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Zhang L, Li J. Molecular Dynamics Simulations on Spike Protein Mutants Binding with Human β Defensin Type 2. J Phys Chem B 2024; 128:415-428. [PMID: 38189674 DOI: 10.1021/acs.jpcb.3c05460] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
Human β defensin type 2 (hBD-2), a cationic cysteine-rich peptide secreted from the human innate immune system, can bind Spike-RBD at the same site as receptor ACE2, thus blocking viral entry into ACE2-expressing cells. In order to find out the impact of CoV-2 mutations on hBD-2's antiviral activity, it is important to investigate the binding and interaction of hBD-2 with RBD mutants. All-atom molecular dynamics simulations were conducted on typical RBD mutants, including N501Y, E484K, P479S, T478I, S477N, N439K, K417N, and N501Y-E484K-K417N, binding with hBD-2. Starting from the stable binding structure of hBD-2 and wt-RBD and ClusPro and HADDOCK docking-predicted initial structures, the RBD variants bound with hBD-2 simulations were set up, and NAMD simulations were conducted. Based on the structure and dynamics analysis, it was found that most RBD variants can still form a similar number of hydrogen bonds with hBD-2, in addition to having a similar-sized buried surface area (BSA) and a similar binding interface to the RBD wildtype. However, the RBD triple mutant formed a less stable binding structure with hBD-2 than other variants. Additionally, the free energy perturbation (FEP) method was applied to calculate the contribution of key mutant residues to the binding and the free energy change caused by the mutations. The result shows that N439K, K417N, and the trimutation increase the binding free energy of RBD with hBD-2; thus, RBD should bind less stably with hBD-2. E484K decreases the binding free energy, thus it should bind more stably with hBD-2, while N501Y, S477N, T478I, and P479S almost do not change the binding free energy with hBD-2. The MM-GBSA method predicted the binding interaction energy which shows that the trimutant should be able to escape the binding with hBD-2 but N501Y should not. The result can provide insight into understanding the functional mechanism of hBD-2 combating SARS-CoV-2 mutants.
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Affiliation(s)
- Liqun Zhang
- Chemical Engineering Department, University of Rhode Island, Kingston, Rhode Island 02881, United States
| | - Jadeson Li
- Newton North High School, 457 Walnut Street, Newton, Massachusetts 02460, United States
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2
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Rucker G, Qin H, Zhang L. Structure, dynamics and free energy studies on the effect of point mutations on SARS-CoV-2 spike protein binding with ACE2 receptor. PLoS One 2023; 18:e0289432. [PMID: 37796794 PMCID: PMC10553274 DOI: 10.1371/journal.pone.0289432] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2023] [Accepted: 09/11/2023] [Indexed: 10/07/2023] Open
Abstract
The ongoing COVID-19 pandemic continues to infect people worldwide, and the virus continues to evolve in significant ways which can pose challenges to the efficiency of available vaccines and therapeutic drugs and cause future pandemic. Therefore, it is important to investigate the binding and interaction of ACE2 with different RBD variants. A comparative study using all-atom MD simulations was conducted on ACE2 binding with 8 different RBD variants, including N501Y, E484K, P479S, T478I, S477N, N439K, K417N and N501Y-E484K-K417N on RBD. Based on the RMSD, RMSF, and DSSP results, overall the binding of RBD variants with ACE2 is stable, and the secondary structure of RBD and ACE2 are consistent after the point mutation. Besides that, a similar buried surface area, a consistent binding interface and a similar amount of hydrogen bonds formed between RBD and ACE2 although the exact residue pairs on the binding interface were modified. The change of binding free energy from point mutation was predicted using the free energy perturbation (FEP) method. It is found that N501Y, N439K, and K417N can strengthen the binding of RBD with ACE2, while E484K and P479S weaken the binding, and S477N and T478I have negligible effect on the binding. Point mutations modified the dynamic correlation of residues in RBD based on the dihedral angle covariance matrix calculation. Doing dynamic network analysis, a common intrinsic network community extending from the tail of RBD to central, then to the binding interface region was found, which could communicate the dynamics in the binding interface region to the tail thus to the other sections of S protein. The result can supply unique methodology and molecular insight on studying the molecular structure and dynamics of possible future pandemics and design novel drugs.
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Affiliation(s)
- George Rucker
- Chemical Engineering Department, Tennessee Technological University, Cookeville, TN, United States of America
| | - Hong Qin
- Computer Science Department, University of Tennessee Chattanooga, Chattanooga, TN, United States of America
| | - Liqun Zhang
- Chemical Engineering Department, University of Rhode Island, Kingston, RI, United States of America
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Chan KC, Song Y, Xu Z, Shang C, Zhou R. SARS-CoV-2 Delta Variant: Interplay between Individual Mutations and Their Allosteric Synergy. Biomolecules 2022; 12:biom12121742. [PMID: 36551170 PMCID: PMC9775976 DOI: 10.3390/biom12121742] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Revised: 11/17/2022] [Accepted: 11/20/2022] [Indexed: 11/25/2022] Open
Abstract
Since its first appearance in April 2021, B.1.617.2, also termed variant Delta, catalyzed one major worldwide wave dominating the second year of coronavirus disease 2019 (COVID-19) pandemic. Despite its quick disappearance worldwide, the strong virulence caused by a few point mutations remains an unsolved problem largely. Along with the other two sublineages, the Delta variant harbors an accumulation of Spike protein mutations, including the previously identified L452R, E484Q, and the newly emerged T478K on its receptor binding domain (RBD). We used molecular dynamics (MD) simulations, in combination with free energy perturbation (FEP) calculations, to examine the effects of two combinative mutation sets, L452R + E484Q and L452R + T478K. Our dynamic trajectories reveal an enhancement in binding affinity between mutated RBD and the common receptor protein angiotensin converting enzyme 2 (ACE2) through a net increase in the buried molecular surface area of the binary complex. This enhanced binding, mediated through Gln493, sets the same stage for all three sublineages due to the presence of L452R mutation. The other mutation component, E484Q or T478K, was found to impact the RBD-ACE2 binding and help the variant to evade several monoclonal antibodies (mAbs) in a distinct manner. Especially for L452R + T478K, synergies between mutations are mediated through a complex residual and water interaction network and further enhance its binding to ACE2. Taking together, this study demonstrates that new variants of SARS-CoV-2 accomplish both "attack" (infection) and "defense" (antibody neutralization escape) with the same "polished sword" (mutated Spike RBD).
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Affiliation(s)
- Kevin C. Chan
- Institute of Quantitative Biology, College of Life Sciences, Zhejiang University, Hangzhou 310058, China
- Shanghai Institute for Advanced Study, Zhejiang University, 799 Dangui Road, Shanghai 201203, China
| | - Yi Song
- Institute of Quantitative Biology, College of Life Sciences, Zhejiang University, Hangzhou 310058, China
| | - Zheng Xu
- BirenTech Research, Shanghai 201112, China
| | - Chun Shang
- Institute of Quantitative Biology, College of Life Sciences, Zhejiang University, Hangzhou 310058, China
| | - Ruhong Zhou
- Institute of Quantitative Biology, College of Life Sciences, Zhejiang University, Hangzhou 310058, China
- Shanghai Institute for Advanced Study, Zhejiang University, 799 Dangui Road, Shanghai 201203, China
- Department of Chemistry, Columbia University, New York, NY 10027, USA
- The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310058, China
- Correspondence:
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He Y, Yu H, Huffman A, Lin AY, Natale DA, Beverley J, Zheng L, Perl Y, Wang Z, Liu Y, Ong E, Wang Y, Huang P, Tran L, Du J, Shah Z, Shah E, Desai R, Huang HH, Tian Y, Merrell E, Duncan WD, Arabandi S, Schriml LM, Zheng J, Masci AM, Wang L, Liu H, Smaili FZ, Hoehndorf R, Pendlington ZM, Roncaglia P, Ye X, Xie J, Tang YW, Yang X, Peng S, Zhang L, Chen L, Hur J, Omenn GS, Athey B, Smith B. A comprehensive update on CIDO: the community-based coronavirus infectious disease ontology. J Biomed Semantics 2022; 13:25. [PMID: 36271389 PMCID: PMC9585694 DOI: 10.1186/s13326-022-00279-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Accepted: 09/13/2022] [Indexed: 11/24/2022] Open
Abstract
Background The current COVID-19 pandemic and the previous SARS/MERS outbreaks of 2003 and 2012 have resulted in a series of major global public health crises. We argue that in the interest of developing effective and safe vaccines and drugs and to better understand coronaviruses and associated disease mechenisms it is necessary to integrate the large and exponentially growing body of heterogeneous coronavirus data. Ontologies play an important role in standard-based knowledge and data representation, integration, sharing, and analysis. Accordingly, we initiated the development of the community-based Coronavirus Infectious Disease Ontology (CIDO) in early 2020. Results As an Open Biomedical Ontology (OBO) library ontology, CIDO is open source and interoperable with other existing OBO ontologies. CIDO is aligned with the Basic Formal Ontology and Viral Infectious Disease Ontology. CIDO has imported terms from over 30 OBO ontologies. For example, CIDO imports all SARS-CoV-2 protein terms from the Protein Ontology, COVID-19-related phenotype terms from the Human Phenotype Ontology, and over 100 COVID-19 terms for vaccines (both authorized and in clinical trial) from the Vaccine Ontology. CIDO systematically represents variants of SARS-CoV-2 viruses and over 300 amino acid substitutions therein, along with over 300 diagnostic kits and methods. CIDO also describes hundreds of host-coronavirus protein-protein interactions (PPIs) and the drugs that target proteins in these PPIs. CIDO has been used to model COVID-19 related phenomena in areas such as epidemiology. The scope of CIDO was evaluated by visual analysis supported by a summarization network method. CIDO has been used in various applications such as term standardization, inference, natural language processing (NLP) and clinical data integration. We have applied the amino acid variant knowledge present in CIDO to analyze differences between SARS-CoV-2 Delta and Omicron variants. CIDO's integrative host-coronavirus PPIs and drug-target knowledge has also been used to support drug repurposing for COVID-19 treatment. Conclusion CIDO represents entities and relations in the domain of coronavirus diseases with a special focus on COVID-19. It supports shared knowledge representation, data and metadata standardization and integration, and has been used in a range of applications. Supplementary Information The online version contains supplementary material available at 10.1186/s13326-022-00279-z.
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Affiliation(s)
- Yongqun He
- University of Michigan Medical School, Ann Arbor, MI, USA.
| | - Hong Yu
- People's Hospital of Guizhou Province, Guiyang, Guizhou, China.
| | | | - Asiyah Yu Lin
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA.,National Center for Ontological Research, Buffalo, NY, USA
| | | | - John Beverley
- National Center for Ontological Research, Buffalo, NY, USA.,The Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA
| | - Ling Zheng
- Computer Science and Software Engineering Department, Monmouth University, West Long Branch, NJ, USA
| | - Yehoshua Perl
- Department of Computer Science, New Jersey Institute of Technology, Newark, NJ, USA
| | - Zhigang Wang
- Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & School of Basic Medicine, Peking Union Medical College, Beijing, China
| | - Yingtong Liu
- University of Michigan Medical School, Ann Arbor, MI, USA
| | - Edison Ong
- University of Michigan Medical School, Ann Arbor, MI, USA
| | - Yang Wang
- University of Michigan Medical School, Ann Arbor, MI, USA.,People's Hospital of Guizhou Province, Guiyang, Guizhou, China
| | - Philip Huang
- University of Michigan Medical School, Ann Arbor, MI, USA
| | - Long Tran
- University of Michigan Medical School, Ann Arbor, MI, USA
| | - Jinyang Du
- University of Michigan Medical School, Ann Arbor, MI, USA
| | - Zalan Shah
- University of Michigan Medical School, Ann Arbor, MI, USA
| | - Easheta Shah
- University of Michigan Medical School, Ann Arbor, MI, USA
| | - Roshan Desai
- University of Michigan Medical School, Ann Arbor, MI, USA
| | - Hsin-Hui Huang
- University of Michigan Medical School, Ann Arbor, MI, USA.,National Yang-Ming University, Taipei, Taiwan
| | - Yujia Tian
- Rutgers University, New Brunswick, NJ, USA
| | | | | | | | - Lynn M Schriml
- University of Maryland School of Medicine, Baltimore, MD, USA
| | - Jie Zheng
- Department of Biology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Anna Maria Masci
- Office of Data Science, National Institute of Environmental Health Sciences, Research Triangle Park, NC, USA
| | | | | | | | - Robert Hoehndorf
- King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Zoë May Pendlington
- European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
| | - Paola Roncaglia
- European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire, UK
| | - Xianwei Ye
- People's Hospital of Guizhou Province, Guiyang, Guizhou, China
| | - Jiangan Xie
- School of Bioinformatics, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Yi-Wei Tang
- Cepheid, Danaher Diagnostic Platform, Shanghai, China
| | - Xiaolin Yang
- Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & School of Basic Medicine, Peking Union Medical College, Beijing, China
| | - Suyuan Peng
- National Institute of Health Data Science, Peking University, Beijing, China
| | - Luxia Zhang
- National Institute of Health Data Science, Peking University, Beijing, China
| | - Luonan Chen
- Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai, China
| | - Junguk Hur
- University of North Dakota School of Medicine and Health Sciences, Grand Forks, ND, USA
| | | | - Brian Athey
- University of Michigan Medical School, Ann Arbor, MI, USA
| | - Barry Smith
- National Center for Ontological Research, Buffalo, NY, USA.,University at Buffalo, Buffalo, NY, 14260, USA
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Chen H, Hu X, Hu Y, Zhou J, Chen M. CoVM2: Molecular Biological Data Integration of SARS-CoV-2 Proteins in a Macro-to-Micro Method. Biomolecules 2022; 12:biom12081067. [PMID: 36008961 PMCID: PMC9405999 DOI: 10.3390/biom12081067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Revised: 07/27/2022] [Accepted: 07/29/2022] [Indexed: 02/01/2023] Open
Abstract
The COVID-19 pandemic has been a major public health event since 2020. Multiple variant strains of SARS-CoV-2, the causative agent of COVID-19, were detected based on the mutation sites in their sequences. These sequence mutations may lead to changes in the protein structures and affect the binding states of SARS-CoV-2 and human proteins. Experimental research on SARS-CoV-2 has accumulated a large amount of structural data and protein-protein interactions (PPIs), but the studies on the SARS-CoV-2–human PPI networks lack integration of physical associations with possible protein docking information. In addition, the docking structures of variant viral proteins with human receptor proteins are still insufficient. This study constructed SARS-CoV-2–human protein–protein interaction network with data integration methods. Crystal structures were collected to map the interaction pairs. The pairs of direct interactions and physical associations were selected and analyzed for variant docking calculations. The study examined the structures of spike (S) glycoprotein of variants Delta B.1.617.2, Omicron BA.1, and Omicron BA.2. The calculated docking structures of S proteins and potential human receptors were obtained. The study integrated binary protein interactions with 3D docking structures to fulfill an extended view of SARS-CoV-2 proteins from a macro- to micro-scale.
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Affiliation(s)
- Hongjun Chen
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou 310058, China; (H.C.); (X.H.); (Y.H.)
| | - Xiaotian Hu
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou 310058, China; (H.C.); (X.H.); (Y.H.)
| | - Yanshi Hu
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou 310058, China; (H.C.); (X.H.); (Y.H.)
| | - Jiawen Zhou
- Chu Kochen Honors College, Zhejiang University, Hangzhou 310058, China;
| | - Ming Chen
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou 310058, China; (H.C.); (X.H.); (Y.H.)
- Institute of Hematology, Zhejiang University School of Medicine, The First Affiliated Hospital, Zhejiang University, Hangzhou 310058, China
- Correspondence: ; Tel.: +86-(0)571-8820-6612
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