In Silico End-to-End Protein–Ligand Interaction Characterization Pipeline: The Case of SARS-CoV-2
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ABSTRACT:
SUBMITTER: Rosario-Ferreira N
PROVIDER: S-EPMC8577377 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection continues to be a global health problem. Despite the current implementation of COVID-19 vaccination schedules, identifying effective antiviral drug treatments for this disease continues to be a priority. A recent study showed that masitinib (MST), a tyrosine kinase inhibitor, blocks the proteolytic activity of SARS-CoV-2 main protease (Mpro ). Although MST is a potential candidate for COVID-19 treatment, a comprehensive analysis of its interaction with Mpro has not been done. In this work, we performed molecular dynamics simulations of the MST-Mpro complex crystal structure. The effect of the protonation states of Mpro H163 residue and MST titratable groups were studied. Furthermore, we identified the MST substituents and Mpro mutations that affect the stability of the complex. Our results provide valuable insights into the design of new MST analogs as potential treatments for COVID-19.
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Project description:Protein-protein interactions (PPIs) offer great opportunities to expand the druggable proteome and therapeutically tackle various diseases, but remain challenging targets for drug discovery. Here, we provide a comprehensive pipeline that combines experimental and computational tools to identify and validate PPI targets and perform early-stage drug discovery. We have developed a machine learning approach that prioritizes interactions by analyzing quantitative data from binary PPI assays or AlphaFold-Multimer predictions. Using the quantitative assay LuTHy together with our machine learning algorithm, we identified high-confidence interactions among SARS-CoV-2 proteins for which we predicted three-dimensional structures using AlphaFold-Multimer. We employed VirtualFlow to target the contact interface of the NSP10-NSP16 SARS-CoV-2 methyltransferase complex by ultra-large virtual drug screening. Thereby, we identified a compound that binds to NSP10 and inhibits its interaction with NSP16, while also disrupting the methyltransferase activity of the complex, and SARS-CoV-2 replication. Overall, this pipeline will help to prioritize PPI targets to accelerate the discovery of early-stage drug candidates targeting protein complexes and pathways.
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Project description:Protein-protein interactions (PPIs) offer great opportunities to expand the druggable proteome and therapeutically tackle various diseases, but remain challenging targets for drug discovery. Here, we provide a comprehensive pipeline that combines experimental and computational tools to identify and validate PPI targets and perform early-stage drug discovery. We have developed a machine learning approach that prioritizes interactions by analyzing quantitative data from binary PPI assays and AlphaFold-Multimer predictions. Using the quantitative assay LuTHy together with our machine learning algorithm, we identified high-confidence interactions among SARS-CoV-2 proteins for which we predicted three-dimensional structures using AlphaFold Multimer. We employed VirtualFlow to target the contact interface of the NSP10-NSP16 SARS-CoV-2 methyltransferase complex by ultra-large virtual drug screening. Thereby, we identified a compound that binds to NSP10 and inhibits its interaction with NSP16, while also disrupting the methyltransferase activity of the complex, and SARS-CoV-2 replication. Overall, this pipeline will help to prioritize PPI targets to accelerate the discovery of early-stage drug candidates targeting protein complexes and pathways.
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Project description:MIEC-SVM is a structure-based method for predicting protein recognition specificity. Here, we present an automated MIEC-SVM pipeline providing an integrated and user-friendly workflow for construction and application of the MIEC-SVM models. This pipeline can handle standard amino acids and those with post-translational modifications (PTMs) or small molecules. Moreover, multi-threading and support to Sun Grid Engine (SGE) are implemented to significantly boost the computational efficiency.The program is available at http://wanglab.ucsd.edu/MIEC-SVM CONTACT: : wei-wang@ucsd.eduSupplementary data available at Bioinformatics online.
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Project description:This study is an attempt to find a suitable therapy using antimicrobial peptides (AMPs) by identifying peptide-protein interaction of AMPs and nucleocapsid protein of SARS and SARS-CoV- 2. The AMPs were shortlisted from the APD3 database (Antimicrobial peptide database) based on various physicochemical parameters. The binding efficacy of AMPs was measured using the lowest energy score of the docked complexes with 10 selected AMPs. For SARS-CoV, AP00180 showed the best pose with a binding affinity value of - 6.4 kcal/mol. Prominent hydrogen bonding interactions were observed between Lys85 (nucleocapsid receptor) and Arg13 (antimicrobial peptide ligand) having the least intermolecular distance of 1.759 Å. For SARS-CoV-2, AP00549 was docked with a binding affinity value of - 3.4 kcal/mol and Arg119 and Glu14 of receptor nucleocapsid protein and ligand AMP having the least intermolecular distance of 2.104 The dynamic simulation was performed at 50 ns to check the stability of the final docked complexes, one with each protein. The two best AMPs were AP00180 (Human Defensin-5) for SARS and AP00549 (Plectasin) for SARS-CoV-2. From positive results of dynamic simulation and previously known knowledge that some AMPs interact with the nucleocapsid of coronaviruses, these AMPs might be used as a potential therapeutic agent for the treatment regime of SARS-CoV-2 and SARS infection.Supplementary informationThe online version contains supplementary material available at 10.1007/s40203-021-00103-z.
| S-EPMC8315091 | biostudies-literature
Project description:The current COVID-19 pandemic has led to a devastating impact across the world. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) (the virus causing COVID-19) is known to use the receptor-binding domain (RBD) at viral surface spike (S) protein to interact with the angiotensin-converting enzyme 2 (ACE2) receptor expressed on many human cell types. The RBD-ACE2 interaction is a crucial step to mediate the host cell entry of SARS-CoV-2. Recent studies indicate that the ACE2 interaction with the SARS-CoV-2 S protein has a higher affinity than its binding with the structurally identical S protein of SARS-CoV-1, the virus causing the 2002-2004 SARS outbreak. However, the biophysical mechanism behind such binding affinity difference is unclear. This study utilizes combined single-molecule force spectroscopy and steered molecular dynamics (SMD) simulation approaches to quantify the specific interactions between SARS-CoV-2 or SARS-CoV-1 RBD and ACE2. Depending on the loading rates, the unbinding forces between SARS-CoV-2 RBD and ACE2 range from 70 to 105 pN and are 30-40% higher than those of SARS-CoV-1 RBD and ACE2 under similar loading rates. SMD results indicate that SARS-CoV-2 RBD interacts with the N-linked glycan on Asn90 of ACE2. This interaction is mostly absent in the SARS-CoV-1 RBD-ACE2 complex. During the SMD simulations, the extra RBD-N-glycan interaction contributes to a greater force and prolonged interaction lifetime. The observation is confirmed by our experimental force spectroscopy study. After removing N-linked glycans on ACE2, its mechanical binding strength with SARS-CoV-2 RBD decreases to a similar level of the SARS-CoV-1 RBD-ACE2 interaction. Together, the study uncovers the mechanism behind the difference in ACE2 binding between SARS-CoV-2 and SARS-CoV-1 and could help develop new strategies to block SARS-CoV-2 entry.
| S-EPMC7886630 | biostudies-literature
Project description:Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the novel coronavirus which caused the coronavirus disease 2019 pandemic and infected more than 12 million victims and resulted in over 560,000 deaths in 213 countries around the world. Having no symptoms in the first week of infection increases the rate of spreading the virus. The increasing rate of the number of infected individuals and its high mortality necessitates an immediate development of proper diagnostic methods and effective treatments. SARS-CoV-2, similar to other viruses, needs to interact with the host proteins to reach the host cells and replicate its genome. Consequently, virus-host protein-protein interaction (PPI) identification could be useful in predicting the behavior of the virus and the design of antiviral drugs. Identification of virus-host PPIs using experimental approaches are very time consuming and expensive. Computational approaches could be acceptable alternatives for many preliminary investigations. In this study, we developed a new method to predict SARS-CoV-2-human PPIs. Our model is a three-layer network in which the first layer contains the most similar Alphainfluenzavirus proteins to SARS-CoV-2 proteins. The second layer contains protein-protein interactions between Alphainfluenzavirus proteins and human proteins. The last layer reveals protein-protein interactions between SARS-CoV-2 proteins and human proteins by using the clustering coefficient network property on the first two layers. To further analyze the results of our prediction network, we investigated human proteins targeted by SARS-CoV-2 proteins and reported the most central human proteins in human PPI network. Moreover, differentially expressed genes of previous researches were investigated and PPIs of SARS-CoV-2-human network, the human proteins of which were related to upregulated genes, were reported.
| S-EPMC7425553 | biostudies-literature
Project description:Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a +sense single-strand RNA virus. The virus has four major surface proteins: spike (S), envelope (E), membrane (M), and nucleocapsid (N), respectively. The constitutive proteins present a high grade of symmetry. Identifying a binding site is difficult. The virion is approximately 50-200 nm in diameter. Angiotensin-converting enzyme 2 (ACE2) acts as the cell receptor for the virus. SARS-CoV-2 has an increased affinity to human ACE2 compared with the original SAR strain. Topological space, and its symmetry, is a critical component in molecular interactions. By exploring this space, a suitable ligand space can be characterized accordingly. A spike protein (S) computational model in a complex with ACE 2 was generated using silica methods. Topological spaces were probed using high computational throughput screening techniques to identify and characterize the topological space of both SARS and SARS-CoV-2 spike protein and its ligand space. In order to identify the symmetry clusters, computational analysis techniques, together with statistical analysis, were utilized. The computations are based on crystallographic protein data bank PDB-based models of constitutive proteins. Cartesian coordinates of component atoms and some cluster maps were generated and analyzed. Dihedral angles were used in order to compute a topological receptor space. This computational study uses a multimodal representation of spike protein interactions with some fragment proteins. The chemical space of the receptors (a dimensional volume) suggests the relevance of the receptor as a drug target. The spike protein S of SARS and SARS-CoV-2 is analyzed and compared. The results suggest a mirror symmetry of SARS and SARS-CoV-2 spike proteins. The results show thatSARS-CoV-2 space is variable and has a distinct topology. In conclusion, surface proteins grant virion variability and symmetry in interactions with a potential complementary target (protein, antibody, ligand). The mirror symmetry of dihedral angle clusters determines a high specificity of the receptor space.
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Project description: Not available
| EMPIAR-11168 | biostudies-other
Project description:BACKGROUND: The design of mutants in protein functional regions, such as the ligand binding sites, is a powerful approach to recognize the determinants of specific protein activities in cellular pathways. For an exhaustive analysis of selected positions of protein structure large scale mutagenesis techniques are often employed, with laborious and time consuming experimental set-up. 'In silico' mutagenesis and screening simulation represents a valid alternative to laboratory methods to drive the 'in vivo' testing toward more focused objectives. RESULTS: We present here a high performance computational procedure for large-scale mutant modelling and subsequent evaluation of the effect on ligand binding affinity. The mutagenesis was performed with a 'saturation' approach, where all 20 natural amino acids were tested in positions involved in ligand binding sites. Each modelled mutant was subjected to molecular docking simulation and stability evaluation. The simulated protein-ligand complexes were screened for their impairment of binding ability based on change of calculated Ki compared to the wild-type.An example of application to the Endothelial Protein C Receptor residues involved in lipid binding is reported. CONCLUSION: The computational pipeline presented in this work is a useful tool for the design of structurally stable mutants with altered affinity for ligand binding, considerably reducing the number of mutants to be experimentally tested. The saturation mutagenesis procedure does not require previous knowledge of functional role of the residues involved and allows extensive exploration of all possible substitutions and their pairwise combinations. Mutants are screened by docking simulation and stability evaluation followed by a rationally driven selection of those presenting the required characteristics. The method can be employed in molecular recognition studies and as a preliminary approach to select models for experimental testing.
| S-EPMC2762068 | biostudies-literature