1
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Comajuncosa-Creus A, Jorba G, Barril X, Aloy P. Comprehensive detection and characterization of human druggable pockets through binding site descriptors. Nat Commun 2024; 15:7917. [PMID: 39256431 PMCID: PMC11387482 DOI: 10.1038/s41467-024-52146-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Accepted: 08/27/2024] [Indexed: 09/12/2024] Open
Abstract
Druggable pockets are protein regions that have the ability to bind organic small molecules, and their characterization is essential in target-based drug discovery. However, deriving pocket descriptors is challenging and existing strategies are often limited in applicability. We introduce PocketVec, an approach to generate pocket descriptors via inverse virtual screening of lead-like molecules. PocketVec performs comparably to leading methodologies while addressing key limitations. Additionally, we systematically search for druggable pockets in the human proteome, using experimentally determined structures and AlphaFold2 models, identifying over 32,000 binding sites across 20,000 protein domains. We then generate PocketVec descriptors for each site and conduct an extensive similarity search, exploring over 1.2 billion pairwise comparisons. Our results reveal druggable pocket similarities not detected by structure- or sequence-based methods, uncovering clusters of similar pockets in proteins lacking crystallized inhibitors and opening the door to strategies for prioritizing chemical probe development to explore the druggable space.
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Affiliation(s)
- Arnau Comajuncosa-Creus
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain
| | - Guillem Jorba
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain
| | - Xavier Barril
- Facultat de Farmàcia and Institut de Biomedicina, Universitat de Barcelona, Barcelona, Catalonia, Spain
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Catalonia, Spain
| | - Patrick Aloy
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain.
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Catalonia, Spain.
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2
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Reim T, Ehrt C, Graef J, Günther S, Meents A, Rarey M. SiteMine: Large-scale binding site similarity searching in protein structure databases. Arch Pharm (Weinheim) 2024; 357:e2300661. [PMID: 38335311 DOI: 10.1002/ardp.202300661] [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: 11/13/2023] [Revised: 01/10/2024] [Accepted: 01/16/2024] [Indexed: 02/12/2024]
Abstract
Drug discovery and design challenges, such as drug repurposing, analyzing protein-ligand and protein-protein complexes, ligand promiscuity studies, or function prediction, can be addressed by protein binding site similarity analysis. Although numerous tools exist, they all have individual strengths and drawbacks with regard to run time, provision of structure superpositions, and applicability to diverse application domains. Here, we introduce SiteMine, an all-in-one database-driven, alignment-providing binding site similarity search tool to tackle the most pressing challenges of binding site comparison. The performance of SiteMine is evaluated on the ProSPECCTs benchmark, showing a promising performance on most of the data sets. The method performs convincingly regarding all quality criteria for reliable binding site comparison, offering a novel state-of-the-art approach for structure-based molecular design based on binding site comparisons. In a SiteMine showcase, we discuss the high structural similarity between cathepsin L and calpain 1 binding sites and give an outlook on the impact of this finding on structure-based drug design. SiteMine is available at https://uhh.de/naomi.
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Affiliation(s)
- Thorben Reim
- ZBH - Center for Bioinformatics, Universität Hamburg, Hamburg, Germany
| | - Christiane Ehrt
- ZBH - Center for Bioinformatics, Universität Hamburg, Hamburg, Germany
| | - Joel Graef
- ZBH - Center for Bioinformatics, Universität Hamburg, Hamburg, Germany
| | - Sebastian Günther
- Center for Free-Electron Laser Science CFEL, Deutsches Elektronen-Synchrotron DESY, Hamburg, Germany
| | - Alke Meents
- Center for Free-Electron Laser Science CFEL, Deutsches Elektronen-Synchrotron DESY, Hamburg, Germany
| | - Matthias Rarey
- ZBH - Center for Bioinformatics, Universität Hamburg, Hamburg, Germany
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3
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Choksi H, Carbone J, Paradis NJ, Bennett L, Bui-Linh C, Wu C. Novel Inhibitors to MmpL3 Transporter of Mycobacterium tuberculosis by Structure-Based High-Throughput Virtual Screening and Molecular Dynamics Simulations. ACS OMEGA 2024; 9:13782-13796. [PMID: 38559933 PMCID: PMC10976370 DOI: 10.1021/acsomega.3c08401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Revised: 02/07/2024] [Accepted: 02/12/2024] [Indexed: 04/04/2024]
Abstract
Tuberculosis (TB)-causing bacterium Mycobacterium tuberculosis (Mtb) utilizes mycolic acids for building the mycobacterial cell wall, which is critical in providing defense against external factors and resisting antibiotic action. MmpL3 is a secondary resistance nodulation division transporter that facilitates the coupled transport of mycolic acid precursor into the periplasm using the proton motive force, thus making it an attractive drug target for TB infection. In 2019, X-ray crystal structures of MmpL3 from M. smegmatis were solved with a promising inhibitor SQ109, which showed promise against drug-resistant TB in Phase II clinical trials. Still, there is a pressing need to discover more effective MmpL3 inhibitors to counteract rising antibiotic resistance. In this study, structure-based high-throughput virtual screening combined with molecular dynamics (MD) simulations identified potential novel MmpL3 inhibitors. Approximately 17 million compounds from the ZINC15 database were screened against the SQ109 binding site on the MmpL3 protein using drug property filters and glide XP docking scores. From this, the top nine compounds and the MmpL3-SQ109 crystal complex structure each underwent 2 × 200 ns MD simulations to probe the inhibitor binding energetics to MmpL3. Four of the nine compounds exhibited stable binding properties and favorable drug properties, suggesting these four compounds could be potential novel inhibitors of MmpL3 for M. tuberculosis.
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Affiliation(s)
| | | | - Nicholas J. Paradis
- Department of Molecular &
Cellular Biosciences, College of Science and Mathematics, Rowan University, Glassboro, New Jersey 08028, United States
| | - Lucas Bennett
- Department of Molecular &
Cellular Biosciences, College of Science and Mathematics, Rowan University, Glassboro, New Jersey 08028, United States
| | - Candice Bui-Linh
- Department of Molecular &
Cellular Biosciences, College of Science and Mathematics, Rowan University, Glassboro, New Jersey 08028, United States
| | - Chun Wu
- Department of Molecular &
Cellular Biosciences, College of Science and Mathematics, Rowan University, Glassboro, New Jersey 08028, United States
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4
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Osman AMA, Arabi AA. Average Electron Density: A Quantitative Tool for Evaluating Non-Classical Bioisosteres of Amides. ACS OMEGA 2024; 9:13172-13182. [PMID: 38524460 PMCID: PMC10955596 DOI: 10.1021/acsomega.3c09732] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 01/12/2024] [Accepted: 02/07/2024] [Indexed: 03/26/2024]
Abstract
Bioisosterism is strategically used in drug design to enhance the pharmacokinetic and pharmacodynamic properties of therapeutic molecules. The average electron density (AED) tool has been used in several studies to quantify similarities among nonclassical bioisosteres of carboxylic acid. In this study, the AED tool is used to quantify the similarities among nonclassical bioisosteres of an amide group. In particular, amide-to-1,2,3-triazole bioisosterism is considered. To evaluate the AED differences exhibited by isomers of nonclassical bioisosteres, both isomers of amide (cis and trans) and 1,2,3-triazole (1,4 and 1,5 disubstituted moieity) were considered. The amide and 1,2,3-triazole bioisosteric moieties were capped with various R groups (R= methyl, hydrogen, and chloro) to account for changes in their environment. Amide-to-triazole bioisosteric substitutions were then explored in a more realistic environment, that is, within the FDA-approved anticancer imatinib drug. The AED tool effectively identified similarities between substantially different moieties, 1,2,3-triazole and amide, showing AED differences of no more than 4%. The AED tool was also proven to be useful in evaluating the contribution of various factors affecting triazole-amide bioisosterism including isomerism and changes in their environment. The AED values of each bioisostere were transferable within a maximum difference of 2.6%, irrespective of the change in environment. The 1,4- and 1,5-disubstituted isomers of 1,2,3-triazole have AED values that differ by less than unity, 0.52%. Similarly, the AED values of the cis- and trans-amide isomers differ by only 1.31%. Overall, the AED quantitative tool not only replicated experimental observations regarding similarities in bioisosteres, but also explained and quantified each contributing factor. This demonstrates the extended utility of the AED tool from nonclassical carboxylic acid bioisosteres to amide equivalents.On the contrary, electrostatic potential maps, usually used in the literature to qualitatively evaluate bioisosterism, were not similar for the 1,2,3-triazole and amide bioisosteres, under different environments. Overall, the AED tool proves to be powerful in quantitatively evaluating and predicting bioisosterism across diverse moieties considering structural and environmental variations, making it valuable in drug design.
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Affiliation(s)
- Alaa MA Osman
- College of Medicine and Health
Sciences, Department of Biochemistry and Molecular Biology, United Arab Emirates University, AlAin P.O. Box: 15551, United Arab Emirates
| | - Alya A. Arabi
- College of Medicine and Health
Sciences, Department of Biochemistry and Molecular Biology, United Arab Emirates University, AlAin P.O. Box: 15551, United Arab Emirates
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5
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Pallante L, Cannariato M, Androutsos L, Zizzi EA, Bompotas A, Hada X, Grasso G, Kalogeras A, Mavroudi S, Di Benedetto G, Theofilatos K, Deriu MA. VirtuousPocketome: a computational tool for screening protein-ligand complexes to identify similar binding sites. Sci Rep 2024; 14:6296. [PMID: 38491261 PMCID: PMC10943019 DOI: 10.1038/s41598-024-56893-7] [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: 01/09/2024] [Accepted: 03/12/2024] [Indexed: 03/18/2024] Open
Abstract
Protein residues within binding pockets play a critical role in determining the range of ligands that can interact with a protein, influencing its structure and function. Identifying structural similarities in proteins offers valuable insights into their function and activation mechanisms, aiding in predicting protein-ligand interactions, anticipating off-target effects, and facilitating the development of therapeutic agents. Numerous computational methods assessing global or local similarity in protein cavities have emerged, but their utilization is impeded by complexity, impractical automation for amino acid pattern searches, and an inability to evaluate the dynamics of scrutinized protein-ligand systems. Here, we present a general, automatic and unbiased computational pipeline, named VirtuousPocketome, aimed at screening huge databases of proteins for similar binding pockets starting from an interested protein-ligand complex. We demonstrate the pipeline's potential by exploring a recently-solved human bitter taste receptor, i.e. the TAS2R46, complexed with strychnine. We pinpointed 145 proteins sharing similar binding sites compared to the analysed bitter taste receptor and the enrichment analysis highlighted the related biological processes, molecular functions and cellular components. This work represents the foundation for future studies aimed at understanding the effective role of tastants outside the gustatory system: this could pave the way towards the rationalization of the diet as a supplement to standard pharmacological treatments and the design of novel tastants-inspired compounds to target other proteins involved in specific diseases or disorders. The proposed pipeline is publicly accessible, can be applied to any protein-ligand complex, and could be expanded to screen any database of protein structures.
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Affiliation(s)
- Lorenzo Pallante
- Department of Mechanical and Aerospace Engineering, Politecnico di Torino, PolitoBIOMedLab, 10129, Torino, Italy
| | - Marco Cannariato
- Department of Mechanical and Aerospace Engineering, Politecnico di Torino, PolitoBIOMedLab, 10129, Torino, Italy
| | | | - Eric A Zizzi
- Department of Mechanical and Aerospace Engineering, Politecnico di Torino, PolitoBIOMedLab, 10129, Torino, Italy
| | - Agorakis Bompotas
- Industrial Systems Institute, Athena Research Center, 265 04, Patras, Greece
| | - Xhesika Hada
- Department of Mechanical and Aerospace Engineering, Politecnico di Torino, PolitoBIOMedLab, 10129, Torino, Italy
| | - Gianvito Grasso
- Dalle Molle Institute for Artificial Intelligence IDSIA USI-SUPSI, 6962, Lugano-Viganello, Switzerland
| | | | - Seferina Mavroudi
- Department of Nursing, School of Health Rehabilitation Sciences, University of Patras, 265 04, Patras, Greece
| | | | | | - Marco A Deriu
- Department of Mechanical and Aerospace Engineering, Politecnico di Torino, PolitoBIOMedLab, 10129, Torino, Italy.
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6
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Xu X, Han W, Ning X, Zang C, Xu C, Zeng C, Pu C, Zhang Y, Chen Y, Liu H. Constructing Innovative Covalent and Noncovalent Compound Libraries: Insights from 3D Protein-Ligand Interactions. J Chem Inf Model 2024; 64:1543-1559. [PMID: 38381562 DOI: 10.1021/acs.jcim.3c01689] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/23/2024]
Abstract
Noncovalent interactions between small-molecule drugs and protein targets assume a pivotal role in drug design. Moreover, the design of covalent inhibitors, forming covalent bonds with amino acid residues, requires rational reactivity for their covalent warheads, presenting a key challenge as well. Understanding the intricacies of these interactions provides a more comprehensive understanding of molecular binding mechanisms, thereby guiding the rational design of potent inhibitors. In this study, we adopted the fragment-based drug design approach, introducing a novel methodology to extract noncovalent and covalent fragments according to distinct three-dimensional (3D) interaction modes from noncovalent and covalent compound libraries. Additionally, we systematically replaced existing ligands with rational fragment substitutions, based on the spatial orientation of fragments in 3D space. Furthermore, we adopted a molecular generation approach to create innovative covalent inhibitors. This process resulted in the recombination of a noncovalent compound library and several covalent compound libraries, constructed by two commonly encountered covalent amino acids: cysteine and serine. We utilized noncovalent ligands in KLIFS and covalent ligands in CovBinderInPDB as examples to recombine noncovalent and covalent libraries. These recombined compound libraries cover a substantial portion of the chemical space present in the original compound libraries and exhibit superior performance in terms of molecular scaffold diversity compared to the original compound libraries and other 11 commercial libraries. We also recombined BTK-focused libraries, and 23 compounds within our libraries have been validated by former researchers to possess potential biological activity. The establishment of these compound libraries provides valuable resources for virtual screening of covalent and noncovalent drugs targeting similar molecular targets.
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Affiliation(s)
- Xiaohe Xu
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing 211198, China
| | - Weijie Han
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing 211198, China
| | - Xiangzhen Ning
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing 211198, China
| | - Chengdong Zang
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing 211198, China
| | - Chengcheng Xu
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing 211198, China
| | - Chen Zeng
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing 211198, China
| | - Chengtao Pu
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing 211198, China
| | - Yanmin Zhang
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing 211198, China
| | - Yadong Chen
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing 211198, China
| | - Haichun Liu
- Laboratory of Molecular Design and Drug Discovery, School of Science, China Pharmaceutical University, 639 Longmian Avenue, Nanjing 211198, China
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7
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Hoogstraten CA, Koenderink JB, van Straaten CE, Scheer-Weijers T, Smeitink JAM, Schirris TJJ, Russel FGM. Pyruvate dehydrogenase is a potential mitochondrial off-target for gentamicin based on in silico predictions and in vitro inhibition studies. Toxicol In Vitro 2024; 95:105740. [PMID: 38036072 DOI: 10.1016/j.tiv.2023.105740] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 11/08/2023] [Accepted: 11/22/2023] [Indexed: 12/02/2023]
Abstract
During the drug development process, organ toxicity leads to an estimated failure of one-third of novel chemical entities. Drug-induced toxicity is increasingly associated with mitochondrial dysfunction, but identifying the underlying molecular mechanisms remains a challenge. Computational modeling techniques have proven to be a good tool in searching for drug off-targets. Here, we aimed to identify mitochondrial off-targets of the nephrotoxic drugs tenofovir and gentamicin using different in silico approaches (KRIPO, ProBis and PDID). Dihydroorotate dehydrogenase (DHODH) and pyruvate dehydrogenase (PDH) were predicted as potential novel off-target sites for tenofovir and gentamicin, respectively. The predicted targets were evaluated in vitro, using (colorimetric) enzymatic activity measurements. Tenofovir did not inhibit DHODH activity, while gentamicin potently reduced PDH activity. In conclusion, the use of in silico methods appeared a valuable approach in predicting PDH as a mitochondrial off-target of gentamicin. Further research is required to investigate the contribution of PDH inhibition to overall renal toxicity of gentamicin.
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Affiliation(s)
- Charlotte A Hoogstraten
- Division of Pharmacology and Toxicology, Department of Pharmacy, Radboud University Medical Center, Nijmegen 6500 HB, the Netherlands; Radboud Center for Mitochondrial Medicine, Radboud University Medical Center, Nijmegen 6500 HB, the Netherlands
| | - Jan B Koenderink
- Division of Pharmacology and Toxicology, Department of Pharmacy, Radboud University Medical Center, Nijmegen 6500 HB, the Netherlands
| | - Carolijn E van Straaten
- Division of Pharmacology and Toxicology, Department of Pharmacy, Radboud University Medical Center, Nijmegen 6500 HB, the Netherlands
| | - Tom Scheer-Weijers
- Division of Pharmacology and Toxicology, Department of Pharmacy, Radboud University Medical Center, Nijmegen 6500 HB, the Netherlands
| | - Jan A M Smeitink
- Radboud Center for Mitochondrial Medicine, Radboud University Medical Center, Nijmegen 6500 HB, the Netherlands; Department of Pediatrics, Amalia Children's Hospital, Radboud University Medical Center, Nijmegen 6500 HB, the Netherlands; Khondrion BV, Nijmegen 6525 EX, the Netherlands
| | - Tom J J Schirris
- Division of Pharmacology and Toxicology, Department of Pharmacy, Radboud University Medical Center, Nijmegen 6500 HB, the Netherlands; Radboud Center for Mitochondrial Medicine, Radboud University Medical Center, Nijmegen 6500 HB, the Netherlands
| | - Frans G M Russel
- Division of Pharmacology and Toxicology, Department of Pharmacy, Radboud University Medical Center, Nijmegen 6500 HB, the Netherlands; Radboud Center for Mitochondrial Medicine, Radboud University Medical Center, Nijmegen 6500 HB, the Netherlands.
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8
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Zhang T, Sun S, Wang R, Li T, Gan B, Zhang Y. BioisoIdentifier: an online free tool to investigate local structural replacements from PDB. J Cheminform 2024; 16:7. [PMID: 38218937 PMCID: PMC10788035 DOI: 10.1186/s13321-024-00801-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Accepted: 01/07/2024] [Indexed: 01/15/2024] Open
Abstract
Within the realm of contemporary medicinal chemistry, bioisosteres are empirically used to enhance potency and selectivity, improve adsorption, distribution, metabolism, excretion and toxicity profiles of drug candidates. It is believed that bioisosteric know-how may help bypass granted patents or generate novel intellectual property for commercialization. Beside the synthetic expertise, the drug discovery process also depends on efficient in silico tools. We hereby present BioisoIdentifier (BII), a web server aiming to uncover bioisosteric information for specific fragment. Using the Protein Data Bank as source, and specific substructures that the user attempt to surrogate as input, BII tries to find suitable fragments that fit well within the local protein active site. BII is a powerful computational tool that offers the ligand design ideas for bioisosteric replacing. For the validation of BII, catechol is conceived as model fragment attempted to be replaced, and many ideas are successfully offered. These outputs are hierarchically grouped according to structural similarity, and clustered based on unsupervised machine learning algorithms. In summary, we constructed a user-friendly interface to enable the viewing of top-ranking molecules for further experimental exploration. This makes BII a highly valuable tool for drug discovery. The BII web server is freely available to researchers and can be accessed at http://www.aifordrugs.cn/index/ . Scientific Contribution: By designing a more optimal computational process for mining bioisosteric replacements from the publicly accessible PDB database, then deployed on a web server for throughly free access for researchers. Additionally, machine learning methods are applied to cluster the bioisosteric replacements searched by the platform, making a scientific contribution to facilitate chemists' selection of appropriate bioisosteric replacements. The number of bioisosteric replacements obtained using BII is significantly larger than the currently available platforms, which expanding the search space for effective local structural replacements.
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Affiliation(s)
- Tinghao Zhang
- Xi'an Institute of Flexible Electronics (IFE) and Xi'an Institute of Biomedical Materials & Engineering (IBME), Northwestern Polytechnical University, 127 West Youyi Road, Xi'an, 710072, China
| | - Shaohua Sun
- Xi'an Institute of Flexible Electronics (IFE) and Xi'an Institute of Biomedical Materials & Engineering (IBME), Northwestern Polytechnical University, 127 West Youyi Road, Xi'an, 710072, China
| | - Runzhou Wang
- School of Management, Xi'an University of Architecture and Technology, Xi'an, 710055, China
| | - Ting Li
- Xi'an Institute of Flexible Electronics (IFE) and Xi'an Institute of Biomedical Materials & Engineering (IBME), Northwestern Polytechnical University, 127 West Youyi Road, Xi'an, 710072, China
| | - Bicheng Gan
- College of Petroleum Engineering, Northeast Petroleum University, Daqing, 163318, Heilongjiang, China
| | - Yuezhou Zhang
- Xi'an Institute of Flexible Electronics (IFE) and Xi'an Institute of Biomedical Materials & Engineering (IBME), Northwestern Polytechnical University, 127 West Youyi Road, Xi'an, 710072, China.
- Ningbo Institute of Northwestern Polytechnical University, Frontiers Science Center for Flexible Electronics (FSCFE), Key Laboratory of Flexible Electronics of Zhejiang Province, Ningbo Institute of Northwestern Polytechnical University, 218 Qingyi Road, Ningbo, 315103, China.
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9
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Dean E, Dominique A, Palillero A, Tran A, Paradis N, Wu C. Probing the Activation Mechanisms of Agonist DPI-287 to Delta-Opioid Receptor and Novel Agonists Using Ensemble-Based Virtual Screening with Molecular Dynamics Simulations. ACS OMEGA 2023; 8:32404-32423. [PMID: 37720760 PMCID: PMC10500586 DOI: 10.1021/acsomega.3c01918] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 08/16/2023] [Indexed: 09/19/2023]
Abstract
Pain drugs targeting mu-opioid receptors face major addiction problems that have caused an epidemic. The delta-opioid receptor (DOR) has shown to not cause addictive effects when bound to an agonist. While the active conformation of the DOR in complex with agonist DPI-287 has been recently solved, there are still no FDA-approved agonists targeting it, providing the opportunity for structure-based virtual screening. In this study, the conformational plasticity of the DOR was probed using molecular dynamics (MD) simulations, identifying two representative conformations from clustering analysis. The two MD conformations as well as the crystal conformation of DOR were used to screen novel compounds from the ZINC database (17 million compounds), in which 69 drugs were picked as potential compounds based on their docking scores. Notably, 37 out of the 69 compounds were obtained from the simulated conformations. The binding stability of the 69 compounds was further investigated using MD simulations. Based on the MM-GBSA binding energy and the predicted drug properties, eight compounds were chosen as the most favorable, six of which were from the simulated conformations. Using a dynamic network model, the communication between the crystal agonist and the top eight molecules with the receptor was analyzed to confirm if these novel compounds share a similar activation mechanism to the crystal ligand. Encouragingly, docking of these eight compounds to the other two opioid receptors (kappa and mu) suggests their good selectivity toward DOR.
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Affiliation(s)
- Emily Dean
- Department of Molecular &
Cellular Biosciences, College of Science and Mathematics, Rowan University, Glassboro, New Jersey 08028, United States
| | - AnneMarie Dominique
- Department of Molecular &
Cellular Biosciences, College of Science and Mathematics, Rowan University, Glassboro, New Jersey 08028, United States
| | - Americus Palillero
- Department of Molecular &
Cellular Biosciences, College of Science and Mathematics, Rowan University, Glassboro, New Jersey 08028, United States
| | - Annie Tran
- Department of Molecular &
Cellular Biosciences, College of Science and Mathematics, Rowan University, Glassboro, New Jersey 08028, United States
| | - Nicholas Paradis
- Department of Molecular &
Cellular Biosciences, College of Science and Mathematics, Rowan University, Glassboro, New Jersey 08028, United States
| | - Chun Wu
- Department of Molecular &
Cellular Biosciences, College of Science and Mathematics, Rowan University, Glassboro, New Jersey 08028, United States
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10
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Li S, Tian T, Zhang Z, Zou Z, Zhao D, Zeng J. PocketAnchor: Learning structure-based pocket representations for protein-ligand interaction prediction. Cell Syst 2023; 14:692-705.e6. [PMID: 37516103 DOI: 10.1016/j.cels.2023.05.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 11/25/2022] [Accepted: 05/19/2023] [Indexed: 07/31/2023]
Abstract
Protein-ligand interactions are essential for cellular activities and drug discovery processes. Appropriately and effectively representing protein features is of vital importance for developing computational approaches, especially data-driven methods, for predicting protein-ligand interactions. However, existing approaches may not fully investigate the features of the ligand-occupying regions in the protein pockets. Here, we design a structure-based protein representation method, named PocketAnchor, for capturing the local environmental and spatial features of protein pockets to facilitate protein-ligand interaction-related learning tasks. We define "anchors" as probe points reaching into the cavities and those located near the surface of proteins, and we design a specific message passing strategy for gathering local information from the atoms and surface neighboring these anchors. Comprehensive evaluation of our method demonstrated its successful applications in pocket detection and binding affinity prediction, which indicated that our anchor-based approach can provide effective protein feature representations for improving the prediction of protein-ligand interactions.
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Affiliation(s)
- Shuya Li
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing 100084, China
| | - Tingzhong Tian
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing 100084, China
| | - Ziting Zhang
- Department of Automation, Tsinghua University, Beijing 100084, China; MOE Key Laboratory of Bioinformatics, Tsinghua University, Beijing 100084, China
| | - Ziheng Zou
- Silexon AI Technology, Nanjing, Jiangsu Province 210023, China
| | - Dan Zhao
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing 100084, China.
| | - Jianyang Zeng
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing 100084, China.
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11
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Zhang T, Jiang S, Li T, Liu Y, Zhang Y. Identified Isosteric Replacements of Ligands' Glycosyl Domain by Data Mining. ACS OMEGA 2023; 8:25165-25184. [PMID: 37483233 PMCID: PMC10357434 DOI: 10.1021/acsomega.3c02243] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Accepted: 06/09/2023] [Indexed: 07/25/2023]
Abstract
Biologically equivalent replacements of key moieties in molecules rationalize scaffold hopping, patent busting, or R-group enumeration. Yet, this information may depend upon the expert-defined space, and might be subjective and biased toward the chemistries they get used to. Most importantly, these practices are often informatively incomplete since they are often compromised by a try-and-error cycle, and although they depict what kind of substructures are suitable for the replacement occurrence, they fail to explain the driving forces to support such interchanges. The protein data bank (PDB) encodes a receptor-ligand interaction pattern and could be an optional source to mine structural surrogates. However, manual decoding of PDB has become almost impossible and redundant to excavate the bioisosteric know-how. Therefore, a text parsing workflow has been developed to automatically extract the local structural replacement of a specific structure from PDB by finding spatial and steric interaction overlaps between the fragments in endogenous ligands and particular ligand fragments. Taking the glycosyl domain for instance, a total of 49 520 replacements that overlap on nucleotide ribose were identified and categorized based on their SMILE codes. A predominately ring system, such as aliphatic and aromatic rings, was observed; yet, amide and sulfonamide replacements also occur. We believe these findings may enlighten medicinal chemists on the structure design and optimization of ligands using the bioisosteric replacement strategy.
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Affiliation(s)
- Tinghao Zhang
- Xi’an
Institute of Flexible Electronics (IFE) and Xi’an Institute
of Biomedical Materials & Engineering (IBME), Northwestern Polytechnical
University, 127 West Youyi Road, Xi’an 710072, China
| | - Shenghao Jiang
- School of
Computer Science, Northwestern Polytechnical
University, 127 West
Youyi Road, Xi’an 710072, China
| | - Ting Li
- Xi’an
Institute of Flexible Electronics (IFE) and Xi’an Institute
of Biomedical Materials & Engineering (IBME), Northwestern Polytechnical
University, 127 West Youyi Road, Xi’an 710072, China
| | - Yan Liu
- Xi’an
Institute of Flexible Electronics (IFE) and Xi’an Institute
of Biomedical Materials & Engineering (IBME), Northwestern Polytechnical
University, 127 West Youyi Road, Xi’an 710072, China
| | - Yuezhou Zhang
- Xi’an
Institute of Flexible Electronics (IFE) and Xi’an Institute
of Biomedical Materials & Engineering (IBME), Northwestern Polytechnical
University, 127 West Youyi Road, Xi’an 710072, China
- Ningbo
Institute of Northwestern Polytechnical University, Frontiers Science
Center for Flexible Electronics (FSCFE), Key laboratory of Flexible
Electronics of Zhejiang Province, Ningbo Institute of Northwestern
Polytechnical University, 218 Qingyi Road, Ningbo 315103, China
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12
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Chen W, Liu X, Zhang S, Chen S. Artificial intelligence for drug discovery: Resources, methods, and applications. MOLECULAR THERAPY. NUCLEIC ACIDS 2023; 31:691-702. [PMID: 36923950 PMCID: PMC10009646 DOI: 10.1016/j.omtn.2023.02.019] [Citation(s) in RCA: 30] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/19/2023]
Abstract
Conventional wet laboratory testing, validations, and synthetic procedures are costly and time-consuming for drug discovery. Advancements in artificial intelligence (AI) techniques have revolutionized their applications to drug discovery. Combined with accessible data resources, AI techniques are changing the landscape of drug discovery. In the past decades, a series of AI-based models have been developed for various steps of drug discovery. These models have been used as complements of conventional experiments and have accelerated the drug discovery process. In this review, we first introduced the widely used data resources in drug discovery, such as ChEMBL and DrugBank, followed by the molecular representation schemes that convert data into computer-readable formats. Meanwhile, we summarized the algorithms used to develop AI-based models for drug discovery. Subsequently, we discussed the applications of AI techniques in pharmaceutical analysis including predicting drug toxicity, drug bioactivity, and drug physicochemical property. Furthermore, we introduced the AI-based models for de novo drug design, drug-target structure prediction, drug-target interaction, and binding affinity prediction. Moreover, we also highlighted the advanced applications of AI in drug synergism/antagonism prediction and nanomedicine design. Finally, we discussed the challenges and future perspectives on the applications of AI to drug discovery.
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Affiliation(s)
- Wei Chen
- State Key Laboratory of Southwestern Chinese Medicine Resources, Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China.,Institute of Herbgenomics, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
| | - Xuesong Liu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Sanyin Zhang
- State Key Laboratory of Southwestern Chinese Medicine Resources, Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China.,Institute of Herbgenomics, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
| | - Shilin Chen
- State Key Laboratory of Southwestern Chinese Medicine Resources, Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China.,Institute of Herbgenomics, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
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13
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Advances in nonclassical phenyl bioisosteres for drug structural optimization. Future Med Chem 2022; 14:1681-1692. [PMID: 36317661 DOI: 10.4155/fmc-2022-0188] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
The phenyl group is the most prevalent ring system and plays an essential role as a pharmacophore or scaffold in marketed drugs. However, the indiscriminate employment of phenyl is also a major cause of poor physicochemical properties of active molecules. Nonclassical phenyl bioisosteres (NPBs) have emerged as effective replacements for phenyl in structural optimization due to their unique steric structures and physicochemical properties. Herein, the effects of widely reported NPBs on physicochemical properties and biological activities, including bicyclo[1.1.1]pentane (BCP), bicyclo[2.1.1]hexanes (BCH), bicyclo[2.2.2]octane (BCO), cubane (CUB) and closo-carboborane, are reviewed. Issues that require consideration while using NPBs and practical solutions to problems frequently encountered in structural optimization using NPBs are also discussed.
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14
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Mind the Gap—Deciphering GPCR Pharmacology Using 3D Pharmacophores and Artificial Intelligence. Pharmaceuticals (Basel) 2022; 15:ph15111304. [DOI: 10.3390/ph15111304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 10/15/2022] [Accepted: 10/17/2022] [Indexed: 11/09/2022] Open
Abstract
G protein-coupled receptors (GPCRs) are amongst the most pharmaceutically relevant and well-studied protein targets, yet unanswered questions in the field leave significant gaps in our understanding of their nuanced structure and function. Three-dimensional pharmacophore models are powerful computational tools in in silico drug discovery, presenting myriad opportunities for the integration of GPCR structural biology and cheminformatics. This review highlights success stories in the application of 3D pharmacophore modeling to de novo drug design, the discovery of biased and allosteric ligands, scaffold hopping, QSAR analysis, hit-to-lead optimization, GPCR de-orphanization, mechanistic understanding of GPCR pharmacology and the elucidation of ligand–receptor interactions. Furthermore, advances in the incorporation of dynamics and machine learning are highlighted. The review will analyze challenges in the field of GPCR drug discovery, detailing how 3D pharmacophore modeling can be used to address them. Finally, we will present opportunities afforded by 3D pharmacophore modeling in the advancement of our understanding and targeting of GPCRs.
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15
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Eguida M, Rognan D. Estimating the Similarity between Protein Pockets. Int J Mol Sci 2022; 23:12462. [PMID: 36293316 PMCID: PMC9604425 DOI: 10.3390/ijms232012462] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Revised: 10/15/2022] [Accepted: 10/16/2022] [Indexed: 10/28/2023] Open
Abstract
With the exponential increase in publicly available protein structures, the comparison of protein binding sites naturally emerged as a scientific topic to explain observations or generate hypotheses for ligand design, notably to predict ligand selectivity for on- and off-targets, explain polypharmacology, and design target-focused libraries. The current review summarizes the state-of-the-art computational methods applied to pocket detection and comparison as well as structural druggability estimates. The major strengths and weaknesses of current pocket descriptors, alignment methods, and similarity search algorithms are presented. Lastly, an exhaustive survey of both retrospective and prospective applications in diverse medicinal chemistry scenarios illustrates the capability of the existing methods and the hurdle that still needs to be overcome for more accurate predictions.
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Affiliation(s)
| | - Didier Rognan
- Laboratoire d’Innovation Thérapeutique, UMR7200 CNRS-Université de Strasbourg, 67400 Illkirch, France
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16
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Wang H, Mulgaonkar N, Pérez LM, Fernando S. ELIXIR-A: An Interactive Visualization Tool for Multi-Target Pharmacophore Refinement. ACS OMEGA 2022; 7:12707-12715. [PMID: 35474832 PMCID: PMC9025992 DOI: 10.1021/acsomega.1c07144] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/18/2021] [Accepted: 03/24/2022] [Indexed: 06/01/2023]
Abstract
Pharmacophore modeling is an important step in computer-aided drug design for identifying interaction points between the receptor and ligand complex. Pharmacophore-based models can be used for de novo drug design, lead identification, and optimization in virtual screening as well as for multi-target drug design. There is a need to develop a user-friendly interface to filter the pharmacophore points resulting from multiple ligand conformations. Here, we present ELIXIR-A, a Python-based pharmacophore refinement tool, to help refine the pharmacophores between multiple ligands from multiple receptors. Furthermore, the output can be easily used in virtual pharmacophore-based screening platforms, thereby contributing to the development of drug discovery.
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Affiliation(s)
- Haoqi Wang
- Biological
and Agricultural Engineering Department, Texas A&M University, College Station, Texas 77843, United States
| | - Nirmitee Mulgaonkar
- Biological
and Agricultural Engineering Department, Texas A&M University, College Station, Texas 77843, United States
| | - Lisa M. Pérez
- High
Performance Research Computing, Texas A&M
University, College
Station, Texas 77843, United States
| | - Sandun Fernando
- Biological
and Agricultural Engineering Department, Texas A&M University, College Station, Texas 77843, United States
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17
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Computational approaches for the design of novel dopamine D2 and serotonin 5-HT2A receptor dual antagonist towards schizophrenia. In Silico Pharmacol 2022; 10:7. [PMID: 35433192 PMCID: PMC8990614 DOI: 10.1007/s40203-022-00121-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Accepted: 02/03/2022] [Indexed: 10/26/2022] Open
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18
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Patel DC, Hausman KR, Arba M, Tran A, Lakernick PM, Wu C. Novel inhibitors to ADP ribose phosphatase of SARS-CoV-2 identified by structure-based high throughput virtual screening and molecular dynamics simulations. Comput Biol Med 2022; 140:105084. [PMID: 34891093 PMCID: PMC8629772 DOI: 10.1016/j.compbiomed.2021.105084] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 11/24/2021] [Accepted: 11/25/2021] [Indexed: 12/16/2022]
Abstract
The outbreak of a new coronavirus (SARS-CoV-2) was first identified in Wuhan, People's Republic of China, in 2019, which has led to a severe, life-threatening form of pneumonia (COVID-19). Research scientists all around the world have been trying to find small molecule drugs to treat COVID-19. In the present study, a conserved macrodomain, ADP Ribose phosphatase (ADRP), of a critical non-structural protein (Nsp3) in all coronaviruses was probed using large-scale Molecular Dynamics (MD) simulations to identify novel inhibitors. In our virtual screening workflow, the recently-solved X-ray complex structure, 6W6Y, with a substrate-mimics was used to screen 17 million ZINC15 compounds using drug property filters and Glide docking scores. The top twenty output compounds each underwent 200 ns MD simulations (i.e. 20 × 200 ns) to validate their individual stability as potential inhibitors. Eight out of the twenty compounds showed stable binding modes in the MD simulations, as well as favorable drug properties from our predctions. Therefore, our computational data suggest that the resulting top eight out of twenty compounds could potentially be novel inhibitors to ADRP of SARS-CoV-2.
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Affiliation(s)
- Dhrumi C Patel
- Department of Molecular & Cellular Biosciences, College of Science and Mathematics, Rowan University, Glassboro, NJ, 08028, United States
| | - Katherine R Hausman
- Department of Molecular & Cellular Biosciences, College of Science and Mathematics, Rowan University, Glassboro, NJ, 08028, United States
| | - Muhammad Arba
- Faculty of Pharmacy, Universitas Halu Oleo, Kendari, 93232, Indonesia
| | - Annie Tran
- Department of Molecular & Cellular Biosciences, College of Science and Mathematics, Rowan University, Glassboro, NJ, 08028, United States
| | - Phillip M Lakernick
- Department of Molecular & Cellular Biosciences, College of Science and Mathematics, Rowan University, Glassboro, NJ, 08028, United States
| | - Chun Wu
- Department of Molecular & Cellular Biosciences, College of Science and Mathematics, Rowan University, Glassboro, NJ, 08028, United States.
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19
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Eguida M, Rognan D. Unexpected similarity between HIV-1 reverse transcriptase and tumor necrosis factor binding sites revealed by computer vision. J Cheminform 2021; 13:90. [PMID: 34814950 PMCID: PMC8609734 DOI: 10.1186/s13321-021-00567-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Accepted: 11/06/2021] [Indexed: 11/10/2022] Open
Abstract
Rationalizing the identification of hidden similarities across the repertoire of druggable protein cavities remains a major hurdle to a true proteome-wide structure-based discovery of novel drug candidates. We recently described a new computational approach (ProCare), inspired by numerical image processing, to identify local similarities in fragment-based subpockets. During the validation of the method, we unexpectedly identified a possible similarity in the binding pockets of two unrelated targets, human tumor necrosis factor alpha (TNF-α) and HIV-1 reverse transcriptase (HIV-1 RT). Microscale thermophoresis experiments confirmed the ProCare prediction as two of the three tested and FDA-approved HIV-1 RT inhibitors indeed bind to soluble human TNF-α trimer. Interestingly, the herein disclosed similarity could be revealed neither by state-of-the-art binding sites comparison methods nor by ligand-based pairwise similarity searches, suggesting that the point cloud registration approach implemented in ProCare, is uniquely suited to identify local and unobvious similarities among totally unrelated targets.
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Affiliation(s)
- Merveille Eguida
- Laboratoire d'Innovation Thérapeutique, UMR 7200 CNRS, Université de Strasbourg, 67400, Illkirch, France
| | - Didier Rognan
- Laboratoire d'Innovation Thérapeutique, UMR 7200 CNRS, Université de Strasbourg, 67400, Illkirch, France.
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20
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Warszycki D, Struski Ł, Śmieja M, Kafel R, Kurczab R. Pharmacoprint: A Combination of a Pharmacophore Fingerprint and Artificial Intelligence as a Tool for Computer-Aided Drug Design. J Chem Inf Model 2021; 61:5054-5065. [PMID: 34547888 DOI: 10.1021/acs.jcim.1c00589] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Structural fingerprints and pharmacophore modeling are methodologies that have been used for at least 2 decades in various fields of cheminformatics, from similarity searching to machine learning (ML). Advances in in silico techniques consequently led to combining both these methodologies into a new approach known as the pharmacophore fingerprint. Herein, we propose a high-resolution, pharmacophore fingerprint called Pharmacoprint that encodes the presence, types, and relationships between pharmacophore features of a molecule. Pharmacoprint was evaluated in classification experiments by using ML algorithms (logistic regression, support vector machines, linear support vector machines, and neural networks) and outperformed other popular molecular fingerprints (i.e., ECFP4, Estate, MACCS, PubChem, Substructure, Klekota-Roth, CDK, Extended, and GraphOnly) and the ChemAxon pharmacophoric features fingerprint. Pharmacoprint consisted of 39 973 bits; several methods were applied for dimensionality reduction, and the best algorithm not only reduced the length of the bit string but also improved the efficiency of the ML tests. Further optimization allowed us to define the best parameter settings for using Pharmacoprint in discrimination tests and for maximizing statistical parameters. Finally, Pharmacoprint generated for three-dimensional (3D) structures with defined hydrogens as input data was applied to neural networks with a supervised autoencoder for selecting the most important bits and allowed us to maximize the Matthews correlation coefficient up to 0.962. The results show the potential of Pharmacoprint as a new, perspective tool for computer-aided drug design.
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Affiliation(s)
- Dawid Warszycki
- Maj Institute of Pharmacology Polish Academy of Sciences, Smetna 12 Street, 31-343, Cracow, Poland
| | - Łukasz Struski
- Faculty of Mathematics and Computer Science, Jagiellonian University, 6 Lojasiewicza Street, 30-348, Cracow, Poland
| | - Marek Śmieja
- Faculty of Mathematics and Computer Science, Jagiellonian University, 6 Lojasiewicza Street, 30-348, Cracow, Poland
| | - Rafał Kafel
- Maj Institute of Pharmacology Polish Academy of Sciences, Smetna 12 Street, 31-343, Cracow, Poland
| | - Rafał Kurczab
- Maj Institute of Pharmacology Polish Academy of Sciences, Smetna 12 Street, 31-343, Cracow, Poland
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21
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Kim J, Park S, Min D, Kim W. Comprehensive Survey of Recent Drug Discovery Using Deep Learning. Int J Mol Sci 2021; 22:9983. [PMID: 34576146 PMCID: PMC8470987 DOI: 10.3390/ijms22189983] [Citation(s) in RCA: 33] [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: 08/24/2021] [Revised: 09/09/2021] [Accepted: 09/10/2021] [Indexed: 02/07/2023] Open
Abstract
Drug discovery based on artificial intelligence has been in the spotlight recently as it significantly reduces the time and cost required for developing novel drugs. With the advancement of deep learning (DL) technology and the growth of drug-related data, numerous deep-learning-based methodologies are emerging at all steps of drug development processes. In particular, pharmaceutical chemists have faced significant issues with regard to selecting and designing potential drugs for a target of interest to enter preclinical testing. The two major challenges are prediction of interactions between drugs and druggable targets and generation of novel molecular structures suitable for a target of interest. Therefore, we reviewed recent deep-learning applications in drug-target interaction (DTI) prediction and de novo drug design. In addition, we introduce a comprehensive summary of a variety of drug and protein representations, DL models, and commonly used benchmark datasets or tools for model training and testing. Finally, we present the remaining challenges for the promising future of DL-based DTI prediction and de novo drug design.
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Affiliation(s)
- Jintae Kim
- KaiPharm Co., Ltd., Seoul 03759, Korea; (J.K.); (S.P.)
| | - Sera Park
- KaiPharm Co., Ltd., Seoul 03759, Korea; (J.K.); (S.P.)
| | - Dongbo Min
- Computer Vision Lab, Department of Computer Science and Engineering, Ewha Womans University, Seoul 03760, Korea
| | - Wankyu Kim
- KaiPharm Co., Ltd., Seoul 03759, Korea; (J.K.); (S.P.)
- System Pharmacology Lab, Department of Life Sciences, Ewha Womans University, Seoul 03760, Korea
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22
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Iconaru LI, Das S, Nourse A, Shelat AA, Zuo J, Kriwacki RW. Small Molecule Sequestration of the Intrinsically Disordered Protein, p27 Kip1, Within Soluble Oligomers. J Mol Biol 2021; 433:167120. [PMID: 34197833 DOI: 10.1016/j.jmb.2021.167120] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 06/04/2021] [Accepted: 06/22/2021] [Indexed: 10/21/2022]
Abstract
Proteins that exhibit intrinsically disordered regions (IDRs) are prevalent in the human proteome and perform diverse biological functions, including signaling and regulation. Due to these important roles, misregulation of intrinsically disordered proteins (IDPs) is associated with myriad human diseases, including neurodegeneration and cancer. The inherent flexibility of IDPs limits the applicability of the traditional structure-based drug design paradigm; therefore, IDPs have long been considered "undruggable". Using NMR spectroscopy and other methods, we previously discovered small, drug-like molecules that bind specifically, albeit weakly, to dynamic clusters of aromatic residues within p27Kip1 (p27), an archetypal disordered protein involved in cell cycle regulation. Here, using synthetic chemistry, NMR spectroscopy and other biophysical methods, we discovered elaborated analogs of our previously reported molecules with 30-fold increased affinity for p27 (apparent Kd = 57 ± 19 μM). Strikingly, using analytical ultracentrifugation methods, we showed that the highest affinity compounds caused p27 to form soluble, disordered oligomers. Based on these observations, we propose that sequestration within soluble oligomers may represent a general strategy for therapeutically targeting disease-associated IDPs in the future.
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Affiliation(s)
- Luigi I Iconaru
- Department of Structural Biology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA; Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA; Present address: Sanofi, Waltham, MA 02451, USA
| | - Sourav Das
- Department of Chemical Biology and Therapeutics, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Amanda Nourse
- Department of Structural Biology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA; Molecular Interactions Analysis Shared Resource, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Anang A Shelat
- Department of Chemical Biology and Therapeutics, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Jian Zuo
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA; Department of Biomedical Sciences, Creighton University School of Medicine, Omaha, NE 68178, USA.
| | - Richard W Kriwacki
- Department of Structural Biology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA; Department of Microbiology, Immunology and Biochemistry, The University of Tennessee Health Science Center, Memphis, TN 38163, USA.
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23
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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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24
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Santana K, do Nascimento LD, Lima e Lima A, Damasceno V, Nahum C, Braga RC, Lameira J. Applications of Virtual Screening in Bioprospecting: Facts, Shifts, and Perspectives to Explore the Chemo-Structural Diversity of Natural Products. Front Chem 2021; 9:662688. [PMID: 33996755 PMCID: PMC8117418 DOI: 10.3389/fchem.2021.662688] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Accepted: 02/25/2021] [Indexed: 12/22/2022] Open
Abstract
Natural products are continually explored in the development of new bioactive compounds with industrial applications, attracting the attention of scientific research efforts due to their pharmacophore-like structures, pharmacokinetic properties, and unique chemical space. The systematic search for natural sources to obtain valuable molecules to develop products with commercial value and industrial purposes remains the most challenging task in bioprospecting. Virtual screening strategies have innovated the discovery of novel bioactive molecules assessing in silico large compound libraries, favoring the analysis of their chemical space, pharmacodynamics, and their pharmacokinetic properties, thus leading to the reduction of financial efforts, infrastructure, and time involved in the process of discovering new chemical entities. Herein, we discuss the computational approaches and methods developed to explore the chemo-structural diversity of natural products, focusing on the main paradigms involved in the discovery and screening of bioactive compounds from natural sources, placing particular emphasis on artificial intelligence, cheminformatics methods, and big data analyses.
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Affiliation(s)
- Kauê Santana
- Instituto de Biodiversidade, Universidade Federal do Oeste do Pará, Santarém, Brazil
| | | | - Anderson Lima e Lima
- Instituto de Ciências Exatas e Naturais, Universidade Federal do Pará, Belém, Brazil
| | - Vinícius Damasceno
- Instituto de Ciências Exatas e Naturais, Universidade Federal do Pará, Belém, Brazil
| | - Claudio Nahum
- Instituto de Ciências Exatas e Naturais, Universidade Federal do Pará, Belém, Brazil
| | | | - Jerônimo Lameira
- Instituto de Ciências Biológicas, Universidade Federal do Pará, Belém, Brazil
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25
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Bhadra A, Yeturu K. Site2Vec: a reference frame invariant algorithm for vector embedding of protein–ligand binding sites. MACHINE LEARNING: SCIENCE AND TECHNOLOGY 2021. [DOI: 10.1088/2632-2153/abad88] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Abstract
Protein–ligand interactions are one of the fundamental types of molecular interactions in living systems. Ligands are small molecules that interact with protein molecules at specific regions on their surfaces called binding sites. Binding sites would also determine ADMET properties of a drug molecule. Tasks such as assessment of protein functional similarity and detection of side effects of drugs need identification of similar binding sites of disparate proteins across diverse pathways. To this end, methods for computing similarities between binding sites are still evolving and is an active area of research even today. Machine learning methods for similarity assessment require feature descriptors of binding sites. Traditional methods based on hand engineered motifs and atomic configurations are not scalable across several thousands of sites. In this regard, deep neural network algorithms are now deployed which can capture very complex input feature space. However, one fundamental challenge in applying deep learning to structures of binding sites is the input representation and the reference frame. We report here a novel algorithm, Site2Vec, that derives reference frame invariant vector embedding of a protein–ligand binding site. The method is based on pairwise distances between representative points and chemical compositions in terms of constituent amino acids of a site. The vector embedding serves as a locality sensitive hash function for proximity queries and determining similar sites. The method has been the top performer with more than 95% quality scores in extensive benchmarking studies carried over 10 data sets and against 23 other site comparison methods in the field. The algorithm serves for high throughput processing and has been evaluated for stability with respect to reference frame shifts, coordinate perturbations and residue mutations. We also provide the method as a standalone executable and a web service hosted at (http://services.iittp.ac.in/bioinfo/home).
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26
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Wong WK, Robinson SA, Bujotzek A, Georges G, Lewis AP, Shi J, Snowden J, Taddese B, Deane CM. Ab-Ligity: identifying sequence-dissimilar antibodies that bind to the same epitope. MAbs 2021; 13:1873478. [PMID: 33448242 PMCID: PMC7833755 DOI: 10.1080/19420862.2021.1873478] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
Solving the structure of an antibody-antigen complex gives atomic level information of the interactions between an antibody and its antigen, but such structures are expensive and hard to obtain. Alternative experimental sources include epitope mapping and binning experiments, which can be used as a surrogate to identify key interacting residues. However, their resolution is usually not sufficient to identify if two antibodies have identical interactions. Computational approaches to this problem have so far been based on the premise that antibodies with similar sequences behave similarly. Such approaches will fail to identify sequence-distant antibodies that target the same epitope. Here, we present Ab-Ligity, a structure-based similarity measure tailored to antibody-antigen interfaces. Using predicted paratopes on model antibody structures, we assessed its ability to identify those antibodies that target highly similar epitopes. Most antibodies adopting similar binding modes can be identified from sequence similarity alone, using methods such as clonotyping. In the challenging subset of antibodies whose sequences differ significantly, Ab-Ligity is still able to predict antibodies that would bind to highly similar epitopes (precision of 0.95 and recall of 0.69). We compared Ab-Ligity's performance to an existing tool for comparing general protein interfaces, InterComp, and showed improved performance on antibody cases achieved in a substantially reduced time. These results suggest that Ab-Ligity will allow the identification of diverse (sequence-dissimilar) antibodies that bind to the same epitopes from large datasets such as immune repertoires. The tool is available at http://opig.stats.ox.ac.uk/resources.
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Affiliation(s)
- Wing Ki Wong
- Department of Statistics, University of Oxford , Oxford, UK
| | | | - Alexander Bujotzek
- Roche Pharma Research and Early Development, Large Molecule Research, Roche Innovation Center Munich , Penzberg, Germany
| | - Guy Georges
- Roche Pharma Research and Early Development, Large Molecule Research, Roche Innovation Center Munich , Penzberg, Germany
| | - Alan P Lewis
- Data and Computational Sciences, GlaxoSmithKline Research and Development , Stevenage, UK
| | | | | | - Bruck Taddese
- Discovery Sciences, BioPharmaceuticals, R&D, AstraZeneca , Cambridge, UK
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Scutellaria baicalensis Flavones as Potent Drugs against Acute Respiratory Injury during SARS-CoV-2 Infection: Structural Biology Approaches. Processes (Basel) 2020. [DOI: 10.3390/pr8111468] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection can result in severe damage to the respiratory system. With no specific treatment to date, it is crucial to identify potent inhibitors of SARS-CoV-2 Chymotrypsin-like protease (3CLpro) that could also modulate the enzymes involved in the respiratory damage that accompanies SARS-CoV-2 infection. Here, flavones isolated from Scutellaria baicalensis (baicalein, baicalin, wogonin, norwogonin, and oroxylin A) were studied as possible compounds in the treatment of SARS-CoV-2 and SARS-CoV-2-induced acute lung injuries. We used structural bioinformatics and cheminformatics to (i) identify the critical molecular features of flavones for their binding activity at human and SARS-CoV-2 enzymes; (ii) predict their drug-likeness and lead-likeness features; (iii) calculate their pharmacokinetic profile, with an emphasis on toxicology; (iv) predict their pharmacodynamic profiles, with the identification of their human body targets involved in the respiratory system injuries; and (v) dock the ligands to SARS-CoV-2 3CLpro. All flavones presented appropriate drug-like and kinetics features, except for baicalin. Flavones could bind to SARS-CoV-2 3CLpro at a similar site, but interact slightly differently with the protease. Flavones’ pharmacodynamic profiles predict that (i) wogonin strongly binds at the cyclooxygenase2 and nitric oxide synthase; (ii) baicalein and norwogonin could modulate lysine-specific demethylase 4D-like and arachidonate 15-lipoxygenase; and (iii) baicalein, wogonin, norwogonin, and oroxylin A bind to SARS-CoV-2 3CLpro. Our results propose these flavones as possible potent drugs against respiratory damage that occurs during SARS-CoV-2 infections, with a strong recommendation for baicalein.
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Eguida M, Rognan D. A Computer Vision Approach to Align and Compare Protein Cavities: Application to Fragment-Based Drug Design. J Med Chem 2020; 63:7127-7142. [DOI: 10.1021/acs.jmedchem.0c00422] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Affiliation(s)
- Merveille Eguida
- UMR 7200 CNRS-Université de Strasbourg, Laboratoire d’Innovation Thérapeutique, 67400 Illkirch, France
| | - Didier Rognan
- UMR 7200 CNRS-Université de Strasbourg, Laboratoire d’Innovation Thérapeutique, 67400 Illkirch, France
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Katigbak J, Li H, Rooklin D, Zhang Y. AlphaSpace 2.0: Representing Concave Biomolecular Surfaces Using β-Clusters. J Chem Inf Model 2020; 60:1494-1508. [PMID: 31995373 PMCID: PMC7093224 DOI: 10.1021/acs.jcim.9b00652] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Modern rational modulator design and structure-function characterization often concentrate on concave regions of biomolecular surfaces, ranging from well-defined small-molecule binding sites to large protein-protein interaction interfaces. Here, we introduce a β-cluster as a pseudomolecular representation of fragment-centric pockets detected by AlphaSpace [J. Chem. Inf. Model. 2015, 55, 1585], a recently developed computational analysis tool for topographical mapping of biomolecular concavities. By mimicking the shape as well as atomic details of potential molecular binders, this new β-cluster representation allows direct pocket-to-ligand shape comparison and can be used to guide ligand optimization. Furthermore, we defined the β-score, the optimal Vina score of the β-cluster, as an indicator of pocket ligandability and developed an ensemble β-cluster approach, which allows one-to-one pocket mapping and comparison among aligned protein structures. We demonstrated the utility of β-cluster representation by applying the approach to a wide variety of problems including binding site detection and comparison, characterization of protein-protein interactions, and fragment-based ligand optimization. These new β-cluster functionalities have been implemented in AlphaSpace 2.0, which is freely available on the web at http://www.nyu.edu/projects/yzhang/AlphaSpace2.
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Affiliation(s)
- Joseph Katigbak
- Department of Chemistry, New York University, New York, New York 10003, United States
| | - Haotian Li
- Department of Chemistry, New York University, New York, New York 10003, United States
| | - David Rooklin
- Department of Chemistry, New York University, New York, New York 10003, United States
| | - Yingkai Zhang
- Department of Chemistry, New York University, New York, New York 10003, United States
- NYU-ECNU Center for Computational Chemistry at NYU Shanghai, Shanghai 200062, China
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30
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Metz P, Tjan MJH, Wu S, Pervaiz M, Hermans S, Shettigar A, Sears CL, Ritschel T, Dutilh BE, Boleij A. Drug Discovery and Repurposing Inhibits a Major Gut Pathogen-Derived Oncogenic Toxin. Front Cell Infect Microbiol 2019; 9:364. [PMID: 31709196 PMCID: PMC6823872 DOI: 10.3389/fcimb.2019.00364] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2019] [Accepted: 10/08/2019] [Indexed: 01/04/2023] Open
Abstract
Objective: The human intestinal microbiome plays an important role in inflammatory bowel disease (IBD) and colorectal cancer (CRC) development. One of the first discovered bacterial mediators involves Bacteroides fragilis toxin (BFT, also named as fragilysin), a metalloprotease encoded by enterotoxigenic Bacteroides fragilis (ETBF) that causes barrier disruption and inflammation of the colon, leads to tumorigenesis in susceptible mice, and is enriched in the mucosa of IBD and CRC patients. Thus, targeted inhibition of BFT may benefit ETBF carrying patients. Design: By applying two complementary in silico drug design techniques, drug repositioning and molecular docking, we predicted potential BFT inhibitory compounds. Top candidates were tested in vitro on the CRC epithelial cell line HT29/c1 for their potential to inhibit key aspects of BFT activity, being epithelial morphology changes, E-cadherin cleavage (a marker for barrier function) and increased IL-8 secretion. Results: The primary bile acid and existing drug chenodeoxycholic acid (CDCA), currently used for treating gallstones, cerebrotendinous xanthomatosis, and constipation, was found to significantly inhibit all evaluated cell responses to BFT exposure. The inhibition of BFT resulted from a direct interaction between CDCA and BFT, as confirmed by an increase in the melting temperature of the BFT protein in the presence of CDCA. Conclusion: Together, our results show the potential of in silico drug discovery to combat harmful human and microbiome-derived proteins and more specifically suggests a potential for retargeting CDCA to inhibit the pro-oncogenic toxin BFT.
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Affiliation(s)
- Paul Metz
- Centre for Molecular and Biomolecular Informatics, Radboud University Medical Center (Radboudumc), Nijmegen, Netherlands.,Department of Pathology, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center (Radboudumc), Nijmegen, Netherlands.,Division of Infectious Diseases, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Martijn J H Tjan
- Department of Pathology, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center (Radboudumc), Nijmegen, Netherlands.,Division of Infectious Diseases, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Shaoguang Wu
- Division of Infectious Diseases, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Mehrosh Pervaiz
- Centre for Molecular and Biomolecular Informatics, Radboud University Medical Center (Radboudumc), Nijmegen, Netherlands
| | - Susanne Hermans
- Centre for Molecular and Biomolecular Informatics, Radboud University Medical Center (Radboudumc), Nijmegen, Netherlands
| | - Aishwarya Shettigar
- Division of Infectious Diseases, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Cynthia L Sears
- Division of Infectious Diseases, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Tina Ritschel
- Centre for Molecular and Biomolecular Informatics, Radboud University Medical Center (Radboudumc), Nijmegen, Netherlands
| | - Bas E Dutilh
- Centre for Molecular and Biomolecular Informatics, Radboud University Medical Center (Radboudumc), Nijmegen, Netherlands.,Theoretical Biology and Bioinformatics, Utrecht University, Utrecht, Netherlands
| | - Annemarie Boleij
- Department of Pathology, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center (Radboudumc), Nijmegen, Netherlands
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Andrade CH, Neves BJ, Melo-Filho CC, Rodrigues J, Silva DC, Braga RC, Cravo PVL. In Silico Chemogenomics Drug Repositioning Strategies for Neglected Tropical Diseases. Curr Med Chem 2019. [DOI: 10.2174/0929867325666180309114824] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Only ~1% of all drug candidates against Neglected Tropical Diseases (NTDs)
have reached clinical trials in the last decades, underscoring the need for new, safe and effective
treatments. In such context, drug repositioning, which allows finding novel indications
for approved drugs whose pharmacokinetic and safety profiles are already known,
emerging as a promising strategy for tackling NTDs. Chemogenomics is a direct descendent
of the typical drug discovery process that involves the systematic screening of chemical
compounds against drug targets in high-throughput screening (HTS) efforts, for the identification
of lead compounds. However, different to the one-drug-one-target paradigm, chemogenomics
attempts to identify all potential ligands for all possible targets and diseases. In
this review, we summarize current methodological development efforts in drug repositioning
that use state-of-the-art computational ligand- and structure-based chemogenomics approaches.
Furthermore, we highlighted the recent progress in computational drug repositioning
for some NTDs, based on curation and modeling of genomic, biological, and chemical data.
Additionally, we also present in-house and other successful examples and suggest possible solutions
to existing pitfalls.
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Affiliation(s)
- Carolina Horta Andrade
- LabMol - Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goias, Goiania, GO, 74605-170, Brazil
| | - Bruno Junior Neves
- LabMol - Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goias, Goiania, GO, 74605-170, Brazil
| | - Cleber Camilo Melo-Filho
- LabMol - Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goias, Goiania, GO, 74605-170, Brazil
| | - Juliana Rodrigues
- LabMol - Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goias, Goiania, GO, 74605-170, Brazil
| | - Diego Cabral Silva
- LabMol - Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goias, Goiania, GO, 74605-170, Brazil
| | - Rodolpho Campos Braga
- LabMol - Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goias, Goiania, GO, 74605-170, Brazil
| | - Pedro Vitor Lemos Cravo
- Laboratory of Cheminformatics, Centro Universitario de Anapolis (UniEVANGELICA), Anapolis, GO, 75083-515, Brazil
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Kumi RO, Issahaku AR, Soremekun OS, Agoni C, Olotu FA, Soliman MES. From the Explored to the Unexplored: Computer-Tailored Drug Design Attempts in the Discovery of Selective Caspase Inhibitors. Comb Chem High Throughput Screen 2019; 22:432-444. [PMID: 31560284 DOI: 10.2174/1386207322666190927143026] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2019] [Revised: 06/19/2019] [Accepted: 08/01/2019] [Indexed: 01/09/2023]
Abstract
The pathophysiological roles of caspases have made them attractive targets in the treatment and amelioration of neurologic diseases. In normal conditions, the expression of caspases is regulated in the brain, while at the onset of neurodegeneration, such as in Alzheimer's disease, they are typically overexpressed. Till date, several therapeutic efforts that include the use of small endogenous binders have been put forward to curtail dysfunctionalities that drive aberrant death in neuronal cells. Caspases are highly homologous, both in structure and in sequence, which leaves us with the question: is it possible to specifically and individually target caspases, while multiple therapeutic attempts to achieve selective targeting have failed! Based on antecedent events, the use of Computer-Aided Drug Design (CADD) methods has significantly contributed to the design of small molecule inhibitors, especially with selective target ability and reduced off-target therapeutic effects. Interestingly, we found out that there still exists an enormous room for the integration of structure/ligand-based drug design techniques towards the development of highly specific reversible and irreversible caspase inhibitors. Therefore, in this review, we highlight drug discovery approaches that have been directed towards caspase inhibition in addition to an insightful focus on applicable CADD techniques for achieving selective targeting in caspase research.
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Affiliation(s)
- Ransford O Kumi
- Molecular Bio-computation and Drug Design Laboratory, School of Health Sciences, University of KwaZulu-Natal, Westville Campus, Durban 4001, South Africa
| | - Abdul R Issahaku
- Molecular Bio-computation and Drug Design Laboratory, School of Health Sciences, University of KwaZulu-Natal, Westville Campus, Durban 4001, South Africa
| | - Opeyemi S Soremekun
- Molecular Bio-computation and Drug Design Laboratory, School of Health Sciences, University of KwaZulu-Natal, Westville Campus, Durban 4001, South Africa
| | - Clement Agoni
- Molecular Bio-computation and Drug Design Laboratory, School of Health Sciences, University of KwaZulu-Natal, Westville Campus, Durban 4001, South Africa
| | - Fisayo A Olotu
- Molecular Bio-computation and Drug Design Laboratory, School of Health Sciences, University of KwaZulu-Natal, Westville Campus, Durban 4001, South Africa
| | - Mahmoud E S Soliman
- Molecular Bio-computation and Drug Design Laboratory, School of Health Sciences, University of KwaZulu-Natal, Westville Campus, Durban 4001, South Africa
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Rifaioglu AS, Atas H, Martin MJ, Cetin-Atalay R, Atalay V, Doğan T. Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases. Brief Bioinform 2019; 20:1878-1912. [PMID: 30084866 PMCID: PMC6917215 DOI: 10.1093/bib/bby061] [Citation(s) in RCA: 236] [Impact Index Per Article: 47.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2018] [Revised: 05/25/2018] [Indexed: 01/16/2023] Open
Abstract
The identification of interactions between drugs/compounds and their targets is crucial for the development of new drugs. In vitro screening experiments (i.e. bioassays) are frequently used for this purpose; however, experimental approaches are insufficient to explore novel drug-target interactions, mainly because of feasibility problems, as they are labour intensive, costly and time consuming. A computational field known as 'virtual screening' (VS) has emerged in the past decades to aid experimental drug discovery studies by statistically estimating unknown bio-interactions between compounds and biological targets. These methods use the physico-chemical and structural properties of compounds and/or target proteins along with the experimentally verified bio-interaction information to generate predictive models. Lately, sophisticated machine learning techniques are applied in VS to elevate the predictive performance. The objective of this study is to examine and discuss the recent applications of machine learning techniques in VS, including deep learning, which became highly popular after giving rise to epochal developments in the fields of computer vision and natural language processing. The past 3 years have witnessed an unprecedented amount of research studies considering the application of deep learning in biomedicine, including computational drug discovery. In this review, we first describe the main instruments of VS methods, including compound and protein features (i.e. representations and descriptors), frequently used libraries and toolkits for VS, bioactivity databases and gold-standard data sets for system training and benchmarking. We subsequently review recent VS studies with a strong emphasis on deep learning applications. Finally, we discuss the present state of the field, including the current challenges and suggest future directions. We believe that this survey will provide insight to the researchers working in the field of computational drug discovery in terms of comprehending and developing novel bio-prediction methods.
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Affiliation(s)
- Ahmet Sureyya Rifaioglu
- Department of Computer Engineering, Middle East Technical University, Ankara, Turkey
- Department of Computer Engineering, İskenderun Technical University, Hatay, Turkey
| | - Heval Atas
- Cancer System Biology Laboratory (CanSyL), Graduate School of Informatics, Middle East Technical University, Ankara, Turkey
| | - Maria Jesus Martin
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL–EBI), Cambridge, Hinxton, UK
| | - Rengul Cetin-Atalay
- Department of Computer Engineering, Middle East Technical University, Ankara, Turkey
| | - Volkan Atalay
- Department of Computer Engineering, Middle East Technical University, Ankara, Turkey
| | - Tunca Doğan
- Cancer System Biology Laboratory (CanSyL), Graduate School of Informatics, Middle East Technical University, Ankara, Turkey and European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL–EBI), Cambridge, Hinxton, UK
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Ehrt C, Brinkjost T, Koch O. Binding site characterization - similarity, promiscuity, and druggability. MEDCHEMCOMM 2019; 10:1145-1159. [PMID: 31391887 DOI: 10.1039/c9md00102f] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/19/2019] [Accepted: 05/31/2019] [Indexed: 12/19/2022]
Abstract
The elucidation of non-obvious binding site similarities has provided useful indications for the establishment of polypharmacology, the identification of potential off-targets, or the repurposing of known drugs. The concept underlying all of these approaches is promiscuous binding which can be analyzed from a ligand-based or a binding site-based perspective. Herein, we applied methods for the automated analysis and comparison of protein binding sites to study promiscuous binding on a novel dataset of sites in complex with ligands sharing common shape and physicochemical properties. We show the suitability of this dataset for the benchmarking of novel binding site comparison methods. Our investigations also reveal promising directions for further in-depth analyses of promiscuity and druggability in a pocket-centered manner. Drawbacks concerning binding site similarity assessment and druggability prediction are outlined, enabling researchers to avoid the typical pitfalls of binding site analyses.
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Affiliation(s)
- Christiane Ehrt
- Faculty of Chemistry and Chemical Biology , TU Dortmund University , Dortmund , Germany
| | - Tobias Brinkjost
- Faculty of Chemistry and Chemical Biology , TU Dortmund University , Dortmund , Germany.,Department of Computer Science , TU Dortmund University , Dortmund , Germany
| | - Oliver Koch
- Faculty of Chemistry and Chemical Biology , TU Dortmund University , Dortmund , Germany
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Fehlmann T, Hutter MC. Conservation and Relevance of Pharmacophore Point Types. J Chem Inf Model 2019; 59:1314-1323. [PMID: 30807146 DOI: 10.1021/acs.jcim.8b00757] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Pharmacophore models in general use a variety of features for distinct chemical characteristics, such as hydrogen-bond properties, lipohilicity, and ionizability. Usually, features have to match onto their identical type. To clarify if this stringent one-to-one assignment is justified, we investigated a set of 581 unique ligands from the BindingDB with known orientation inside the respective binding pockets and conducted a statistical analysis of the likelihood of observed exchanges in between the pharmacophore features, respectively their degree of conservation. To find out if certain features are obsolete, we derived a ranking to determine the most relevant ones. We found that the most conserved one-to-one feature is the negative ionizable (acids), followed by hydrogen-bond donor, positive ionizable (basic nitrogens), hydrogen-bond acceptor, aromatic, nonaromatic π-systems, and other lipophilic characteristics. The most likely exchanges were found between carboxylate groups and hydrogen-bond acceptors and likewise between basic nitrogens and hydrogen-bond donors, which reflects the characteristics of Lewis acids and bases. Exchanges between hydrogen-bond donors and hydrogen-bond acceptors are hardly more likely than by chance. The kind of target (e.g., kinase, phosphatase, protease, phosphodiesterase, nuclear receptor, metal-containing, or transmembrane protein) did not show substantial influence on the degree of conservation. The relevance of the actual pharmacophore features was found to be strongly dependent on the applied ranking scheme. Mutual information ranks all hydrophobic features as least important, whereas the aromatic feature is put into second place by using a geometric series. Both ranking schemes see the negative ionizable feature of higher significance than the positively ionizable feature.
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Affiliation(s)
- Tobias Fehlmann
- Center for Bioinformatics , Saarland University , Campus E2.1 , 66123 Saarbruecken , Germany
| | - Michael C Hutter
- Center for Bioinformatics , Saarland University , Campus E2.1 , 66123 Saarbruecken , Germany
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36
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Abstract
INTRODUCTION The success of binding site comparisons in drug discovery is based on the recognized fact that many different proteins have similar binding sites. Indeed, binding site comparisons have found many uses in drug development and have the potential to dramatically cut the cost and shorten the time necessary for the development of new drugs. Areas covered: The authors review recent methods for comparing protein binding sites and their use in drug repurposing and polypharmacology. They examine emerging fields including the use of binding site comparisons in precision medicine, the prediction of structured water molecules, the search for targets of natural compounds, and their application in the development of protein-based drugs by loop modeling and for comparison of RNA binding sites. Expert opinion: Binding site comparisons have produced many interesting results in drug development, but relatively little work has been done on protein-protein interaction sites, which are particularly relevant in view of the success of biological drugs. Growth of protein loop modeling for modulating biological drugs is anticipated. The fusion of currently distinct methods for the comparison of RNA and protein binding sites into a single comprehensive approach could allow the search for new selective ribosomal antibiotics and initiate pharmaceutical research into other nucleoproteins.
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Affiliation(s)
- Janez Konc
- a Theory Department , National Institute of Chemistry , Ljubljana , Slovenia.,b Faculty of Pharmacy , University of Ljubljana , Ljubljana , Slovenia.,c Faculty of Mathematics , Natural Sciences and Information Technologies, University of Primorska , Koper , Slovenia.,d Faculty of Chemistry and Chemical Technology , University of Maribor , Maribor , Slovenia
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37
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Sydow D, Burggraaff L, Szengel A, van Vlijmen HWT, IJzerman AP, van Westen GJP, Volkamer A. Advances and Challenges in Computational Target Prediction. J Chem Inf Model 2019; 59:1728-1742. [DOI: 10.1021/acs.jcim.8b00832] [Citation(s) in RCA: 58] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Affiliation(s)
- Dominique Sydow
- In silico Toxicology, Institute of Physiology, Charité − Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany
| | - Lindsey Burggraaff
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, P.O. Box 9502, 2300 RA, Leiden, The Netherlands
| | - Angelika Szengel
- In silico Toxicology, Institute of Physiology, Charité − Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany
| | - Herman W. T. van Vlijmen
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, P.O. Box 9502, 2300 RA, Leiden, The Netherlands
- Computational Chemistry, Janssen Research & Development, Turnhoutseweg 30, B-2340 Beerse, Belgium
| | - Adriaan P. IJzerman
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, P.O. Box 9502, 2300 RA, Leiden, The Netherlands
| | - Gerard J. P. van Westen
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Leiden University, P.O. Box 9502, 2300 RA, Leiden, The Netherlands
| | - Andrea Volkamer
- In silico Toxicology, Institute of Physiology, Charité − Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany
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Bousis S, Setyawati I, Diamanti E, Slotboom DJ, Hirsch AKH. Energy-Coupling Factor Transporters as Novel Antimicrobial Targets. ADVANCED THERAPEUTICS 2019. [DOI: 10.1002/adtp.201800066] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Affiliation(s)
- Spyridon Bousis
- Helmholtz Institute for Pharmaceutical Research Saarland (HIPS) - Helmholtz Centre for Infection Research (HZI); Department of Drug Design and Optimization; Campus Building E8.1 66123 Saarbrücken Germany
- Stratingh Institute for Chemistry; University of Groningen; Nijenborgh 7 9747AG Groningen The Netherlands
- Department of Pharmacy; Saarland University; Saarbrücken, Campus Building E8.1 66123 Saarbrücken Germany
| | - Inda Setyawati
- Groningen Biomolecular Sciences and Biotechnology Institute; University of Groningen; Nijenborgh 4 9747AG Groningen The Netherlands
- Department of Biochemistry; Bogor Agricultural University; Dramaga 16680 Bogor Indonesia
| | - Eleonora Diamanti
- Helmholtz Institute for Pharmaceutical Research Saarland (HIPS) - Helmholtz Centre for Infection Research (HZI); Department of Drug Design and Optimization; Campus Building E8.1 66123 Saarbrücken Germany
- Stratingh Institute for Chemistry; University of Groningen; Nijenborgh 7 9747AG Groningen The Netherlands
| | - Dirk J. Slotboom
- Groningen Biomolecular Sciences and Biotechnology Institute; University of Groningen; Nijenborgh 4 9747AG Groningen The Netherlands
- Department of Biochemistry; Bogor Agricultural University; Dramaga 16680 Bogor Indonesia
| | - Anna K. H. Hirsch
- Helmholtz Institute for Pharmaceutical Research Saarland (HIPS) - Helmholtz Centre for Infection Research (HZI); Department of Drug Design and Optimization; Campus Building E8.1 66123 Saarbrücken Germany
- Stratingh Institute for Chemistry; University of Groningen; Nijenborgh 7 9747AG Groningen The Netherlands
- Department of Pharmacy; Saarland University; Saarbrücken, Campus Building E8.1 66123 Saarbrücken Germany
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Blöcher R, Rodarte Ramírez A, Castro-Escarpulli G, Curiel-Quesada E, Reyes-Arellano A. Design, Synthesis, and Evaluation of Alkyl-Quinoxalin-2(1 H)-One Derivatives as Anti- Quorum Sensing Molecules, Inhibiting Biofilm Formation in Aeromonas caviae Sch3. Molecules 2018; 23:molecules23123075. [PMID: 30477243 PMCID: PMC6321446 DOI: 10.3390/molecules23123075] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2018] [Revised: 11/18/2018] [Accepted: 11/20/2018] [Indexed: 01/05/2023] Open
Abstract
With the increasing antibiotic resistance of bacterial strains, alternative methods for infection control are in high demand. Quorum sensing (QS) is the bacterial communication system based on small molecules. QS is enables bacterial biofilm formation and pathogenic development. The interruption of QS has become a target for drug discovery, but remains in the early experimental phase. In this study, we synthesized a set of six compounds based on a scaffold (alkyl-quinoxalin-2(1H)-one), new in the anti-QS of Gram-negative bacteria Aeromonas caviae Sch3. By quantifying biofilm formation, we were able to monitor the effect of these compounds from concentrations of 1 to 100 µM. Significant reduction in biofilm formation was achieved by 3-hexylylquinoxalin-2(1H)-one (11), 3-hexylylquinoxalin-2(1H)-one-6-carboxylic acid (12), and 3-heptylylquinoxalin-2(1H)-one-6-carboxylic acid (14), ranging from 11% to 59% inhibition of the biofilm. This pilot study contributes to the development of anti-QS compounds to overcome the clinical challenge of resistant bacteria strains.
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Affiliation(s)
- René Blöcher
- Escuela Nacional de Ciencias Biológicas del Instituto Politécnico Nacional (ENCB-IPN), Departamento de Química Orgánica, Ciudad de México 11340, México.
| | - Ariel Rodarte Ramírez
- Escuela Nacional de Ciencias Biológicas del Instituto Politécnico Nacional (ENCB-IPN), Laboratorio de Investigación Clínica y Ambiental, Departamento de Microbiología, Ciudad de México 11340, México.
| | - Graciela Castro-Escarpulli
- Escuela Nacional de Ciencias Biológicas del Instituto Politécnico Nacional (ENCB-IPN), Laboratorio de Investigación Clínica y Ambiental, Departamento de Microbiología, Ciudad de México 11340, México.
| | - Everardo Curiel-Quesada
- Escuela Nacional de Ciencias Biológicas del Instituto Politécnico Nacional (ENCB-IPN), Departamento de Bioquímica, Ciudad de México 11340, México.
| | - Alicia Reyes-Arellano
- Escuela Nacional de Ciencias Biológicas del Instituto Politécnico Nacional (ENCB-IPN), Departamento de Química Orgánica, Ciudad de México 11340, México.
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40
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Ehrt C, Brinkjost T, Koch O. A benchmark driven guide to binding site comparison: An exhaustive evaluation using tailor-made data sets (ProSPECCTs). PLoS Comput Biol 2018; 14:e1006483. [PMID: 30408032 PMCID: PMC6224041 DOI: 10.1371/journal.pcbi.1006483] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2018] [Accepted: 09/02/2018] [Indexed: 11/24/2022] Open
Abstract
The automated comparison of protein-ligand binding sites provides useful insights into yet unexplored site similarities. Various stages of computational and chemical biology research can benefit from this knowledge. The search for putative off-targets and the establishment of polypharmacological effects by comparing binding sites led to promising results for numerous projects. Although many cavity comparison methods are available, a comprehensive analysis to guide the choice of a tool for a specific application is wanting. Moreover, the broad variety of binding site modeling approaches, comparison algorithms, and scoring metrics impedes this choice. Herein, we aim to elucidate strengths and weaknesses of binding site comparison methodologies. A detailed benchmark study is the only possibility to rationalize the selection of appropriate tools for different scenarios. Specific evaluation data sets were developed to shed light on multiple aspects of binding site comparison. An assembly of all applied benchmark sets (ProSPECCTs–Protein Site Pairs for the Evaluation of Cavity Comparison Tools) is made available for the evaluation and optimization of further and still emerging methods. The results indicate the importance of such analyses to facilitate the choice of a methodology that complies with the requirements of a specific scientific challenge. Binding site similarities are useful in the context of promiscuity prediction, drug repurposing, the analysis of protein-ligand and protein-protein complexes, function prediction, and further fields of general interest in chemical biology and biochemistry. Many years of research have led to the development of a multitude of methods for binding site analysis and comparison. On the one hand, their availability supports research. On the other hand, the huge number of methods hampers the efficient selection of a specific tool. Our research is dedicated to the analysis of different cavity comparison tools. We use several binding site data sets to establish guidelines which can be applied to ensure a successful application of comparison methods by circumventing potential pitfalls.
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Affiliation(s)
- Christiane Ehrt
- Faculty of Chemistry and Chemical Biology, TU Dortmund University, Dortmund, Germany
| | - Tobias Brinkjost
- Faculty of Chemistry and Chemical Biology, TU Dortmund University, Dortmund, Germany
- Department of Computer Science, TU Dortmund University, Dortmund, Germany
| | - Oliver Koch
- Faculty of Chemistry and Chemical Biology, TU Dortmund University, Dortmund, Germany
- * E-mail: ,
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41
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Miszta P, Jakowiecki J, Rutkowska E, Turant M, Latek D, Filipek S. Approaches for Differentiation and Interconverting GPCR Agonists and Antagonists. Methods Mol Biol 2018; 1705:265-296. [PMID: 29188567 DOI: 10.1007/978-1-4939-7465-8_12] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
Abstract
Predicting the functional preferences of the ligands was always a highly demanding task, much harder that predicting whether a ligand can bind to the receptor. This is because of significant similarities of agonists, antagonists and inverse agonists which are binding usually in the same binding site of the receptor and only small structural changes can push receptor toward a particular activation state. For G protein-coupled receptors, due to a large progress in crystallization techniques and also in receptor thermal stabilization, it was possible to obtain a large number of high-quality structures of complexes of these receptors with agonists and non-agonists. Additionally, the long-time-scale molecular dynamics simulations revealed how the activation processes of GPCRs can take place. Using both theoretical and experimental knowledge it was possible to employ many clever and sophisticated methods which can help to differentiate agonists and non-agonists, so one can interconvert them in search of the optimal drug.
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Affiliation(s)
- Przemysław Miszta
- Biological and Chemical Research Centre, Faculty of Chemistry, University of Warsaw, ul. Pasteura 1, 02-093, Warsaw, Poland
| | - Jakub Jakowiecki
- Biological and Chemical Research Centre, Faculty of Chemistry, University of Warsaw, ul. Pasteura 1, 02-093, Warsaw, Poland
| | - Ewelina Rutkowska
- Biological and Chemical Research Centre, Faculty of Chemistry, University of Warsaw, ul. Pasteura 1, 02-093, Warsaw, Poland
| | - Maria Turant
- Biological and Chemical Research Centre, Faculty of Chemistry, University of Warsaw, ul. Pasteura 1, 02-093, Warsaw, Poland
| | - Dorota Latek
- Biological and Chemical Research Centre, Faculty of Chemistry, University of Warsaw, ul. Pasteura 1, 02-093, Warsaw, Poland
| | - Sławomir Filipek
- Biological and Chemical Research Centre, Faculty of Chemistry, University of Warsaw, ul. Pasteura 1, 02-093, Warsaw, Poland.
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42
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Chemical Diversity in the G Protein-Coupled Receptor Superfamily. Trends Pharmacol Sci 2018; 39:494-512. [PMID: 29576399 DOI: 10.1016/j.tips.2018.02.004] [Citation(s) in RCA: 59] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2017] [Revised: 02/05/2018] [Accepted: 02/06/2018] [Indexed: 12/20/2022]
Abstract
G protein-coupled receptors (GPCRs) are the largest family of cell signaling transmembrane proteins that can be modulated by a plethora of chemical compounds. Systematic cheminformatics analysis of structurally and pharmacologically characterized GPCR ligands shows that cocrystallized GPCR ligands cover a significant part of chemical ligand space, despite their limited number. Many GPCR ligands and substructures interact with multiple receptors, providing a basis for polypharmacological ligand design. Experimentally determined GPCR structures represent a variety of binding sites and receptor-ligand interactions that can be translated to chemically similar ligands for which structural data are lacking. This integration of structural, pharmacological, and chemical information on GPCR-ligand interactions enables the extension of the structural GPCR-ligand interactome and the structure-based design of novel modulators of GPCR function.
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43
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Kooistra AJ, Vass M, McGuire R, Leurs R, de Esch IJP, Vriend G, Verhoeven S, de Graaf C. 3D-e-Chem: Structural Cheminformatics Workflows for Computer-Aided Drug Discovery. ChemMedChem 2018; 13:614-626. [PMID: 29337438 PMCID: PMC5900740 DOI: 10.1002/cmdc.201700754] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2017] [Revised: 01/11/2018] [Indexed: 01/06/2023]
Abstract
eScience technologies are needed to process the information available in many heterogeneous types of protein-ligand interaction data and to capture these data into models that enable the design of efficacious and safe medicines. Here we present scientific KNIME tools and workflows that enable the integration of chemical, pharmacological, and structural information for: i) structure-based bioactivity data mapping, ii) structure-based identification of scaffold replacement strategies for ligand design, iii) ligand-based target prediction, iv) protein sequence-based binding site identification and ligand repurposing, and v) structure-based pharmacophore comparison for ligand repurposing across protein families. The modular setup of the workflows and the use of well-established standards allows the re-use of these protocols and facilitates the design of customized computer-aided drug discovery workflows.
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Affiliation(s)
- Albert J. Kooistra
- Centre for Molecular and Biomolecular Informatics (CMBI)Radboud University Medical Center (RadboudUMC)NijmegenThe Netherlands
- Division of Medicinal Chemistry, Faculty of Science, Amsterdam Institute for Molecules, Medicines and Systems (AIMMS)Vrije Universiteit AmsterdamAmsterdamThe Netherlands
| | - Márton Vass
- Division of Medicinal Chemistry, Faculty of Science, Amsterdam Institute for Molecules, Medicines and Systems (AIMMS)Vrije Universiteit AmsterdamAmsterdamThe Netherlands
| | - Ross McGuire
- Centre for Molecular and Biomolecular Informatics (CMBI)Radboud University Medical Center (RadboudUMC)NijmegenThe Netherlands
- BioAxis Research, Pivot ParkOssThe Netherlands
| | - Rob Leurs
- Division of Medicinal Chemistry, Faculty of Science, Amsterdam Institute for Molecules, Medicines and Systems (AIMMS)Vrije Universiteit AmsterdamAmsterdamThe Netherlands
| | - Iwan J. P. de Esch
- Division of Medicinal Chemistry, Faculty of Science, Amsterdam Institute for Molecules, Medicines and Systems (AIMMS)Vrije Universiteit AmsterdamAmsterdamThe Netherlands
| | - Gert Vriend
- Centre for Molecular and Biomolecular Informatics (CMBI)Radboud University Medical Center (RadboudUMC)NijmegenThe Netherlands
| | | | - Chris de Graaf
- Division of Medicinal Chemistry, Faculty of Science, Amsterdam Institute for Molecules, Medicines and Systems (AIMMS)Vrije Universiteit AmsterdamAmsterdamThe Netherlands
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44
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BoBER: web interface to the base of bioisosterically exchangeable replacements. J Cheminform 2017; 9:62. [PMID: 29234984 PMCID: PMC5727005 DOI: 10.1186/s13321-017-0251-x] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2017] [Accepted: 12/04/2017] [Indexed: 11/10/2022] Open
Abstract
We describe a novel freely available web server Base of Bioisosterically Exchangeable Replacements (BoBER), which implements an interface to a database of bioisosteric and scaffold hopping replacements. Bioisosterism and scaffold hopping are key concepts in drug design and optimization, and can be defined as replacements of biologically active compound's fragments with other fragments to improve activity, reduce toxicity, change bioavailability or to diversify the scaffold space. Our web server enables fast and user-friendly searches for bioisosteric and scaffold replacements which were obtained by mining the whole Protein Data Bank. The working of the web server is presented on an existing MurF inhibitor as example. BoBER web server enables medicinal chemists to quickly search for and get new and unique ideas about possible bioisosteric or scaffold hopping replacements that could be used to improve hit or lead drug-like compounds.
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45
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Swier LJYM, Monjas L, Reeßing F, Oudshoorn RC, Aisyah, Primke T, Bakker MM, van Olst E, Ritschel T, Faustino I, Marrink SJ, Hirsch AKH, Slotboom DJ. Insight into the complete substrate-binding pocket of ThiT by chemical and genetic mutations. MEDCHEMCOMM 2017; 8:1121-1130. [PMID: 30108823 DOI: 10.1039/c7md00079k] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/17/2017] [Accepted: 03/24/2017] [Indexed: 01/12/2023]
Abstract
Energy-coupling factor (ECF) transporters are involved in the uptake of micronutrients in bacteria. The transporters capture the substrate by high-affinity binding proteins, the so-called S-components. Here, we present the analysis of two regions of the substrate-binding pocket of the thiamine-specific S-component in Lactococcus lactis, ThiT. First, interaction of the thiazolium ring of thiamine with residues Trp34, His125 and Glu84 by π-π-stacking and cation-π is studied, and second, the part of the binding pocket that extends from the hydroxyl group. We mutated either the transported ligand (chemically) or the protein (genetically). Surprisingly, modifications in the thiazolium ring by introducing substituents with opposite electronic effects had similar effects on the binding affinity. We hypothesize that the electronic effects are superseeded by steric effects of the added substituents, which renders the study of isolated interactions difficult. Amino acid substitutions in ThiT indicate that the electrostatic interaction facilitated by residue Glu84 of ThiT and thiamine is necessary for picomolar affinity. Deazathiamine derivatives that explore the subpocket of the binding site extending from the hydroxyl group of thiamine bind with high affinity to ThiT and may be developed into selective inhibitors of thiamine transport by ECF transporters. Molecular-dynamics simulations suggest that two of these derivatives may not only bind to ThiT, but could also be transported.
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Affiliation(s)
- L J Y M Swier
- Groningen Biomolecular Sciences and Biotechnology Institute , University of Groningen , Nijenborgh 4 , 9747 AG Groningen , The Netherlands . ; Tel: +31 50 363 4187
| | - L Monjas
- Stratingh Institute for Chemistry , University of Groningen , Nijenborgh 7 , 9747 AG Groningen , The Netherlands . ; Tel: +31 50 363 4275
| | - F Reeßing
- Stratingh Institute for Chemistry , University of Groningen , Nijenborgh 7 , 9747 AG Groningen , The Netherlands . ; Tel: +31 50 363 4275
| | - R C Oudshoorn
- Groningen Biomolecular Sciences and Biotechnology Institute , University of Groningen , Nijenborgh 4 , 9747 AG Groningen , The Netherlands . ; Tel: +31 50 363 4187
| | - Aisyah
- Groningen Biomolecular Sciences and Biotechnology Institute , University of Groningen , Nijenborgh 4 , 9747 AG Groningen , The Netherlands . ; Tel: +31 50 363 4187
| | - T Primke
- Groningen Biomolecular Sciences and Biotechnology Institute , University of Groningen , Nijenborgh 4 , 9747 AG Groningen , The Netherlands . ; Tel: +31 50 363 4187
| | - M M Bakker
- Stratingh Institute for Chemistry , University of Groningen , Nijenborgh 7 , 9747 AG Groningen , The Netherlands . ; Tel: +31 50 363 4275
| | - E van Olst
- Groningen Biomolecular Sciences and Biotechnology Institute , University of Groningen , Nijenborgh 4 , 9747 AG Groningen , The Netherlands . ; Tel: +31 50 363 4187
| | - T Ritschel
- Centre for Molecular and Biomolecular Informatics (CMBI) , Radboudumc , 6525 GA Nijmegen , The Netherlands
| | - I Faustino
- Groningen Biomolecular Sciences and Biotechnology Institute , University of Groningen , Nijenborgh 4 , 9747 AG Groningen , The Netherlands . ; Tel: +31 50 363 4187
| | - S J Marrink
- Groningen Biomolecular Sciences and Biotechnology Institute , University of Groningen , Nijenborgh 4 , 9747 AG Groningen , The Netherlands . ; Tel: +31 50 363 4187
| | - A K H Hirsch
- Stratingh Institute for Chemistry , University of Groningen , Nijenborgh 7 , 9747 AG Groningen , The Netherlands . ; Tel: +31 50 363 4275
| | - D J Slotboom
- Groningen Biomolecular Sciences and Biotechnology Institute , University of Groningen , Nijenborgh 4 , 9747 AG Groningen , The Netherlands . ; Tel: +31 50 363 4187
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46
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Zhang Y, Borrel A, Ghemtio L, Regad L, Boije af Gennäs G, Camproux AC, Yli-Kauhaluoma J, Xhaard H. Structural Isosteres of Phosphate Groups in the Protein Data Bank. J Chem Inf Model 2017; 57:499-516. [DOI: 10.1021/acs.jcim.6b00519] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Affiliation(s)
| | - Alexandre Borrel
- Laboratoire
Molécules Thérapeutiques in silico (MTi), UMRS-973, Université Paris Diderot, Sorbonne Paris Cité, INSERM, F-75013 Paris, France
| | | | - Leslie Regad
- Laboratoire
Molécules Thérapeutiques in silico (MTi), UMRS-973, Université Paris Diderot, Sorbonne Paris Cité, INSERM, F-75013 Paris, France
| | | | - Anne-Claude Camproux
- Laboratoire
Molécules Thérapeutiques in silico (MTi), UMRS-973, Université Paris Diderot, Sorbonne Paris Cité, INSERM, F-75013 Paris, France
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47
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McGuire R, Verhoeven S, Vass M, Vriend G, de Esch IJP, Lusher SJ, Leurs R, Ridder L, Kooistra AJ, Ritschel T, de Graaf C. 3D-e-Chem-VM: Structural Cheminformatics Research Infrastructure in a Freely Available Virtual Machine. J Chem Inf Model 2017; 57:115-121. [PMID: 28125221 PMCID: PMC5342320 DOI: 10.1021/acs.jcim.6b00686] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
![]()
3D-e-Chem-VM is an open source, freely
available Virtual Machine
(http://3d-e-chem.github.io/3D-e-Chem-VM/) that integrates cheminformatics and bioinformatics tools for the
analysis of protein–ligand interaction data. 3D-e-Chem-VM consists
of software libraries, and database and workflow tools that can analyze
and combine small molecule and protein structural information in a
graphical programming environment. New chemical and biological data
analytics tools and workflows have been developed for the efficient
exploitation of structural and pharmacological protein–ligand
interaction data from proteomewide databases (e.g., ChEMBLdb and PDB),
as well as customized information systems focused on, e.g., G protein-coupled
receptors (GPCRdb) and protein kinases (KLIFS). The integrated structural
cheminformatics research infrastructure compiled in the 3D-e-Chem-VM
enables the design of new approaches in virtual ligand screening (Chemdb4VS),
ligand-based metabolism prediction (SyGMa), and structure-based protein
binding site comparison and bioisosteric replacement for ligand design
(KRIPOdb).
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Affiliation(s)
- Ross McGuire
- Centre for Molecular and Biomolecular Informatics (CMBI), Radboudumc , 6525 GA Nijmegen, The Netherlands.,BioAxis Research , Pivot Park, 5349 AE Oss, The Netherlands
| | - Stefan Verhoeven
- Netherlands eScience Center , 1098 XG Amsterdam, The Netherlands
| | - Márton Vass
- Division of Medicinal Chemistry, Faculty of Sciences, Amsterdam Institute for Molecules, Medicines and Systems (AIMMS), Vrije Universiteit Amsterdam , 1081 HZ Amsterdam, The Netherlands
| | - Gerrit Vriend
- Centre for Molecular and Biomolecular Informatics (CMBI), Radboudumc , 6525 GA Nijmegen, The Netherlands
| | - Iwan J P de Esch
- Division of Medicinal Chemistry, Faculty of Sciences, Amsterdam Institute for Molecules, Medicines and Systems (AIMMS), Vrije Universiteit Amsterdam , 1081 HZ Amsterdam, The Netherlands
| | - Scott J Lusher
- Centre for Molecular and Biomolecular Informatics (CMBI), Radboudumc , 6525 GA Nijmegen, The Netherlands.,Netherlands eScience Center , 1098 XG Amsterdam, The Netherlands
| | - Rob Leurs
- Division of Medicinal Chemistry, Faculty of Sciences, Amsterdam Institute for Molecules, Medicines and Systems (AIMMS), Vrije Universiteit Amsterdam , 1081 HZ Amsterdam, The Netherlands
| | - Lars Ridder
- Netherlands eScience Center , 1098 XG Amsterdam, The Netherlands
| | - Albert J Kooistra
- Centre for Molecular and Biomolecular Informatics (CMBI), Radboudumc , 6525 GA Nijmegen, The Netherlands.,Division of Medicinal Chemistry, Faculty of Sciences, Amsterdam Institute for Molecules, Medicines and Systems (AIMMS), Vrije Universiteit Amsterdam , 1081 HZ Amsterdam, The Netherlands
| | - Tina Ritschel
- Centre for Molecular and Biomolecular Informatics (CMBI), Radboudumc , 6525 GA Nijmegen, The Netherlands
| | - Chris de Graaf
- Division of Medicinal Chemistry, Faculty of Sciences, Amsterdam Institute for Molecules, Medicines and Systems (AIMMS), Vrije Universiteit Amsterdam , 1081 HZ Amsterdam, The Netherlands
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48
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Molecular interaction fingerprint approaches for GPCR drug discovery. Curr Opin Pharmacol 2016; 30:59-68. [PMID: 27479316 DOI: 10.1016/j.coph.2016.07.007] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2016] [Revised: 07/11/2016] [Accepted: 07/12/2016] [Indexed: 01/23/2023]
Abstract
Protein-ligand interaction fingerprints (IFPs) are binary 1D representations of the 3D structure of protein-ligand complexes encoding the presence or absence of specific interactions between the binding pocket amino acids and the ligand. Various implementations of IFPs have been developed and successfully applied for post-processing molecular docking results for G Protein-Coupled Receptor (GPCR) ligand binding mode prediction and virtual ligand screening. Novel interaction fingerprint methods enable structural chemogenomics and polypharmacology predictions by complementing the increasing amount of GPCR structural data. Machine learning methods are increasingly used to derive relationships between bioactivity data and fingerprint descriptors of chemical and structural information of binding sites, ligands, and protein-ligand interactions. Factors that influence the application of IFPs include structure preparation, binding site definition, fingerprint similarity assessment, and data processing and these factors pose challenges as well possibilities to optimize interaction fingerprint methods for GPCR drug discovery.
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49
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Ehrt C, Brinkjost T, Koch O. Impact of Binding Site Comparisons on Medicinal Chemistry and Rational Molecular Design. J Med Chem 2016; 59:4121-51. [PMID: 27046190 DOI: 10.1021/acs.jmedchem.6b00078] [Citation(s) in RCA: 66] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Modern rational drug design not only deals with the search for ligands binding to interesting and promising validated targets but also aims to identify the function and ligands of yet uncharacterized proteins having impact on different diseases. Additionally, it contributes to the design of inhibitors with distinct selectivity patterns and the prediction of possible off-target effects. The identification of similarities between binding sites of various proteins is a useful approach to cope with those challenges. The main scope of this perspective is to describe applications of different protein binding site comparison approaches to outline their applicability and impact on molecular design. The article deals with various substantial application domains and provides some outstanding examples to show how various binding site comparison methods can be applied to promote in silico drug design workflows. In addition, we will also briefly introduce the fundamental principles of different protein binding site comparison methods.
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Affiliation(s)
- Christiane Ehrt
- Faculty of Chemistry and Chemical Biology, TU Dortmund University , Otto-Hahn-Straße 6, 44227 Dortmund, Germany
| | - Tobias Brinkjost
- Faculty of Chemistry and Chemical Biology, TU Dortmund University , Otto-Hahn-Straße 6, 44227 Dortmund, Germany.,Department of Computer Science, TU Dortmund University , Otto-Hahn-Straße 14, 44224 Dortmund, Germany
| | - Oliver Koch
- Faculty of Chemistry and Chemical Biology, TU Dortmund University , Otto-Hahn-Straße 6, 44227 Dortmund, Germany
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50
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Azad CS, Narula AK. An operational transformation of 3-carboxy-4-quinolones into 3-nitro-4-quinolones via ipso-nitration using polysaccharide supported copper nanoparticles: synthesis of 3-tetrazolyl bioisosteres of 3-carboxy-4-quinolones as antibacterial agents. RSC Adv 2016. [DOI: 10.1039/c5ra26909a] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
Abstract
The 3-nitro-4-quinolones have been synthesized via ipso nitration of 3-carboxy-4-quinolones by chitosan supported Cu nano-particles. The 3-nitro derivatives were converted into their 3-tetrazolyl bioisosteres which showed increased antibacterial activity.
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Affiliation(s)
- Chandra S. Azad
- “Hygeia” Centre of Excellence in Pharmaceutical Sciences (CEPS)
- GGS Indraprastha University
- New Delhi
- India
| | - Anudeep K. Narula
- “Hygeia” Centre of Excellence in Pharmaceutical Sciences (CEPS)
- GGS Indraprastha University
- New Delhi
- India
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