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Burley SK, Piehl DW, Vallat B, Zardecki C. RCSB Protein Data Bank: supporting research and education worldwide through explorations of experimentally determined and computationally predicted atomic level 3D biostructures. IUCRJ 2024; 11:279-286. [PMID: 38597878 PMCID: PMC11067742 DOI: 10.1107/s2052252524002604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Accepted: 03/19/2024] [Indexed: 04/11/2024]
Abstract
The Protein Data Bank (PDB) was established as the first open-access digital data resource in biology and medicine in 1971 with seven X-ray crystal structures of proteins. Today, the PDB houses >210 000 experimentally determined, atomic level, 3D structures of proteins and nucleic acids as well as their complexes with one another and small molecules (e.g. approved drugs, enzyme cofactors). These data provide insights into fundamental biology, biomedicine, bioenergy and biotechnology. They proved particularly important for understanding the SARS-CoV-2 global pandemic. The US-funded Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) and other members of the Worldwide Protein Data Bank (wwPDB) partnership jointly manage the PDB archive and support >60 000 `data depositors' (structural biologists) around the world. wwPDB ensures the quality and integrity of the data in the ever-expanding PDB archive and supports global open access without limitations on data usage. The RCSB PDB research-focused web portal at https://www.rcsb.org/ (RCSB.org) supports millions of users worldwide, representing a broad range of expertise and interests. In addition to retrieving 3D structure data, PDB `data consumers' access comparative data and external annotations, such as information about disease-causing point mutations and genetic variations. RCSB.org also provides access to >1 000 000 computed structure models (CSMs) generated using artificial intelligence/machine-learning methods. To avoid doubt, the provenance and reliability of experimentally determined PDB structures and CSMs are identified. Related training materials are available to support users in their RCSB.org explorations.
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Affiliation(s)
- Stephen K. Burley
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Research Collaboratory for Structural Biology Protein Data Bank, San Diego Supercomputer Center, University of California, San Diego, La Jolla, CA 92093, USA
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Rutgers Cancer Institute of New Jersey, New Brunswick, NJ 08901, USA
| | - Dennis W. Piehl
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Brinda Vallat
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Rutgers Cancer Institute of New Jersey, New Brunswick, NJ 08901, USA
| | - Christine Zardecki
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
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2
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Lubin JH, Martinusen SG, Zardecki C, Olivas C, Bacorn M, Balogun M, Slaton EW, Wu AW, Sakeer S, Hudson BP, Denard CA, Burley SK, Khare SD. A comprehensive survey of coronaviral main protease active site diversity in 3D: Identifying and analyzing drug discovery targets in search of broad specificity inhibitors for the next coronavirus pandemic. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.30.526101. [PMID: 36778399 PMCID: PMC9915488 DOI: 10.1101/2023.01.30.526101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Although the rapid development of therapeutic responses to combat SARS-CoV-2 represents a great human achievement, it also demonstrates untapped potential for advanced pandemic preparedness. Cross-species efficacy against multiple human coronaviruses by the main protease (MPro) inhibitor nirmatrelvir raises the question of its breadth of inhibition and our preparedness against future coronaviral threats. Herein, we describe sequence and structural analyses of 346 unique MPro enzymes from all coronaviruses represented in the NCBI Virus database. Cognate substrates of these representative proteases were inferred from their polyprotein sequences. We clustered MPro sequences based on sequence identity and AlphaFold2-predicted structures, showing approximate correspondence with known viral subspecies. Predicted structures of five representative MPros bound to their inferred cognate substrates showed high conservation in protease:substrate interaction modes, with some notable differences. Yeast-based proteolysis assays of the five representatives were able to confirm activity of three on inferred cognate substrates, and demonstrated that of the three, only one was effectively inhibited by nirmatrelvir. Our findings suggest that comprehensive preparedness against future potential coronaviral threats will require continued inhibitor development. Our methods may be applied to candidate coronaviral MPro inhibitors to evaluate in advance the breadth of their inhibition and identify target coronaviruses potentially meriting advanced development of alternative countermeasures.
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Affiliation(s)
- Joseph H. Lubin
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA
| | | | - Christine Zardecki
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA
| | - Cassandra Olivas
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA
- California State University Stanislaus, Turlock, California, USA
| | - Mickayla Bacorn
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA
- University Of Maryland, Baltimore County, Baltimore, Maryland, USA
| | - MaryAgnes Balogun
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA
- Morgan State University, Baltimore, Maryland, USA
| | - Ethan W. Slaton
- Department of Chemical Engineering, University of Florida, Gainesville, Florida, USA
| | - Amy Wu Wu
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA
- University of Puerto Rico – Mayagüez, Mayagüez, Puerto Rico
| | - Sarah Sakeer
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA
| | - Brian P. Hudson
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA
| | - Carl A. Denard
- Department of Chemical Engineering, University of Florida, Gainesville, Florida, USA
| | - Stephen K. Burley
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA
- Rutgers Cancer Institute of New Jersey, Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, New Brunswick, New Jersey, USA
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California, San Diego, La Jolla, California, USA
| | - Sagar D. Khare
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA
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3
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Lubin JH, Zardecki C, Dolan EM, Lu C, Shen Z, Dutta S, Westbrook JD, Hudson BP, Goodsell DS, Williams JK, Voigt M, Sarma V, Xie L, Venkatachalam T, Arnold S, Alfaro Alvarado LH, Catalfano K, Khan A, McCarthy E, Staggers S, Tinsley B, Trudeau A, Singh J, Whitmore L, Zheng H, Benedek M, Currier J, Dresel M, Duvvuru A, Dyszel B, Fingar E, Hennen EM, Kirsch M, Khan AA, Labrie‐Cleary C, Laporte S, Lenkeit E, Martin K, Orellana M, Ortiz‐Alvarez de la Campa M, Paredes I, Wheeler B, Rupert A, Sam A, See K, Soto Zapata S, Craig PA, Hall BL, Jiang J, Koeppe JR, Mills SA, Pikaart MJ, Roberts R, Bromberg Y, Hoyer JS, Duffy S, Tischfield J, Ruiz FX, Arnold E, Baum J, Sandberg J, Brannigan G, Khare SD, Burley SK. Evolution of the SARS-CoV-2 proteome in three dimensions (3D) during the first 6 months of the COVID-19 pandemic. Proteins 2022; 90:1054-1080. [PMID: 34580920 PMCID: PMC8661935 DOI: 10.1002/prot.26250] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Revised: 08/26/2021] [Accepted: 09/16/2021] [Indexed: 01/18/2023]
Abstract
Understanding the molecular evolution of the SARS-CoV-2 virus as it continues to spread in communities around the globe is important for mitigation and future pandemic preparedness. Three-dimensional structures of SARS-CoV-2 proteins and those of other coronavirusess archived in the Protein Data Bank were used to analyze viral proteome evolution during the first 6 months of the COVID-19 pandemic. Analyses of spatial locations, chemical properties, and structural and energetic impacts of the observed amino acid changes in >48 000 viral isolates revealed how each one of 29 viral proteins have undergone amino acid changes. Catalytic residues in active sites and binding residues in protein-protein interfaces showed modest, but significant, numbers of substitutions, highlighting the mutational robustness of the viral proteome. Energetics calculations showed that the impact of substitutions on the thermodynamic stability of the proteome follows a universal bi-Gaussian distribution. Detailed results are presented for potential drug discovery targets and the four structural proteins that comprise the virion, highlighting substitutions with the potential to impact protein structure, enzyme activity, and protein-protein and protein-nucleic acid interfaces. Characterizing the evolution of the virus in three dimensions provides testable insights into viral protein function and should aid in structure-based drug discovery efforts as well as the prospective identification of amino acid substitutions with potential for drug resistance.
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Affiliation(s)
- Joseph H. Lubin
- Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Department of Chemistry and Chemical BiologyRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Christine Zardecki
- Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Research Collaboratory for Structural Bioinformatics Protein Data BankRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Elliott M. Dolan
- Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Department of Chemistry and Chemical BiologyRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Changpeng Lu
- Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Zhuofan Shen
- Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Department of Chemistry and Chemical BiologyRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Shuchismita Dutta
- Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Research Collaboratory for Structural Bioinformatics Protein Data BankRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Rutgers Cancer Institute of New Jersey, Robert Wood Johnson Medical School, Rutgers, The State University of New JerseyNew BrunswickNew JerseyUSA
| | - John D. Westbrook
- Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Research Collaboratory for Structural Bioinformatics Protein Data BankRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Rutgers Cancer Institute of New Jersey, Robert Wood Johnson Medical School, Rutgers, The State University of New JerseyNew BrunswickNew JerseyUSA
| | - Brian P. Hudson
- Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Research Collaboratory for Structural Bioinformatics Protein Data BankRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - David S. Goodsell
- Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Research Collaboratory for Structural Bioinformatics Protein Data BankRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Rutgers Cancer Institute of New Jersey, Robert Wood Johnson Medical School, Rutgers, The State University of New JerseyNew BrunswickNew JerseyUSA
- The Scripps Research InstituteLa JollaCaliforniaUSA
| | - Jonathan K. Williams
- Department of Chemistry and Chemical BiologyRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Maria Voigt
- Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Research Collaboratory for Structural Bioinformatics Protein Data BankRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Vidur Sarma
- Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Lingjun Xie
- Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Department of Chemistry and Chemical BiologyRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Thejasvi Venkatachalam
- Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Steven Arnold
- Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | | | | | - Aaliyah Khan
- University of Maryland Baltimore CountyBaltimoreMarylandUSA
| | | | | | | | | | | | | | - Helen Zheng
- Watchung Hills Regional High SchoolWarrenNew JerseyUSA
| | | | | | - Mark Dresel
- Department of Chemistry and Chemical BiologyRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | | | | | | | | | | | | | | | | | - Evan Lenkeit
- Department of Chemistry and Chemical BiologyRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | | | | | | | | | | | | | - Andrew Sam
- Department of Chemistry and Chemical BiologyRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Katherine See
- Rochester Institute of TechnologyRochesterNew YorkUSA
| | | | - Paul A. Craig
- Rochester Institute of TechnologyRochesterNew YorkUSA
| | | | - Jennifer Jiang
- Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | | | | | | | | | - Yana Bromberg
- Department of Biochemistry and MicrobiologyRutgers, The State University of New JerseyNew BrunswickNew JerseyUSA
| | - J. Steen Hoyer
- Department of Ecology, Evolution and Natural Resources, School of Environmental and Biological SciencesRutgers, The State University of New JerseyNew BrunswickNew JerseyUSA
| | - Siobain Duffy
- Department of Ecology, Evolution and Natural Resources, School of Environmental and Biological SciencesRutgers, The State University of New JerseyNew BrunswickNew JerseyUSA
| | - Jay Tischfield
- Department of GeneticsRutgers, The State University of New Jersey, and Human Genetics Institute of New JerseyPiscatawayNew JerseyUSA
| | - Francesc X. Ruiz
- Center for Advanced Biotechnology and MedicineRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Eddy Arnold
- Department of Chemistry and Chemical BiologyRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Center for Advanced Biotechnology and MedicineRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Jean Baum
- Department of Chemistry and Chemical BiologyRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
| | - Jesse Sandberg
- Center for Computational and Integrative BiologyRutgers, The State University of New JerseyCamdenNew JerseyUSA
| | - Grace Brannigan
- Center for Computational and Integrative BiologyRutgers, The State University of New JerseyCamdenNew JerseyUSA
- Department of PhysicsRutgers, The State University of New JerseyCamdenNew JerseyUSA
| | - Sagar D. Khare
- Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Department of Chemistry and Chemical BiologyRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Rutgers Cancer Institute of New Jersey, Robert Wood Johnson Medical School, Rutgers, The State University of New JerseyNew BrunswickNew JerseyUSA
| | - Stephen K. Burley
- Institute for Quantitative Biomedicine, Rutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Department of Chemistry and Chemical BiologyRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Research Collaboratory for Structural Bioinformatics Protein Data BankRutgers, The State University of New JerseyPiscatawayNew JerseyUSA
- Rutgers Cancer Institute of New Jersey, Robert Wood Johnson Medical School, Rutgers, The State University of New JerseyNew BrunswickNew JerseyUSA
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer CenterUniversity of CaliforniaSan Diego, La JollaCaliforniaUSA
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4
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Jeffries PR, Bushardt RL, DuBose-Morris R, Hood C, Kardong-Edgren S, Pintz C, Posey L, Sikka N. The Role of Technology in Health Professions Education During the COVID-19 Pandemic. ACADEMIC MEDICINE : JOURNAL OF THE ASSOCIATION OF AMERICAN MEDICAL COLLEGES 2022; 97:S104-S109. [PMID: 34789662 PMCID: PMC8855755 DOI: 10.1097/acm.0000000000004523] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
The COVID-19 pandemic has sparked radical shifts in the ways that both health care and health professions education are delivered. Before the pandemic, some degree programs were offered fully online or in a hybrid format, but in-person learning was considered essential to the education and training of health professionals. Similarly, even as the use of telehealth was slowly expanding, most health care visits were conducted in-person. The need to maintain a safe physical distance during the pandemic rapidly increased the online provision of health care and health professions education, accelerating technology adoption in both academic and professional health care settings. Many health care professionals, educators, and patients have had to adapt to new communication modalities, often with little or no preparation. Before the pandemic, the need for cost-effective, robust methodologies to enable teaching across distances electronically was recognized. During the pandemic, online learning and simulation became essential and were often the only means available for continuity of education and clinical training. This paper reviews the transition to online health professions education and delivery during the COVID-19 pandemic and provides recommendations for moving forward.
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Affiliation(s)
- Pamela R. Jeffries
- P.R. Jeffries is professor and dean, School of Nursing, Vanderbilt University, Nashville, Tennessee
| | - Reamer L. Bushardt
- R.L. Bushardt is professor and senior associate dean, School of Medicine and Health Sciences, The George Washington University, Washington, DC
| | - Ragan DuBose-Morris
- R. DuBose-Morris is associate professor, Academic Affairs Faculty, Medical University of South Carolina, Charleston, South Carolina
| | - Colton Hood
- C. Hood is assistant professor, Department of Emergency Medicine, The George Washington University, Washington, DC
| | - Suzan Kardong-Edgren
- S. Kardong-Edgren is associate professor, Department of Health Professions Education, MGH Institute of Health Professions, Boston, Massachusetts
| | - Christine Pintz
- C. Pintz is professor, School of Nursing, The George Washington University, Washington, DC
| | - Laurie Posey
- L. Posey is associate professor, School of Nursing, The George Washington University, Washington, DC
| | - Neal Sikka
- N. Sikka is professor, Department of Emergency Medicine, The GW Medical Faculty Associates, Washington, DC
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5
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Lubin JH, Zardecki C, Dolan EM, Lu C, Shen Z, Dutta S, Westbrook JD, Hudson BP, Goodsell DS, Williams JK, Voigt M, Sarma V, Xie L, Venkatachalam T, Arnold S, Alvarado LHA, Catalfano K, Khan A, McCarthy E, Staggers S, Tinsley B, Trudeau A, Singh J, Whitmore L, Zheng H, Benedek M, Currier J, Dresel M, Duvvuru A, Dyszel B, Fingar E, Hennen EM, Kirsch M, Khan AA, Labrie-Cleary C, Laporte S, Lenkeit E, Martin K, Orellana M, de la Campa MOA, Paredes I, Wheeler B, Rupert A, Sam A, See K, Zapata SS, Craig PA, Hall BL, Jiang J, Koeppe JR, Mills SA, Pikaart MJ, Roberts R, Bromberg Y, Hoyer JS, Duffy S, Tischfield J, Ruiz FX, Arnold E, Baum J, Sandberg J, Brannigan G, Khare SD, Burley SK. Evolution of the SARS-CoV-2 proteome in three dimensions (3D) during the first six months of the COVID-19 pandemic. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2020. [PMID: 33299989 DOI: 10.1101/2020.12.01.406637] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Three-dimensional structures of SARS-CoV-2 and other coronaviral proteins archived in the Protein Data Bank were used to analyze viral proteome evolution during the first six months of the COVID-19 pandemic. Analyses of spatial locations, chemical properties, and structural and energetic impacts of the observed amino acid changes in >48,000 viral proteome sequences showed how each one of the 29 viral study proteins have undergone amino acid changes. Structural models computed for every unique sequence variant revealed that most substitutions map to protein surfaces and boundary layers with a minority affecting hydrophobic cores. Conservative changes were observed more frequently in cores versus boundary layers/surfaces. Active sites and protein-protein interfaces showed modest numbers of substitutions. Energetics calculations showed that the impact of substitutions on the thermodynamic stability of the proteome follows a universal bi-Gaussian distribution. Detailed results are presented for six drug discovery targets and four structural proteins comprising the virion, highlighting substitutions with the potential to impact protein structure, enzyme activity, and functional interfaces. Characterizing the evolution of the virus in three dimensions provides testable insights into viral protein function and should aid in structure-based drug discovery efforts as well as the prospective identification of amino acid substitutions with potential for drug resistance.
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