scholarly journals Dynamic residue interaction network analysis of the oseltamivir binding site of N1 neuraminidase and its H274Y mutation site conferring drug resistance in influenza A virus

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11552
Author(s):  
Mohini Yadav ◽  
Manabu Igarashi ◽  
Norifumi Yamamoto
Keyword(s):  
Binding Site ◽  
Binding Affinity ◽  
Molecular Mechanisms ◽  
Interaction Network ◽  
Influenza Viruses ◽  
Dynamics Simulation ◽  
Mutation Site ◽  
Residue Interaction ◽  
H274y Mutation ◽  
Interface Residues

Background Oseltamivir (OTV)-resistant influenza virus exhibits His-to-Tyr mutation at residue 274 (H274Y) in N1 neuraminidase (NA). However, the molecular mechanisms by which the H274Y mutation in NA reduces its binding affinity to OTV have not been fully elucidated. Methods In this study, we used dynamic residue interaction network (dRIN) analysis based on molecular dynamics simulation to investigate the correlation between the OTV binding site of NA and its H274Y mutation site. Results dRIN analysis revealed that the OTV binding site and H274Y mutation site of NA interact via the three interface residues connecting them. H274Y mutation significantly enhanced the interaction between residue 274 and the three interface residues in NA, thereby significantly decreasing the interaction between OTV and its surrounding loop 150 residues. Thus, we concluded that such changes in residue interactions could reduce the binding affinity of OTV to NA, resulting in drug resistant influenza viruses. Using dRIN analysis, we succeeded in understanding the characteristic changes in residue interactions due to H274Y mutation, which can elucidate the molecular mechanism of reduction in OTV binding affinity to influenza NA. Finally, the dRIN analysis used in this study can be widely applied to various systems such as individual proteins, protein-ligand complexes, and protein-protein complexes, to characterize the dynamic aspects of the interactions.

scholarly journals Ketoamide Resistance and Hepatitis C Virus Fitness in Val55 Variants of the NS3 Serine Protease

2012 ◽  
Vol 56 (4) ◽  
pp. 1907-1915 ◽  
Author(s):  
Christoph Welsch ◽  
Sabine Schweizer ◽  
Tetsuro Shimakami ◽  
Francisco S. Domingues ◽  
Seungtaek Kim ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Hepatitis C Virus ◽  
Hepatitis C ◽  
Molecular Mechanisms ◽  
Interaction Network ◽  
Rna Replication ◽  
Viral Fitness ◽  
Dynamics Simulation ◽  
Structural Flexibility ◽  
Residue Interaction

ABSTRACTDrug-resistant viral variants are a major issue in the use of direct-acting antiviral agents in chronic hepatitis C. Ketoamides are potent inhibitors of the NS3 protease, with V55A identified as mutation associated with resistance to boceprevir. Underlying molecular mechanisms are only partially understood. We applied a comprehensive sequence analysis to characterize the natural variability at Val55 within dominant worldwide patient strains. A residue-interaction network and molecular dynamics simulation were applied to identify mechanisms for ketoamide resistance and viral fitness in Val55 variants. An infectious H77S.3 cell culture system was used for variant phenotype characterization. We measured antiviral 50% effective concentration (EC50) and fold changes, as well as RNA replication and infectious virus yields from viral RNAs containing variants. Val55 was found highly conserved throughout all hepatitis C virus (HCV) genotypes. The conservative V55A and V55I variants were identified from HCV genotype 1a strains with no variants in genotype 1b. Topology measures from a residue-interaction network of the protease structure suggest a potential Val55 key role for modulation of molecular changes in the protease ligand-binding site. Molecular dynamics showed variants with constricted binding pockets and a loss of H-bonded interactions upon boceprevir binding to the variant proteases. These effects might explain low-level boceprevir resistance in the V55A variant, as well as the Val55 variant, reduced RNA replication capacity. Higher structural flexibility was found in the wild-type protease, whereas variants showed lower flexibility. Reduced structural flexibility could impact the Val55 variant's ability to adapt for NS3 domain-domain interaction and might explain the virus yield drop observed in variant strains.

scholarly journals Theoretical insights into the molecular mechanism of I117V mutation in neuraminidase mediated reduction of oseltamivir drug susceptibility in A/H5N1 influenza virus

2021 ◽  
Vol 3 ◽  
pp. e19
Author(s):  
Mohini Yadav ◽  
Manabu Igarashi ◽  
Norifumi Yamamoto
Keyword(s):  
Binding Site ◽  
Binding Affinity ◽  
Conformational Changes ◽  
Md Simulations ◽  
Side Chain ◽  
Electrostatic Repulsion ◽  
H5n1 Influenza Virus ◽  
Mutation Site ◽  
H5n1 Influenza ◽  
Β Sheet

The substitution of Ile to Val at residue 117 (I117V) of neuraminidase (NA) reduces the susceptibility of the A/H5N1 influenza virus to oseltamivir (OTV). However, the molecular mechanism by which the I117V mutation affects the intermolecular interactions between NA and OTV has not been fully elucidated. In this study, we performed molecular dynamics (MD) simulations to analyze the characteristic conformational changes that contribute to the reduced binding affinity of NA to OTV after the I117V mutation. The results of MD simulations revealed that after the I117V mutation in NA, the changes in the secondary structure around the mutation site had a noticeable effect on the residue interactions in the OTV-binding site. In the case of the WT NA-OTV complex, the positively charged side chain of R118, located in the β-sheet region, frequently interacted with the negatively charged side chain of E119, which is an amino acid residue in the OTV-binding site. This can reduce the electrostatic repulsion of E119 toward D151, which is also a negatively charged residue in the OTV-binding site, so that both E119 and D151 simultaneously form hydrogen bonds with OTV more frequently, which greatly contributes to the binding affinity of NA to OTV. After the I117V mutation in NA, the side chain of R118 interacted with the side chain of E119 less frequently, likely because of the decreased tendency of R118 to form a β-sheet structure. As a result, the electrostatic repulsion of E119 toward D151 is greater than that of the WT case, making it difficult for both E119 and D151 to simultaneously form hydrogen bonds with OTV, which in turn reduces the binding affinity of NA to OTV. Hence, after the I117V mutation in NA, influenza viruses are less susceptible to OTV because of conformational changes in residues of R118, E119, and D151 around the mutation site and in the binding site.

scholarly journals Design of a New α-1-C-Alkyl-DAB Derivative Acting as a Pharmacological Chaperone for β-Glucocerebrosidase Using Ligand Docking and Molecular Dynamics Simulation

Molecules ◽  
2018 ◽  
Vol 23 (10) ◽  
pp. 2683 ◽  
Author(s):  
Izumi Nakagome ◽  
Atsushi Kato ◽  
Noriyuki Yamaotsu ◽  
Tomoki Yoshida ◽  
Shin-ichiro Ozawa ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Binding Site ◽  
Binding Affinity ◽  
Gaucher Disease ◽  
Point Mutations ◽  
Dynamics Simulation ◽  
Ligand Docking ◽  
Pharmacological Chaperone ◽  
Pharmacological Chaperones ◽  
Dynamics Simulations

Some point mutations in β-glucocerebrosidase cause either improper folding or instability of this protein, resulting in Gaucher disease. Pharmacological chaperones bind to the mutant enzyme and stabilize this enzyme; thus, pharmacological chaperone therapy was proposed as a potential treatment for Gaucher disease. The binding affinities of α-1-C-alkyl 1,4-dideoxy-1,4-imino-d-arabinitol (DAB) derivatives, which act as pharmacological chaperones for β-glucocerebrosidase, abruptly increased upon elongation of their alkyl chain. In this study, the primary causes of such an increase in binding affinity were analyzed using protein–ligand docking and molecular dynamics simulations. We found that the activity cliff between α-1-C-heptyl-DAB and α-1-C-octyl-DAB was due to the shape and size of the hydrophobic binding site accommodating the alkyl chains, and that the interaction with this hydrophobic site controlled the binding affinity of the ligands well. Furthermore, based on the aromatic/hydrophobic properties of the binding site, a 7-(tetralin-2-yl)-heptyl-DAB compound was designed and synthesized. This compound had significantly enhanced activity. The design strategy in consideration of aromatic interactions in the hydrophobic pocket was useful for generating effective pharmacological chaperones for the treatment of Gaucher disease.

scholarly journals MitImpact 3: modeling the residue interaction network of the Respiratory Chain subunits

2020 ◽  
Vol 49 (D1) ◽  
pp. D1282-D1288
Author(s):  
Stefano Castellana ◽  
Tommaso Biagini ◽  
Francesco Petrizzelli ◽  
Luca Parca ◽  
Noemi Panzironi ◽  
...  
Keyword(s):  
Respiratory Chain ◽  
Interaction Network ◽  
Complex Trait ◽  
Eukaryotic Cell ◽  
Acid Sites ◽  
Dynamics Simulation ◽  
Functional Annotations ◽  
Residue Interaction ◽  
New Information ◽  
Human Mitochondrial Genome

Abstract Numerous lines of evidence have shown that the interaction between the nuclear and mitochondrial genomes ensures the efficient functioning of the OXPHOS complexes, with substantial implications in bioenergetics, adaptation, and disease. Their interaction is a fascinating and complex trait of the eukaryotic cell that MitImpact explores with its third major release. MitImpact expands its collection of genomic, clinical, and functional annotations of all non-synonymous substitutions of the human mitochondrial genome with new information on putative Compensated Pathogenic Deviations and co-varying amino acid sites of the Respiratory Chain subunits. It further provides evidence of energetic and structural residue compensation by techniques of molecular dynamics simulation. MitImpact is freely accessible at http://mitimpact.css-mendel.it.

scholarly journals In Silico Designing and Analysis of Inhibitors against Target Protein Identified through Host-Pathogen Protein Interactions in Malaria

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Monika Samant ◽  
Nidhi Chadha ◽  
Anjani K. Tiwari ◽  
Yasha Hasija
Keyword(s):  
Plasmodium Falciparum ◽  
Binding Affinity ◽  
Protein Interactions ◽  
Molecular Mechanisms ◽  
Biological Significance ◽  
Interaction Network ◽  
Protein Protein Interaction ◽  
Pathogen Protein ◽  
Complete Protein

Malaria, a life-threatening blood disease, has been a major concern in the field of healthcare. One of the severe forms of malaria is caused by the parasite Plasmodium falciparum which is initiated through protein interactions of pathogen with the host proteins. It is essential to analyse the protein-protein interactions among the host and pathogen for better understanding of the process and characterizing specific molecular mechanisms involved in pathogen persistence and survival. In this study, a complete protein-protein interaction network of human host and Plasmodium falciparum has been generated by integration of the experimental data and computationally predicting interactions using the interolog method. The interacting proteins were filtered according to their biological significance and functional roles. α-tubulin was identified as a potential protein target and inhibitors were designed against it by modification of amiprophos methyl. Docking and binding affinity analysis showed two modified inhibitors exhibiting better docking scores of −10.5 kcal/mol and −10.43 kcal/mol and an improved binding affinity of −83.80 kJ/mol and −98.16 kJ/mol with the target. These inhibitors can further be tested and validated in vivo for their properties as an antimalarial drug.

scholarly journals Prediction of interface residue based on features of residue interaction network extracted using shapley value centrality measure

2018 ◽  
Author(s):  
Babita Pandey ◽  
Sarneet K Chawla ◽  
Devendra K Pandey
Keyword(s):  
Game Theory ◽  
Shapley Value ◽  
Molecular Mechanisms ◽  
Spatial Relationship ◽  
Interaction Network ◽  
Interface Residue ◽  
Protein Protein Interaction ◽  
Residue Interaction ◽  
Interface Prediction

Protein-Protein interaction plays an important role in the life processes. Molecular mechanisms of the related processes can be better understood with the help of interface prediction. In this work, we use game theory concept of Shapley value to analyse the spatial relationship between residues in residue interaction network. Four features are extracted from network using shapley value and given as input to ACO for optimization. Our experiment shows that optimized feature set, significantly improves the result of normal classifier and accuracy from 80% to 85%. These findings are useful for identifying protein-like complex networks. The presented results suggest that the feature selection by Shapley value and optimization by ACO improves the classification of protein structure at great extent less computational complexity.

Molecular Docking and Dynamics Simulation of Natural Phenolic Compounds with GSK-3β: A Putative Target to Combat Mortality in Patients with COVID-19

2021 ◽  
Vol 14 ◽  
Author(s):  
Zahra Khamverdi ◽  
Zeinab Mohamadi ◽  
Amir Taherkhani
Keyword(s):  
Amino Acids ◽  
Molecular Docking ◽  
Phenolic Compounds ◽  
Binding Affinity ◽  
Active Sites ◽  
Interaction Network ◽  
Md Simulations ◽  
Dynamics Simulation ◽  
Discovery Studio ◽  
Gsk 3Β

Objective: In this study, molecular docking analysis was performed to evaluate the binding affinity of 52 plant-based phenolics with the GSK-3β active sites. Moreover, Molecular Dynamics (MD) simulation was conducted to investigate the stability of interactions between the topranked phenolics and residues within the GSK-3β active sites. Methods: Molecular docking and MD simulations were performed using AutoDock and Discovery Studio Client software, respectively. Thereafter, pharmacokinetic and toxicological properties of top inhibitors were predicted using bioinformatics web tools. This study aimed to identify the most effective amino acids involved in the inhibition of GSK-3β based on the most stabilizing interactions between the residues and compounds, and also by considering the degree centrality in the ligand-amino acid interaction network for GSK-3β. Results: It was observed that procyanidin and amentoflavone could bind to the GSK-3β active sites at the picomolar (pM) scale as well as the binding affinity of ∆G binding < -13 kcal/mol, while the inhibition constant for theaflavin 3’-gallate, procyanidin B4, and rutin was calculated at the nanomolar (nM) scale, suggesting that these phenolic compounds can be considered as potential effective GSK-3β inhibitors. Furthermore, Val70, Ala83, Val135, and Tyr134 were found to be the most important amino acids involved in the inhibition of GSK-3β. Conclusion: The results of the current study may be useful in the prevention of several human disorders, including COVID-19, cancers, Alzheimer’s disease, diabetes mellitus, and cardiovascular diseases. However, wet-lab experiments need to be performed in the future.

scholarly journals Prediction of interface residue based on features of residue interaction network extracted using shapley value centrality measure

2018 ◽  
Author(s):  
Babita Pandey ◽  
Sarneet K Chawla ◽  
Devendra K Pandey
Keyword(s):  
Game Theory ◽  
Shapley Value ◽  
Molecular Mechanisms ◽  
Spatial Relationship ◽  
Interaction Network ◽  
Interface Residue ◽  
Protein Protein Interaction ◽  
Residue Interaction ◽  
Interface Prediction

Protein-Protein interaction plays an important role in the life processes. Molecular mechanisms of the related processes can be better understood with the help of interface prediction. In this work, we use game theory concept of Shapley value to analyse the spatial relationship between residues in residue interaction network. Four features are extracted from network using shapley value and given as input to ACO for optimization. Our experiment shows that optimized feature set, significantly improves the result of normal classifier and accuracy from 80% to 85%. These findings are useful for identifying protein-like complex networks. The presented results suggest that the feature selection by Shapley value and optimization by ACO improves the classification of protein structure at great extent less computational complexity.

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