Unveiling Mutation Effects on the Structural Dynamics of the Main Protease from SARS-CoV-2 with Hybrid Simulation Methods

Philot, Eric Allison; Gasparini, P.; Mattos, Jean Carlos de; Magro, Ângelo J.; Torres-Bonfim, Enndrew; Quiroz, R.C.N.; Klioussof, André; Pérahia, David; Scott, Ana Lígia

doi:10.1101/2021.07.17.452787

Cited by 2 publications

(7 citation statements)

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“…P108S: This mutant is reported only in the B.1.1.7 variant. The substitution showed a significant variation in potential energy ( Philot et al., 2021 ); nevertheless, the SDM and Dynamut2 scores suggest no substantial change in the structure.…”

Section: Discussionmentioning

confidence: 95%

“…T45I: This mutation in the domain I region of SARS-CoV-2 M pro is reported in variants B.1.1.7, B.1.351, P.1, B1.617, B.1.429 + B.1.427 ( Philot et al., 2021 ). It presented a polar to non-polar substitution due to which there is a reduction in its hydrogen bonding potential.…”

Section: Discussionmentioning

confidence: 95%

“…K90R: This mutant lies in the domain-I region; it has shown relevant modifications in its potential energy concerning the WT SARS-CoV-2 M pro ( Philot et al., 2021 ). SDM has indicated this substitution as destabilizing, and the Dynamut2 score suggests the substitution will increase the flexibility of the protein.…”

Section: Discussionmentioning

confidence: 99%

“…K90R mutant is reported in the variants B.1.1.7, B.1.351, P.1, B1.617, which are mainly are our variant of concern. Furthermore, a lower energy configuration and a more extensive dimeric interface have resulted from the mutant K90R ( Philot et al., 2021 ).…”

Section: Discussionmentioning

confidence: 99%

“…P99L: It is reported in the B.1.1.7 variant only. The Dynamut2 score suggests the mutant will induce stability to the structure, whereas SDM shows destabilizing; a higher probability of the formation of dimeric interfaces was reported than WT SARS-CoV-2 M pro ( Sheik Amamuddy et al., 2020 ; Philot et al., 2021 ).…”

Section: Discussionmentioning

confidence: 99%

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Genomic Variations in the Structural Proteins of SARS-CoV-2 and Their Deleterious Impact on Pathogenesis: A Comparative Genomics Approach

Mohammad

Choudhury

Habib

et al. 2021

Front. Cell. Infect. Microbiol.

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A continual rise in severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection causing coronavirus disease (COVID-19) has become a global threat. The main problem comes when SARS-CoV-2 gets mutated with the rising infection and becomes more lethal for humankind than ever. Mutations in the structural proteins of SARS-CoV-2, i.e., the spike surface glycoprotein (S), envelope (E), membrane (M) and nucleocapsid (N), and replication machinery enzymes, i.e., main protease (Mpro) and RNA-dependent RNA polymerase (RdRp) creating more complexities towards pathogenesis and the available COVID-19 therapeutic strategies. This study analyzes how a minimal variation in these enzymes, especially in S protein at the genomic/proteomic level, affects pathogenesis. The structural variations are discussed in light of the failure of small molecule development in COVID-19 therapeutic strategies. We have performed in-depth sequence- and structure-based analyses of these proteins to get deeper insights into the mechanism of pathogenesis, structure-function relationships, and development of modern therapeutic approaches. Structural and functional consequences of the selected mutations on these proteins and their association with SARS-CoV-2 virulency and human health are discussed in detail in the light of our comparative genomics analysis.

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Section: Discussionmentioning

confidence: 95%

Section: Discussionmentioning

confidence: 95%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Genomic Variations in the Structural Proteins of SARS-CoV-2 and Their Deleterious Impact on Pathogenesis: A Comparative Genomics Approach

Mohammad

Choudhury

Habib

et al. 2021

Front. Cell. Infect. Microbiol.

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Evaluation of Mutual Information and Feature Selection for SARS-CoV-2 Respiratory Infection

et al. 2023

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This study aims to develop a predictive model for SARS-CoV-2 using machine-learning techniques and to explore various feature selection methods to enhance the accuracy of predictions. A precise forecast of the SARS-CoV-2 respiratory infections spread can help with efficient planning and resource allocation. The proposed model utilizes stochastic regression to capture the virus transmission’s stochastic nature, considering data uncertainties. Feature selection techniques are employed to identify the most relevant and informative features contributing to prediction accuracy. Furthermore, the study explores the use of neighbor embedding and Sammon mapping algorithms to visualize high-dimensional SARS-CoV-2 respiratory infection data in a lower-dimensional space, enabling better interpretation and understanding of the underlying patterns. The application of machine-learning techniques for predicting SARS-CoV-2 respiratory infections, the use of statistical measures in healthcare, including confirmed cases, deaths, and recoveries, and an analysis of country-wise dynamics of the pandemic using machine-learning models are used. Our analysis involves the performance of various algorithms, including neural networks (NN), decision trees (DT), random forests (RF), the Adam optimizer (AD), hyperparameters (HP), stochastic regression (SR), neighbor embedding (NE), and Sammon mapping (SM). A pre-processed and feature-extracted SARS-CoV-2 respiratory infection dataset is combined with ADHPSRNESM to form a new orchestration in the proposed model for a perfect prediction to increase the precision of accuracy. The findings of this research can contribute to public health efforts by enabling policymakers and healthcare professionals to make informed decisions based on accurate predictions, ultimately aiding in managing and controlling the SARS-CoV-2 pandemic.

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Unveiling Mutation Effects on the Structural Dynamics of the Main Protease from SARS-CoV-2 with Hybrid Simulation Methods

Cited by 2 publications

References 59 publications

Genomic Variations in the Structural Proteins of SARS-CoV-2 and Their Deleterious Impact on Pathogenesis: A Comparative Genomics Approach

Genomic Variations in the Structural Proteins of SARS-CoV-2 and Their Deleterious Impact on Pathogenesis: A Comparative Genomics Approach

Evaluation of Mutual Information and Feature Selection for SARS-CoV-2 Respiratory Infection

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