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Beltrao P. The power of scientific collaborations and the future of structural biology. Nat Struct Mol Biol 2024:10.1038/s41594-024-01358-8. [PMID: 39009854 DOI: 10.1038/s41594-024-01358-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/17/2024]
Affiliation(s)
- Pedro Beltrao
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.
- Swiss Institute of Bioinformatics, Lausanne, Switzerland.
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2
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Pal D, Dey S, Ghosh P, Bhattacharya DK, Das S, Maji B. A unique approach for protein secondary structure comparison under TOPS representation. J Biomol Struct Dyn 2024:1-13. [PMID: 38698728 DOI: 10.1080/07391102.2024.2333449] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 03/15/2024] [Indexed: 05/05/2024]
Abstract
To unravel the intricate connection between protein function and protein structure, it is imperative to comprehensively evaluate protein secondary structure similarity from various perspectives. While numerous techniques have been suggested for comparing protein secondary structure elements (SSE), there continues to be a substantial need for finding alternative ways of comparing the same. In this paper, Topology of Protein Structure (TOPS) representations of protein secondary structures are considered to offer a new alignment-free method for evaluating similarities/dissimilarities of protein secondary structures. Initially, a two-dimensional numerical representation of the SSE is created, associating each point with a mass reflecting its frequency of occurrence. Then the means of coordinate values are determined by averaging weighted sums, and these mean values are subsequently used to calculate moments-of-inertia. Next, a four-component descriptor is generated out of the eigenvalues of the matrix and the mean values of the represented coordinates. Thereafter, Manhattan distance measure is used to obtain the distance matrix. This is finally applied to obtain the phylogenetic trees under the use of NJ method. SSE considered in the proposed method comprises 36-elements from the Chew-Kedem database giving five different taxa: globin, alpha-beta, tim-barrel, beta, and alpha. Phylogenetic trees were created for these SSE through the application of various methods: Clustal-Omega, LZ-Complexity, SED, TOPS + and TOC, to facilitate comparative analysis. Phylogenetic tree of the proposed method outperformed results of the previous methods when applied to the same SSE. Therefore, the method effectively constructs phylogenetic tree for analyzing protein secondary structure comparison.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Debrupa Pal
- Computer Application, Narula Institute of Technology, Kolkata, India
- Electronics and Communication Engineering, National Institute of Technology, Durgapur, India
| | - Sudeshna Dey
- Computer Science and Engineering, Narula Institute of Technology, Kolkata, India
| | - Papri Ghosh
- Computer Science and Engineering, Narula Institute of Technology, Kolkata, India
| | | | - Subhram Das
- Computer Science and Engineering, Narula Institute of Technology, Kolkata, India
| | - Bansibadan Maji
- Electronics and Communication Engineering, National Institute of Technology, Durgapur, India
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3
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Genomics-based strategies toward the identification of a Z-ISO carotenoid biosynthetic enzyme suitable for structural studies. Methods Enzymol 2022; 671:171-205. [PMID: 35878977 DOI: 10.1016/bs.mie.2021.12.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
Over the past 20years, structural genomics efforts have proven enormously successful for the determination of integral membrane protein structures, particularly for those of prokaryotic origin. However, traditional genomic expansion screens have included up to hundreds of targets, necessitating the use of robotics and other automation not available to most laboratories. Moreover, such large-scale screens of eukaryotic targets are not easily performed at such a scale. To have broader appeal, traditional structural genomic approaches need to be modified and improved such that they are feasible for most laboratories and especially so for proteins from eukaryotic organisms. One such refinement, termed "microgenomic expansion," has been recently described. This approach improves the process of target selection by making target screening a two-step process, with a minimal number of targets tested at each step. Microgenomic expansion methods are applied here theoretically to a project that has the objective of acquiring a structure for the plant 15-cis-ζ-carotene isomerase, Z-ISO.
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4
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Diep P, Cadavid JL, Yakunin AF, McGuigan AP, Mahadevan R. REVOLVER: A low-cost automated protein purifier based on parallel preparative gravity column workflows. HARDWAREX 2022; 11:e00291. [PMID: 35509899 PMCID: PMC9058827 DOI: 10.1016/j.ohx.2022.e00291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/12/2021] [Revised: 03/01/2022] [Accepted: 03/08/2022] [Indexed: 06/14/2023]
Abstract
Protein purification is a ubiquitous procedure in biochemistry and the life sciences, and represents a key step in the protein production pipeline. The need for scalable and parallel protein purification systems is driven by the demands for increasing the throughput of recombinant protein characterization. Therefore, automating the process to simultaneously handle multiple samples with minimal human intervention is highly desirable, yet there are only a handful of such systems that have been developed, all of which are closed source and expensive. To address this challenge, we present REVOLVER, a 3D-printed programmable protein purification system based on gravity-column workflows and controlled by Arduino boards that can be built for under $130 USD. REVOLVER takes a cell lysate sample and completes a full protein purification process with almost no human intervention and yields results indistinguishable from those obtained by an experienced biochemist when purifying a real-world protein sample. We further present and describe MULTI-VOLVER, a scalable version of the REVOLVER that allows for parallel purification of up to six samples and can be built for under $250 USD. Both systems can help accelerate protein purification and ultimately link them to bio-foundries for protein characterization and engineering.
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Affiliation(s)
- Patrick Diep
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, Canada
| | - Jose L. Cadavid
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, Canada
| | - Alexander F. Yakunin
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, Canada
- Centre for Environmental Biotechnology, Bangor University, Bangor, United Kingdom
| | - Alison P. McGuigan
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, Canada
| | - Radhakrishnan Mahadevan
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, Canada
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5
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Puhl AC, Ekins S. Advancing the Research and Development of Enzyme Replacement Therapies for Lysosomal Storage Diseases. GEN BIOTECHNOLOGY 2022; 1:156-162. [PMID: 35706761 PMCID: PMC9192161 DOI: 10.1089/genbio.2021.0013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
With the increasing interest in developing gene therapies for rare diseases, it is easy to overlook that there are numerous rare lysosomal storage diseases (LSD) with treatments that have been approved by regulatory agencies in the United States and Europe. These primarily consist of enzyme replacement therapies (ERT), which are recombinant human proteins that are delivered for the life of the patient via different routes and may have distinct safety and distribution advantages over gene therapies. The research and development of ERT is a lengthy and expensive process, which is usually performed in academic laboratories before transfer to pharmaceutical companies and is hence a process ripe for disruption. There may still be considerable scientific and investment potential for ERT, however we need to develop a pipeline of proteins analogous to what has been created in some open science efforts as well as apply technologies to decrease manufacturing costs. In this Perspective, we illustrate the opportunity to fill the rare LSD treatment gap with ERTs while gene therapies are in development for these life-shortening diseases.
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Affiliation(s)
- Ana C. Puhl
- Collaborations Pharmaceuticals, Inc., Raleigh, North Carolina, USA
- Address correspondence to: Ana C. Puhl, Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, USA.
| | - Sean Ekins
- Collaborations Pharmaceuticals, Inc., Raleigh, North Carolina, USA
- Address correspondence to: Sean Ekins, Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, USA.
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6
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Cousido-Siah A, Carneiro L, Kostmann C, Ecsedi P, Nyitray L, Trave G, Gogl G. A scalable strategy to solve structures of PDZ domains and their complexes. ACTA CRYSTALLOGRAPHICA SECTION D STRUCTURAL BIOLOGY 2022; 78:509-516. [DOI: 10.1107/s2059798322001784] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Accepted: 02/16/2022] [Indexed: 11/10/2022]
Abstract
The human PDZome represents one of the largest globular domain families in the human proteome, with 266 instances. These globular domains typically interact with C-terminal peptide motifs found in thousands of human proteins. Despite previous efforts, not all PDZ domains have experimentally solved structures and most of their complexes remain to be solved. Here, a simple and cost-effective strategy is proposed for the crystallization of PDZ domains and their complexes. A human annexin A2 fusion tag was used as a crystallization chaperone and the structures of nine PDZ domains were solved, including five domains that had not yet been solved. Finally, these novel experimental structures were compared with AlphaFold predictions and it is speculated how predictions and experimental methods could cooperate in order to investigate the structural landscapes of entire domain families and interactomes.
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7
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Witkin JM. The romantic age of pharmacological science. Pharmacol Biochem Behav 2022; 214:173354. [DOI: 10.1016/j.pbb.2022.173354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 02/08/2022] [Accepted: 02/09/2022] [Indexed: 11/25/2022]
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8
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Zhang J, Ghadermarzi S, Katuwawala A, Kurgan L. DNAgenie: accurate prediction of DNA-type-specific binding residues in protein sequences. Brief Bioinform 2021; 22:6355416. [PMID: 34415020 DOI: 10.1093/bib/bbab336] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 07/02/2021] [Accepted: 07/28/2021] [Indexed: 01/02/2023] Open
Abstract
Efforts to elucidate protein-DNA interactions at the molecular level rely in part on accurate predictions of DNA-binding residues in protein sequences. While there are over a dozen computational predictors of the DNA-binding residues, they are DNA-type agnostic and significantly cross-predict residues that interact with other ligands as DNA binding. We leverage a custom-designed machine learning architecture to introduce DNAgenie, first-of-its-kind predictor of residues that interact with A-DNA, B-DNA and single-stranded DNA. DNAgenie uses a comprehensive physiochemical profile extracted from an input protein sequence and implements a two-step refinement process to provide accurate predictions and to minimize the cross-predictions. Comparative tests on an independent test dataset demonstrate that DNAgenie outperforms the current methods that we adapt to predict residue-level interactions with the three DNA types. Further analysis finds that the use of the second (refinement) step leads to a substantial reduction in the cross predictions. Empirical tests show that DNAgenie's outputs that are converted to coarse-grained protein-level predictions compare favorably against recent tools that predict which DNA-binding proteins interact with double-stranded versus single-stranded DNAs. Moreover, predictions from the sequences of the whole human proteome reveal that the results produced by DNAgenie substantially overlap with the known DNA-binding proteins while also including promising leads for several hundred previously unknown putative DNA binders. These results suggest that DNAgenie is a valuable tool for the sequence-based characterization of protein functions. The DNAgenie's webserver is available at http://biomine.cs.vcu.edu/servers/DNAgenie/.
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Affiliation(s)
- Jian Zhang
- School of Computer and Information Technology at the Xinyang Normal University, No.237, Nanhu Road, Xinyang 464000, Henan Province, P.R. China
| | - Sina Ghadermarzi
- Department of Computer Science at the Virginia Commonwealth University, 401 West Main Street, Room E4225, Richmond, Virginia 23284, USA
| | - Akila Katuwawala
- Department of Computer Science from the Virginia Commonwealth University, 401 West Main Street, Room E4225, Richmond, Virginia 23284, USA
| | - Lukasz Kurgan
- Department of Computer Science at the Virginia Commonwealth University, 401 West Main Street, Room E4225, Richmond, Virginia 23284, USA
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9
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Torres PHM, Rossi AD, Blundell TL. ProtCHOIR: a tool for proteome-scale generation of homo-oligomers. Brief Bioinform 2021; 22:bbab182. [PMID: 34015821 PMCID: PMC8574958 DOI: 10.1093/bib/bbab182] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 04/04/2021] [Accepted: 04/20/2021] [Indexed: 01/10/2023] Open
Abstract
The rapid developments in gene sequencing technologies achieved in the recent decades, along with the expansion of knowledge on the three-dimensional structures of proteins, have enabled the construction of proteome-scale databases of protein models such as the Genome3D and ModBase. Nevertheless, although gene products are usually expressed as individual polypeptide chains, most biological processes are associated with either transient or stable oligomerisation. In the PDB databank, for example, ~40% of the deposited structures contain at least one homo-oligomeric interface. Unfortunately, databases of protein models are generally devoid of multimeric structures. To tackle this particular issue, we have developed ProtCHOIR, a tool that is able to generate homo-oligomeric structures in an automated fashion, providing detailed information for the input protein and output complex. ProtCHOIR requires input of either a sequence or a protomeric structure that is queried against a pre-constructed local database of homo-oligomeric structures, then extensively analyzed using well-established tools such as PSI-Blast, MAFFT, PISA and Molprobity. Finally, MODELLER is employed to achieve the construction of the homo-oligomers. The output complex is thoroughly analyzed taking into account its stereochemical quality, interfacial stabilities, hydrophobicity and conservation profile. All these data are then summarized in a user-friendly HTML report that can be saved or printed as a PDF file. The software is easily parallelizable and also outputs a comma-separated file with summary statistics that can straightforwardly be concatenated as a spreadsheet-like document for large-scale data analyses. As a proof-of-concept, we built oligomeric models for the Mabellini Mycobacterium abscessus structural proteome database. ProtCHOIR can be run as a web-service and the code can be obtained free-of-charge at http://lmdm.biof.ufrj.br/protchoir.
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10
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Diwan GD, Carlos Gonzalez-Sanchez J, Apic G, Russell RB. Next generation protein structure predictions and genetic variant interpretation. J Mol Biol 2021; 433:167180. [PMID: 34358547 DOI: 10.1016/j.jmb.2021.167180] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Revised: 07/24/2021] [Accepted: 07/26/2021] [Indexed: 10/20/2022]
Abstract
The need to make sense of the thousands of genetic variants uncovered every day in terms of pathology or biological mechanism is acute. Many insights into how genetic changes impact protein function can be gleaned if three-dimensional structures of the associated proteins are available. The availability of a highly accurate method of predicting structures from amino acid sequences is thus potentially a great boost to those wanting to understand genetic changes. In this paper we discuss the current state of protein structures known for the human and other proteomes and how better structure predictions might impact on variant interpretation efforts. For the human proteome in particular, the state of the available structural data suggests that the impact on variant interpretation might be less than anticipated. We also discuss additional efforts in structure prediction that could further aid the understanding of genetic variants.
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Affiliation(s)
- Gaurav D Diwan
- BioQuant, Heidelberg University, Im Neuenheimer Feld 267, Heidelberg, Germany; Heidelberg University Biochemistry Center (BZH), Im Neuenheimer Feld
| | - Juan Carlos Gonzalez-Sanchez
- BioQuant, Heidelberg University, Im Neuenheimer Feld 267, Heidelberg, Germany; Heidelberg University Biochemistry Center (BZH), Im Neuenheimer Feld
| | - Gordana Apic
- BioQuant, Heidelberg University, Im Neuenheimer Feld 267, Heidelberg, Germany; Heidelberg University Biochemistry Center (BZH), Im Neuenheimer Feld
| | - Robert B Russell
- BioQuant, Heidelberg University, Im Neuenheimer Feld 267, Heidelberg, Germany; Heidelberg University Biochemistry Center (BZH), Im Neuenheimer Feld.
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11
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Guterres H, Park SJ, Zhang H, Im W. CHARMM-GUI LBS Finder & Refiner for Ligand Binding Site Prediction and Refinement. J Chem Inf Model 2021; 61:3744-3751. [PMID: 34296608 DOI: 10.1021/acs.jcim.1c00561] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
A protein performs its task by binding a variety of ligands in its local region that is also known as the ligand-binding-site (LBS). Therefore, accurate prediction, characterization, and refinement of LBS can facilitate protein functional annotations and structure-based drug design. In this work, we present CHARMM-GUI LBS Finder & Refiner (https://www.charmm-gui.org/input/lbsfinder) that predicts potential LBS, offers interactive features for local LBS structure analysis, and prepares various molecular dynamics (MD) systems and inputs by setting up distance restraint potentials for LBS structure refinement. LBS Finder & Refiner supports 5 different commonly used simulation programs, such as NAMD, AMBER, GROMACS, GENESIS, and OpenMM, for LBS structure refinement together with hydrogen mass repartitioning. The capability of LBS Finder & Refiner is illustrated through LBS structure predictions and refinements of 48 modeled and 20 apo benchmark target proteins. Overall, successful LBS structure predictions and refinements are seen in our benchmark tests. We hope that LBS Finder & Refiner is useful to predict, characterize, and refine potential LBS on any given protein of interest.
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Affiliation(s)
- Hugo Guterres
- Departments of Biological Sciences, Chemistry, Bioengineering, and Computer Science and Engineering, Lehigh University, Bethlehem, Pennsylvania 18015, United States
| | - Sang-Jun Park
- Departments of Biological Sciences, Chemistry, Bioengineering, and Computer Science and Engineering, Lehigh University, Bethlehem, Pennsylvania 18015, United States
| | - Han Zhang
- Departments of Biological Sciences, Chemistry, Bioengineering, and Computer Science and Engineering, Lehigh University, Bethlehem, Pennsylvania 18015, United States
| | - Wonpil Im
- Departments of Biological Sciences, Chemistry, Bioengineering, and Computer Science and Engineering, Lehigh University, Bethlehem, Pennsylvania 18015, United States
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12
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Ghadermarzi S, Krawczyk B, Song J, Kurgan L. XRRpred: Accurate Predictor of Crystal Structure Quality from Protein Sequence. Bioinformatics 2021; 37:4366-4374. [PMID: 34247234 DOI: 10.1093/bioinformatics/btab509] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Revised: 06/10/2021] [Accepted: 07/06/2021] [Indexed: 11/12/2022] Open
Abstract
MOTIVATION X-ray crystallography was used to produce nearly 90% of protein structures. These efforts were supported by numerous sequence-based tools that accurately predict crystallizable proteins. However, protein structures vary widely in their quality, typically measured with resolution and R-free. This impacts the ability to use these structures for some applications including rational drug design and molecular docking and motivates development of methods that accurately predict structure quality. RESULTS We introduce XRRpred, the first predictor of the resolution and R-free values from protein sequences. XRRpred relies on original sequence profiles, hand-crafted features, empirically selected and parametrized regressors, and modern resampling techniques. Using an independent test dataset, we show that XRRpred provides accurate predictions of resolution and R-free. We demonstrate that XRRpred's predictions correctly model relationship between the resolution and R-free and reproduce structure quality relations between structural classes of proteins. We also show that XRRpred significantly outperforms indirect alternative ways to predict the structure quality that include predictors of crystallization propensity and an alignment-based approach. XRRpred is available as a convenient webserver that allows batch predictions and offers informative visualization of the results. AVAILABILITY http://biomine.cs.vcu.edu/servers/XRRPred/.
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Affiliation(s)
- Sina Ghadermarzi
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, USA
| | - Bartosz Krawczyk
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, USA
| | - Jiangning Song
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia.,Monash Centre for Data Science, Faculty of Information Technology, Monash University, Melbourne, VIC 3800, Australia
| | - Lukasz Kurgan
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, USA
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Merlotti A, Menichetti G, Fariselli P, Capriotti E, Remondini D. Network-based strategies for protein characterization. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2021; 127:217-248. [PMID: 34340768 DOI: 10.1016/bs.apcsb.2021.05.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
Protein structure characterization is fundamental to understand protein properties, such as folding process and protein resistance to thermal stress, up to unveiling organism pathologies (e.g., prion disease). In this chapter, we provide an overview on how the spectral properties of the networks reconstructed from the Protein Contact Map (PCM) can be used to generate informative observables. As a specific case study, we apply two different network approaches to an example protein dataset, for the aim of discriminating protein folding state, and for the reconstruction of protein 3D structure.
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Affiliation(s)
| | - Giulia Menichetti
- Center for Complex Network Research, Department of Physics, Northeastern University, Boston, MA, United States; Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Piero Fariselli
- Department of Medical Sciences, University of Torino, Turin, Italy
| | - Emidio Capriotti
- Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
| | - Daniel Remondini
- Department of Physics and Astronomy, University of Bologna, Bologna, Italy.
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14
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Structural genomics and the Protein Data Bank. J Biol Chem 2021; 296:100747. [PMID: 33957120 PMCID: PMC8166929 DOI: 10.1016/j.jbc.2021.100747] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Revised: 04/16/2021] [Accepted: 04/30/2021] [Indexed: 12/14/2022] Open
Abstract
The field of Structural Genomics arose over the last 3 decades to address a large and rapidly growing divergence between microbial genomic, functional, and structural data. Several international programs took advantage of the vast genomic sequence information and evaluated the feasibility of structure determination for expanded and newly discovered protein families. As a consequence, structural genomics has developed structure-determination pipelines and applied them to a wide range of novel, uncharacterized proteins, often from “microbial dark matter,” and later to proteins from human pathogens. Advances were especially needed in protein production and rapid de novo structure solution. The experimental three-dimensional models were promptly made public, facilitating structure determination of other members of the family and helping to understand their molecular and biochemical functions. Improvements in experimental methods and databases resulted in fast progress in molecular and structural biology. The Protein Data Bank structure repository played a central role in the coordination of structural genomics efforts and the structural biology community as a whole. It facilitated development of standards and validation tools essential for maintaining high quality of deposited structural data.
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15
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Grabowski M, Macnar JM, Cymborowski M, Cooper DR, Shabalin IG, Gilski M, Brzezinski D, Kowiel M, Dauter Z, Rupp B, Wlodawer A, Jaskolski M, Minor W. Rapid response to emerging biomedical challenges and threats. IUCRJ 2021; 8:395-407. [PMID: 33953926 PMCID: PMC8086160 DOI: 10.1107/s2052252521003018] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Accepted: 03/22/2021] [Indexed: 05/13/2023]
Abstract
As part of the global mobilization to combat the present pandemic, almost 100 000 COVID-19-related papers have been published and nearly a thousand models of macromolecules encoded by SARS-CoV-2 have been deposited in the Protein Data Bank within less than a year. The avalanche of new structural data has given rise to multiple resources dedicated to assessing the correctness and quality of structural data and models. Here, an approach to evaluate the massive amounts of such data using the resource https://covid19.bioreproducibility.org is described, which offers a template that could be used in large-scale initiatives undertaken in response to future biomedical crises. Broader use of the described methodology could considerably curtail information noise and significantly improve the reproducibility of biomedical research.
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Affiliation(s)
- Marek Grabowski
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, Virginia, USA
| | - Joanna M. Macnar
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, Virginia, USA
- College of Inter-Faculty Individual Studies in Mathematics and Natural Sciences, University of Warsaw, Warsaw, Poland
- Faculty of Chemistry, Biological and Chemical Research Center, University of Warsaw, Warsaw, Poland
| | - Marcin Cymborowski
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, Virginia, USA
| | - David R. Cooper
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, Virginia, USA
| | - Ivan G. Shabalin
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, Virginia, USA
| | - Miroslaw Gilski
- Department of Crystallography, Faculty of Chemistry, A. Mickiewicz University, Poznan, Poland
- Center for Biocrystallographic Research, Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznan, Poland
| | - Dariusz Brzezinski
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, Virginia, USA
- Center for Biocrystallographic Research, Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznan, Poland
- Institute of Computing Science, Poznan University of Technology, Poznan, Poland
| | - Marcin Kowiel
- Center for Biocrystallographic Research, Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznan, Poland
| | - Zbigniew Dauter
- Center for Structural Biology, National Cancer Institute, Frederick, Maryland, USA
| | - Bernhard Rupp
- k.-k Hofkristallamt, San Diego, California, USA
- Institute of Genetic Epidemiology, Medical University Innsbruck, Innsbruck, Austria
| | - Alexander Wlodawer
- Center for Structural Biology, National Cancer Institute, Frederick, Maryland, USA
| | - Mariusz Jaskolski
- Department of Crystallography, Faculty of Chemistry, A. Mickiewicz University, Poznan, Poland
- Center for Biocrystallographic Research, Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznan, Poland
| | - Wladek Minor
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, Virginia, USA
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16
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Rodriguez-Esteban R. Biomedical articles share annotations with their citation neighbors. BMC Bioinformatics 2021; 22:95. [PMID: 33637047 PMCID: PMC7912518 DOI: 10.1186/s12859-021-04044-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Accepted: 02/16/2021] [Indexed: 11/24/2022] Open
Abstract
Background Numerous efforts have been poured into annotating the wealth of knowledge contained in biomedical articles. Thanks to such efforts, it is now possible to quantitatively explore relations between these annotations and the citation network at large scale. Results With the aid of several large and small annotation databases, this study shows that articles share annotations with their citation neighborhood to the point that the neighborhood’s most common annotations are likely to be those appearing in the article. Conclusions These findings posit that an article’s citation neighborhood defines to a large extent the article’s annotated content. Thus, citations should be considered as a foundation for future knowledge management and annotation of biomedical articles.
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Affiliation(s)
- Raul Rodriguez-Esteban
- Roche Innovation Center Basel, Roche Pharmaceutical Research and Early Development, 4070, Basel, Switzerland.
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17
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Grabowski M, Cooper DR, Brzezinski D, Macnar JM, Shabalin IG, Cymborowski M, Otwinowski Z, Minor W. Synchrotron Radiation as a Tool for Macromolecular X-Ray Crystallography: a XXI Century Perspective. NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH. SECTION B, BEAM INTERACTIONS WITH MATERIALS AND ATOMS 2021; 489:30-40. [PMID: 33603257 PMCID: PMC7886262 DOI: 10.1016/j.nimb.2020.12.016] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Intense X-rays available at powerful synchrotron beamlines provide macromolecular crystallographers with an incomparable tool for investigating biological phenomena on an atomic scale. The resulting insights into the mechanism's underlying biological processes have played an essential role and shaped biomedical sciences during the last 30 years, considered the "golden age" of structural biology. In this review, we analyze selected aspects of the impact of synchrotron radiation on structural biology. Synchrotron beamlines have been used to determine over 70% of all macromolecular structures deposited into the Protein Data Bank (PDB). These structures were deposited by over 13,000 different research groups. Interestingly, despite the impressive advances in synchrotron technologies, the median resolution of macromolecular structures determined using synchrotrons has remained constant throughout the last 30 years, at about 2 Å. Similarly, the median times from the data collection to the deposition and release have not changed significantly. We describe challenges to reproducibility related to recording all relevant data and metadata during the synchrotron experiments, including diffraction images. Finally, we discuss some of the recent opinions suggesting a diminishing importance of X-ray crystallography due to impressive advances in Cryo-EM and theoretical modeling. We believe that synchrotrons of the future will increasingly evolve towards a life science center model, where X-ray crystallography, Cryo-EM, and other experimental and computational resources and knowledge are encompassed within a versatile research facility. The recent response of crystallographers to the COVID-19 pandemic suggests that X-ray crystallography conducted at synchrotron beamlines will continue to play an essential role in structural biology and drug discovery for years to come.
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Affiliation(s)
- Marek Grabowski
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, VA22903, USA
| | - David R. Cooper
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, VA22903, USA
| | - Dariusz Brzezinski
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, VA22903, USA
- Institute of Computing Science, Poznan University of Technology, Poznan, Poland
- Center for Biocrystallographic Research, Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznan, Poland
| | - Joanna M. Macnar
- College of Inter-Faculty Individual Studies in Mathematics and Natural Sciences, University of Warsaw, Warsaw, Poland
- Faculty of Chemistry, Biological and Chemical Research Center, University of Warsaw, Warsaw, Poland
| | - Ivan G. Shabalin
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, VA22903, USA
| | - Marcin Cymborowski
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, VA22903, USA
| | - Zbyszek Otwinowski
- Department of Biophysics, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Wladek Minor
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, VA22903, USA
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18
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Residue-based pharmacophore approaches to study protein-protein interactions. Curr Opin Struct Biol 2021; 67:205-211. [PMID: 33486430 DOI: 10.1016/j.sbi.2020.12.016] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Revised: 12/04/2020] [Accepted: 12/28/2020] [Indexed: 01/22/2023]
Abstract
This review focuses on pharmacophore approaches in researching protein interfaces that bind protein ligands. Pharmacophore descriptions of binding interfaces that employ molecular dynamics simulation can account for effects of solvation and conformational flexibility. In addition, these calculations provide an approximation to entropic considerations and as such, a better approximation of the free energy of binding. Residue-based pharmacophore approaches can facilitate a variety of drug discovery tasks such as the identification of receptor-ligand partners, identifying their binding poses, designing protein interfaces for selectivity, or defining a reduced mutational combinatorial exploration for subsequent experimental engineering techniques by orders of magnitudes.
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19
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Wilson IA, Stanfield RL. 50 Years of structural immunology. J Biol Chem 2021; 296:100745. [PMID: 33957119 PMCID: PMC8163984 DOI: 10.1016/j.jbc.2021.100745] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Revised: 03/24/2021] [Accepted: 04/30/2021] [Indexed: 12/12/2022] Open
Abstract
Fifty years ago, the first landmark structures of antibodies heralded the dawn of structural immunology. Momentum then started to build toward understanding how antibodies could recognize the vast universe of potential antigens and how antibody-combining sites could be tailored to engage antigens with high specificity and affinity through recombination of germline genes (V, D, J) and somatic mutation. Equivalent groundbreaking structures in the cellular immune system appeared some 15 to 20 years later and illustrated how processed protein antigens in the form of peptides are presented by MHC molecules to T cell receptors. Structures of antigen receptors in the innate immune system then explained their inherent specificity for particular microbial antigens including lipids, carbohydrates, nucleic acids, small molecules, and specific proteins. These two sides of the immune system act immediately (innate) to particular microbial antigens or evolve (adaptive) to attain high specificity and affinity to a much wider range of antigens. We also include examples of other key receptors in the immune system (cytokine receptors) that regulate immunity and inflammation. Furthermore, these antigen receptors use a limited set of protein folds to accomplish their various immunological roles. The other main players are the antigens themselves. We focus on surface glycoproteins in enveloped viruses including SARS-CoV-2 that enable entry and egress into host cells and are targets for the antibody response. This review covers what we have learned over the past half century about the structural basis of the immune response to microbial pathogens and how that information can be utilized to design vaccines and therapeutics.
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MESH Headings
- Adaptive Immunity
- Allergy and Immunology/history
- Animals
- Antibodies, Viral/chemistry
- Antibodies, Viral/genetics
- Antibodies, Viral/immunology
- Antibody Specificity
- Antigen Presentation
- Antigens, Viral/chemistry
- Antigens, Viral/genetics
- Antigens, Viral/immunology
- COVID-19/immunology
- COVID-19/virology
- Crystallography/history
- Crystallography/methods
- History, 20th Century
- History, 21st Century
- Humans
- Immunity, Innate
- Protein Folding
- Protein Interaction Domains and Motifs
- Receptors, Antigen, T-Cell/chemistry
- Receptors, Antigen, T-Cell/genetics
- Receptors, Antigen, T-Cell/immunology
- Receptors, Cytokine/chemistry
- Receptors, Cytokine/genetics
- Receptors, Cytokine/immunology
- SARS-CoV-2/immunology
- SARS-CoV-2/pathogenicity
- V(D)J Recombination
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Affiliation(s)
- Ian A Wilson
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, California, USA; The Skaggs Institute for Chemical Biology, The Scripps Research Institute, La Jolla, California, USA.
| | - Robyn L Stanfield
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, California, USA
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20
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Zhu L, Chen X, Abola EE, Jing L, Liu W. Serial Crystallography for Structure-Based Drug Discovery. Trends Pharmacol Sci 2020; 41:830-839. [PMID: 32950259 PMCID: PMC7572805 DOI: 10.1016/j.tips.2020.08.009] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2020] [Revised: 07/17/2020] [Accepted: 08/25/2020] [Indexed: 02/07/2023]
Abstract
Rational drug discovery has greatly accelerated the development of safer and more efficacious therapeutics, assisted significantly by insights from experimentally determined 3D structures of ligands in complex with their targets. Serial crystallography (SX) with X-ray free-electron lasers has enabled structural determination using micrometer- or nanometer-size crystals. This technology, applied in the past decade to solve structures of notoriously difficult-to-study drug targets at room temperature, has now been adapted for use in synchrotron radiation facilities. Ultrashort time scales allow time-resolved characterization of dynamic structural changes and pave the road to study the molecular mechanisms by 'molecular movie.' This article summarizes the latest progress in SX technology and deliberates its demanding applications in future structure-based drug discovery.
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Affiliation(s)
- Lan Zhu
- Biodesign Center for Applied Structural Discovery, Biodesign Institute, Arizona State University, Tempe, AZ 85287, USA; School of Molecular Sciences, Arizona State University, Tempe, AZ 85287, USA
| | - Xiaoyu Chen
- Biodesign Center for Applied Structural Discovery, Biodesign Institute, Arizona State University, Tempe, AZ 85287, USA; School of Molecular Sciences, Arizona State University, Tempe, AZ 85287, USA
| | - Enrique E Abola
- Biodesign Center for Applied Structural Discovery, Biodesign Institute, Arizona State University, Tempe, AZ 85287, USA; School of Molecular Sciences, Arizona State University, Tempe, AZ 85287, USA
| | - Liang Jing
- Biodesign Center for Applied Structural Discovery, Biodesign Institute, Arizona State University, Tempe, AZ 85287, USA; School of Molecular Sciences, Arizona State University, Tempe, AZ 85287, USA
| | - Wei Liu
- Biodesign Center for Applied Structural Discovery, Biodesign Institute, Arizona State University, Tempe, AZ 85287, USA; School of Molecular Sciences, Arizona State University, Tempe, AZ 85287, USA.
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21
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Wen B, Zeng W, Liao Y, Shi Z, Savage SR, Jiang W, Zhang B. Deep Learning in Proteomics. Proteomics 2020; 20:e1900335. [PMID: 32939979 PMCID: PMC7757195 DOI: 10.1002/pmic.201900335] [Citation(s) in RCA: 70] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Revised: 09/14/2020] [Indexed: 12/17/2022]
Abstract
Proteomics, the study of all the proteins in biological systems, is becoming a data-rich science. Protein sequences and structures are comprehensively catalogued in online databases. With recent advancements in tandem mass spectrometry (MS) technology, protein expression and post-translational modifications (PTMs) can be studied in a variety of biological systems at the global scale. Sophisticated computational algorithms are needed to translate the vast amount of data into novel biological insights. Deep learning automatically extracts data representations at high levels of abstraction from data, and it thrives in data-rich scientific research domains. Here, a comprehensive overview of deep learning applications in proteomics, including retention time prediction, MS/MS spectrum prediction, de novo peptide sequencing, PTM prediction, major histocompatibility complex-peptide binding prediction, and protein structure prediction, is provided. Limitations and the future directions of deep learning in proteomics are also discussed. This review will provide readers an overview of deep learning and how it can be used to analyze proteomics data.
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Affiliation(s)
- Bo Wen
- Lester and Sue Smith Breast CenterBaylor College of MedicineHoustonTX77030USA
- Department of Molecular and Human GeneticsBaylor College of MedicineHoustonTX77030USA
| | - Wen‐Feng Zeng
- Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS)Chinese Academy of SciencesInstitute of Computing TechnologyBeijing100190China
| | - Yuxing Liao
- Lester and Sue Smith Breast CenterBaylor College of MedicineHoustonTX77030USA
- Department of Molecular and Human GeneticsBaylor College of MedicineHoustonTX77030USA
| | - Zhiao Shi
- Lester and Sue Smith Breast CenterBaylor College of MedicineHoustonTX77030USA
- Department of Molecular and Human GeneticsBaylor College of MedicineHoustonTX77030USA
| | - Sara R. Savage
- Lester and Sue Smith Breast CenterBaylor College of MedicineHoustonTX77030USA
- Department of Molecular and Human GeneticsBaylor College of MedicineHoustonTX77030USA
| | - Wen Jiang
- Lester and Sue Smith Breast CenterBaylor College of MedicineHoustonTX77030USA
- Department of Molecular and Human GeneticsBaylor College of MedicineHoustonTX77030USA
| | - Bing Zhang
- Lester and Sue Smith Breast CenterBaylor College of MedicineHoustonTX77030USA
- Department of Molecular and Human GeneticsBaylor College of MedicineHoustonTX77030USA
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22
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Kim G, Jang S, Lee E, Song JJ. EMPAS: Electron Microscopy Screening for Endogenous Protein Architectures. Mol Cells 2020; 43:804-812. [PMID: 32975210 PMCID: PMC7528680 DOI: 10.14348/molcells.2020.0163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Revised: 08/11/2020] [Accepted: 08/14/2020] [Indexed: 11/27/2022] Open
Abstract
In cells, proteins form macromolecular complexes to execute their own unique roles in biological processes. Conventional structural biology methods adopt a bottom-up approach starting from defined sets of proteins to investigate the structures and interactions of protein complexes. However, this approach does not reflect the diverse and complex landscape of endogenous molecular architectures. Here, we introduce a top-down approach called Electron Microscopy screening for endogenous Protein ArchitectureS (EMPAS) to investigate the diverse and complex landscape of endogenous macromolecular architectures in an unbiased manner. By applying EMPAS, we discovered a spiral architecture and identified it as AdhE. Furthermore, we performed screening to examine endogenous molecular architectures of human embryonic stem cells (hESCs), mouse brains, cyanobacteria and plant leaves, revealing their diverse repertoires of molecular architectures. This study suggests that EMPAS may serve as a tool to investigate the molecular architectures of endogenous macromolecular proteins.
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Affiliation(s)
- Gijeong Kim
- Department of Biological Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 344, Korea
- These authors contributed equally to this work
| | - Seongmin Jang
- Department of Biological Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 344, Korea
- These authors contributed equally to this work
| | - Eunhye Lee
- Department of Biological Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 344, Korea
| | - Ji-Joon Song
- Department of Biological Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 344, Korea
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23
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Brzezinski D, Dauter Z, Minor W, Jaskolski M. On the evolution of the quality of macromolecular models in the PDB. FEBS J 2020; 287:2685-2698. [PMID: 32311227 PMCID: PMC7340579 DOI: 10.1111/febs.15314] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2019] [Revised: 03/02/2020] [Accepted: 03/26/2020] [Indexed: 01/06/2023]
Abstract
Crystallographic models of biological macromolecules have been ranked using the quality criteria associated with them in the Protein Data Bank (PDB). The outcomes of this quality analysis have been correlated with time and with the journals that published papers based on those models. The results show that the overall quality of PDB structures has substantially improved over the last ten years, but this period of progress was preceded by several years of stagnation or even depression. Moreover, the study shows that the historically observed negative correlation between journal impact and the quality of structural models presented therein seems to disappear as time progresses.
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Affiliation(s)
- Dariusz Brzezinski
- Center for Biocrystallographic ResearchInstitute of Bioorganic ChemistryPolish Academy of SciencesPoznanPoland
- Institute of Computing SciencePoznan University of TechnologyPoland
- Center for Artificial Intelligence and Machine LearningPoznan University of TechnologyPoland
- Department of Molecular Physiology and Biological PhysicsUniversity of VirginiaCharlottesvilleVAUSA
| | - Zbigniew Dauter
- Synchrotron Radiation Research SectionMacromolecular Crystallography LaboratoryNational Cancer InstituteArgonne National LaboratoryArgonneILUSA
| | - Wladek Minor
- Department of Molecular Physiology and Biological PhysicsUniversity of VirginiaCharlottesvilleVAUSA
| | - Mariusz Jaskolski
- Center for Biocrystallographic ResearchInstitute of Bioorganic ChemistryPolish Academy of SciencesPoznanPoland
- Department of CrystallographyFaculty of ChemistryA. Mickiewicz UniversityPoznanPoland
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24
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Yan J, Cheng J, Kurgan L, Uversky VN. Structural and functional analysis of "non-smelly" proteins. Cell Mol Life Sci 2020; 77:2423-2440. [PMID: 31486849 PMCID: PMC11105052 DOI: 10.1007/s00018-019-03292-1] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2019] [Revised: 08/21/2019] [Accepted: 08/28/2019] [Indexed: 01/09/2023]
Abstract
Cysteine and aromatic residues are major structure-promoting residues. We assessed the abundance, structural coverage, and functional characteristics of the "non-smelly" proteins, i.e., proteins that do not contain cysteine residues (C-depleted) or cysteine and aromatic residues (CFYWH-depleted), across 817 proteomes from all domains of life. The analysis revealed that although these proteomes contained significant levels of the C-depleted proteins, with prokaryotes being significantly more enriched in such proteins than eukaryotes, the CFYWH-depleted proteins were relatively rare, accounting for about 0.05% of proteomes. Furthermore, CFYWH-depleted proteins were virtually never found in PDB. Depletion in cysteine and in aromatic residues was associated with the substantially increased intrinsic disorder levels across all domains of life. Archaeal and eukaryotic organisms with higher levels of the C-depleted proteins were shown to have higher levels of the intrinsic disorder and lower levels of structural coverage. We also showed that the "non-smelly" proteins typically did not independently fold into monomeric structures, and instead, they fold by interacting with nucleic acids as constituents of the ribosome and nucleosome complexes. They were shown to be involved in translation, transcription, nucleosome assembly, transmembrane transport, and protein folding functions, all of which are known to be associated with the intrinsic disorder. Our data suggested that, in general, structure of monomeric proteins is crucially dependent on the presence of cysteine and aromatic residues.
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Affiliation(s)
- Jing Yan
- Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada
| | - Jianlin Cheng
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, USA
| | - Lukasz Kurgan
- Department of Computer Science, Virginia Commonwealth University, 401 West Main Street, Room E4225, Richmond, VA, 23284, USA.
| | - Vladimir N Uversky
- Department of Molecular Medicine and USF Health Byrd Alzheimer's Research Institute, Morsani College of Medicine, University of South Florida, 12901 Bruce B. Downs Blvd., MDC07, Tampa, FL, 33612, USA.
- Protein Research Group, Institute for Biological Instrumentation of the Russian Academy of Sciences, 142290, Pushchino, Moscow Region, Russia.
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25
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Martín-Galiano AJ, McConnell MJ. Using Omics Technologies and Systems Biology to Identify Epitope Targets for the Development of Monoclonal Antibodies Against Antibiotic-Resistant Bacteria. Front Immunol 2019; 10:2841. [PMID: 31921119 PMCID: PMC6914692 DOI: 10.3389/fimmu.2019.02841] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2019] [Accepted: 11/19/2019] [Indexed: 12/12/2022] Open
Abstract
Over the past few decades, antimicrobial resistance has emerged as an important threat to public health due to the global dissemination of multidrug-resistant strains from several bacterial species. This worrisome trend, in addition to the paucity of new antibiotics with novel mechanisms of action in the development pipeline, warrants the development of non-antimicrobial approaches to combating infection caused by these isolates. Monoclonal antibodies (mAbs) have emerged as highly effective molecules for the treatment of multiple diseases. However, in spite of the fact that antibodies play an important role in protective immunity against bacteria, only three mAb therapies have been approved for clinical use in the treatment of bacterial infections. In the present review, we briefly outline the therapeutic potential of mAbs in the treatment of bacterial diseases and discuss how their development can be facilitated when assisted by “omics” technologies and interpreted under a systems biology paradigm. Specifically, methods employing large genomic, transcriptomic, structural, and proteomic datasets allow for the rational identification of epitopes. Ideally, these include those that are present in the majority of circulating isolates, highly conserved at the amino acid level, surface-exposed, located on antigens essential for virulence, and expressed during critical stages of infection. Therefore, these knowledge-based approaches can contribute to the identification of high-value epitopes for the development of effective mAbs against challenging bacterial clones.
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Affiliation(s)
- Antonio J Martín-Galiano
- Intrahospital Infections Laboratory, National Centre for Microbiology, Instituto de Salud Carlos III, Majadahonda, Spain
| | - Michael J McConnell
- Intrahospital Infections Laboratory, National Centre for Microbiology, Instituto de Salud Carlos III, Majadahonda, Spain
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26
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High-Throughput Crystallization Pipeline at the Crystallography Core Facility of the Institut Pasteur. Molecules 2019; 24:molecules24244451. [PMID: 31817305 PMCID: PMC6943606 DOI: 10.3390/molecules24244451] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Revised: 12/02/2019] [Accepted: 12/03/2019] [Indexed: 11/25/2022] Open
Abstract
The availability of whole-genome sequence data, made possible by significant advances in DNA sequencing technology, led to the emergence of structural genomics projects in the late 1990s. These projects not only significantly increased the number of 3D structures deposited in the Protein Data Bank in the last two decades, but also influenced present crystallographic strategies by introducing automation and high-throughput approaches in the structure-determination pipeline. Today, dedicated crystallization facilities, many of which are open to the general user community, routinely set up and track thousands of crystallization screening trials per day. Here, we review the current methods for high-throughput crystallization and procedures to obtain crystals suitable for X-ray diffraction studies, and we describe the crystallization pipeline implemented in the medium-scale crystallography platform at the Institut Pasteur (Paris) as an example.
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27
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Granados Moreno P, Ali-Khan SE, Capps B, Caulfield T, Chalaud D, Edwards A, Gold ER, Rahimzadeh V, Thorogood A, Auld D, Bertier G, Breden F, Caron R, César PM, Cook-Deegan R, Doerr M, Duncan R, Issa AM, Reichman J, Simard J, So D, Vanamala S, Joly Y. Open science precision medicine in Canada: Points to consider. Facets (Ott) 2019. [DOI: 10.1139/facets-2018-0034] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Open science can significantly influence the development and translational process of precision medicine in Canada. Precision medicine presents a unique opportunity to improve disease prevention and healthcare, as well as to reduce health-related expenditures. However, the development of precision medicine also brings about economic challenges, such as costly development, high failure rates, and reduced market size in comparison with the traditional blockbuster drug development model. Open science, characterized by principles of open data sharing, fast dissemination of knowledge, cumulative research, and cooperation, presents a unique opportunity to address these economic challenges while also promoting the public good. The Centre of Genomics and Policy at McGill University organized a stakeholders’ workshop in Montreal in March 2018. The workshop entitled “Could Open be the Yellow Brick Road to Precision Medicine?” provided a forum for stakeholders to share experiences and identify common objectives, challenges, and needs to be addressed to promote open science initiatives in precision medicine. The rich presentations and exchanges that took place during the meeting resulted in this consensus paper containing key considerations for open science precision medicine in Canada. Stakeholders would benefit from addressing these considerations as to promote a more coherent and dynamic open science ecosystem for precision medicine.
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Affiliation(s)
- Palmira Granados Moreno
- Centre of Genomics and Policy, Department of Human Genetics, McGill University, Montréal, QC H3A 0G1, Canada
| | - Sarah E. Ali-Khan
- Centre for Intellectual Property and Policy, Faculty of Law, McGill University, Montreal, QC H3A 1W9, Canada
| | - Benjamin Capps
- Department of Bioethics, Faculty of Medicine, Dalhousie University, Halifax, NS B3H 4R2, Canada
| | - Timothy Caulfield
- Health Law Institute, Faculty of Law and School of Public Health, University of Alberta, Edmonton, AB T6G 2H5, Canada
| | - Damien Chalaud
- Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Aled Edwards
- Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
- Structural Genomics Consortium, University of Toronto, Toronto, ON M5G 1L6, Canada
| | - E. Richard Gold
- Centre for Intellectual Property and Policy, Faculty of Law, McGill University, Montreal, QC H3A 1W9, Canada
| | - Vasiliki Rahimzadeh
- Centre of Genomics and Policy, Department of Human Genetics, McGill University, Montréal, QC H3A 0G1, Canada
| | - Adrian Thorogood
- Centre of Genomics and Policy, Department of Human Genetics, McGill University, Montréal, QC H3A 0G1, Canada
| | - Daniel Auld
- McGill University and Genome Quebec Innovation Centre, Montreal, QC H3A 0G1, Canada
| | - Gabrielle Bertier
- Centre of Genomics and Policy, Department of Human Genetics, McGill University, Montréal, QC H3A 0G1, Canada
| | - Felix Breden
- Department of Biological Sciences, Simon Fraser University, Burnaby, BC V5A 1S6, Canada
| | - Roxanne Caron
- Centre of Genomics and Policy, Department of Human Genetics, McGill University, Montréal, QC H3A 0G1, Canada
| | - Priscilla M.D.G. César
- Centre for Intellectual Property and Policy, Faculty of Law, McGill University, Montreal, QC H3A 1W9, Canada
| | - Robert Cook-Deegan
- School for the Future of Innovation in Society, Barrett & O’Connor Washington Center, Arizona State University, Washington, DC 20006, USA
| | | | - Ross Duncan
- Public Health Agency of Canada, Ottawa, ON K1A 0K9, Canada
| | - Amalia M. Issa
- Centre of Genomics and Policy, Department of Human Genetics, McGill University, Montréal, QC H3A 0G1, Canada
- Department of Family Medicine, McGill University, Montreal, QC H3S 1Z1, Canada
- Personalized Medicine & Targeted Therapeutics, Philadelphia, PA 19803, USA
- Health Policy & Pharmaceutical Sciences, University of the Sciences in Philadelphia, Philadelphia, PA 19104, USA
| | | | - Jacques Simard
- Genomics Center, Centre Hospitalier Universitaire de Quebec-Laval University, Quebec City, QC G1V 4G2, Canada
| | - Derek So
- Centre of Genomics and Policy, Department of Human Genetics, McGill University, Montréal, QC H3A 0G1, Canada
| | - Sandeep Vanamala
- Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Yann Joly
- Centre of Genomics and Policy, Department of Human Genetics, McGill University, Montréal, QC H3A 0G1, Canada
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28
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Buchholz PCF, Ferrario V, Pohl M, Gardossi L, Pleiss J. Navigating within thiamine diphosphate-dependent decarboxylases: Sequences, structures, functional positions, and binding sites. Proteins 2019; 87:774-785. [PMID: 31070804 DOI: 10.1002/prot.25706] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2019] [Revised: 04/23/2019] [Accepted: 05/05/2019] [Indexed: 11/10/2022]
Abstract
Thiamine diphosphate-dependent decarboxylases catalyze both cleavage and formation of CC bonds in various reactions, which have been assigned to different homologous sequence families. This work compares 53 ThDP-dependent decarboxylases with known crystal structures. Both sequence and structural information were analyzed synergistically and data were analyzed for global and local properties by means of statistical approaches (principle component analysis and principal coordinate analysis) enabling complexity reduction. The different results obtained both locally and globally, that is, individual positions compared with the overall protein sequence or structure, revealed challenges in the assignment of separated homologous families. The methods applied herein support the comparison of enzyme families and the identification of functionally relevant positions. The findings for the family of ThDP-dependent decarboxylases underline that global sequence identity alone is not sufficient to distinguish enzyme function. Instead, local sequence similarity, defined by comparisons of structurally equivalent positions, allows for a better navigation within several groups of homologous enzymes. The differentiation between homologous sequences is further enhanced by taking structural information into account, such as BioGPS analysis of the active site properties or pairwise structural superimpositions. The methods applied herein are expected to be transferrable to other enzyme families, to facilitate family assignments for homologous protein sequences.
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Affiliation(s)
- Patrick C F Buchholz
- Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Stuttgart, Germany
| | - Valerio Ferrario
- Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Stuttgart, Germany.,Laboratory of Applied and Computational Biocatalysis, Department of Chemical and Pharmaceutical Sciences, Università degli Studi di Trieste, Trieste, Italy
| | - Martina Pohl
- Forschungszentrum Jülich GmbH, IBG-1: Biotechnology, Jülich, Germany
| | - Lucia Gardossi
- Laboratory of Applied and Computational Biocatalysis, Department of Chemical and Pharmaceutical Sciences, Università degli Studi di Trieste, Trieste, Italy
| | - Jürgen Pleiss
- Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Stuttgart, Germany
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29
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Oldfield CJ, Chen K, Kurgan L. Computational Prediction of Secondary and Supersecondary Structures from Protein Sequences. Methods Mol Biol 2019; 1958:73-100. [PMID: 30945214 DOI: 10.1007/978-1-4939-9161-7_4] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Many new methods for the sequence-based prediction of the secondary and supersecondary structures have been developed over the last several years. These and older sequence-based predictors are widely applied for the characterization and prediction of protein structure and function. These efforts have produced countless accurate predictors, many of which rely on state-of-the-art machine learning models and evolutionary information generated from multiple sequence alignments. We describe and motivate both types of predictions. We introduce concepts related to the annotation and computational prediction of the three-state and eight-state secondary structure as well as several types of supersecondary structures, such as β hairpins, coiled coils, and α-turn-α motifs. We review 34 predictors focusing on recent tools and provide detailed information for a selected set of 14 secondary structure and 3 supersecondary structure predictors. We conclude with several practical notes for the end users of these predictive methods.
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Affiliation(s)
- Christopher J Oldfield
- Department of Computer Science, College of Engineering, Virginia Commonwealth University, Richmond, VA, USA
| | - Ke Chen
- School of Computer Science and Software Engineering, Tianjin Polytechnic University, Tianjin, People's Republic of China
| | - Lukasz Kurgan
- Department of Computer Science, College of Engineering, Virginia Commonwealth University, Richmond, VA, USA.
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30
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Brady NG, Li M, Ma Y, Gumbart JC, Bruce BD. Non-detergent isolation of a cyanobacterial photosystem I using styrene maleic acid alternating copolymers. RSC Adv 2019; 9:31781-31796. [PMID: 35527920 PMCID: PMC9072662 DOI: 10.1039/c9ra04619d] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2019] [Accepted: 10/02/2019] [Indexed: 11/21/2022] Open
Abstract
Trimeric Photosystem I (PSI) from the thermophilic cyanobacteriumThermosynechococcus elongatus(Te) is the largest membrane protein complex to be encapsulated within a SMALP to date.
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Affiliation(s)
- Nathan G. Brady
- Department of Biochemistry & Cellular and Molecular Biology
- University of Tennessee at Knoxville
- Knoxville
- USA
| | - Meng Li
- Department of Biochemistry & Cellular and Molecular Biology
- University of Tennessee at Knoxville
- Knoxville
- USA
- Bredesen Center for Interdisciplinary Research and Education
| | - Yue Ma
- Department of Biochemistry & Cellular and Molecular Biology
- University of Tennessee at Knoxville
- Knoxville
- USA
| | | | - Barry D. Bruce
- Department of Biochemistry & Cellular and Molecular Biology
- University of Tennessee at Knoxville
- Knoxville
- USA
- Bredesen Center for Interdisciplinary Research and Education
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31
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Toti D, Viet Hung L, Tortosa V, Brandi V, Polticelli F. LIBRA-WA: a web application for ligand binding site detection and protein function recognition. Bioinformatics 2018; 34:878-880. [PMID: 29126218 PMCID: PMC6192203 DOI: 10.1093/bioinformatics/btx715] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2017] [Accepted: 11/04/2017] [Indexed: 02/04/2023] Open
Abstract
Summary Recently, LIBRA, a tool for active/ligand binding site prediction, was described. LIBRA's effectiveness was comparable to similar state-of-the-art tools; however, its scoring scheme, output presentation, dependence on local resources and overall convenience were amenable to improvements. To solve these issues, LIBRA-WA, a web application based on an improved LIBRA engine, has been developed, featuring a novel scoring scheme consistently improving LIBRA's performance, and a refined algorithm that can identify binding sites hosted at the interface between different subunits. LIBRA-WA also sports additional functionalities like ligand clustering and a completely redesigned interface for an easier analysis of the output. Extensive tests on 373 apoprotein structures indicate that LIBRA-WA is able to identify the biologically relevant ligand/ligand binding site in 357 cases (∼96%), with the correct prediction ranking first in 349 cases (∼98% of the latter, ∼94% of the total). The earlier stand-alone tool has also been updated and dubbed LIBRA+, by integrating LIBRA-WA's improved engine for cross-compatibility purposes. Availability and implementation LIBRA-WA and LIBRA+ are available at: http://www.computationalbiology.it/software.html. Contact polticel@uniroma3.it. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Daniele Toti
- Department of Sciences, University of Roma Tre, 00146 Rome, Italy
| | - Le Viet Hung
- Department of Science and Technology, Nguyen Tat Thanh University, Ho chi Minh City, Vietnam
| | | | - Valentina Brandi
- Department of Sciences, University of Roma Tre, 00146 Rome, Italy
| | - Fabio Polticelli
- Department of Sciences, University of Roma Tre, 00146 Rome, Italy.,National Institute of Nuclear Physics, Roma Tre Section, 00146 Rome, Italy
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32
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Hu G, Wang K, Song J, Uversky VN, Kurgan L. Taxonomic Landscape of the Dark Proteomes: Whole-Proteome Scale Interplay Between Structural Darkness, Intrinsic Disorder, and Crystallization Propensity. Proteomics 2018; 18:e1800243. [PMID: 30198635 DOI: 10.1002/pmic.201800243] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2018] [Revised: 08/30/2018] [Indexed: 12/14/2022]
Abstract
Growth rate of the protein sequence universe dramatically exceeds the speed of expansion for the protein structure universe, generating an immense dark proteome that includes proteins with unknown structure. A whole-proteome scale analysis of 5.4 million proteins from 987 proteomes in the three domains of life and viruses to systematically dissect an interplay between structural coverage, degree of putative intrinsic disorder, and predicted propensity for structure determination is performed. It has been found that Archaean and Bacterial proteomes have relatively high structural coverage and low amounts of disorder, whereas Eukaryotic and Viral proteomes are characterized by a broad spread of structural coverage and higher disorder levels. The analysis reveals that dark proteomes (i.e., proteomes containing high fractions of proteins with unknown structure) have significantly elevated amounts of intrinsic disorder and are predicted to be difficult to solve structurally. Although the majority of dark proteomes are of viral origin, many dark viral proteomes have at least modest crystallization propensity and only a handful of them are enriched in the intrinsic disorder. The disorder, structural coverage, and propensity are mapped for structural determination onto a novel proteome-level sequence similarity network to analyze the interplay of these characteristics in the taxonomic landscape.
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Affiliation(s)
- Gang Hu
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin, 300071, P. R. China
| | - Kui Wang
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin, 300071, P. R. China
| | - Jiangning Song
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia.,Monash Centre for Data Science, Faculty of Information Technology, Monash University, Melbourne, VIC 3800, Australia
| | - Vladimir N Uversky
- Department of Molecular Medicine and USF Health Byrd Alzheimer's Research Institute, Morsani College of Medicine, University of South Florida, Tampa, 33612, USA.,Institute for Biological Instrumentation, Russian Academy of Sciences, Pushchino, 142290, Russia
| | - Lukasz Kurgan
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA, 23284, USA
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33
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Wang H, Feng L, Webb GI, Kurgan L, Song J, Lin D. Critical evaluation of bioinformatics tools for the prediction of protein crystallization propensity. Brief Bioinform 2018; 19:838-852. [PMID: 28334201 PMCID: PMC6171492 DOI: 10.1093/bib/bbx018] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2016] [Revised: 01/19/2017] [Indexed: 12/11/2022] Open
Abstract
X-ray crystallography is the main tool for structural determination of proteins. Yet, the underlying crystallization process is costly, has a high attrition rate and involves a series of trial-and-error attempts to obtain diffraction-quality crystals. The Structural Genomics Consortium aims to systematically solve representative structures of major protein-fold classes using primarily high-throughput X-ray crystallography. The attrition rate of these efforts can be improved by selection of proteins that are potentially easier to be crystallized. In this context, bioinformatics approaches have been developed to predict crystallization propensities based on protein sequences. These approaches are used to facilitate prioritization of the most promising target proteins, search for alternative structural orthologues of the target proteins and suggest designs of constructs capable of potentially enhancing the likelihood of successful crystallization. We reviewed and compared nine predictors of protein crystallization propensity. Moreover, we demonstrated that integrating selected outputs from multiple predictors as candidate input features to build the predictive model results in a significantly higher predictive performance when compared to using these predictors individually. Furthermore, we also introduced a new and accurate predictor of protein crystallization propensity, Crysf, which uses functional features extracted from UniProt as inputs. This comprehensive review will assist structural biologists in selecting the most appropriate predictor, and is also beneficial for bioinformaticians to develop a new generation of predictive algorithms.
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Affiliation(s)
- Huilin Wang
- Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, China
| | | | - Geoffrey I Webb
- Monash Centre for Data Science, Faculty of Information Technology, Monash University, Australia
| | - Lukasz Kurgan
- Department of Computer Science, Virginia Commonwealth University, USA
| | - Jiangning Song
- Department of Biochemistry and Molecular Biology, Monash University, Australia
| | - Donghai Lin
- Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, China
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34
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Bryant JM, Blind RD. Signaling through non-membrane nuclear phosphoinositide binding proteins in human health and disease. J Lipid Res 2018; 60:299-311. [PMID: 30201631 DOI: 10.1194/jlr.r088518] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2018] [Revised: 08/22/2018] [Indexed: 12/22/2022] Open
Abstract
Phosphoinositide membrane signaling is critical for normal physiology, playing well-known roles in diverse human pathologies. The basic mechanisms governing phosphoinositide signaling within the nucleus, however, have remained deeply enigmatic owing to their presence outside the nuclear membranes. Over 40% of nuclear phosphoinositides can exist in this non-membrane state, held soluble in the nucleoplasm by nuclear proteins that remain largely unidentified. Recently, two nuclear proteins responsible for solubilizing phosphoinositides were identified, steroidogenic factor-1 (SF-1; NR5A1) and liver receptor homolog-1 (LRH-1; NR5A2), along with two enzymes that directly remodel these phosphoinositide/protein complexes, phosphatase and tensin homolog (PTEN; MMAC) and inositol polyphosphate multikinase (IPMK; ipk2). These new footholds now permit the assignment of physiological functions for nuclear phosphoinositides in human diseases, such as endometriosis, nonalcoholic fatty liver disease/steatohepatitis, glioblastoma, and hepatocellular carcinoma. The unique nature of nuclear phosphoinositide signaling affords extraordinary clinical opportunities for new biomarkers, diagnostics, and therapeutics. Thus, phosphoinositide biology within the nucleus may represent the next generation of low-hanging fruit for new drugs, not unlike what has occurred for membrane phosphatidylinositol 3-kinase drug development. This review connects recent basic science discoveries in nuclear phosphoinositide signaling to clinical pathologies, with the hope of inspiring development of new therapies.
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Affiliation(s)
- Jamal M Bryant
- Departments of Pharmacology, Biochemistry, and Medicine, Division of Diabetes, Endocrinology, and Metabolism, Vanderbilt Diabetes Research and Training Center, Vanderbilt University School of Medicine, Nashville, TN 37232
| | - Raymond D Blind
- Departments of Pharmacology, Biochemistry, and Medicine, Division of Diabetes, Endocrinology, and Metabolism, Vanderbilt Diabetes Research and Training Center, Vanderbilt University School of Medicine, Nashville, TN 37232
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35
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Oke M, Agbalajobi R, Osifeso M, Muhammad B, Lawal H, Mai M, Adegunle Q. Design and implementation of structural bioinformatics projects for biological sciences undergraduate students. BIOCHEMISTRY AND MOLECULAR BIOLOGY EDUCATION : A BIMONTHLY PUBLICATION OF THE INTERNATIONAL UNION OF BIOCHEMISTRY AND MOLECULAR BIOLOGY 2018; 46:547-554. [PMID: 30369034 DOI: 10.1002/bmb.21169] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/04/2018] [Revised: 08/21/2018] [Accepted: 09/03/2018] [Indexed: 06/08/2023]
Abstract
Contemporary biology is currently undergoing a revolution, driven by the availability of high-throughput technologies and a wide variety of bioinformatics tools. However, bioinformatics education and practice is still in its infancy in most of the African continent. Consequently, concerted efforts have been made in recent years to incorporate bioinformatics modules into biological sciences curriculum of African Universities. Despite this, one aspect of bioinformatics that is yet to be incorporated is structural bioinformatics. In this article, we report on a structural bioinformatics project carried out by final year project students in a Nigerian university. The target protein was the thermoacidophilic Sulfolobus islandicus rod-shaped virus 1 (SIRV1) Rep protein, which was further characterized using various free, user-friendly and online sequence-based and structure-based bioinformatics tools. This exercise gave students the opportunity to generate new data, interpret the data, and acquire collaborative research skills. In this report, emphasis is placed on analysis of the data generated to further encourage analytical skills. By sharing this experience, it is anticipated that other similar institutions would adopt parallel strategies to expose undergraduate students to structural biology, and increase awareness of freely available bioinformatics tools for tackling pertinent biological questions. © 2018 International Union of Biochemistry and Molecular Biology, 46(5):547-554, 2018.
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Affiliation(s)
- Muse Oke
- Department of Biological Sciences, Fountain University, Oke-Osun, Osogbo, Nigeria
| | - Ramon Agbalajobi
- Department of Biological Sciences, Fountain University, Oke-Osun, Osogbo, Nigeria
| | | | - Babagana Muhammad
- Department of Biological Sciences, Fountain University, Oke-Osun, Osogbo, Nigeria
| | - Halimat Lawal
- Department of Biological Sciences, Fountain University, Oke-Osun, Osogbo, Nigeria
| | - Muhammad Mai
- Department of Biological Sciences, Fountain University, Oke-Osun, Osogbo, Nigeria
| | - Quadri Adegunle
- Department of Biological Sciences, Fountain University, Oke-Osun, Osogbo, Nigeria
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36
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Wang C, Kurgan L. Review and comparative assessment of similarity-based methods for prediction of drug–protein interactions in the druggable human proteome. Brief Bioinform 2018; 20:2066-2087. [DOI: 10.1093/bib/bby069] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2018] [Revised: 06/26/2018] [Accepted: 07/10/2018] [Indexed: 12/18/2022] Open
Abstract
AbstractDrug–protein interactions (DPIs) underlie the desired therapeutic actions and the adverse side effects of a significant majority of drugs. Computational prediction of DPIs facilitates research in drug discovery, characterization and repurposing. Similarity-based methods that do not require knowledge of protein structures are particularly suitable for druggable genome-wide predictions of DPIs. We review 35 high-impact similarity-based predictors that were published in the past decade. We group them based on three types of similarities and their combinations that they use. We discuss and compare key aspects of these methods including source databases, internal databases and their predictive models. Using our novel benchmark database, we perform comparative empirical analysis of predictive performance of seven types of representative predictors that utilize each type of similarity individually and all possible combinations of similarities. We assess predictive quality at the database-wide DPI level and we are the first to also include evaluation over individual drugs. Our comprehensive analysis shows that predictors that use more similarity types outperform methods that employ fewer similarities, and that the model combining all three types of similarities secures area under the receiver operating characteristic curve of 0.93. We offer a comprehensive analysis of sensitivity of predictive performance to intrinsic and extrinsic characteristics of the considered predictors. We find that predictive performance is sensitive to low levels of similarities between sequences of the drug targets and several extrinsic properties of the input drug structures, drug profiles and drug targets. The benchmark database and a webserver for the seven predictors are freely available at http://biomine.cs.vcu.edu/servers/CONNECTOR/.
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Affiliation(s)
- Chen Wang
- Computer Science Department, Virginia Commonwealth University, Richmond, VA 23284, USA
| | - Lukasz Kurgan
- Computer Science Department, Virginia Commonwealth University, Richmond, VA 23284, USA
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37
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Getting Momentum: From Biocatalysis to Advanced Synthetic Biology. Trends Biochem Sci 2018; 43:180-198. [DOI: 10.1016/j.tibs.2018.01.003] [Citation(s) in RCA: 58] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2017] [Revised: 01/08/2018] [Accepted: 01/10/2018] [Indexed: 11/20/2022]
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38
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Meng F, Wang C, Kurgan L. fDETECT webserver: fast predictor of propensity for protein production, purification, and crystallization. BMC Bioinformatics 2018; 18:580. [PMID: 29295714 PMCID: PMC6389161 DOI: 10.1186/s12859-017-1995-z] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2017] [Accepted: 12/06/2017] [Indexed: 02/26/2023] Open
Abstract
Background Development of predictors of propensity of protein sequences for successful crystallization has been actively pursued for over a decade. A few novel methods that expanded the scope of these predictions to address additional steps of protein production and structure determination pipelines were released in recent years. The predictive performance of the current methods is modest. This is because the only input that they use is the protein sequence and since the experimental annotations of these data might be inconsistent given that they were collected across many laboratories and centers. However, even these modest levels of predictive quality are still practical compared to the reported low success rates of crystallization, which are below 10%. We focus on another important aspect related to a high computational cost of running the predictors that offer the expanded scope. Results We introduce a novel fDETECT webserver that provides very fast and modestly accurate predictions of the success of protein production, purification, crystallization, and structure determination. Empirical tests on two datasets demonstrate that fDETECT is more accurate than the only other similarly fast method, and similarly accurate and three orders of magnitude faster than the currently most accurate predictors. Our method predicts a single protein in about 120 milliseconds and needs less than an hour to generate the four predictions for an entire human proteome. Moreover, we empirically show that fDETECT secures similar levels of predictive performance when compared with four representative methods that only predict success of crystallization, while it also provides the other three predictions. A webserver that implements fDETECT is available at http://biomine.cs.vcu.edu/servers/fDETECT/. Conclusions fDETECT is a computational tool that supports target selection for protein production and X-ray crystallography-based structure determination. It offers predictive quality that matches or exceeds other state-of-the-art tools and is especially suitable for the analysis of large protein sets.
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Affiliation(s)
- Fanchi Meng
- Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada
| | - Chen Wang
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA, USA
| | - Lukasz Kurgan
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA, USA.
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39
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Gao J, Wu Z, Hu G, Wang K, Song J, Joachimiak A, Kurgan L. Survey of Predictors of Propensity for Protein Production and Crystallization with Application to Predict Resolution of Crystal Structures. Curr Protein Pept Sci 2018; 19:200-210. [PMID: 28933304 PMCID: PMC7001581 DOI: 10.2174/1389203718666170921114437] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2017] [Revised: 09/14/2017] [Accepted: 09/14/2017] [Indexed: 11/22/2022]
Abstract
Selection of proper targets for the X-ray crystallography will benefit biological research community immensely. Several computational models were proposed to predict propensity of successful protein production and diffraction quality crystallization from protein sequences. We reviewed a comprehensive collection of 22 such predictors that were developed in the last decade. We found that almost all of these models are easily accessible as webservers and/or standalone software and we demonstrated that some of them are widely used by the research community. We empirically evaluated and compared the predictive performance of seven representative methods. The analysis suggests that these methods produce quite accurate propensities for the diffraction-quality crystallization. We also summarized results of the first study of the relation between these predictive propensities and the resolution of the crystallizable proteins. We found that the propensities predicted by several methods are significantly higher for proteins that have high resolution structures compared to those with the low resolution structures. Moreover, we tested a new meta-predictor, MetaXXC, which averages the propensities generated by the three most accurate predictors of the diffraction-quality crystallization. MetaXXC generates putative values of resolution that have modest levels of correlation with the experimental resolutions and it offers the lowest mean absolute error when compared to the seven considered methods. We conclude that protein sequences can be used to fairly accurately predict whether their corresponding protein structures can be solved using X-ray crystallography. Moreover, we also ascertain that sequences can be used to reasonably well predict the resolution of the resulting protein crystals.
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Affiliation(s)
- Jianzhao Gao
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin, People’s Republic of China
| | - Zhonghua Wu
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin, People’s Republic of China
| | - Gang Hu
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin, People’s Republic of China
| | - Kui Wang
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin, People’s Republic of China
| | - Jiangning Song
- Infection and Immunity Program, Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Australia
- ARC Centre of Excellence in Advanced Molecular Imaging, Monash University, Melbourne, Australia
| | - Andrzej Joachimiak
- Midwest Center for Structural Genomics, Argonne, USA
- Structural Biology Center, Biosciences, Argonne National Laboratory, Argonne, USA
- Department of Biochemistry and Molecular Biology, University of Chicago, Chicago, USA
| | - Lukasz Kurgan
- Department of Computer Science, Virginia Commonwealth University, Richmond, USA
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40
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Abstract
In this review, we describe how the interplay among science, technology and community interests contributed to the evolution of four structural biology data resources. We present the method by which data deposited by scientists are prepared for worldwide distribution, and argue that data archiving in a trusted repository must be an integral part of any scientific investigation.
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Affiliation(s)
- Helen M. Berman
- Center for Integrative Proteomics Research, Institute for Quantitative Biomedicine, Department of Chemistry and Chemical Biology, 174 Frelinghuysen Road, Piscataway New Jersey 08854
| | - Catherine L. Lawson
- Center for Integrative Proteomics Research, Institute for Quantitative Biomedicine, Department of Chemistry and Chemical Biology, 174 Frelinghuysen Road, Piscataway New Jersey 08854
| | - Brinda Vallat
- Center for Integrative Proteomics Research, Institute for Quantitative Biomedicine, Department of Chemistry and Chemical Biology, 174 Frelinghuysen Road, Piscataway New Jersey 08854
| | - Margaret J. Gabanyi
- Center for Integrative Proteomics Research, Institute for Quantitative Biomedicine, Department of Chemistry and Chemical Biology, 174 Frelinghuysen Road, Piscataway New Jersey 08854
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41
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Blaxter M. Imagining Sisyphus happy: DNA barcoding and the unnamed majority. Philos Trans R Soc Lond B Biol Sci 2017; 371:rstb.2015.0329. [PMID: 27481781 PMCID: PMC4971181 DOI: 10.1098/rstb.2015.0329] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/29/2016] [Indexed: 01/21/2023] Open
Abstract
The vast majority of life on the Earth is physically small, and is classifiable as micro- or meiobiota. These organisms are numerically dominant and it is likely that they are also abundantly speciose. By contrast, the vast majority of taxonomic effort has been expended on ‘charismatic megabionts’: larger organisms where a wealth of morphology has facilitated Linnaean species definition. The hugely successful Linnaean project is unlikely to be extensible to the totality of approximately 10 million species in a reasonable time frame and thus alternative toolkits and methodologies need to be developed. One such toolkit is DNA barcoding, particularly in its metabarcoding or metagenetics mode, where organisms are identified purely by the presence of a diagnostic DNA sequence in samples that are not processed for morphological identification. Building on secure Linnaean foundations, classification of unknown (and unseen) organisms to molecular operational taxonomic units (MOTUs) and deployment of these MOTUs in biodiversity science promises a rewarding resolution to the Sisyphean task of naming all the world's species. This article is part of the themed issue ‘From DNA barcodes to biomes’.
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Affiliation(s)
- Mark Blaxter
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh EH9 3FL, UK
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42
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Kim HN, Seok SH, Lee YS, Won HS, Seo MD. Crystal structure and functional characterization of SF216 from Shigella flexneri. FEBS Lett 2017; 591:3692-3703. [PMID: 28983914 DOI: 10.1002/1873-3468.12873] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2017] [Revised: 10/01/2017] [Accepted: 10/02/2017] [Indexed: 12/18/2022]
Abstract
Shigella flexneri is a Gram-negative anaerobic bacterium that causes highly infectious bacterial dysentery in humans. Here, we solved the crystal structure of SF216, a hypothetical protein from the S. flexneri 5a strain M90T, at 1.7 Å resolution. The crystal structure of SF216 represents a homotrimer stabilized by intersubunit interactions and ion-mediated electrostatic interactions. Each subunit consists of three β-strands and five α-helices with the β-β-β-α-α-α-α-α topology. Based on the structural information, we also demonstrate that SF216 shows weak ribonuclease activity by a fluorescence quenching assay. Furthermore, we identify potential druggable pockets (putative hot spots) on the surface of the SF216 structure by computational mapping.
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Affiliation(s)
- Ha-Neul Kim
- Department of Molecular Science and Technology, Ajou University, Suwon, Gyeonggi, Korea.,College of Pharmacy, Ajou University, Suwon, Gyeonggi, Korea
| | | | - Yoo-Sup Lee
- Department of Molecular Science and Technology, Ajou University, Suwon, Gyeonggi, Korea
| | - Hyung-Sik Won
- Department of Biotechnology, Research Institute and College of Biomedical and Health Science (RIBHS), Konkuk University, Chungju, Chungbuk, Korea
| | - Min-Duk Seo
- Department of Molecular Science and Technology, Ajou University, Suwon, Gyeonggi, Korea.,College of Pharmacy, Ajou University, Suwon, Gyeonggi, Korea
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43
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Porebski PJ, Sroka P, Zheng H, Cooper DR, Minor W. Molstack-Interactive visualization tool for presentation, interpretation, and validation of macromolecules and electron density maps. Protein Sci 2017; 27:86-94. [PMID: 28815771 DOI: 10.1002/pro.3272] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2017] [Revised: 08/11/2017] [Accepted: 08/14/2017] [Indexed: 11/07/2022]
Abstract
Our understanding of the world of biomolecular structures is based upon the interpretation of macromolecular models, of which ∼90% are an interpretation of electron density maps. This structural information guides scientific progress and exploration in many biomedical disciplines. The Protein Data Bank's web portals have made these structures available for mass scientific consumption and greatly broaden the scope of information presented in scientific publications. The portals provide numerous quality metrics; however, the portion of the structure that is most vital for interpretation of the function may have the most difficult to interpret electron density and this ambiguity is not reflected by any single metric. The possible consequences of basing research on suboptimal models make it imperative to inspect the agreement of a model with its experimental evidence. Molstack, a web-based interactive publishing platform for structural data, allows users to present density maps and structural models by displaying a collection of maps and models, including different interpretation of one's own data, re-refinements, and corrections of existing structures. Molstack organizes the sharing and dissemination of these structural models along with their experimental evidence as an interactive session. Molstack was designed with three groups of users in mind; researchers can present the evidence of their interpretation, reviewers and readers can independently judge the experimental evidence of the authors' conclusions, and other researchers can present or even publish their new hypotheses in the context of prior results. The server is available at http://molstack.bioreproducibility.org.
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Affiliation(s)
- Przemyslaw J Porebski
- Department of Biological Physics & Molecular Physiology, University of Virginia, Charlottesville, Virginia
| | - Piotr Sroka
- Department of Biological Physics & Molecular Physiology, University of Virginia, Charlottesville, Virginia
| | - Heping Zheng
- Department of Biological Physics & Molecular Physiology, University of Virginia, Charlottesville, Virginia
| | - David R Cooper
- Department of Biological Physics & Molecular Physiology, University of Virginia, Charlottesville, Virginia
| | - Wladek Minor
- Department of Biological Physics & Molecular Physiology, University of Virginia, Charlottesville, Virginia
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Lefurgy ST, Mundorff EC. A 13-week research-based biochemistry laboratory curriculum. BIOCHEMISTRY AND MOLECULAR BIOLOGY EDUCATION : A BIMONTHLY PUBLICATION OF THE INTERNATIONAL UNION OF BIOCHEMISTRY AND MOLECULAR BIOLOGY 2017; 45:437-448. [PMID: 28251763 DOI: 10.1002/bmb.21054] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/21/2016] [Revised: 01/15/2017] [Accepted: 02/12/2017] [Indexed: 06/06/2023]
Abstract
Here, we present a 13-week research-based biochemistry laboratory curriculum designed to provide the students with the experience of engaging in original research while introducing foundational biochemistry laboratory techniques. The laboratory experience has been developed around the directed evolution of an enzyme chosen by the instructor, with mutations designed by the students. Ideal enzymes for this curriculum are able to be structurally modeled, solubly expressed, and monitored for activity by UV/Vis spectroscopy, and an example curriculum for haloalkane dehalogenase is given. Unique to this curriculum is a successful implementation of saturation mutagenesis and high-throughput screening of enzyme function, along with bioinformatics analysis, homology modeling, structural analysis, protein expression and purification, polyacrylamide gel electrophoresis, UV/Vis spectroscopy, and enzyme kinetics. Each of these techniques is carried out using a novel student-designed mutant library or enzyme variant unique to the lab team and, importantly, not described previously in the literature. Use of a well-established set of protocols promotes student data quality. Publication may result from the original student-generated hypotheses and data, either from the class as a whole or individual students that continue their independent projects upon course completion. © 2017 by The International Union of Biochemistry and Molecular Biology, 45(5):437-448, 2017.
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Affiliation(s)
- Scott T Lefurgy
- Department of Chemistry, Hofstra University, 151 Hofstra University, Hempstead, NY, 11549
| | - Emily C Mundorff
- Department of Chemistry, Hofstra University, 151 Hofstra University, Hempstead, NY, 11549
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Cramer P. Structural Molecular Biology-A Personal Reflection on the Occasion of John Kendrew's 100th Birthday. J Mol Biol 2017; 429:2603-2610. [PMID: 28501586 DOI: 10.1016/j.jmb.2017.05.007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2017] [Accepted: 05/08/2017] [Indexed: 10/19/2022]
Abstract
Here, I discuss the development and future of structural molecular biology, concentrating on the eukaryotic transcription machinery and reflecting on John Kendrew's legacy from a personal perspective.
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Affiliation(s)
- Patrick Cramer
- Max Planck Institute for Biophysical Chemistry, Department of Molecular Biology, Am Fassberg 11, 37077 Göttingen, Germany.
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Abstract
The ProFunc web server is a tool for helping identify the function of a given protein whose 3D coordinates have been experimentally determined or homology modeled. It uses a cocktail of both sequence- and structure-based methods to identify matches to other proteins that may, in turn, suggest the query protein's most likely function. The server was originally developed to aid the worldwide structural genomics effort at the start of the millennium. It accepts a file containing the protein's 3D coordinates in PDB format, and, when processing is complete, sends an email containing a link to the password-protected result pages. The results include an at-a-glance summary, as well as separate pages containing more detailed analyses. The server can be found at: http://www.ebi.ac.uk/thornton-srv/databases/profunc .
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Affiliation(s)
- Roman A Laskowski
- European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK.
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Protein-RNA interactions: structural biology and computational modeling techniques. Biophys Rev 2016; 8:359-367. [PMID: 28510023 DOI: 10.1007/s12551-016-0223-9] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2016] [Accepted: 09/20/2016] [Indexed: 12/30/2022] Open
Abstract
RNA-binding proteins are functionally diverse within cells, being involved in RNA-metabolism, translation, DNA damage repair, and gene regulation at both the transcriptional and post-transcriptional levels. Much has been learnt about their interactions with RNAs through structure determination techniques and computational modeling. This review gives an overview of the structural data currently available for protein-RNA complexes, and discusses the technical issues facing structural biologists working to solve their structures. The review focuses on three techniques used to solve the 3-dimensional structure of protein-RNA complexes at atomic resolution, namely X-ray crystallography, solution nuclear magnetic resonance (NMR) and cryo-electron microscopy (cryo-EM). The review then focuses on the main computational modeling techniques that use these atomic resolution data: discussing the prediction of RNA-binding sites on unbound proteins, docking proteins, and RNAs, and modeling the molecular dynamics of the systems. In conclusion, the review looks at the future directions this field of research might take.
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Lobb B, Doxey AC. Novel function discovery through sequence and structural data mining. Curr Opin Struct Biol 2016; 38:53-61. [DOI: 10.1016/j.sbi.2016.05.017] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2016] [Revised: 05/17/2016] [Accepted: 05/24/2016] [Indexed: 01/30/2023]
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Minor W, Dauter Z, Jaskolski M. The young person's guide to the PDB. Postepy Biochem 2016; 62:242-249. [PMID: 28132477 PMCID: PMC5610703 DOI: 10.18388/pb.2016_1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2016] [Accepted: 07/06/2016] [Indexed: 06/06/2023]
Abstract
The Protein Data Bank (PDB), created in 1971 when merely seven protein crystal structures were known, today holds over 120, 000 experimentally-determined three-dimensional models of macromolecules, including gigantic structures comprised of hundreds of thousands of atoms, such as ribosomes and viruses. Most of the deposits come from X-ray crystallography experiments, with important contributions also made by NMR spectroscopy and, recently, by the fast growing Cryo-Electron Microscopy. Although the determination of a macromolecular crystal structure is now facilitated by advanced experimental tools and by sophisticated software, it is still a highly complicated research process requiring specialized training, skill, experience and a bit of luck. Understanding the plethora of structural information provided by the PDB requires that its users (consumers) have at least a rudimentary initiation. This is the purpose of this educational overview.
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Affiliation(s)
- Wladek Minor
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, VA 22908, USA
| | - Zbigniew Dauter
- Macromolecular Crystallography Laboratory, National Cancer Institute, Argonne National Laboratory, Argonne, IL 60439, USA
| | - Mariusz Jaskolski
- Department of Crystallography, Faculty of Chemistry, A. Mickiewicz University, Poznan, Poland
- Center for Biocrystallographic Research, Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznan, Poland
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