scholarly journals Effects of SARS-CoV-2 Mutations on Protein Structures and Intraviral Protein-Protein Interactions

2020 ◽  
Author(s):  
Siqi Wu ◽  
Chang Tian ◽  
Panpan Liu ◽  
Dongjie Guo ◽  
Wei Zheng ◽  
...  
Keyword(s):  
Protein Structure ◽  
Binding Affinity ◽  
Protein Interactions ◽  
Hot Spots ◽  
Protein Structures ◽  
Virus Genome ◽  
Incidence Rates ◽  
Open Reading Frames ◽  
Synonymous Mutations ◽  
Novel Coronavirus

AbstractSince 2019, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causing coronavirus disease 2019 (COVID-19) has infected ten millions of people across the globe, and massive mutations in virus genome have occurred during the rapid spread of this novel coronavirus. Variance in protein sequence might lead to change in protein structure and interaction, then further affect the viral physiological characteristics, which could bring tremendous influence on the pandemic. In this study, we investigated 18 non-synonymous mutations in SARS-CoV-2 genome which incidence rates were all ≥1% as of July 15th, 2020, then modeled the mutated protein structures and compared them with the reference ones. The results showed that four types of mutations could cause dramatic changes in protein structures (RMSD ≥5.0 Å), which were Q57H and G251V in open reading frames 3a (ORF3a), S194L and R203K/G204R in nucleocapsid (N). Next, we found that these mutations could affect the binding affinity of intraviral protein interactions. In addition, the hot spots within these docking complexes were altered, among which the mutation Q57H was involved in both Orf3a-Orf8 and Orf3a-S protein interactions. Besides, these mutations were widely distributed all over the world, and their occurrences fluctuated as time went on. Notably, the incidences of R203K/G204R in N and Q57H in Orf3a were both over 50% in some countries. Overall, our findings suggest that SARS-CoV-2 mutations can change viral protein structure, binding affinity and hot spots of the interface, thereby may have impacts on SARS-CoV-2 transmission, diagnosis and treatment of COVID-19.

scholarly journals Insights into Cross-species Evolution of Novel Human Coronavirus 2019-nCoV and Defining Immune Determinants for Vaccine Development

2020 ◽  
Author(s):  
Arunachalam Ramaiah ◽  
Vaithilingaraja Arumugaswami
Keyword(s):  
Binding Affinity ◽  
Vaccine Development ◽  
Asia Pacific ◽  
Potential Candidate ◽  
T Cell Epitopes ◽  
Live Animal ◽  
Synonymous Mutations ◽  
Wide Range ◽  
Novel Coronavirus ◽  
N Proteins

ABSTRACTNovel Coronavirus (nCoV) outbreak in the city of Wuhan, China during December 2019, has now spread to various countries across the globe triggering a heightened containment effort. This human pathogen is a member of betacoronavirus genus carrying 30 kilobase of single positive-sense RNA genome. Understanding the evolution, zoonotic transmission, and source of this novel virus would help accelerating containment and prevention efforts. The present study reported detailed analysis of 2019-nCoV genome evolution and potential candidate peptides for vaccine development. This nCoV genotype might have been evolved from a bat-CoV by accumulating non-synonymous mutations, indels, and recombination events. Structural proteins Spike (S), and Membrane (M) had extensive mutational changes, whereas Envelope (E) and Nucleocapsid (N) proteins were very conserved suggesting differential selection pressures exerted on 2019-nCoV during evolution. Interestingly, 2019-nCoV Spike protein contains a 39 nucleotide sequence insertion relative to SARS-like bat-SL-CoVZC45/2017. Furthermore, we identified eight high binding affinity (HBA) CD4 T-cell epitopes in the S, E, M and N proteins, which can be commonly recognized by HLA-DR alleles of Asia and Asia-Pacific Region population. These immunodominant epitopes can be incorporated in universal subunit CoV vaccine. Diverse HLA types and variations in the epitope binding affinity may contribute to the wide range of immunopathological outcomes of circulating virus in humans. Our findings emphasize the requirement for continuous surveillance of CoV strains in live animal markets to better understand the viral adaptation to human host and to develop practical solutions to prevent the emergence of novel pathogenic CoV strains.

scholarly journals INSILICO METHOD FOR PREDICTION OF MAXIMUM BINDING AFFINITY AND LIGAND – PROTEIN INTERACTION STUDIES ON ALZHEIMER’S DISEASE

2020 ◽  
Vol 8 (11) ◽  
pp. 362-370
Author(s):  
Karthika Perampattu Baskaran ◽  
Arunagiri Arumugam ◽  
Ruckmani Kandasamy ◽  
Shanmugarathinam Alagarsamy
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Molecular Docking ◽  
Protein Structure ◽  
Binding Affinity ◽  
Protein Interactions ◽  
Protein Crystal ◽  
Hydrogen Bond Formation ◽  
Discovery Studio ◽  
Protein Crystal Structure

The aim of this study is to perform the molecular docking, identifying the drug likeness, ADME properties of drugs, Ligand-Protein interactions using different softwares. Due to the excess activity of Acetylcholinesterase, plaque formation and tau protein aggregation in the brain is the main cause for the Alzheimer’s disease. The interaction of Donepezil, Rivastigmine and Chlorzoxazone against AChE protein crystal structure (4EY5, 4EY6, 4EY7) using molecular docking were analyzed. Docking results of Rivastigmine and Chlorzoxazone were compared with Donepezil (widely used drug for Alzheimer’s disease) to identify the binding affinity. To verify whether Chlorzoxazone could act similarly as effective drug of Donepezil and also finding in which protein structure, ligands could bind effectively were employed using BIOVIA Discovery Studio software. Among those ligands interaction with all protein structure, 4EY7 on Rivastigmine (-7.1 kcal/mol) exhibits maximum binding affinity. The interactions of three ligands were compared with one another, in that Hydrogen bond formation of Chlorzoxazone and Donepezil with 4EY6 and 4EY7 interacting the similar aminoacids residues (4EY6-ARG165; 4EY7-ASP74) were studied using insilico studies .

scholarly journals A 3D Structural Interactome to Explore the Impact of Evolutionary Divergence, Population Variation, and Small-molecule Drugs on SARS-CoV-2-Human Protein-Protein Interactions

2020 ◽  
Author(s):  
Shayne D. Wierbowski ◽  
Siqi Liang ◽  
You Chen ◽  
Nicole M. Andre ◽  
Steven M. Lipkin ◽  
...  
Keyword(s):  
Peer Review ◽  
Binding Affinity ◽  
Protein Interactions ◽  
Protein Structures ◽  
Population Variation ◽  
Easy Access ◽  
Evolutionary Divergence ◽  
Viral Agent ◽  
Protein Protein Interactions ◽  
The Impact

AbstractThe recent COVID-19 pandemic has sparked a global public health crisis. Vital to the development of informed treatments for this disease is a comprehensive understanding of the molecular interactions involved in disease pathology. One lens through which we can better understand this pathology is through the network of protein-protein interactions between its viral agent, SARS-CoV-2, and its human host. For instance, increased infectivity of SARS-CoV-2 compared to SARS-CoV can be explained by rapid evolution along the interface between the Spike protein and its human receptor (ACE2) leading to increased binding affinity. Sequence divergences that modulate other protein-protein interactions may further explain differences in transmission and virulence in this novel coronavirus. To facilitate these comparisons, we combined homology-based structural modeling with the ECLAIR pipeline for interface prediction at residue resolution, and molecular docking with PyRosetta. This enabled us to compile a novel 3D structural interactome meta-analysis for the published interactome network between SARS-CoV-2 and human. This resource includes docked structures for all interactions with protein structures, enrichment analysis of variation along interfaces, predicted ΔΔG between SARS-CoV and SARS-CoV-2 variants for each interaction, predicted impact of natural human population variation on binding affinity, and a further prioritized set of drug repurposing candidates predicted to overlap with protein interfaces†. All predictions are available online† for easy access and are continually updated when new interactions are published.† Some sections of this pre-print have been redacted to comply with current bioRxiv policy restricting the dissemination of purely in silico results predicting potential therapies for SARS-CoV-2 that have not undergone thorough peer-review. The results section titled “Prioritization of Candidate Inhibitors of SARS-CoV-2-Human Interactions Through Binding Site Comparison,” Figure 4, Supplemental Table 9, and all links to our web resource have been removed. Blank headers left in place to preserve structure and item numbering. Our full manuscript will be published in an appropriate journal following peer-review.

Sequence Specific Dihedral Angle Distribution: Application in Protein Structure Prediction and Evaluation

1970 ◽  
Vol 19 (2) ◽  
pp. 217-226
Author(s):  
S. M. Minhaz Ud-Dean ◽  
Mahdi Muhammad Moosa
Keyword(s):  
Protein Structure ◽  
Dihedral Angle ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Protein Structures ◽  
Angle Distribution ◽  
Ramachandran Plot ◽  
Specific Data ◽  
Specific Distribution ◽  
Structure Evaluation

Protein structure prediction and evaluation is one of the major fields of computational biology. Estimation of dihedral angle can provide information about the acceptability of both theoretically predicted and experimentally determined structures. Here we report on the sequence specific dihedral angle distribution of high resolution protein structures available in PDB and have developed Sasichandran, a tool for sequence specific dihedral angle prediction and structure evaluation. This tool will allow evaluation of a protein structure in pdb format from the sequence specific distribution of Ramachandran angles. Additionally, it will allow retrieval of the most probable Ramachandran angles for a given sequence along with the sequence specific data. Key words: Torsion angle, φ-ψ distribution, sequence specific ramachandran plot, Ramasekharan, protein structure appraisal D.O.I. 10.3329/ptcb.v19i2.5439 Plant Tissue Cult. & Biotech. 19(2): 217-226, 2009 (December)

Current Trends in Biotherapeutic Higher Order Structure Characterization by Irreversible Covalent Footprinting Mass Spectrometry

2019 ◽  
Vol 26 (1) ◽  
pp. 35-43 ◽  
Author(s):  
Natalie K. Garcia ◽  
Galahad Deperalta ◽  
Aaron T. Wecksler
Keyword(s):  
Mass Spectrometry ◽  
Protein Structure ◽  
Protein Interactions ◽  
Quaternary Structure ◽  
Solvent Accessibility ◽  
Higher Order ◽  
Structure Characterization ◽  
Order Structure ◽  
Protein Footprinting ◽  
Wide Range

Background: Biotherapeutics, particularly monoclonal antibodies (mAbs), are a maturing class of drugs capable of treating a wide range of diseases. Therapeutic function and solutionstability are linked to the proper three-dimensional organization of the primary sequence into Higher Order Structure (HOS) as well as the timescales of protein motions (dynamics). Methods that directly monitor protein HOS and dynamics are important for mapping therapeutically relevant protein-protein interactions and assessing properly folded structures. Irreversible covalent protein footprinting Mass Spectrometry (MS) tools, such as site-specific amino acid labeling and hydroxyl radical footprinting are analytical techniques capable of monitoring the side chain solvent accessibility influenced by tertiary and quaternary structure. Here we discuss the methodology, examples of biotherapeutic applications, and the future directions of irreversible covalent protein footprinting MS in biotherapeutic research and development. Conclusion: Bottom-up mass spectrometry using irreversible labeling techniques provide valuable information for characterizing solution-phase protein structure. Examples range from epitope mapping and protein-ligand interactions, to probing challenging structures of membrane proteins. By paring these techniques with hydrogen-deuterium exchange, spectroscopic analysis, or static-phase structural data such as crystallography or electron microscopy, a comprehensive understanding of protein structure can be obtained.

Analysis of Oncogene Protein Structure Using Small World Network Concept

2020 ◽  
Vol 15 (7) ◽  
pp. 732-740
Author(s):  
Neetu Kumari ◽  
Anshul Verma
Keyword(s):  
Amino Acids ◽  
Protein Structure ◽  
Degree Distribution ◽  
Protein Structures ◽  
Small World ◽  
Extreme Condition ◽  
Centrality Measures ◽  
Small World Network ◽  
Network Concept ◽  
Oncogene Protein

Background: The basic building block of a body is protein which is a complex system whose structure plays a key role in activation, catalysis, messaging and disease states. Therefore, careful investigation of protein structure is necessary for the diagnosis of diseases and for the drug designing. Protein structures are described at their different levels of complexity: primary (chain), secondary (helical), tertiary (3D), and quaternary structure. Analyzing complex 3D structure of protein is a difficult task but it can be analyzed as a network of interconnection between its component, where amino acids are considered as nodes and interconnection between them are edges. Objective: Many literature works have proven that the small world network concept provides many new opportunities to investigate network of biological systems. The objective of this paper is analyzing the protein structure using small world concept. Methods: Protein is analyzed using small world network concept, specifically where extreme condition is having a degree distribution which follows power law. For the correct verification of the proposed approach, dataset of the Oncogene protein structure is analyzed using Python programming. Results: Protein structure is plotted as network of amino acids (Residue Interaction Graph (RIG)) using distance matrix of nodes with given threshold, then various centrality measures (i.e., degree distribution, Degree-Betweenness correlation, and Betweenness-Closeness correlation) are calculated for 1323 nodes and graphs are plotted. Conclusion: Ultimately, it is concluded that there exist hubs with higher centrality degree but less in number, and they are expected to be robust toward harmful effects of mutations with new functions.

scholarly journals Functional alterations caused by mutations reflect evolutionary trends of SARS-CoV-2

2021 ◽  
Author(s):  
Liang Cheng ◽  
Xudong Han ◽  
Zijun Zhu ◽  
Changlu Qi ◽  
Ping Wang ◽  
...  
Keyword(s):  
Reference Genome ◽  
Sequence Data ◽  
Purifying Selection ◽  
Virus Genome ◽  
Receptor Binding Domain ◽  
Evolutionary Trends ◽  
Synonymous Mutations ◽  
Almost All ◽  
Virus Strains ◽  
New Mutations

Abstract Since the first report of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in December 2019, the COVID-19 pandemic has spread rapidly worldwide. Due to the limited virus strains, few key mutations that would be very important with the evolutionary trends of virus genome were observed in early studies. Here, we downloaded 1809 sequence data of SARS-CoV-2 strains from GISAID before April 2020 to identify mutations and functional alterations caused by these mutations. Totally, we identified 1017 nonsynonymous and 512 synonymous mutations with alignment to reference genome NC_045512, none of which were observed in the receptor-binding domain (RBD) of the spike protein. On average, each of the strains could have about 1.75 new mutations each month. The current mutations may have few impacts on antibodies. Although it shows the purifying selection in whole-genome, ORF3a, ORF8 and ORF10 were under positive selection. Only 36 mutations occurred in 1% and more virus strains were further analyzed to reveal linkage disequilibrium (LD) variants and dominant mutations. As a result, we observed five dominant mutations involving three nonsynonymous mutations C28144T, C14408T and A23403G and two synonymous mutations T8782C, and C3037T. These five mutations occurred in almost all strains in April 2020. Besides, we also observed two potential dominant nonsynonymous mutations C1059T and G25563T, which occurred in most of the strains in April 2020. Further functional analysis shows that these mutations decreased protein stability largely, which could lead to a significant reduction of virus virulence. In addition, the A23403G mutation increases the spike-ACE2 interaction and finally leads to the enhancement of its infectivity. All of these proved that the evolution of SARS-CoV-2 is toward the enhancement of infectivity and reduction of virulence.

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