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Burley SK, Wu-Wu A, Dutta S, Ganesan S, Zheng SXF. Impact of structural biology and the protein data bank on us fda new drug approvals of low molecular weight antineoplastic agents 2019-2023. Oncogene 2024:10.1038/s41388-024-03077-2. [PMID: 38886570 DOI: 10.1038/s41388-024-03077-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2024] [Revised: 06/04/2024] [Accepted: 06/05/2024] [Indexed: 06/20/2024]
Abstract
Open access to three-dimensional atomic-level biostructure information from the Protein Data Bank (PDB) facilitated discovery/development of 100% of the 34 new low molecular weight, protein-targeted, antineoplastic agents approved by the US FDA 2019-2023. Analyses of PDB holdings, the scientific literature, and related documents for each drug-target combination revealed that the impact of structural biologists and public-domain 3D biostructure data was broad and substantial, ranging from understanding target biology (100% of all drug targets), to identifying a given target as likely druggable (100% of all targets), to structure-guided drug discovery (>80% of all new small-molecule drugs, made up of 50% confirmed and >30% probable cases). In addition to aggregate impact assessments, illustrative case studies are presented for six first-in-class small-molecule anti-cancer drugs, including a selective inhibitor of nuclear export targeting Exportin 1 (selinexor, Xpovio), an ATP-competitive CSF-1R receptor tyrosine kinase inhibitor (pexidartinib,Turalia), a non-ATP-competitive inhibitor of the BCR-Abl fusion protein targeting the myristoyl binding pocket within the kinase catalytic domain of Abl (asciminib, Scemblix), a covalently-acting G12C KRAS inhibitor (sotorasib, Lumakras or Lumykras), an EZH2 methyltransferase inhibitor (tazemostat, Tazverik), and an agent targeting the basic-Helix-Loop-Helix transcription factor HIF-2α (belzutifan, Welireg).
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Affiliation(s)
- Stephen K Burley
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA.
- Rutgers Cancer Institute of New Jersey, Robert Wood Johnson Medical School, New Brunswick, NJ, 08903, USA.
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California, San Diego, La Jolla, CA, 92093, USA.
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA.
| | - Amy Wu-Wu
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA
| | - Shuchismita Dutta
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA
- Rutgers Cancer Institute of New Jersey, Robert Wood Johnson Medical School, New Brunswick, NJ, 08903, USA
| | - Shridar Ganesan
- Rutgers Cancer Institute of New Jersey, Robert Wood Johnson Medical School, New Brunswick, NJ, 08903, USA
| | - Steven X F Zheng
- Rutgers Cancer Institute of New Jersey, Robert Wood Johnson Medical School, New Brunswick, NJ, 08903, USA
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2
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de Medeiros WF, Gomes AFT, Aguiar AJFC, de Queiroz JLC, Bezerra IWL, da Silva-Maia JK, Piuvezam G, Morais AHDA. Anti-Obesity Therapeutic Targets Studied In Silico and In Vivo: A Systematic Review. Int J Mol Sci 2024; 25:4699. [PMID: 38731918 PMCID: PMC11083175 DOI: 10.3390/ijms25094699] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2024] [Revised: 04/12/2024] [Accepted: 04/17/2024] [Indexed: 05/13/2024] Open
Abstract
In the age of information technology and the additional computational search tools and software available, this systematic review aimed to identify potential therapeutic targets for obesity, evaluated in silico and subsequently validated in vivo. The systematic review was initially guided by the research question "What therapeutic targets have been used in in silico analysis for the treatment of obesity?" and structured based on the acronym PECo (P, problem; E, exposure; Co, context). The systematic review protocol was formulated and registered in PROSPERO (CRD42022353808) in accordance with the Preferred Reporting Items Checklist for Systematic Review and Meta-Analysis Protocols (PRISMA-P), and the PRISMA was followed for the systematic review. The studies were selected according to the eligibility criteria, aligned with PECo, in the following databases: PubMed, ScienceDirect, Scopus, Web of Science, BVS, and EMBASE. The search strategy yielded 1142 articles, from which, based on the evaluation criteria, 12 were included in the systematic review. Only seven these articles allowed the identification of both in silico and in vivo reassessed therapeutic targets. Among these targets, five were exclusively experimental, one was exclusively theoretical, and one of the targets presented an experimental portion and a portion obtained by modeling. The predominant methodology used was molecular docking and the most studied target was Human Pancreatic Lipase (HPL) (n = 4). The lack of methodological details resulted in more than 50% of the papers being categorized with an "unclear risk of bias" across eight out of the eleven evaluated criteria. From the current systematic review, it seems evident that integrating in silico methodologies into studies of potential drug targets for the exploration of new therapeutic agents provides an important tool, given the ongoing challenges in controlling obesity.
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Affiliation(s)
- Wendjilla F. de Medeiros
- Nutrition Postgraduate Program, Center for Health Sciences, Federal University of Rio Grande do Norte, Natal 59078-900, Brazil; (W.F.d.M.); (A.F.T.G.); (I.W.L.B.); (J.K.d.S.-M.)
| | - Ana Francisca T. Gomes
- Nutrition Postgraduate Program, Center for Health Sciences, Federal University of Rio Grande do Norte, Natal 59078-900, Brazil; (W.F.d.M.); (A.F.T.G.); (I.W.L.B.); (J.K.d.S.-M.)
| | - Ana Júlia F. C. Aguiar
- Biochemistry and Molecular Biology Postgraduate Program, Biosciences Center, Federal University of Rio Grande do Norte, Natal 59078-970, Brazil; (A.J.F.C.A.); (J.L.C.d.Q.)
| | - Jaluza Luana C. de Queiroz
- Biochemistry and Molecular Biology Postgraduate Program, Biosciences Center, Federal University of Rio Grande do Norte, Natal 59078-970, Brazil; (A.J.F.C.A.); (J.L.C.d.Q.)
| | - Ingrid Wilza L. Bezerra
- Nutrition Postgraduate Program, Center for Health Sciences, Federal University of Rio Grande do Norte, Natal 59078-900, Brazil; (W.F.d.M.); (A.F.T.G.); (I.W.L.B.); (J.K.d.S.-M.)
- Department of Nutrition, Center for Health Sciences, Federal University of Rio Grande do Norte, Natal 59078-900, Brazil
| | - Juliana Kelly da Silva-Maia
- Nutrition Postgraduate Program, Center for Health Sciences, Federal University of Rio Grande do Norte, Natal 59078-900, Brazil; (W.F.d.M.); (A.F.T.G.); (I.W.L.B.); (J.K.d.S.-M.)
- Department of Nutrition, Center for Health Sciences, Federal University of Rio Grande do Norte, Natal 59078-900, Brazil
| | - Grasiela Piuvezam
- Public Health Postgraduate Program, Center for Health Sciences, Federal University of Rio Grande do Norte, Natal 59078-400, Brazil;
- Public Health Department, Federal University of Rio Grande do Norte, Natal 59078-900, Brazil
| | - Ana Heloneida de A. Morais
- Nutrition Postgraduate Program, Center for Health Sciences, Federal University of Rio Grande do Norte, Natal 59078-900, Brazil; (W.F.d.M.); (A.F.T.G.); (I.W.L.B.); (J.K.d.S.-M.)
- Biochemistry and Molecular Biology Postgraduate Program, Biosciences Center, Federal University of Rio Grande do Norte, Natal 59078-970, Brazil; (A.J.F.C.A.); (J.L.C.d.Q.)
- Department of Nutrition, Center for Health Sciences, Federal University of Rio Grande do Norte, Natal 59078-900, Brazil
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3
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Kunnakkattu IR, Choudhary P, Pravda L, Nadzirin N, Smart OS, Yuan Q, Anyango S, Nair S, Varadi M, Velankar S. PDBe CCDUtils: an RDKit-based toolkit for handling and analysing small molecules in the Protein Data Bank. J Cheminform 2023; 15:117. [PMID: 38042830 PMCID: PMC10693035 DOI: 10.1186/s13321-023-00786-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Accepted: 11/17/2023] [Indexed: 12/04/2023] Open
Abstract
While the Protein Data Bank (PDB) contains a wealth of structural information on ligands bound to macromolecules, their analysis can be challenging due to the large amount and diversity of data. Here, we present PDBe CCDUtils, a versatile toolkit for processing and analysing small molecules from the PDB in PDBx/mmCIF format. PDBe CCDUtils provides streamlined access to all the metadata for small molecules in the PDB and offers a set of convenient methods to compute various properties using RDKit, such as 2D depictions, 3D conformers, physicochemical properties, scaffolds, common fragments, and cross-references to small molecule databases using UniChem. The toolkit also provides methods for identifying all the covalently attached chemical components in a macromolecular structure and calculating similarity among small molecules. By providing a broad range of functionality, PDBe CCDUtils caters to the needs of researchers in cheminformatics, structural biology, bioinformatics and computational chemistry.
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Affiliation(s)
- Ibrahim Roshan Kunnakkattu
- Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Preeti Choudhary
- Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Lukas Pravda
- Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Nurul Nadzirin
- Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Oliver S Smart
- Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Qi Yuan
- Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Stephen Anyango
- Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Sreenath Nair
- Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Mihaly Varadi
- Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Sameer Velankar
- Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK.
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4
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Elkaeed EB, Alsfouk BA, Ibrahim TH, Arafa RK, Elkady H, Ibrahim IM, Eissa IH, Metwaly AM. Computer-assisted drug discovery of potential natural inhibitors of the SARS-CoV-2 RNA-dependent RNA polymerase through a multi-phase in silico approach. Antivir Ther 2023; 28:13596535231199838. [PMID: 37669909 DOI: 10.1177/13596535231199838] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/07/2023]
Abstract
BACKGROUND The COVID-19 pandemic has led to significant loss of life and economic disruption worldwide. Currently, there are limited effective treatments available for this disease. SARS-CoV-2 RNA-dependent RNA polymerase (SARS-CoV-2 RdRp) has been identified as a potential target for drug development against COVID-19. Natural products have been shown to possess antiviral properties, making them a promising source for developing drugs against SARS-CoV-2. OBJECTIVES The objective of this study is to identify the most effective natural inhibitors of SARS-CoV-2 RdRp among a set of 4924 African natural products using a multi-phase in silico approach. METHODS The study utilized remdesivir (RTP), the co-crystallized ligand of RdRp, as a starting point to select compounds that have the most similar chemical structures among the examined set of compounds. Molecular fingerprints and structure similarity studies were carried out in the first part of the study. The second part of the study included molecular docking against SARS-CoV-2 RdRp (PDB ID: 7BV2) and Molecular Dynamics (MD) simulations including the calculation of RMSD, RMSF, Rg, SASA, hydrogen bonding, and PLIP. Moreover, the calculations of Molecular mechanics with generalised Born and surface area solvation (MM-GBSA) Lennard-Jones and Columbic electrostatic interaction energies have been conducted. Additionally, in silico ADMET and toxicity studies were performed to examine the drug likeness degrees of the selected compounds. RESULTS Eight compounds were identified as the most effective natural inhibitors of SARS-CoV-2 RdRp. These compounds are kaempferol 3-galactoside, kaempferol 3-O-β-D-glucopyranoside, mangiferin methyl ether, luteolin 7-O-β-D-glucopyranoside, quercetin-O-β-D-3-glucopyranoside, 1-methoxy-3-indolylmethyl glucosinolate, naringenin, and asphodelin A 4'-O-β-D-glucopyranoside. CONCLUSION The results of this study provide valuable information for the development of natural product-based drugs against COVID-19. However, the elected compounds should be further studied in vitro and in vivo to confirm their efficacy in treating COVID-19.
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Affiliation(s)
- Eslam B Elkaeed
- Department of Pharmaceutical Sciences, College of Pharmacy, AlMaarefa University, Riyadh, Saudi Arabia
| | - Bshra A Alsfouk
- Department of Pharmaceutical Sciences, College of Pharmacy, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
| | - Tuqa H Ibrahim
- Drug Design and Discovery Lab, Zewail City of Science and Technology, Cairo, Egypt
- Biomedical Sciences Program, University of Science and Technology, Zewail City of Science and Technology, Cairo, Egypt
| | - Reem K Arafa
- Drug Design and Discovery Lab, Zewail City of Science and Technology, Cairo, Egypt
- Biomedical Sciences Program, University of Science and Technology, Zewail City of Science and Technology, Cairo, Egypt
| | - Hazem Elkady
- Pharmaceutical Medicinal Chemistry and Drug Design Department, Faculty of Pharmacy (Boys), Al-Azhar University, Cairo, Egypt
| | - Ibrahim M Ibrahim
- Biophysics Department, Faculty of Science, Cairo University, Giza, Egypt
| | - Ibrahim H Eissa
- Pharmaceutical Medicinal Chemistry and Drug Design Department, Faculty of Pharmacy (Boys), Al-Azhar University, Cairo, Egypt
| | - Ahmed M Metwaly
- Pharmacognosy and Medicinal Plants Department, Faculty of Pharmacy (Boys), Al-Azhar University, Cairo, Egypt
- Biopharmaceutical Products Research Department, Genetic Engineering and Biotechnology Research Institute, City of Scientific Research and Technological Applications (SRTA-City), Alexandria, Egypt
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5
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Yuan S, Xia L, Wang C, Wu F, Zhang B, Pan C, Fan Z, Lei X, Stevens RC, Sali A, Sun L, Shui W. Conformational Dynamics of the Activated GLP-1 Receptor-G s Complex Revealed by Cross-Linking Mass Spectrometry and Integrative Structure Modeling. ACS CENTRAL SCIENCE 2023; 9:992-1007. [PMID: 37252352 PMCID: PMC10214531 DOI: 10.1021/acscentsci.3c00063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Indexed: 05/31/2023]
Abstract
Despite advances in characterizing the structures and functions of G protein-coupled receptors (GPCRs), our understanding of GPCR activation and signaling is still limited by the lack of information on conformational dynamics. It is particularly challenging to study the dynamics of GPCR complexes with their signaling partners because of their transient nature and low stability. Here, by combining cross-linking mass spectrometry (CLMS) with integrative structure modeling, we map the conformational ensemble of an activated GPCR-G protein complex at near-atomic resolution. The integrative structures describe heterogeneous conformations for a high number of potential alternative active states of the GLP-1 receptor-Gs complex. These structures show marked differences from the previously determined cryo-EM structure, especially at the receptor-Gs interface and in the interior of the Gs heterotrimer. Alanine-scanning mutagenesis coupled with pharmacological assays validates the functional significance of 24 interface residue contacts only observed in the integrative structures, yet absent in the cryo-EM structure. Through the integration of spatial connectivity data from CLMS with structure modeling, our study provides a new approach that is generalizable to characterizing the conformational dynamics of GPCR signaling complexes.
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Affiliation(s)
- Shijia Yuan
- iHuman
Institute, ShanghaiTech University, Shanghai 201210, China
- School
of Life Science and Technology, ShanghaiTech
University, Shanghai 201210, China
- University
of Chinese Academy of Sciences, Beijing 100049, China
| | - Lisha Xia
- iHuman
Institute, ShanghaiTech University, Shanghai 201210, China
- School
of Life Science and Technology, ShanghaiTech
University, Shanghai 201210, China
- University
of Chinese Academy of Sciences, Beijing 100049, China
| | - Chenxi Wang
- iHuman
Institute, ShanghaiTech University, Shanghai 201210, China
- School
of Life Science and Technology, ShanghaiTech
University, Shanghai 201210, China
- University
of Chinese Academy of Sciences, Beijing 100049, China
| | - Fan Wu
- Structure
Therapeutics, South San Francisco, California 94080, United States
| | - Bingjie Zhang
- iHuman
Institute, ShanghaiTech University, Shanghai 201210, China
| | - Chen Pan
- National
Facility for Protein Science in Shanghai, Shanghai Advanced Research
Institute, Chinese Academy of Science, Shanghai 201210, China
| | - Zhiran Fan
- Biocreater
(WuHan) Biotechnology Co., Ltd, Wuhan 430075, China
| | - Xiaoguang Lei
- Beijing
National Laboratory for Molecular Sciences, State Key Laboratory of
Natural and Biomimetic Drugs, Key Laboratory of Bioorganic Chemistry
and Molecular Engineering of Ministry of Education, Department of
Chemical Biology, College of Chemistry and Molecular Engineering,
Peking-Tsinghua Center for Life Sciences, Peking University, Beijing 100871, China
| | - Raymond C. Stevens
- iHuman
Institute, ShanghaiTech University, Shanghai 201210, China
- School
of Life Science and Technology, ShanghaiTech
University, Shanghai 201210, China
- Structure
Therapeutics, South San Francisco, California 94080, United States
| | - Andrej Sali
- Quantitative
Biosciences Institute, University of California,
San Francisco, San Francisco, California 94143, United States
- Department
of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, California 94143, United States
- Department
of Pharmaceutical Chemistry, University
of California, San Francisco, San
Francisco, California 94143, United States
| | - Liping Sun
- iHuman
Institute, ShanghaiTech University, Shanghai 201210, China
| | - Wenqing Shui
- iHuman
Institute, ShanghaiTech University, Shanghai 201210, China
- School
of Life Science and Technology, ShanghaiTech
University, Shanghai 201210, China
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6
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Revillo Imbernon J, Chiesa L, Kellenberger E. Mining the Protein Data Bank to inspire fragment library design. Front Chem 2023; 11:1089714. [PMID: 36846858 PMCID: PMC9950109 DOI: 10.3389/fchem.2023.1089714] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Accepted: 01/27/2023] [Indexed: 02/12/2023] Open
Abstract
The fragment approach has emerged as a method of choice for drug design, as it allows difficult therapeutic targets to be addressed. Success lies in the choice of the screened chemical library and the biophysical screening method, and also in the quality of the selected fragment and structural information used to develop a drug-like ligand. It has recently been proposed that promiscuous compounds, i.e., those that bind to several proteins, present an advantage for the fragment approach because they are likely to give frequent hits in screening. In this study, we searched the Protein Data Bank for fragments with multiple binding modes and targeting different sites. We identified 203 fragments represented by 90 scaffolds, some of which are not or hardly present in commercial fragment libraries. By contrast to other available fragment libraries, the studied set is enriched in fragments with a marked three-dimensional character (download at 10.5281/zenodo.7554649).
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Affiliation(s)
- Julia Revillo Imbernon
- Laboratoire d’Innovation Thérapeutique, Faculté de Pharmacie, UMR7200 CNRS Université de Strasbourg, Illkirch-Graffenstaden, France
| | - Luca Chiesa
- Laboratoire d’Innovation Thérapeutique, Faculté de Pharmacie, UMR7200 CNRS Université de Strasbourg, Illkirch-Graffenstaden, France
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7
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Burley SK, Bhikadiya C, Bi C, Bittrich S, Chao H, Chen L, Craig PA, Crichlow GV, Dalenberg K, Duarte JM, Dutta S, Fayazi M, Feng Z, Flatt JW, Ganesan S, Ghosh S, Goodsell DS, Green RK, Guranovic V, Henry J, Hudson BP, Khokhriakov I, Lawson CL, Liang Y, Lowe R, Peisach E, Persikova I, Piehl DW, Rose Y, Sali A, Segura J, Sekharan M, Shao C, Vallat B, Voigt M, Webb B, Westbrook JD, Whetstone S, Young JY, Zalevsky A, Zardecki C. RCSB Protein Data Bank (RCSB.org): delivery of experimentally-determined PDB structures alongside one million computed structure models of proteins from artificial intelligence/machine learning. Nucleic Acids Res 2023; 51:D488-D508. [PMID: 36420884 PMCID: PMC9825554 DOI: 10.1093/nar/gkac1077] [Citation(s) in RCA: 155] [Impact Index Per Article: 155.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 10/17/2022] [Accepted: 11/02/2022] [Indexed: 11/27/2022] Open
Abstract
The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB), founding member of the Worldwide Protein Data Bank (wwPDB), is the US data center for the open-access PDB archive. As wwPDB-designated Archive Keeper, RCSB PDB is also responsible for PDB data security. Annually, RCSB PDB serves >10 000 depositors of three-dimensional (3D) biostructures working on all permanently inhabited continents. RCSB PDB delivers data from its research-focused RCSB.org web portal to many millions of PDB data consumers based in virtually every United Nations-recognized country, territory, etc. This Database Issue contribution describes upgrades to the research-focused RCSB.org web portal that created a one-stop-shop for open access to ∼200 000 experimentally-determined PDB structures of biological macromolecules alongside >1 000 000 incorporated Computed Structure Models (CSMs) predicted using artificial intelligence/machine learning methods. RCSB.org is a 'living data resource.' Every PDB structure and CSM is integrated weekly with related functional annotations from external biodata resources, providing up-to-date information for the entire corpus of 3D biostructure data freely available from RCSB.org with no usage limitations. Within RCSB.org, PDB structures and the CSMs are clearly identified as to their provenance and reliability. Both are fully searchable, and can be analyzed and visualized using the full complement of RCSB.org web portal capabilities.
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Affiliation(s)
- Stephen K Burley
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Rutgers Cancer Institute of New Jersey, New Brunswick, NJ 08901, USA
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093, USA
| | - Charmi Bhikadiya
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093, USA
| | - Chunxiao Bi
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093, USA
| | - Sebastian Bittrich
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093, USA
| | - Henry Chao
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Li Chen
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Paul A Craig
- School of Chemistry and Materials Science, Rochester Institute of Technology, Rochester, NY 14623, USA
| | - Gregg V Crichlow
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Kenneth Dalenberg
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Jose M Duarte
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093, USA
| | - Shuchismita Dutta
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Rutgers Cancer Institute of New Jersey, New Brunswick, NJ 08901, USA
| | - Maryam Fayazi
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Zukang Feng
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Justin W Flatt
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Sai Ganesan
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, Quantitative Biosciences Institute, University of California San Francisco, San Francisco, CA 94158, USA
| | - Sutapa Ghosh
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - David S Goodsell
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Rutgers Cancer Institute of New Jersey, New Brunswick, NJ 08901, USA
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - Rachel Kramer Green
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Vladimir Guranovic
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Jeremy Henry
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093, USA
| | - Brian P Hudson
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Igor Khokhriakov
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093, USA
| | - Catherine L Lawson
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Yuhe Liang
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Robert Lowe
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Ezra Peisach
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Irina Persikova
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Dennis W Piehl
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Yana Rose
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093, USA
| | - Andrej Sali
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, Quantitative Biosciences Institute, University of California San Francisco, San Francisco, CA 94158, USA
| | - Joan Segura
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093, USA
| | - Monica Sekharan
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Chenghua Shao
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Brinda Vallat
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Maria Voigt
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Ben Webb
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, Quantitative Biosciences Institute, University of California San Francisco, San Francisco, CA 94158, USA
| | - John D Westbrook
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Rutgers Cancer Institute of New Jersey, New Brunswick, NJ 08901, USA
| | - Shamara Whetstone
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Jasmine Y Young
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Arthur Zalevsky
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, Quantitative Biosciences Institute, University of California San Francisco, San Francisco, CA 94158, USA
| | - Christine Zardecki
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
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8
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Abstract
Within the family of purinergic receptors, the P2X1 receptor is a ligand-gated ion channel that plays a role in urogenital, immune and cardiovascular function. Specifically, the P2X1 receptor has been implicated in controlling smooth muscle contractions of the vas deferens and therefore has emerged as an exciting drug target for male contraception. In addition, the P2X1 receptor contributes to smooth muscle contractions of the bladder and is a target to treat bladder dysfunction. Finally, platelets and neutrophils have populations of P2X1 receptors that could be targeted for thrombosis and inflammatory conditions. Drugs that specifically target the P2X1 receptor have been challenging to develop, and only recently have small molecule antagonists of the P2X1 receptor been available. However, these ligands need further biological validation for appropriate selectivity and drug-like properties before they will be suitable for use in preclinical models of disease. Although the atomic structure of the P2X1 receptor has yet to be determined, the recent discovery of several other P2X receptor structures and improvements in the field of structural biology suggests that this is now a distinct possibility. Such efforts may significantly improve drug discovery efforts at the P2X1 receptor.
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9
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Burley SK, Bhikadiya C, Bi C, Bittrich S, Chao H, Chen L, Craig PA, Crichlow GV, Dalenberg K, Duarte JM, Dutta S, Fayazi M, Feng Z, Flatt JW, Ganesan SJ, Ghosh S, Goodsell DS, Green RK, Guranovic V, Henry J, Hudson BP, Khokhriakov I, Lawson CL, Liang Y, Lowe R, Peisach E, Persikova I, Piehl DW, Rose Y, Sali A, Segura J, Sekharan M, Shao C, Vallat B, Voigt M, Webb B, Westbrook JD, Whetstone S, Young JY, Zalevsky A, Zardecki C. RCSB Protein Data bank: Tools for visualizing and understanding biological macromolecules in 3D. Protein Sci 2022; 31:e4482. [PMID: 36281733 PMCID: PMC9667899 DOI: 10.1002/pro.4482] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 10/17/2022] [Accepted: 10/19/2022] [Indexed: 12/14/2022]
Abstract
Now in its 52nd year of continuous operations, the Protein Data Bank (PDB) is the premiere open-access global archive housing three-dimensional (3D) biomolecular structure data. It is jointly managed by the Worldwide Protein Data Bank (wwPDB) partnership. The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) is funded by the National Science Foundation, National Institutes of Health, and US Department of Energy and serves as the US data center for the wwPDB. RCSB PDB is also responsible for the security of PDB data in its role as wwPDB-designated Archive Keeper. Every year, RCSB PDB serves tens of thousands of depositors of 3D macromolecular structure data (coming from macromolecular crystallography, nuclear magnetic resonance spectroscopy, electron microscopy, and micro-electron diffraction). The RCSB PDB research-focused web portal (RCSB.org) makes PDB data available at no charge and without usage restrictions to many millions of PDB data consumers around the world. The RCSB PDB training, outreach, and education web portal (PDB101.RCSB.org) serves nearly 700 K educators, students, and members of the public worldwide. This invited Tools Issue contribution describes how RCSB PDB (i) is organized; (ii) works with wwPDB partners to process new depositions; (iii) serves as the wwPDB-designated Archive Keeper; (iv) enables exploration and 3D visualization of PDB data via RCSB.org; and (v) supports training, outreach, and education via PDB101.RCSB.org. New tools and features at RCSB.org are presented using examples drawn from high-resolution structural studies of proteins relevant to treatment of human cancers by targeting immune checkpoints.
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Affiliation(s)
- Stephen K. Burley
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Cancer Institute of New Jersey, Rutgers, The State University of New JerseyNew BrunswickNew JerseyUSA
- Research Collaboratory for Structural Bioinformatics Protein Data BankSan Diego Supercomputer Center, University of CaliforniaLa JollaCaliforniaUSA
- Department of Chemistry and Chemical Biology, RutgersThe State University of New JerseyPiscatawayNew JerseyUSA
| | - Charmi Bhikadiya
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Chunxiao Bi
- Research Collaboratory for Structural Bioinformatics Protein Data BankSan Diego Supercomputer Center, University of CaliforniaLa JollaCaliforniaUSA
| | - Sebastian Bittrich
- Research Collaboratory for Structural Bioinformatics Protein Data BankSan Diego Supercomputer Center, University of CaliforniaLa JollaCaliforniaUSA
| | - Henry Chao
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Li Chen
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Paul A. Craig
- School of Chemistry and Materials ScienceRochester Institute of TechnologyRochesterNew YorkUSA
| | - Gregg V. Crichlow
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Kenneth Dalenberg
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Jose M. Duarte
- Research Collaboratory for Structural Bioinformatics Protein Data BankSan Diego Supercomputer Center, University of CaliforniaLa JollaCaliforniaUSA
| | - Shuchismita Dutta
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Cancer Institute of New Jersey, Rutgers, The State University of New JerseyNew BrunswickNew JerseyUSA
| | - Maryam Fayazi
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Zukang Feng
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Justin W. Flatt
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Sai J. Ganesan
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Department of Bioengineering and Therapeutic SciencesQuantitative Biosciences Institute, University of CaliforniaSan FranciscoCaliforniaUSA
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Department of Pharmaceutical ChemistryQuantitative Biosciences Institute, University of CaliforniaSan FranciscoCaliforniaUSA
| | - Sutapa Ghosh
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - David S. Goodsell
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Cancer Institute of New Jersey, Rutgers, The State University of New JerseyNew BrunswickNew JerseyUSA
- Department of Integrative Structural and Computational BiologyThe Scripps Research InstituteLa JollaCaliforniaUSA
| | - Rachel Kramer Green
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Vladimir Guranovic
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Jeremy Henry
- Research Collaboratory for Structural Bioinformatics Protein Data BankSan Diego Supercomputer Center, University of CaliforniaLa JollaCaliforniaUSA
| | - Brian P. Hudson
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Igor Khokhriakov
- Research Collaboratory for Structural Bioinformatics Protein Data BankSan Diego Supercomputer Center, University of CaliforniaLa JollaCaliforniaUSA
| | - Catherine L. Lawson
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Yuhe Liang
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Robert Lowe
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Ezra Peisach
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Irina Persikova
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Dennis W. Piehl
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Yana Rose
- Research Collaboratory for Structural Bioinformatics Protein Data BankSan Diego Supercomputer Center, University of CaliforniaLa JollaCaliforniaUSA
| | - Andrej Sali
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Department of Bioengineering and Therapeutic SciencesQuantitative Biosciences Institute, University of CaliforniaSan FranciscoCaliforniaUSA
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Department of Pharmaceutical ChemistryQuantitative Biosciences Institute, University of CaliforniaSan FranciscoCaliforniaUSA
| | - Joan Segura
- Research Collaboratory for Structural Bioinformatics Protein Data BankSan Diego Supercomputer Center, University of CaliforniaLa JollaCaliforniaUSA
| | - Monica Sekharan
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Chenghua Shao
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Brinda Vallat
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Maria Voigt
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Benjamin Webb
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Department of Bioengineering and Therapeutic SciencesQuantitative Biosciences Institute, University of CaliforniaSan FranciscoCaliforniaUSA
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Department of Pharmaceutical ChemistryQuantitative Biosciences Institute, University of CaliforniaSan FranciscoCaliforniaUSA
| | - John D. Westbrook
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Shamara Whetstone
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Jasmine Y. Young
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Arthur Zalevsky
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Department of Bioengineering and Therapeutic SciencesQuantitative Biosciences Institute, University of CaliforniaSan FranciscoCaliforniaUSA
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Department of Pharmaceutical ChemistryQuantitative Biosciences Institute, University of CaliforniaSan FranciscoCaliforniaUSA
| | - Christine Zardecki
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
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10
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Shao C, Bittrich S, Wang S, Burley SK. Assessing PDB macromolecular crystal structure confidence at the individual amino acid residue level. Structure 2022; 30:1385-1394.e3. [PMID: 36049478 PMCID: PMC9547844 DOI: 10.1016/j.str.2022.08.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 06/24/2022] [Accepted: 08/05/2022] [Indexed: 11/22/2022]
Abstract
Approximately 87% of the more than 190,000 atomic-level three-dimensional (3D) biostructures in the PDB were determined using macromolecular crystallography (MX). Agreement between 3D atomic coordinates and experimental data for >100 million individual amino acid residues occurring within ∼150,000 PDB MX structures was analyzed in detail. The real-space correlation coefficient (RSCC) calculated using the 3D atomic coordinates for each residue and experimental-data-derived electron density enables outlier detection of unreliable atomic coordinates (particularly important for poorly resolved side-chain atoms) and ready evaluation of local structure quality by PDB users. For human protein MX structures in PDB, comparisons of the per-residue RSCC metric with AlphaFold2-computed structure model confidence (pLDDT-predicted local distance difference test) document (1) that RSCC values and pLDDT scores are correlated (median correlation coefficient ∼0.41), and (2) that experimentally determined MX structures (3.5 Å resolution or better) are more reliable than AlphaFold2-computed structure models and should be used preferentially whenever possible.
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Affiliation(s)
- Chenghua Shao
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA; Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA.
| | - Sebastian Bittrich
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093, USA
| | - Sijian Wang
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA; Department of Statistics, Rutgers, The State University of New Jersey, New Brunswick, NJ 08903, USA
| | - Stephen K Burley
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA; Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA; Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093, USA; Rutgers Cancer Institute of New Jersey, Robert Wood Johnson Medical School, New Brunswick, NJ 08903, USA; Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA.
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11
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Burley SK, Berman HM, Duarte JM, Feng Z, Flatt JW, Hudson BP, Lowe R, Peisach E, Piehl DW, Rose Y, Sali A, Sekharan M, Shao C, Vallat B, Voigt M, Westbrook JD, Young JY, Zardecki C. Protein Data Bank: A Comprehensive Review of 3D Structure Holdings and Worldwide Utilization by Researchers, Educators, and Students. Biomolecules 2022; 12:1425. [PMID: 36291635 PMCID: PMC9599165 DOI: 10.3390/biom12101425] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 09/23/2022] [Accepted: 09/26/2022] [Indexed: 11/18/2022] Open
Abstract
The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB), funded by the United States National Science Foundation, National Institutes of Health, and Department of Energy, supports structural biologists and Protein Data Bank (PDB) data users around the world. The RCSB PDB, a founding member of the Worldwide Protein Data Bank (wwPDB) partnership, serves as the US data center for the global PDB archive housing experimentally-determined three-dimensional (3D) structure data for biological macromolecules. As the wwPDB-designated Archive Keeper, RCSB PDB is also responsible for the security of PDB data and weekly update of the archive. RCSB PDB serves tens of thousands of data depositors (using macromolecular crystallography, nuclear magnetic resonance spectroscopy, electron microscopy, and micro-electron diffraction) annually working on all permanently inhabited continents. RCSB PDB makes PDB data available from its research-focused web portal at no charge and without usage restrictions to many millions of PDB data consumers around the globe. It also provides educators, students, and the general public with an introduction to the PDB and related training materials through its outreach and education-focused web portal. This review article describes growth of the PDB, examines evolution of experimental methods for structure determination viewed through the lens of the PDB archive, and provides a detailed accounting of PDB archival holdings and their utilization by researchers, educators, and students worldwide.
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Affiliation(s)
- Stephen K. Burley
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ 08901, USA
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093, USA
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Helen M. Berman
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Jose M. Duarte
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093, USA
| | - Zukang Feng
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Justin W. Flatt
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Brian P. Hudson
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Robert Lowe
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Ezra Peisach
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Dennis W. Piehl
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Yana Rose
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093, USA
| | - Andrej Sali
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, Quantitative Biosciences Institute, University of California San Francisco, San Francisco, CA 94158, USA
| | - Monica Sekharan
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Chenghua Shao
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Brinda Vallat
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ 08901, USA
| | - Maria Voigt
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - John D. Westbrook
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ 08901, USA
| | - Jasmine Y. Young
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Christine Zardecki
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
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12
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Kushwaha V, Capalash N. Aminoacyl-tRNA synthetase (AARS) as an attractive drug target in neglected tropical trypanosomatid diseases-Leishmaniasis, Human African Trypanosomiasis and Chagas disease. Mol Biochem Parasitol 2022; 251:111510. [PMID: 35988745 DOI: 10.1016/j.molbiopara.2022.111510] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 08/09/2022] [Accepted: 08/16/2022] [Indexed: 10/15/2022]
Abstract
TriTryp diseases (Leishmaniasis, Human African Trypanosomiasis (HAT), and Chagas disease) are devastating parasitic neglected tropical diseases (NTDs) that affect billions of people in developing countries, cause high mortality in humans, and impose a large socio-economic burden. The current treatment options against tritryp diseases are suboptimal and challenging due to the emergence of resistance against available tritryp drugs. Hence, designing and developing effective anti-tritryp drugs with novel targets are required. Aminoacyl-tRNA synthetases (AARSs) involved in specific aminoacylation of transfer RNAs (tRNAs), interrupt protein synthesis through inhibitors, and retard the parasite growth. AaRSs have long been studied as therapeutic targets in bacteria, and three aaRS inhibitors, mupirocin (against IleRS), tavaborole AN2690 (against LeuRS), and halofuginone (against ProRS), are already in clinical practice. The structural differences between tritryp and human aaRSs and the presence of unique sequences (N-terminal domain/C-terminal domain/catalytic domain) make them potential target for developing selective inhibitors. Drugs based on a single aaRS target developed by high-throughput screening (HTS) are less effective due to the emergence of resistance. However, designing multi-targeted drugs may be a better strategy for resistance development. In this perspective, we discuss the characteristics of tritryp aaRSs, sequence conservation in their orthologs and their peculiarities, recent advancements towards the single-target and multi-target aaRS inhibitors developed through rational design.
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Affiliation(s)
- Vikas Kushwaha
- Department of Biotechnology, Panjab University, Sector-25, South Campus, Chandigarh 160025, India.
| | - Neena Capalash
- Department of Biotechnology, Panjab University, Sector-25, South Campus, Chandigarh 160025, India.
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13
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Integration of machine learning with computational structural biology of plants. Biochem J 2022; 479:921-928. [PMID: 35484946 DOI: 10.1042/bcj20200942] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 04/01/2022] [Accepted: 04/06/2022] [Indexed: 11/17/2022]
Abstract
Computational structural biology of proteins has developed rapidly in recent decades with the development of new computational tools and the advancement of computing hardware. However, while these techniques have widely been used to make advancements in human medicine, these methods have seen less utilization in the plant sciences. In the last several years, machine learning methods have gained popularity in computational structural biology. These methods have enabled the development of new tools which are able to address the major challenges that have hampered the wide adoption of the computational structural biology of plants. This perspective examines the remaining challenges in computational structural biology and how the development of machine learning techniques enables more in-depth computational structural biology of plants.
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14
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Nazarova AL, Katritch V. It all clicks together: In silico drug discovery becoming mainstream. Clin Transl Med 2022; 12:e766. [PMID: 35377970 PMCID: PMC8979333 DOI: 10.1002/ctm2.766] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 03/01/2022] [Indexed: 11/25/2022] Open
Affiliation(s)
- Antonina L Nazarova
- Department of Quantitative and Computational Biology, Department of Chemistry, Bridge Institute, USC Michelson Center for Convergent Biosciences, University of Southern California, Los Angeles, California, USA
| | - Vsevolod Katritch
- Department of Quantitative and Computational Biology, Department of Chemistry, Bridge Institute, USC Michelson Center for Convergent Biosciences, University of Southern California, Los Angeles, California, USA
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15
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Vasina M, Velecký J, Planas-Iglesias J, Marques SM, Skarupova J, Damborsky J, Bednar D, Mazurenko S, Prokop Z. Tools for computational design and high-throughput screening of therapeutic enzymes. Adv Drug Deliv Rev 2022; 183:114143. [PMID: 35167900 DOI: 10.1016/j.addr.2022.114143] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2021] [Revised: 02/04/2022] [Accepted: 02/09/2022] [Indexed: 12/16/2022]
Abstract
Therapeutic enzymes are valuable biopharmaceuticals in various biomedical applications. They have been successfully applied for fibrinolysis, cancer treatment, enzyme replacement therapies, and the treatment of rare diseases. Still, there is a permanent demand to find new or better therapeutic enzymes, which would be sufficiently soluble, stable, and active to meet specific medical needs. Here, we highlight the benefits of coupling computational approaches with high-throughput experimental technologies, which significantly accelerate the identification and engineering of catalytic therapeutic agents. New enzymes can be identified in genomic and metagenomic databases, which grow thanks to next-generation sequencing technologies exponentially. Computational design and machine learning methods are being developed to improve catalytically potent enzymes and predict their properties to guide the selection of target enzymes. High-throughput experimental pipelines, increasingly relying on microfluidics, ensure functional screening and biochemical characterization of target enzymes to reach efficient therapeutic enzymes.
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Affiliation(s)
- Michal Vasina
- Loschmidt Laboratories, Department of Experimental Biology, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic; Loschmidt Laboratories, RECETOX, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic; International Clinical Research Centre, St. Anne's University Hospital, Pekarska 53, Brno, Czech Republic
| | - Jan Velecký
- Loschmidt Laboratories, Department of Experimental Biology, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic; Loschmidt Laboratories, RECETOX, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic
| | - Joan Planas-Iglesias
- Loschmidt Laboratories, Department of Experimental Biology, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic; Loschmidt Laboratories, RECETOX, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic; International Clinical Research Centre, St. Anne's University Hospital, Pekarska 53, Brno, Czech Republic
| | - Sergio M Marques
- Loschmidt Laboratories, Department of Experimental Biology, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic; Loschmidt Laboratories, RECETOX, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic; International Clinical Research Centre, St. Anne's University Hospital, Pekarska 53, Brno, Czech Republic
| | - Jana Skarupova
- Loschmidt Laboratories, Department of Experimental Biology, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic; Loschmidt Laboratories, RECETOX, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic
| | - Jiri Damborsky
- Loschmidt Laboratories, Department of Experimental Biology, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic; Loschmidt Laboratories, RECETOX, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic; International Clinical Research Centre, St. Anne's University Hospital, Pekarska 53, Brno, Czech Republic; Enantis, INBIT, Kamenice 34, Brno, Czech Republic
| | - David Bednar
- Loschmidt Laboratories, Department of Experimental Biology, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic; Loschmidt Laboratories, RECETOX, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic; International Clinical Research Centre, St. Anne's University Hospital, Pekarska 53, Brno, Czech Republic.
| | - Stanislav Mazurenko
- Loschmidt Laboratories, Department of Experimental Biology, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic; Loschmidt Laboratories, RECETOX, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic; International Clinical Research Centre, St. Anne's University Hospital, Pekarska 53, Brno, Czech Republic.
| | - Zbynek Prokop
- Loschmidt Laboratories, Department of Experimental Biology, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic; Loschmidt Laboratories, RECETOX, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic; International Clinical Research Centre, St. Anne's University Hospital, Pekarska 53, Brno, Czech Republic.
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16
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Eissa IH, Alesawy MS, Saleh AM, Elkaeed EB, Alsfouk BA, El-Attar AAMM, Metwaly AM. Ligand and Structure-Based In Silico Determination of the Most Promising SARS-CoV-2 nsp16-nsp10 2'- o-Methyltransferase Complex Inhibitors among 3009 FDA Approved Drugs. Molecules 2022; 27:2287. [PMID: 35408684 PMCID: PMC9000629 DOI: 10.3390/molecules27072287] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Revised: 03/27/2022] [Accepted: 03/28/2022] [Indexed: 12/15/2022] Open
Abstract
As a continuation of our earlier work against SARS-CoV-2, seven FDA-approved drugs were designated as the best SARS-CoV-2 nsp16-nsp10 2'-o-methyltransferase (2'OMTase) inhibitors through 3009 compounds. The in silico inhibitory potential of the examined compounds against SARS-CoV-2 nsp16-nsp10 2'-o-methyltransferase (PDB ID: (6W4H) was conducted through a multi-step screening approach. At the beginning, molecular fingerprints experiment with SAM (S-Adenosylmethionine), the co-crystallized ligand of the targeted enzyme, unveiled the resemblance of 147 drugs. Then, a structural similarity experiment recommended 26 compounds. Therefore, the 26 compounds were docked against 2'OMTase to reveal the potential inhibitory effect of seven promising compounds (Protirelin, (1187), Calcium folinate (1913), Raltegravir (1995), Regadenoson (2176), Ertapenem (2396), Methylergometrine (2532), and Thiamine pyrophosphate hydrochloride (2612)). Out of the docked ligands, Ertapenem (2396) showed an ideal binding mode like that of the co-crystallized ligand (SAM). It occupied all sub-pockets of the active site and bound the crucial amino acids. Accordingly, some MD simulation experiments (RMSD, RMSF, Rg, SASA, and H-bonding) have been conducted for the 2'OMTase-Ertapenem complex over 100 ns. The performed MD experiments verified the correct binding mode of Ertapenem against 2'OMTase exhibiting low energy and optimal dynamics. Finally, MM-PBSA studies indicated that Ertapenem bonded advantageously to the targeted protein with a free energy value of -43 KJ/mol. Furthermore, the binding free energy analysis revealed the essential amino acids of 2'OMTase that served positively to the binding. The achieved results bring hope to find a treatment for COVID-19 via in vitro and in vivo studies for the pointed compounds.
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Affiliation(s)
- Ibrahim H. Eissa
- Pharmaceutical Medicinal Chemistry and Drug Design Department, Faculty of Pharmacy (Boys), Al-Azhar University, Cairo 11884, Egypt; (M.S.A.); (A.M.S.)
| | - Mohamed S. Alesawy
- Pharmaceutical Medicinal Chemistry and Drug Design Department, Faculty of Pharmacy (Boys), Al-Azhar University, Cairo 11884, Egypt; (M.S.A.); (A.M.S.)
| | - Abdulrahman M. Saleh
- Pharmaceutical Medicinal Chemistry and Drug Design Department, Faculty of Pharmacy (Boys), Al-Azhar University, Cairo 11884, Egypt; (M.S.A.); (A.M.S.)
| | - Eslam B. Elkaeed
- Department of Pharmaceutical Sciences, College of Pharmacy, Almaarefa University, Riyadh 13713, Saudi Arabia;
| | - Bshra A. Alsfouk
- Department of Pharmaceutical Sciences, College of Pharmacy, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia;
| | - Abdul-Aziz M. M. El-Attar
- Pharmaceutical Analytical Chemistry Department, Faculty of Pharmacy, Al-Azhar University, Cairo 11884, Egypt;
| | - Ahmed M. Metwaly
- Pharmacognosy and Medicinal Plants Department, Faculty of Pharmacy (Boys), Al-Azhar University, Cairo 11884, Egypt
- Biopharmaceutical Products Research Department, Genetic Engineering and Biotechnology Research Institute, City of Scientific Research and Technological Applications (SRTA-City), Alexandria 21934, Egypt
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17
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Binette V, Mousseau N, Tuffery P. A Generalized Attraction-Repulsion Potential and Revisited Fragment Library Improves PEP-FOLD Peptide Structure Prediction. J Chem Theory Comput 2022; 18:2720-2736. [PMID: 35298162 DOI: 10.1021/acs.jctc.1c01293] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Fast and accurate structure prediction is essential to the study of peptide function, molecular targets, and interactions and has been the subject of considerable efforts in the past decade. In this work, we present improvements to the popular simplified PEP-FOLD technique for small peptide structure prediction. PEP-FOLD originality is threefold: (i) it uses a predetermined structural alphabet, (ii) it uses a sequential algorithm to reconstruct the tridimensional structures of these peptides in a discrete space using a fragment library, and (iii) it assesses the energy of these structures using a coarse-grained representation in which all of the backbone atoms but the α-hydrogen are present, and the side chain corresponds to a unique bead. In former versions of PEP-FOLD, a van der Waals formulation was used for non-bonded interactions, with each side chain being associated with a fixed radius. Here, we explore the relevance of using instead a generalized formulation in which not only the optimal distance of interaction and the energy at this distance are parameters but also the distance at which the potential is zero. This allows each side chain to be associated with a different radius and potential energy shape, depending on its interaction partner, and in principle to make more effective the coarse-grained representation. In addition, the new PEP-FOLD version is associated with an updated library of fragments. We show that these modifications lead to important improvements for many of the problematic targets identified with the former PEP-FOLD version while maintaining already correct predictions. The improvement is in terms of both model ranking and model accuracy. We also compare the PEP-FOLD enhanced version to state-of-the-art techniques for both peptide and structure predictions: APPTest, RaptorX, and AlphaFold2. We find that the new predictions are superior, in particular with respect to the prediction of small β-targets, to those of APPTest and RaptorX and bring, with its original approach, additional understanding on folded structures, even when less precise than AlphaFold2. With their strong physical influence, the revised structural library and coarse-grained potential offer, however, the means for a deeper understanding of the nature of folding and open a solid basis for studying flexibility and other dynamical properties not accessible to IA structure prediction approaches.
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Affiliation(s)
- Vincent Binette
- Départment de Physique, Université de Montréal, Case postale 6128, succursale Centre-ville, Montréal, QC H3C 3J7, Canada
| | - Normand Mousseau
- Départment de Physique, Université de Montréal, Case postale 6128, succursale Centre-ville, Montréal, QC H3C 3J7, Canada
| | - Pierre Tuffery
- Université de Paris, INSERM U1133, CNRS UMR 8251, F-75205 Paris, France
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18
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Kobauri P, Galenkamp NS, Schulte AM, de Vries J, Simeth NA, Maglia G, Thallmair S, Kolarski D, Szymanski W, Feringa BL. Hypothesis-Driven, Structure-Based Design in Photopharmacology: The Case of eDHFR Inhibitors. J Med Chem 2022; 65:4798-4817. [PMID: 35258959 PMCID: PMC8958501 DOI: 10.1021/acs.jmedchem.1c01962] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
![]()
Photopharmacology
uses light to regulate the biological activity
of drugs. This precise control is obtained through the incorporation
of molecular photoswitches into bioactive molecules. A major challenge
for photopharmacology is the rational design of photoswitchable drugs
that show light-induced activation. Computer-aided drug design is
an attractive approach toward more effective, targeted design. Herein,
we critically evaluated different structure-based approaches for photopharmacology
with Escherichia coli dihydrofolate reductase (eDHFR)
as a case study. Through the iterative examination of our hypotheses,
we progressively tuned the design of azobenzene-based, photoswitchable
eDHFR inhibitors in five design–make–switch–test–analyze
cycles. Targeting a hydrophobic subpocket of the enzyme and a specific
salt bridge only with the thermally metastable cis-isomer emerged as the most promising design strategy. We identified
three inhibitors that could be activated upon irradiation and reached
potencies in the low-nanomolar range. Above all, this systematic study
provided valuable insights for future endeavors toward rational photopharmacology.
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Affiliation(s)
- Piermichele Kobauri
- Stratingh Institute for Chemistry, University of Groningen, Nijenborgh 4, 9747 AG Groningen, The Netherlands
| | - Nicole S Galenkamp
- Groningen Biomolecular Science and Biotechnology Institute, University of Groningen, Nijenborgh 4, 9747 AG Groningen, The Netherlands
| | - Albert M Schulte
- Stratingh Institute for Chemistry, University of Groningen, Nijenborgh 4, 9747 AG Groningen, The Netherlands
| | - Jisk de Vries
- Stratingh Institute for Chemistry, University of Groningen, Nijenborgh 4, 9747 AG Groningen, The Netherlands
| | - Nadja A Simeth
- Stratingh Institute for Chemistry, University of Groningen, Nijenborgh 4, 9747 AG Groningen, The Netherlands.,Institute for Organic and Biomolecular Chemistry, University of Goettingen, Tammannstr. 2, 37077 Göttingen, Germany
| | - Giovanni Maglia
- Groningen Biomolecular Science and Biotechnology Institute, University of Groningen, Nijenborgh 4, 9747 AG Groningen, The Netherlands
| | - Sebastian Thallmair
- Groningen Biomolecular Sciences and Biotechnology Institute and Zernike Institute for Advanced Materials, University of Groningen, Nijenborgh 7, 9747 AG Groningen, The Netherlands.,Frankfurt Institute for Advanced Studies, Ruth-Moufang-Straße 1, 60438 Frankfurt am Main, Germany
| | - Dušan Kolarski
- Stratingh Institute for Chemistry, University of Groningen, Nijenborgh 4, 9747 AG Groningen, The Netherlands.,DWI-Leibniz Institut für interaktive Materialien e.V., RWTH Aachen University, Forckenbeckstraße 50, 52074 Aachen, Germany
| | - Wiktor Szymanski
- Stratingh Institute for Chemistry, University of Groningen, Nijenborgh 4, 9747 AG Groningen, The Netherlands.,Department of Radiology, Medical Imaging Center, University of Groningen, University Medical Center Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands
| | - Ben L Feringa
- Stratingh Institute for Chemistry, University of Groningen, Nijenborgh 4, 9747 AG Groningen, The Netherlands
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19
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Simplified quality assessment for small-molecule ligands in the Protein Data Bank. Structure 2022; 30:252-262.e4. [PMID: 35026162 PMCID: PMC8849442 DOI: 10.1016/j.str.2021.10.003] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Revised: 09/14/2021] [Accepted: 10/06/2021] [Indexed: 02/05/2023]
Abstract
More than 70% of the experimentally determined macromolecular structures in the Protein Data Bank (PDB) contain small-molecule ligands. Quality indicators of ∼643,000 ligands present in ∼106,000 PDB X-ray crystal structures have been analyzed. Ligand quality varies greatly with regard to goodness of fit between ligand structure and experimental data, deviations in bond lengths and angles from known chemical structures, and inappropriate interatomic clashes between the ligand and its surroundings. Based on principal component analysis, correlated quality indicators of ligand structure have been aggregated into two largely orthogonal composite indicators measuring goodness of fit to experimental data and deviation from ideal chemical structure. Ranking of the composite quality indicators across the PDB archive enabled construction of uniformly distributed composite ranking score. This score is implemented at RCSB.org to compare chemically identical ligands in distinct PDB structures with easy-to-interpret two-dimensional ligand quality plots, allowing PDB users to quickly assess ligand structure quality and select the best exemplars.
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20
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Goodsell DS, Burley SK. RCSB Protein Data Bank resources for structure-facilitated design of mRNA vaccines for existing and emerging viral pathogens. Structure 2022; 30:55-68.e2. [PMID: 34739839 PMCID: PMC8567414 DOI: 10.1016/j.str.2021.10.008] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Revised: 09/17/2021] [Accepted: 10/14/2021] [Indexed: 01/11/2023]
Abstract
Structural biologists provide direct insights into the molecular bases of human health and disease. The open-access Protein Data Bank (PDB) stores and delivers three-dimensional (3D) biostructure data that facilitate discovery and development of therapeutic agents and diagnostic tools. We are in the midst of a revolution in vaccinology. Non-infectious mRNA vaccines have been proven during the coronavirus disease 2019 (COVID-19) pandemic. This new technology underpins nimble discovery and clinical development platforms that use knowledge of 3D viral protein structures for societal benefit. The RCSB PDB supports vaccine designers through expert biocuration and rigorous validation of 3D structures; open-access dissemination of structure information; and search, visualization, and analysis tools for structure-guided design efforts. This resource article examines the structural biology underpinning the success of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) mRNA vaccines and enumerates some of the many protein structures in the PDB archive that could guide design of new countermeasures against existing and emerging viral pathogens.
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Affiliation(s)
- David S Goodsell
- RCSB Protein Data Bank and Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA; Rutgers Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ 08903, USA; Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - Stephen K Burley
- RCSB Protein Data Bank and Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA; Rutgers Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ 08903, USA; Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California, San Diego, CA 92093, USA; Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA.
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21
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Burley SK, Bhikadiya C, Bi C, Bittrich S, Chen L, Crichlow GV, Duarte JM, Dutta S, Fayazi M, Feng Z, Flatt JW, Ganesan SJ, Goodsell DS, Ghosh S, Kramer Green R, Guranovic V, Henry J, Hudson BP, Lawson CL, Liang Y, Lowe R, Peisach E, Persikova I, Piehl DW, Rose Y, Sali A, Segura J, Sekharan M, Shao C, Vallat B, Voigt M, Westbrook JD, Whetstone S, Young JY, Zardecki C. RCSB Protein Data Bank: Celebrating 50 years of the PDB with new tools for understanding and visualizing biological macromolecules in 3D. Protein Sci 2022; 31:187-208. [PMID: 34676613 PMCID: PMC8740825 DOI: 10.1002/pro.4213] [Citation(s) in RCA: 69] [Impact Index Per Article: 34.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Revised: 10/12/2021] [Accepted: 10/12/2021] [Indexed: 01/03/2023]
Abstract
The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB), funded by the US National Science Foundation, National Institutes of Health, and Department of Energy, has served structural biologists and Protein Data Bank (PDB) data consumers worldwide since 1999. RCSB PDB, a founding member of the Worldwide Protein Data Bank (wwPDB) partnership, is the US data center for the global PDB archive housing biomolecular structure data. RCSB PDB is also responsible for the security of PDB data, as the wwPDB-designated Archive Keeper. Annually, RCSB PDB serves tens of thousands of three-dimensional (3D) macromolecular structure data depositors (using macromolecular crystallography, nuclear magnetic resonance spectroscopy, electron microscopy, and micro-electron diffraction) from all inhabited continents. RCSB PDB makes PDB data available from its research-focused RCSB.org web portal at no charge and without usage restrictions to millions of PDB data consumers working in every nation and territory worldwide. In addition, RCSB PDB operates an outreach and education PDB101.RCSB.org web portal that was used by more than 800,000 educators, students, and members of the public during calendar year 2020. This invited Tools Issue contribution describes (i) how the archive is growing and evolving as new experimental methods generate ever larger and more complex biomolecular structures; (ii) the importance of data standards and data remediation in effective management of the archive and facile integration with more than 50 external data resources; and (iii) new tools and features for 3D structure analysis and visualization made available during the past year via the RCSB.org web portal.
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Affiliation(s)
- Stephen K. Burley
- Research Collaboratory for Structural Bioinformatics Protein Data BankRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Institute for Quantitative BiomedicineRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Cancer Institute of New JerseyRutgers, The State University of New JerseyNew BrunswickNew JerseyUSA
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer CenterUniversity of CaliforniaLa JollaCaliforniaUSA
- Department of Chemistry and Chemical BiologyRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Charmi Bhikadiya
- Research Collaboratory for Structural Bioinformatics Protein Data BankRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Institute for Quantitative BiomedicineRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Chunxiao Bi
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer CenterUniversity of CaliforniaLa JollaCaliforniaUSA
| | - Sebastian Bittrich
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer CenterUniversity of CaliforniaLa JollaCaliforniaUSA
| | - Li Chen
- Research Collaboratory for Structural Bioinformatics Protein Data BankRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Institute for Quantitative BiomedicineRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Gregg V. Crichlow
- Research Collaboratory for Structural Bioinformatics Protein Data BankRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Institute for Quantitative BiomedicineRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Jose M. Duarte
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer CenterUniversity of CaliforniaLa JollaCaliforniaUSA
| | - Shuchismita Dutta
- Research Collaboratory for Structural Bioinformatics Protein Data BankRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Institute for Quantitative BiomedicineRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Cancer Institute of New JerseyRutgers, The State University of New JerseyNew BrunswickNew JerseyUSA
| | - Maryam Fayazi
- Research Collaboratory for Structural Bioinformatics Protein Data BankRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Institute for Quantitative BiomedicineRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Zukang Feng
- Research Collaboratory for Structural Bioinformatics Protein Data BankRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Institute for Quantitative BiomedicineRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Justin W. Flatt
- Research Collaboratory for Structural Bioinformatics Protein Data BankRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Institute for Quantitative BiomedicineRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Sai J. Ganesan
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, Quantitative Biosciences InstituteUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | - David S. Goodsell
- Research Collaboratory for Structural Bioinformatics Protein Data BankRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Institute for Quantitative BiomedicineRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Cancer Institute of New JerseyRutgers, The State University of New JerseyNew BrunswickNew JerseyUSA
- Department of Integrative Structural and Computational BiologyThe Scripps Research InstituteLa JollaCaliforniaUSA
| | - Sutapa Ghosh
- Research Collaboratory for Structural Bioinformatics Protein Data BankRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Institute for Quantitative BiomedicineRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Rachel Kramer Green
- Research Collaboratory for Structural Bioinformatics Protein Data BankRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Institute for Quantitative BiomedicineRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Vladimir Guranovic
- Research Collaboratory for Structural Bioinformatics Protein Data BankRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Institute for Quantitative BiomedicineRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Jeremy Henry
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer CenterUniversity of CaliforniaLa JollaCaliforniaUSA
| | - Brian P. Hudson
- Research Collaboratory for Structural Bioinformatics Protein Data BankRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Institute for Quantitative BiomedicineRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Catherine L. Lawson
- Research Collaboratory for Structural Bioinformatics Protein Data BankRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Institute for Quantitative BiomedicineRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Yuhe Liang
- Research Collaboratory for Structural Bioinformatics Protein Data BankRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Institute for Quantitative BiomedicineRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Robert Lowe
- Research Collaboratory for Structural Bioinformatics Protein Data BankRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Institute for Quantitative BiomedicineRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Ezra Peisach
- Research Collaboratory for Structural Bioinformatics Protein Data BankRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Institute for Quantitative BiomedicineRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Irina Persikova
- Research Collaboratory for Structural Bioinformatics Protein Data BankRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Institute for Quantitative BiomedicineRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Dennis W. Piehl
- Research Collaboratory for Structural Bioinformatics Protein Data BankRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Institute for Quantitative BiomedicineRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Yana Rose
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer CenterUniversity of CaliforniaLa JollaCaliforniaUSA
| | - Andrej Sali
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, Quantitative Biosciences InstituteUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | - Joan Segura
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer CenterUniversity of CaliforniaLa JollaCaliforniaUSA
| | - Monica Sekharan
- Research Collaboratory for Structural Bioinformatics Protein Data BankRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Institute for Quantitative BiomedicineRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Chenghua Shao
- Research Collaboratory for Structural Bioinformatics Protein Data BankRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Institute for Quantitative BiomedicineRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Brinda Vallat
- Research Collaboratory for Structural Bioinformatics Protein Data BankRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Institute for Quantitative BiomedicineRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Maria Voigt
- Research Collaboratory for Structural Bioinformatics Protein Data BankRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Institute for Quantitative BiomedicineRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - John D. Westbrook
- Research Collaboratory for Structural Bioinformatics Protein Data BankRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Institute for Quantitative BiomedicineRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Cancer Institute of New JerseyRutgers, The State University of New JerseyNew BrunswickNew JerseyUSA
| | - Shamara Whetstone
- Research Collaboratory for Structural Bioinformatics Protein Data BankRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Institute for Quantitative BiomedicineRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Jasmine Y. Young
- Research Collaboratory for Structural Bioinformatics Protein Data BankRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Institute for Quantitative BiomedicineRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Christine Zardecki
- Research Collaboratory for Structural Bioinformatics Protein Data BankRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Institute for Quantitative BiomedicineRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
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22
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Helliwell JR. Pre- and Post-publication Verification for Reproducible Data Mining in Macromolecular Crystallography. Methods Mol Biol 2022; 2449:235-261. [PMID: 35507266 DOI: 10.1007/978-1-0716-2095-3_10] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Like an article narrative is deemed by an editor and referees to be worthy of being a version of record on acceptance as a publication, so must the underpinning data also be scrutinized before passing it as a version of record. Indeed without the underpinning data, a study and its conclusions cannot be reproduced at any stage of evaluation, pre- or post-publication. Likewise, an independent study without its own underpinning data also cannot be reproduced let alone be considered a replicate of the first study. The PDB is a modern marvel of achievement providing an organized open access to depositor and user of the data held there opening numerous applications. Methods for modeling protein structures and for determination of structures are still improving their precision, and artifacts of the method exist. So their accuracy is realized if they are reproduced by other methods. It is on such foundations that reproducible data mining is based. Data rates are expanding considerably be they at synchrotrons, the X-ray free electron lasers (XFELs), electron cryomicroscopes (cryoEM), or at the neutron facilities. The work of a person as a referee or user with a narrative and its underpinning data may well be complemented in future by artificial intelligence with machine learning, the former for specific refereeing and the latter for the more general validation, both ideally before publication. Examples are described involving rhenium theranostics, the anti-cancer platins and the SARS-CoV-2 main protease.
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Affiliation(s)
- John R Helliwell
- Department of Chemistry, University of Manchester, Manchester, UK.
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23
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Zardecki C, Dutta S, Goodsell DS, Lowe R, Voigt M, Burley SK. PDB-101: Educational resources supporting molecular explorations through biology and medicine. Protein Sci 2022; 31:129-140. [PMID: 34601771 PMCID: PMC8740840 DOI: 10.1002/pro.4200] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 09/24/2021] [Accepted: 09/28/2021] [Indexed: 01/03/2023]
Abstract
The Protein Data Bank (PDB) archive is a rich source of information in the form of atomic-level three-dimensional (3D) structures of biomolecules experimentally determined using macromolecular crystallography, nuclear magnetic resonance (NMR) spectroscopy, and electron microscopy (3DEM). Originally established in 1971 as a resource for protein crystallographers to freely exchange data, today PDB data drive research and education across scientific disciplines. In 2011, the online portal PDB-101 was launched to support teachers, students, and the general public in PDB archive exploration (pdb101.rcsb.org). Maintained by the Research Collaboratory for Structural Bioinformatics PDB, PDB-101 aims to help train the next generation of PDB users and to promote the overall importance of structural biology and protein science to nonexperts. Regularly published features include the highly popular Molecule of the Month series, 3D model activities, molecular animation videos, and educational curricula. Materials are organized into various categories (Health and Disease, Molecules of Life, Biotech and Nanotech, and Structures and Structure Determination) and searchable by keyword. A biennial health focus frames new resource creation and provides topics for annual video challenges for high school students. Web analytics document that PDB-101 materials relating to fundamental topics (e.g., hemoglobin, catalase) are highly accessed year-on-year. In addition, PDB-101 materials created in response to topical health matters (e.g., Zika, measles, coronavirus) are well received. PDB-101 shows how learning about the diverse shapes and functions of PDB structures promotes understanding of all aspects of biology, from the central dogma of biology to health and disease to biological energy.
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Affiliation(s)
- Christine Zardecki
- Research Collaboratory for Structural Bioinformatics Protein Data BankRutgers, The State University of New JerseyPiscatawayNew JerseyUSA,Institute for Quantitative BiomedicineRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Shuchismita Dutta
- Research Collaboratory for Structural Bioinformatics Protein Data BankRutgers, The State University of New JerseyPiscatawayNew JerseyUSA,Institute for Quantitative BiomedicineRutgers, The State University of New JerseyPiscatawayNew JerseyUSA,Rutgers Cancer Institute of New JerseyRutgers, The State University of New JerseyNew BrunswickNew JerseyUSA
| | - David S. Goodsell
- Research Collaboratory for Structural Bioinformatics Protein Data BankRutgers, The State University of New JerseyPiscatawayNew JerseyUSA,Institute for Quantitative BiomedicineRutgers, The State University of New JerseyPiscatawayNew JerseyUSA,Rutgers Cancer Institute of New JerseyRutgers, The State University of New JerseyNew BrunswickNew JerseyUSA,Department of Integrative Structural and Computational BiologyThe Scripps Research InstituteLa JollaCaliforniaUSA
| | - Robert Lowe
- Research Collaboratory for Structural Bioinformatics Protein Data BankRutgers, The State University of New JerseyPiscatawayNew JerseyUSA,Institute for Quantitative BiomedicineRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Maria Voigt
- Research Collaboratory for Structural Bioinformatics Protein Data BankRutgers, The State University of New JerseyPiscatawayNew JerseyUSA,Institute for Quantitative BiomedicineRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Stephen K. Burley
- Research Collaboratory for Structural Bioinformatics Protein Data BankRutgers, The State University of New JerseyPiscatawayNew JerseyUSA,Institute for Quantitative BiomedicineRutgers, The State University of New JerseyPiscatawayNew JerseyUSA,Rutgers Cancer Institute of New JerseyRutgers, The State University of New JerseyNew BrunswickNew JerseyUSA,Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer CenterUniversity of California San DiegoLa JollaCaliforniaUSA,Department of Chemistry and Chemical BiologyRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
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24
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BEHZADI PAYAM, GAJDÁCS MÁRIÓ. Worldwide Protein Data Bank (wwPDB): A virtual treasure for research in biotechnology. Eur J Microbiol Immunol (Bp) 2021; 11:77-86. [PMID: 34908533 PMCID: PMC8830413 DOI: 10.1556/1886.2021.00020] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2021] [Accepted: 11/23/2021] [Indexed: 12/25/2022] Open
Abstract
The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RSCB PDB) provides a wide range of digital data regarding biology and biomedicine. This huge internet resource involves a wide range of important biological data, obtained from experiments around the globe by different scientists. The Worldwide Protein Data Bank (wwPDB) represents a brilliant collection of 3D structure data associated with important and vital biomolecules including nucleic acids (RNAs and DNAs) and proteins. Moreover, this database accumulates knowledge regarding function and evolution of biomacromolecules which supports different disciplines such as biotechnology. 3D structure, functional characteristics and phylogenetic properties of biomacromolecules give a deep understanding of the biomolecules' characteristics. An important advantage of the wwPDB database is the data updating time, which is done every week. This updating process helps users to have the newest data and information for their projects. The data and information in wwPDB can be a great support to have an accurate imagination and illustrations of the biomacromolecules in biotechnology. As demonstrated by the SARS-CoV-2 pandemic, rapidly reliable and accessible biological data for microbiology, immunology, vaccinology, and drug development are critical to address many healthcare-related challenges that are facing humanity. The aim of this paper is to introduce the readers to wwPDB, and to highlight the importance of this database in biotechnology, with the expectation that the number of scientists interested in the utilization of Protein Data Bank's resources will increase substantially in the coming years.
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Affiliation(s)
- PAYAM BEHZADI
- Department of Microbiology, College of Basic Sciences, Shahr-e-Qods Branch, Islamic Azad University, Tehran, 37541-374, Iran
| | - MÁRIÓ GAJDÁCS
- Department of Oral Biology and Experimental Dental Research, Faculty of Dentistry, University of Szeged, 6720, Szeged, Hungary,*Corresponding author. Tel.: +36-62-342-532. E-mail:
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25
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Wigge C, Stefanovic A, Radjainia M. The rapidly evolving role of cryo-EM in drug design. DRUG DISCOVERY TODAY. TECHNOLOGIES 2021; 38:91-102. [PMID: 34895645 DOI: 10.1016/j.ddtec.2020.12.003] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Revised: 12/09/2020] [Accepted: 12/22/2020] [Indexed: 01/18/2023]
Abstract
Since the early 2010s, cryo-electron microscopy (cryo-EM) has evolved to a mainstream structural biology method in what has been dubbed the "resolution revolution". Pharma companies also began to use cryo-EM in drug discovery, evidenced by a growing number of industry publications. Hitherto limited in resolution, throughput and attainable molecular weight, cryo-EM is rapidly overcoming its main limitations for more widespread use through a new wave of technological advances. This review discusses how cryo-EM has already impacted drug discovery, and how the state-of-the-art is poised to further revolutionize its application to previously intractable proteins as well as new use cases.
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Affiliation(s)
- Christoph Wigge
- Thermo Fisher Scientific, Achtseweg Noord 5, 5651 GG Eindhoven, The Netherlands
| | | | - Mazdak Radjainia
- Thermo Fisher Scientific, Achtseweg Noord 5, 5651 GG Eindhoven, The Netherlands.
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26
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Kidera A, Moritsugu K, Ekimoto T, Ikeguchi M. Allosteric Regulation of 3CL Protease of SARS-CoV-2 and SARS-CoV Observed in the Crystal Structure Ensemble. J Mol Biol 2021; 433:167324. [PMID: 34717972 PMCID: PMC8550881 DOI: 10.1016/j.jmb.2021.167324] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Revised: 10/16/2021] [Accepted: 10/18/2021] [Indexed: 01/08/2023]
Abstract
The 3C-like protease (3CLpro) of SARS-CoV-2 is a potential therapeutic target for COVID-19. Importantly, it has an abundance of structural information solved as a complex with various drug candidate compounds. Collecting these crystal structures (83 Protein Data Bank (PDB) entries) together with those of the highly homologous 3CLpro of SARS-CoV (101 PDB entries), we constructed the crystal structure ensemble of 3CLpro to analyze the dynamic regulation of its catalytic function. The structural dynamics of the 3CLpro dimer observed in the ensemble were characterized by the motions of four separate loops (the C-loop, E-loop, H-loop, and Linker) and the C-terminal domain III on the rigid core of the chymotrypsin fold. Among the four moving loops, the C-loop (also known as the oxyanion binding loop) causes the order (active)-disorder (collapsed) transition, which is regulated cooperatively by five hydrogen bonds made with the surrounding residues. The C-loop, E-loop, and Linker constitute the major ligand binding sites, which consist of a limited variety of binding residues including the substrate binding subsites. Ligand binding causes a ligand size dependent conformational change to the E-loop and Linker, which further stabilize the C-loop via the hydrogen bond between the C-loop and E-loop. The T285A mutation from SARS-CoV 3CLpro to SARS-CoV-2 3CLpro significantly closes the interface of the domain III dimer and allosterically stabilizes the active conformation of the C-loop via hydrogen bonds with Ser1 and Gly2; thus, SARS-CoV-2 3CLpro seems to have increased activity relative to that of SARS-CoV 3CLpro.
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Affiliation(s)
- Akinori Kidera
- Graduate School of Medical Life Science, Yokohama City University, 1-7-29 Suehiro-cho, Tsurumi, Yokohama 230-0045, Japan.
| | - Kei Moritsugu
- Graduate School of Medical Life Science, Yokohama City University, 1-7-29 Suehiro-cho, Tsurumi, Yokohama 230-0045, Japan
| | - Toru Ekimoto
- Graduate School of Medical Life Science, Yokohama City University, 1-7-29 Suehiro-cho, Tsurumi, Yokohama 230-0045, Japan
| | - Mitsunori Ikeguchi
- Graduate School of Medical Life Science, Yokohama City University, 1-7-29 Suehiro-cho, Tsurumi, Yokohama 230-0045, Japan
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27
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Burley SK, Arap W, Pasqualini R. Predicting Proteome-Scale Protein Structure with Artificial Intelligence. N Engl J Med 2021; 385:2191-2194. [PMID: 34874637 DOI: 10.1056/nejmcibr2113027] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Affiliation(s)
- Stephen K Burley
- From the Research Collaboratory for Structural Bioinformatics Protein Data Bank, the Institute for Quantitative Biomedicine, and the Department of Chemistry and Chemical Biology, Rutgers, the State University of New Jersey (S.K.B.), and the Rutgers Cancer Institute of New Jersey, New Brunswick (S.K.B.) and Newark (W.A., R.P.); and the Division of Hematology-Oncology, Department of Medicine (W.A.), and the Division of Cancer Biology, Department of Radiation Oncology (R.P.), Rutgers New Jersey Medical School, Newark; and the Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California, San Diego, San Diego (S.K.B.)
| | - Wadih Arap
- From the Research Collaboratory for Structural Bioinformatics Protein Data Bank, the Institute for Quantitative Biomedicine, and the Department of Chemistry and Chemical Biology, Rutgers, the State University of New Jersey (S.K.B.), and the Rutgers Cancer Institute of New Jersey, New Brunswick (S.K.B.) and Newark (W.A., R.P.); and the Division of Hematology-Oncology, Department of Medicine (W.A.), and the Division of Cancer Biology, Department of Radiation Oncology (R.P.), Rutgers New Jersey Medical School, Newark; and the Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California, San Diego, San Diego (S.K.B.)
| | - Renata Pasqualini
- From the Research Collaboratory for Structural Bioinformatics Protein Data Bank, the Institute for Quantitative Biomedicine, and the Department of Chemistry and Chemical Biology, Rutgers, the State University of New Jersey (S.K.B.), and the Rutgers Cancer Institute of New Jersey, New Brunswick (S.K.B.) and Newark (W.A., R.P.); and the Division of Hematology-Oncology, Department of Medicine (W.A.), and the Division of Cancer Biology, Department of Radiation Oncology (R.P.), Rutgers New Jersey Medical School, Newark; and the Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California, San Diego, San Diego (S.K.B.)
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28
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Sheik Amamuddy O, Afriyie Boateng R, Barozi V, Wavinya Nyamai D, Tastan Bishop Ö. Novel dynamic residue network analysis approaches to study allosteric modulation: SARS-CoV-2 M pro and its evolutionary mutations as a case study. Comput Struct Biotechnol J 2021; 19:6431-6455. [PMID: 34849191 PMCID: PMC8613987 DOI: 10.1016/j.csbj.2021.11.016] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Revised: 11/09/2021] [Accepted: 11/13/2021] [Indexed: 01/15/2023] Open
Abstract
The rational search for allosteric modulators and the allosteric mechanisms of these modulators in the presence of mutations is a relatively unexplored field. Here, we established novel in silico approaches and applied them to SARS-CoV-2 main protease (Mpro) as a case study. First, we identified six potential allosteric modulators. Then, we focused on understanding the allosteric effects of these modulators on each of its protomers. We introduced a new combinatorial approach and dynamic residue network (DRN) analysis algorithms to examine patterns of change and conservation of critical nodes, according to five independent criteria of network centrality. We observed highly conserved network hubs for each averaged DRN metric on the basis of their existence in both protomers in the absence and presence of all ligands (persistent hubs). We also detected ligand specific signal changes. Using eigencentrality (EC) persistent hubs and ligand introduced hubs we identified a residue communication path connecting the allosteric binding site to the catalytic site. Finally, we examined the effects of the mutations on the behavior of the protein in the presence of selected potential allosteric modulators and investigated the ligand stability. One crucial outcome was to show that EC centrality hubs form an allosteric communication path between the allosteric ligand binding site to the active site going through the interface residues of domains I and II; and this path was either weakened or lost in the presence of some of the mutations. Overall, the results revealed crucial aspects that need to be considered in rational computational drug discovery.
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Affiliation(s)
| | | | - Victor Barozi
- Research Unit in Bioinformatics (RUBi), Department of Biochemistry and Microbiology, Rhodes University, Makhanda, South Africa
| | - Dorothy Wavinya Nyamai
- Research Unit in Bioinformatics (RUBi), Department of Biochemistry and Microbiology, Rhodes University, Makhanda, South Africa
| | - Özlem Tastan Bishop
- Research Unit in Bioinformatics (RUBi), Department of Biochemistry and Microbiology, Rhodes University, Makhanda, South Africa
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29
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Shao C, Feng Z, Westbrook JD, Peisach E, Berrisford J, Ikegawa Y, Kurisu G, Velankar S, Burley SK, Young JY. Modernized uniform representation of carbohydrate molecules in the Protein Data Bank. Glycobiology 2021; 31:1204-1218. [PMID: 33978738 PMCID: PMC8457362 DOI: 10.1093/glycob/cwab039] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 04/05/2021] [Accepted: 04/25/2021] [Indexed: 12/12/2022] Open
Abstract
Since 1971, the Protein Data Bank (PDB) has served as the single global archive for experimentally determined 3D structures of biological macromolecules made freely available to the global community according to the FAIR principles of Findability-Accessibility-Interoperability-Reusability. During the first 50 years of continuous PDB operations, standards for data representation have evolved to better represent rich and complex biological phenomena. Carbohydrate molecules present in more than 14,000 PDB structures have recently been reviewed and remediated to conform to a new standardized format. This machine-readable data representation for carbohydrates occurring in the PDB structures and the corresponding reference data improves the findability, accessibility, interoperability and reusability of structural information pertaining to these molecules. The PDB Exchange MacroMolecular Crystallographic Information File data dictionary now supports (i) standardized atom nomenclature that conforms to International Union of Pure and Applied Chemistry-International Union of Biochemistry and Molecular Biology (IUPAC-IUBMB) recommendations for carbohydrates, (ii) uniform representation of branched entities for oligosaccharides, (iii) commonly used linear descriptors of carbohydrates developed by the glycoscience community and (iv) annotation of glycosylation sites in proteins. For the first time, carbohydrates in PDB structures are consistently represented as collections of standardized monosaccharides, which precisely describe oligosaccharide structures and enable improved carbohydrate visualization, structure validation, robust quantitative and qualitative analyses, search for dendritic structures and classification. The uniform representation of carbohydrate molecules in the PDB described herein will facilitate broader usage of the resource by the glycoscience community and researchers studying glycoproteins.
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Affiliation(s)
- Chenghua Shao
- Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB), Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Zukang Feng
- Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB), Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - John D Westbrook
- Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB), Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Rutgers Cancer Institute of New Jersey, Robert Wood Johnson Medical School, New Brunswick, NJ 08903, USA
| | - Ezra Peisach
- Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB), Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - John Berrisford
- Protein Data Bank in Europe (PDBe), European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Yasuyo Ikegawa
- Protein Data Bank Japan (PDBj), Institute for Protein Research, Osaka University, Osaka 565-0871, Japan
| | - Genji Kurisu
- Protein Data Bank Japan (PDBj), Institute for Protein Research, Osaka University, Osaka 565-0871, Japan
| | - Sameer Velankar
- Protein Data Bank in Europe (PDBe), European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Stephen K Burley
- Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB), Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Rutgers Cancer Institute of New Jersey, Robert Wood Johnson Medical School, New Brunswick, NJ 08903, USA
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California, La Jolla, San Diego, CA 92093, USA
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Jasmine Y Young
- Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB), Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
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30
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CAVIAR: a method for automatic cavity detection, description and decomposition into subcavities. J Comput Aided Mol Des 2021; 35:737-750. [PMID: 34050420 DOI: 10.1007/s10822-021-00390-w] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Accepted: 05/11/2021] [Indexed: 10/21/2022]
Abstract
The accurate description of protein binding sites is essential to the determination of similarity and the application of machine learning methods to relate the binding sites to observed functions. This work describes CAVIAR, a new open source tool for generating descriptors for binding sites, using protein structures in PDB and mmCIF format as well as trajectory frames from molecular dynamics simulations as input. The applicability of CAVIAR descriptors is showcased by computing machine learning predictions of binding site ligandability. The method can also automatically assign subcavities, even in the absence of a bound ligand. The defined subpockets mimic the empirical definitions used in medicinal chemistry projects. It is shown that the experimental binding affinity scales relatively well with the number of subcavities filled by the ligand, with compounds binding to more than three subcavities having nanomolar or better affinities to the target. The CAVIAR descriptors and methods can be used in any machine learning-based investigations of problems involving binding sites, from protein engineering to hit identification. The full software code is available on GitHub and a conda package is hosted on Anaconda cloud.
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31
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Waman VP, Sen N, Varadi M, Daina A, Wodak SJ, Zoete V, Velankar S, Orengo C. The impact of structural bioinformatics tools and resources on SARS-CoV-2 research and therapeutic strategies. Brief Bioinform 2021; 22:742-768. [PMID: 33348379 PMCID: PMC7799268 DOI: 10.1093/bib/bbaa362] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Revised: 11/06/2020] [Accepted: 11/09/2020] [Indexed: 01/18/2023] Open
Abstract
SARS-CoV-2 is the causative agent of COVID-19, the ongoing global pandemic. It has posed a worldwide challenge to human health as no effective treatment is currently available to combat the disease. Its severity has led to unprecedented collaborative initiatives for therapeutic solutions against COVID-19. Studies resorting to structure-based drug design for COVID-19 are plethoric and show good promise. Structural biology provides key insights into 3D structures, critical residues/mutations in SARS-CoV-2 proteins, implicated in infectivity, molecular recognition and susceptibility to a broad range of host species. The detailed understanding of viral proteins and their complexes with host receptors and candidate epitope/lead compounds is the key to developing a structure-guided therapeutic design. Since the discovery of SARS-CoV-2, several structures of its proteins have been determined experimentally at an unprecedented speed and deposited in the Protein Data Bank. Further, specialized structural bioinformatics tools and resources have been developed for theoretical models, data on protein dynamics from computer simulations, impact of variants/mutations and molecular therapeutics. Here, we provide an overview of ongoing efforts on developing structural bioinformatics tools and resources for COVID-19 research. We also discuss the impact of these resources and structure-based studies, to understand various aspects of SARS-CoV-2 infection and therapeutic development. These include (i) understanding differences between SARS-CoV-2 and SARS-CoV, leading to increased infectivity of SARS-CoV-2, (ii) deciphering key residues in the SARS-CoV-2 involved in receptor-antibody recognition, (iii) analysis of variants in host proteins that affect host susceptibility to infection and (iv) analyses facilitating structure-based drug and vaccine design against SARS-CoV-2.
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Affiliation(s)
| | | | | | - Antoine Daina
- Molecular Modeling Group at SIB, Swiss Institute of Bioinformatics
| | | | - Vincent Zoete
- Department of Fundamental Oncology at the University of Lausanne and Group leader at SIB
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Antila HS, M. Ferreira T, Ollila OHS, Miettinen MS. Using Open Data to Rapidly Benchmark Biomolecular Simulations: Phospholipid Conformational Dynamics. J Chem Inf Model 2021; 61:938-949. [PMID: 33496579 PMCID: PMC7903423 DOI: 10.1021/acs.jcim.0c01299] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Indexed: 01/08/2023]
Abstract
Molecular dynamics (MD) simulations are widely used to monitor time-resolved motions of biomacromolecules, although it often remains unknown how closely the conformational dynamics correspond to those occurring in real life. Here, we used a large set of open-access MD trajectories of phosphatidylcholine (PC) lipid bilayers to benchmark the conformational dynamics in several contemporary MD models (force fields) against nuclear magnetic resonance (NMR) data available in the literature: effective correlation times and spin-lattice relaxation rates. We found none of the tested MD models to fully reproduce the conformational dynamics. That said, the dynamics in CHARMM36 and Slipids are more realistic than in the Amber Lipid14, OPLS-based MacRog, and GROMOS-based Berger force fields, whose sampling of the glycerol backbone conformations is too slow. The performance of CHARMM36 persists when cholesterol is added to the bilayer, and when the hydration level is reduced. However, for conformational dynamics of the PC headgroup, both with and without cholesterol, Slipids provides the most realistic description because CHARMM36 overestimates the relative weight of ∼1 ns processes in the headgroup dynamics. We stress that not a single new simulation was run for the present work. This demonstrates the worth of open-access MD trajectory databanks for the indispensable step of any serious MD study: benchmarking the available force fields. We believe this proof of principle will inspire other novel applications of MD trajectory databanks and thus aid in developing biomolecular MD simulations into a true computational microscope-not only for lipid membranes but for all biomacromolecular systems.
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Affiliation(s)
- Hanne S. Antila
- Department
of Theory and Bio-Systems, Max Planck Institute
of Colloids and Interfaces, 14424 Potsdam, Germany
| | - Tiago M. Ferreira
- NMR
Group−Institute for Physics, Martin-Luther
University Halle-Wittenberg, 06120 Halle (Saale), Germany
| | | | - Markus S. Miettinen
- Department
of Theory and Bio-Systems, Max Planck Institute
of Colloids and Interfaces, 14424 Potsdam, Germany
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33
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Burley SK, Bhikadiya C, Bi C, Bittrich S, Chen L, Crichlow GV, Christie CH, Dalenberg K, Di Costanzo L, Duarte JM, Dutta S, Feng Z, Ganesan S, Goodsell DS, Ghosh S, Green RK, Guranović V, Guzenko D, Hudson BP, Lawson C, Liang Y, Lowe R, Namkoong H, Peisach E, Persikova I, Randle C, Rose A, Rose Y, Sali A, Segura J, Sekharan M, Shao C, Tao YP, Voigt M, Westbrook J, Young JY, Zardecki C, Zhuravleva M. RCSB Protein Data Bank: powerful new tools for exploring 3D structures of biological macromolecules for basic and applied research and education in fundamental biology, biomedicine, biotechnology, bioengineering and energy sciences. Nucleic Acids Res 2021; 49:D437-D451. [PMID: 33211854 PMCID: PMC7779003 DOI: 10.1093/nar/gkaa1038] [Citation(s) in RCA: 773] [Impact Index Per Article: 257.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Revised: 10/14/2020] [Accepted: 11/17/2020] [Indexed: 12/14/2022] Open
Abstract
The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB), the US data center for the global PDB archive and a founding member of the Worldwide Protein Data Bank partnership, serves tens of thousands of data depositors in the Americas and Oceania and makes 3D macromolecular structure data available at no charge and without restrictions to millions of RCSB.org users around the world, including >660 000 educators, students and members of the curious public using PDB101.RCSB.org. PDB data depositors include structural biologists using macromolecular crystallography, nuclear magnetic resonance spectroscopy, 3D electron microscopy and micro-electron diffraction. PDB data consumers accessing our web portals include researchers, educators and students studying fundamental biology, biomedicine, biotechnology, bioengineering and energy sciences. During the past 2 years, the research-focused RCSB PDB web portal (RCSB.org) has undergone a complete redesign, enabling improved searching with full Boolean operator logic and more facile access to PDB data integrated with >40 external biodata resources. New features and resources are described in detail using examples that showcase recently released structures of SARS-CoV-2 proteins and host cell proteins relevant to understanding and addressing the COVID-19 global pandemic.
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Affiliation(s)
- Stephen K Burley
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ 08901, USA
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California, La Jolla, CA 92093, USA
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Charmi Bhikadiya
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Chunxiao Bi
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California, La Jolla, CA 92093, USA
| | - Sebastian Bittrich
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California, La Jolla, CA 92093, USA
| | - Li Chen
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Gregg V Crichlow
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Cole H Christie
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California, La Jolla, CA 92093, USA
| | - Kenneth Dalenberg
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Luigi Di Costanzo
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Jose M Duarte
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California, La Jolla, CA 92093, USA
| | - Shuchismita Dutta
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ 08901, USA
| | - Zukang Feng
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Sai Ganesan
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Department of Biotherapeutic Sciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - David S Goodsell
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Center for Computational Structural Biology, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - Sutapa Ghosh
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Rachel Kramer Green
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Vladimir Guranović
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Dmytro Guzenko
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California, La Jolla, CA 92093, USA
| | - Brian P Hudson
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Catherine L Lawson
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Yuhe Liang
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Robert Lowe
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Harry Namkoong
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Ezra Peisach
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Irina Persikova
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Chris Randle
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California, La Jolla, CA 92093, USA
| | - Alexander Rose
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California, La Jolla, CA 92093, USA
| | - Yana Rose
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California, La Jolla, CA 92093, USA
| | - Andrej Sali
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Department of Biotherapeutic Sciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Joan Segura
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California, La Jolla, CA 92093, USA
| | - Monica Sekharan
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Chenghua Shao
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Yi-Ping Tao
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Maria Voigt
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - John D Westbrook
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ 08901, USA
| | - Jasmine Y Young
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Christine Zardecki
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Marina Zhuravleva
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
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34
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Abstract
Protein Data Bank is the single worldwide archive of experimentally determined macromolecular structure data. Established in 1971 as the first open access data resource in biology, the PDB archive is managed by the worldwide Protein Data Bank (wwPDB) consortium which has four partners-the RCSB Protein Data Bank (RCSB PDB; rcsb.org), the Protein Data Bank Japan (PDBj; pdbj.org), the Protein Data Bank in Europe (PDBe; pdbe.org), and BioMagResBank (BMRB; www.bmrb.wisc.edu ). The PDB archive currently includes ~175,000 entries. The wwPDB has established a number of task forces and working groups that bring together experts form the community who provide recommendations on improving data standards and data validation for improving data quality and integrity. The wwPDB members continue to develop the joint deposition, biocuration, and validation system (OneDep) to improve data quality and accommodate new data from emerging techniques such as 3DEM. Each PDB entry contains coordinate model and associated metadata for all experimentally determined atomic structures, experimental data for the traditional structure determination techniques (X-ray crystallography and nuclear magnetic resonance (NMR) spectroscopy), validation reports, and additional information on quaternary structures. The wwPDB partners are committed to following the FAIR (Findability, Accessibility, Interoperability, and Reproducibility) principles and have implemented a DOI resolution mechanism that provides access to all the relevant files for a given PDB entry. On average, >250 new entries are added to the archive every week and made available by each wwPDB partner via FTP area. The wwPDB partner sites also develop data access and analysis tools and make these available via their websites. wwPDB continues to work with experts in the community to establish a federation of archives for archiving structures determined using integrative/hybrid method where multiple experimental techniques are used.
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Affiliation(s)
- Sameer Velankar
- Protein Data Bank in Europe, European Molecular Biology Laboratory-European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, UK.
| | - Stephen K Burley
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, USA.,Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ, USA.,Rutgers Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ, USA.,Skaggs School of Pharmacy and Pharmaceutical Sciences and San Diego Supercomputer Center, University of California, San Diego, La Jolla, CA, USA
| | - Genji Kurisu
- Protein Data Bank Japan, Institute for Protein Research, Osaka University, Suita, Osaka, Japan
| | - Jeffrey C Hoch
- BioMagResBank, Department of Molecular Biology and Biophysics, UConn Health, Farmington, CT, USA
| | - John L Markley
- BioMagResBank, Biochemistry Department, University of Wisconsin-Madison, Madison, WI, USA
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35
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Burley SK. Impact of structural biologists and the Protein Data Bank on small-molecule drug discovery and development. J Biol Chem 2021; 296:100559. [PMID: 33744282 PMCID: PMC8059052 DOI: 10.1016/j.jbc.2021.100559] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Revised: 02/02/2021] [Accepted: 03/16/2021] [Indexed: 12/12/2022] Open
Abstract
The Protein Data Bank (PDB) is an international core data resource central to fundamental biology, biomedicine, bioenergy, and biotechnology/bioengineering. Now celebrating its 50th anniversary, the PDB houses >175,000 experimentally determined atomic structures of proteins, nucleic acids, and their complexes with one another and small molecules and drugs. The importance of three-dimensional (3D) biostructure information for research and education obtains from the intimate link between molecular form and function evident throughout biology. Among the most prolific consumers of PDB data are biomedical researchers, who rely on the open access resource as the authoritative source of well-validated, expertly curated biostructures. This review recounts how the PDB grew from just seven protein structures to contain more than 49,000 structures of human proteins that have proven critical for understanding their roles in human health and disease. It then describes how these structures are used in academe and industry to validate drug targets, assess target druggability, characterize how tool compounds and other small-molecules bind to drug targets, guide medicinal chemistry optimization of binding affinity and selectivity, and overcome challenges during preclinical drug development. Three case studies drawn from oncology exemplify how structural biologists and open access to PDB structures impacted recent regulatory approvals of antineoplastic drugs.
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Affiliation(s)
- Stephen K Burley
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA; Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA; Rutgers Cancer Institute of New Jersey, Robert Wood Johnson Medical School, New Brunswick, New Jersey, USA; Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California, San Diego, La Jolla, California, USA; Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, California, USA.
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36
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Sali A. From integrative structural biology to cell biology. J Biol Chem 2021; 296:100743. [PMID: 33957123 PMCID: PMC8203844 DOI: 10.1016/j.jbc.2021.100743] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Revised: 04/09/2021] [Accepted: 04/30/2021] [Indexed: 12/16/2022] Open
Abstract
Integrative modeling is an increasingly important tool in structural biology, providing structures by combining data from varied experimental methods and prior information. As a result, molecular architectures of large, heterogeneous, and dynamic systems, such as the ∼52-MDa Nuclear Pore Complex, can be mapped with useful accuracy, precision, and completeness. Key challenges in improving integrative modeling include expanding model representations, increasing the variety of input data and prior information, quantifying a match between input information and a model in a Bayesian fashion, inventing more efficient structural sampling, as well as developing better model validation, analysis, and visualization. In addition, two community-level challenges in integrative modeling are being addressed under the auspices of the Worldwide Protein Data Bank (wwPDB). First, the impact of integrative structures is maximized by PDB-Development, a prototype wwPDB repository for archiving, validating, visualizing, and disseminating integrative structures. Second, the scope of structural biology is expanded by linking the wwPDB resource for integrative structures with archives of data that have not been generally used for structure determination but are increasingly important for computing integrative structures, such as data from various types of mass spectrometry, spectroscopy, optical microscopy, proteomics, and genetics. To address the largest of modeling problems, a type of integrative modeling called metamodeling is being developed; metamodeling combines different types of input models as opposed to different types of data to compute an output model. Collectively, these developments will facilitate the structural biology mindset in cell biology and underpin spatiotemporal mapping of the entire cell.
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Affiliation(s)
- Andrej Sali
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, the Department of Bioengineering and Therapeutic Sciences, the Quantitative Biosciences Institute (QBI), and the Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, California, USA.
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37
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dos Santos R, Romão MJ, Roque ACA, Carvalho AL. Magnetic particles used in a new approach for designed protein crystallization. CrystEngComm 2021. [DOI: 10.1039/d0ce01529f] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Designed protein crystallization using magnetic particles as additives in the crystallization of model case studies.
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Affiliation(s)
- Raquel dos Santos
- UCIBIO
- Chemistry Department
- School of Science and Technology
- NOVA University of Lisbon
- 2829-516 Caparica
| | - Maria João Romão
- UCIBIO
- Chemistry Department
- School of Science and Technology
- NOVA University of Lisbon
- 2829-516 Caparica
| | - Ana Cecília A. Roque
- UCIBIO
- Chemistry Department
- School of Science and Technology
- NOVA University of Lisbon
- 2829-516 Caparica
| | - Ana Luísa Carvalho
- UCIBIO
- Chemistry Department
- School of Science and Technology
- NOVA University of Lisbon
- 2829-516 Caparica
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38
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Self-Nano-Emulsifying Drug-Delivery Systems: From the Development to the Current Applications and Challenges in Oral Drug Delivery. Pharmaceutics 2020; 12:pharmaceutics12121194. [PMID: 33317067 PMCID: PMC7764143 DOI: 10.3390/pharmaceutics12121194] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Revised: 12/04/2020] [Accepted: 12/05/2020] [Indexed: 12/31/2022] Open
Abstract
Approximately one third of newly discovered drug molecules show insufficient water solubility and therefore low oral bio-availability. Self-nano-emulsifying drug-delivery systems (SNEDDSs) are one of the emerging strategies developed to tackle the issues associated with their oral delivery. SNEDDSs are composed of an oil phase, surfactant, and cosurfactant or cosolvent. SNEDDSs characteristics, their ability to dissolve a drug, and in vivo considerations are determinant factors in the choice of SNEDDSs excipients. A SNEDDS formulation can be optimized through phase diagram approach or statistical design of experiments. The characterization of SNEDDSs includes multiple orthogonal methods required to fully control SNEDDS manufacture, stability, and biological fate. Encapsulating a drug in SNEDDSs can lead to increased solubilization, stability in the gastro-intestinal tract, and absorption, resulting in enhanced bio-availability. The transformation of liquid SNEDDSs into solid dosage forms has been shown to increase the stability and patient compliance. Supersaturated, mucus-permeating, and targeted SNEDDSs can be developed to increase efficacy and patient compliance. Self-emulsification approach has been successful in oral drug delivery. The present review gives an insight of SNEDDSs for the oral administration of both lipophilic and hydrophilic compounds from the experimental bench to marketed products.
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39
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Deciphering collaborative sidechain motions in proteins during molecular dynamics simulations. Sci Rep 2020; 10:15901. [PMID: 32985550 PMCID: PMC7522237 DOI: 10.1038/s41598-020-72766-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Accepted: 06/25/2020] [Indexed: 12/15/2022] Open
Abstract
The dynamic structure of proteins is essential for their functions and may include large conformational transitions which can be studied by molecular dynamics (MD) simulations. However, details of these transitions are difficult to automatically track. To facilitate their analysis, we developed two scores of correlation between sidechain dihedral angles. The CIRCULAR and OMES scores are computed from, respectively, dihedral angle values and rotamer distributions. As a case study, we applied our methods to an activation-like transition of the chemokine receptor CXCR4, observed during accelerated MD simulations. The principal component analysis of the correlation matrices was consistent with the networking structure of the top ranking pairs. Both scores identify a set of residues whose “collaborative” sidechain rotamerization immediately preceded or accompanied the conformational transition of CXCR4. Detailed analysis of the sequential order of these rotamerizations suggests that an allosteric mechanism, involving the outward motion of an asparagine residue in transmembrane helix 3, might be a prerequisite to the large scale conformational transition of CXCR4. This case study provides the proof-of-concept that the correlation methods developed here are valuable exploratory techniques to help decipher complex reactional pathways.
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40
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Goodsell DS, Burley SK. RCSB Protein Data Bank tools for 3D structure-guided cancer research: human papillomavirus (HPV) case study. Oncogene 2020; 39:6623-6632. [PMID: 32939013 PMCID: PMC7581513 DOI: 10.1038/s41388-020-01461-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2020] [Revised: 07/30/2020] [Accepted: 09/04/2020] [Indexed: 11/21/2022]
Abstract
Atomic-level three-dimensional (3D) structure data for biological macromolecules often prove critical to dissecting and understanding the precise mechanisms of action of cancer-related proteins and their diverse roles in oncogenic transformation, proliferation, and metastasis. They are also used extensively to identify potentially druggable targets and facilitate discovery and development of both small-molecule and biologic drugs that are today benefiting individuals diagnosed with cancer around the world. 3D structures of biomolecules (including proteins, DNA, RNA, and their complexes with one another, drugs, and other small molecules) are freely distributed by the open-access Protein Data Bank (PDB). This global data repository is used by millions of scientists and educators working in the areas of drug discovery, vaccine design, and biomedical and biotechnology research. The US Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) provides an integrated portal to the PDB archive that streamlines access for millions of worldwide PDB data consumers worldwide. Herein, we review online resources made available free of charge by the RCSB PDB to basic and applied researchers, healthcare providers, educators and their students, patients and their families, and the curious public. We exemplify the value of understanding cancer-related proteins in 3D with a case study focused on human papillomavirus.
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Affiliation(s)
- David S Goodsell
- Research Collaboratory for Structural Bioinformatics Protein Data Bank and Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA. .,Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, 92037, USA.
| | - Stephen K Burley
- Research Collaboratory for Structural Bioinformatics Protein Data Bank and Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA. .,Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ, 08854, USA. .,Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, and the Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA, 92093, USA. .,Rutgers Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ, 08903, USA.
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41
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Khan ZM, Real AM, Marsiglia WM, Chow A, Duffy ME, Yerabolu JR, Scopton AP, Dar AC. Structural basis for the action of the drug trametinib at KSR-bound MEK. Nature 2020; 588:509-514. [PMID: 32927473 PMCID: PMC7746607 DOI: 10.1038/s41586-020-2760-4] [Citation(s) in RCA: 78] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2019] [Accepted: 09/04/2020] [Indexed: 12/21/2022]
Abstract
The MAPK/ERK Kinase MEK is a shared effector of the frequent cancer drivers KRAS and BRAF that has long been pursued as a drug target in oncology1, and more recently in immunotherapy2,3 and aging4. However, many MEK inhibitors (MEKi) are limited due to on-target toxicities5–7 and drug resistance8–10. Accordingly, a molecular understanding of the structure and function of MEK within physiological complexes could provide a template for the design of safer and more effective therapies. Here we report X-ray crystal structures of MEK bound to the scaffold KSR (Kinase Suppressor of Ras) with various MEKi, including the clinical drug trametinib. The structures reveal an unexpected mode of binding in which trametinib directly engages KSR at the MEK interface. Through complexation, KSR remodels the prototypical MEKi allosteric pocket thereby impacting binding and kinetics, including drug residence time. Moreover, trametinib binds KSR-MEK but disrupts the related RAF-MEK complex through a mechanism that exploits evolutionarily conserved interface residues that distinguish these subcomplexes. Based on these insights we created trametiglue, which limits adaptive resistance to MEKi through enhanced interfacial binding. Together, our results reveal the plasticity of an interface pocket within MEK subcomplexes that has implications for the design of next generation drugs targeting the RAS pathway.
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Affiliation(s)
- Zaigham M Khan
- Department of Oncological Sciences, The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Department of Pharmacological Sciences, The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Alexander M Real
- Department of Oncological Sciences, The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Department of Pharmacological Sciences, The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - William M Marsiglia
- Department of Oncological Sciences, The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Department of Pharmacological Sciences, The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Arthur Chow
- Department of Oncological Sciences, The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Department of Pharmacological Sciences, The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Mary E Duffy
- Department of Oncological Sciences, The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Department of Pharmacological Sciences, The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jayasudhan R Yerabolu
- Department of Oncological Sciences, The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Department of Pharmacological Sciences, The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Alex P Scopton
- Department of Oncological Sciences, The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Department of Pharmacological Sciences, The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Arvin C Dar
- Department of Oncological Sciences, The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA. .,Department of Pharmacological Sciences, The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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42
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Lynch ML, Dudek MF, Bowman SE. A Searchable Database of Crystallization Cocktails in the PDB: Analyzing the Chemical Condition Space. PATTERNS (NEW YORK, N.Y.) 2020; 1:100024. [PMID: 32776019 PMCID: PMC7409820 DOI: 10.1016/j.patter.2020.100024] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/31/2019] [Revised: 03/22/2020] [Accepted: 03/30/2020] [Indexed: 10/26/2022]
Abstract
Nearly 90% of structural models in the Protein Data Bank (PDB), the central resource worldwide for three-dimensional structural information, are currently derived from macromolecular crystallography (MX). A major bottleneck in determining MX structures is finding conditions in which a biomolecule will crystallize. Here, we present a searchable database of the chemicals associated with successful crystallization experiments from the PDB. We use these data to examine the relationship between protein secondary structure and average molecular weight of polyethylene glycol and to investigate patterns in crystallization conditions. Our analyses reveal striking patterns of both redundancy of chemical compositions in crystallization experiments and extreme sparsity of specific chemical combinations, underscoring the challenges faced in generating predictive models for de novo optimal crystallization experiments.
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Affiliation(s)
- Miranda L. Lynch
- High-Throughput Crystallization Screening Center, Hauptman-Woodward Medical Research Institute, Buffalo, NY 14203, USA
| | - Max F. Dudek
- University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Sarah E.J. Bowman
- High-Throughput Crystallization Screening Center, Hauptman-Woodward Medical Research Institute, Buffalo, NY 14203, USA
- Department of Biochemistry, Jacobs School of Medicine & Biomedical Sciences at the University at Buffalo, Buffalo, NY 14203, USA
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43
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Berman HM, Vallat B, Lawson CL. The data universe of structural biology. IUCRJ 2020; 7:630-638. [PMID: 32695409 PMCID: PMC7340255 DOI: 10.1107/s205225252000562x] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2020] [Accepted: 04/21/2020] [Indexed: 05/05/2023]
Abstract
The Protein Data Bank (PDB) has grown from a small data resource for crystallographers to a worldwide resource serving structural biology. The history of the growth of the PDB and the role that the community has played in developing standards and policies are described. This article also illustrates how other biophysics communities are collaborating with the worldwide PDB to create a network of interoperating data resources. This network will expand the capabilities of structural biology and enable the determination and archiving of increasingly complex structures.
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Affiliation(s)
- Helen M. Berman
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Department of Biological Sciences and Bridge Institute, University of Southern California, Los Angeles, CA 90089, USA
| | - Brinda Vallat
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Catherine L. Lawson
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
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44
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45
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Westbrook JD, Soskind R, Hudson BP, Burley SK. Impact of the Protein Data Bank on antineoplastic approvals. Drug Discov Today 2020; 25:837-850. [PMID: 32068073 DOI: 10.1016/j.drudis.2020.02.002] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2019] [Revised: 01/08/2020] [Accepted: 02/07/2020] [Indexed: 12/14/2022]
Abstract
Open access to 3D structure information from the Protein Data Bank (PDB) facilitated discovery and development of >90% of the 79 new antineoplastic agents (54 small molecules, 25 biologics) with known molecular targets approved by the FDA 2010-2018. Analyses of PDB holdings, the scientific literature and related documents for each drug-target combination revealed that the impact of public-domain 3D structure data was broad and substantial, ranging from understanding target biology (∼95% of all targets) to identifying a given target as probably druggable (∼95% of all targets) to structure-guided lead optimization (>70% of all small-molecule drugs). In addition to aggregate impact assessments, illustrative case studies are presented for three protein kinase inhibitors, an allosteric enzyme inhibitor and seven advanced-stage melanoma therapeutics.
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Affiliation(s)
- John D Westbrook
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Rose Soskind
- Ernest Mario School of Pharmacy, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Brian P Hudson
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Stephen K Burley
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA; Rutgers Cancer Institute of New Jersey, Robert Wood Johnson Medical School, New Brunswick, NJ 08903, USA; Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California, San Diego, La Jolla, CA 92093, USA; Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, CA 92093, USA.
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46
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Spaggiari G, Di Pizio A, Cozzini P. Sweet, umami and bitter taste receptors: State of the art of in silico molecular modeling approaches. Trends Food Sci Technol 2020. [DOI: 10.1016/j.tifs.2019.12.002] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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47
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Goodsell DS, Zardecki C, Di Costanzo L, Duarte JM, Hudson BP, Persikova I, Segura J, Shao C, Voigt M, Westbrook JD, Young JY, Burley SK. RCSB Protein Data Bank: Enabling biomedical research and drug discovery. Protein Sci 2019; 29:52-65. [PMID: 31531901 DOI: 10.1002/pro.3730] [Citation(s) in RCA: 184] [Impact Index Per Article: 36.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2019] [Revised: 09/09/2019] [Accepted: 09/10/2019] [Indexed: 12/11/2022]
Abstract
Analyses of publicly available structural data reveal interesting insights into the impact of the three-dimensional (3D) structures of protein targets important for discovery of new drugs (e.g., G-protein-coupled receptors, voltage-gated ion channels, ligand-gated ion channels, transporters, and E3 ubiquitin ligases). The Protein Data Bank (PDB) archive currently holds > 155,000 atomic-level 3D structures of biomolecules experimentally determined using crystallography, nuclear magnetic resonance spectroscopy, and electron microscopy. The PDB was established in 1971 as the first open-access, digital-data resource in biology, and is now managed by the Worldwide PDB partnership (wwPDB; wwPDB.org). US PDB operations are the responsibility of the Research Collaboratory for Structural Bioinformatics PDB (RCSB PDB). The RCSB PDB serves millions of RCSB.org users worldwide by delivering PDB data integrated with ∼40 external biodata resources, providing rich structural views of fundamental biology, biomedicine, and energy sciences. Recently published work showed that the PDB archival holdings facilitated discovery of ∼90% of the 210 new drugs approved by the US Food and Drug Administration 2010-2016. We review user-driven development of RCSB PDB services, examine growth of the PDB archive in terms of size and complexity, and present examples and opportunities for structure-guided drug discovery for challenging targets (e.g., integral membrane proteins).
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Affiliation(s)
- David S Goodsell
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, New Jersey.,Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, New Jersey.,The Scripps Research Institute, La Jolla, California
| | - Christine Zardecki
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, New Jersey.,Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, New Jersey
| | - Luigi Di Costanzo
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, New Jersey.,Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, New Jersey
| | - Jose M Duarte
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California, San Diego, California
| | - Brian P Hudson
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, New Jersey.,Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, New Jersey
| | - Irina Persikova
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, New Jersey.,Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, New Jersey
| | - Joan Segura
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California, San Diego, California
| | - Chenghua Shao
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, New Jersey.,Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, New Jersey
| | - Maria Voigt
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, New Jersey.,Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, New Jersey
| | - John D Westbrook
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, New Jersey.,Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, New Jersey
| | - Jasmine Y Young
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, New Jersey.,Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, New Jersey
| | - Stephen K Burley
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, New Jersey.,Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, New Jersey.,Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California, San Diego, California.,Rutgers Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, New Jersey
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48
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Porebski PJ, Bokota G, Venkataramany BS, Minor W. Molstack: A platform for interactive presentations of electron density and cryo-EM maps and their interpretations. Protein Sci 2019; 29:120-127. [PMID: 31605409 DOI: 10.1002/pro.3747] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2019] [Revised: 10/07/2019] [Accepted: 10/08/2019] [Indexed: 12/21/2022]
Abstract
In the Special Issue on Tools for Protein Science in 2018, we presented Molstack: a concept of a cloud-based platform for sharing electron density maps and their interpretations. Molstack is a web platform that allows the interactive visualization of density maps through the simultaneous presentation of multiple datasets and models in a way that allows for easy pairwise comparison. We anticipated that the users of this conceptually simple platform would find many different uses for their projects, and we were not mistaken. We have observed researchers use Molstack to present experimental evidence for their models in the form of electron density maps, omit maps, and anomalous difference density maps. Users also employed Molstack to present alternative interpretations of densities, including rerefinements and speculative interpretations. While we anticipated these types of projects to be the main use cases, we were pleased to see Molstack used to display superpositions of different models, as a tool for story-driven presentations, and for collaboration as well. Here, we present developments in the platform that were driven by user feedback, highlight several cases that used Molstack to enhance the publication, and discuss possible directions for the platform.
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Affiliation(s)
- Przemyslaw J Porebski
- Department of Molecular Physiology & Biological Physics, University of Virginia, Charlottesville, Virginia
| | - Grzegorz Bokota
- Department of Molecular Physiology & Biological Physics, University of Virginia, Charlottesville, Virginia.,Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Warsaw, Poland
| | - Barat S Venkataramany
- Department of Molecular Physiology & Biological Physics, University of Virginia, Charlottesville, Virginia
| | - Wladek Minor
- Department of Molecular Physiology & Biological Physics, University of Virginia, Charlottesville, Virginia
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49
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Geng H, Chen F, Ye J, Jiang F. Applications of Molecular Dynamics Simulation in Structure Prediction of Peptides and Proteins. Comput Struct Biotechnol J 2019; 17:1162-1170. [PMID: 31462972 PMCID: PMC6709365 DOI: 10.1016/j.csbj.2019.07.010] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Revised: 07/07/2019] [Accepted: 07/23/2019] [Indexed: 12/21/2022] Open
Abstract
Compared with rapid accumulation of protein sequences from high-throughput DNA sequencing, obtaining experimental 3D structures of proteins is still much more difficult, making protein structure prediction (PSP) potentially very useful. Currently, a vast majority of PSP efforts are based on data mining of known sequences, structures and their relationships (informatics-based). However, if closely related template is not available, these methods are usually much less reliable than experiments. They may also be problematic in predicting the structures of naturally occurring or designed peptides. On the other hand, physics-based methods including molecular dynamics (MD) can utilize our understanding of detailed atomic interactions determining biomolecular structures. In this mini-review, we show that all-atom MD can predict structures of cyclic peptides and other peptide foldamers with accuracy similar to experiments. Then, some notable successes in reproducing experimental 3D structures of small proteins through MD simulations (some with replica-exchange) of the folding were summarized. We also describe advancements of MD-based refinement of structure models, and the integration of limited experimental or bioinformatics data into MD-based structure modeling.
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Affiliation(s)
- Hao Geng
- Lab of Computational Chemistry and Drug Design, State Key Laboratory of Chemical Oncogenomics, Peking University Shenzhen Graduate School, Shenzhen 518055, China
| | - Fangfang Chen
- Guangdong and Shenzhen Key Laboratory of Male Reproductive Medicine and Genetics, Peking University Shenzhen Hospital, Shenzhen PKU-HKUST Medical Center, Shenzhen 518036, China
| | - Jing Ye
- Guangdong and Shenzhen Key Laboratory of Male Reproductive Medicine and Genetics, Peking University Shenzhen Hospital, Shenzhen PKU-HKUST Medical Center, Shenzhen 518036, China
| | - Fan Jiang
- Lab of Computational Chemistry and Drug Design, State Key Laboratory of Chemical Oncogenomics, Peking University Shenzhen Graduate School, Shenzhen 518055, China
- NanoAI Biotech Co.,Ltd., Silicon Valley Compound, Longhua District, Shenzhen 518109, China
- Corresponding author at: Lab of Computational Chemistry and Drug Design, State Key Laboratory of Chemical Oncogenomics, Peking University Shenzhen Graduate School, Shenzhen 518055, China.
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50
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Towards an African Light Source. Biophys Rev 2019; 11:499-507. [PMID: 31301018 DOI: 10.1007/s12551-019-00578-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2019] [Accepted: 06/28/2019] [Indexed: 10/26/2022] Open
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