In silico characterization of mutations circulating in SARS-CoV-2 structural proteins

Area of Science:

• Virology
• Computational Biology
• Genomics

Background:

• The SARS-CoV-2 pandemic has caused millions of deaths globally.
• Viral genome sequencing provides crucial insights into viral dynamics and evolution.
• Understanding mutations is key to developing effective countermeasures.

Purpose of the Study:

• To identify and characterize mutations in SARS-CoV-2 genomes.
• To investigate the functional and structural impact of identified mutations on viral proteins.
• To utilize computational approaches for predicting mutation effects.

Main Methods:

• Utilized the Meta-CATS algorithm for comparative analysis of 829 SARS-CoV-2 genomes.
• Employed computational tools including Normal Mode Analysis (NMA), C-α Discrete Molecular Dynamics (DMD), and all-atom Molecular Dynamics (MD) simulations.
• Used PredictSNP predictor to assess the nature (neutral or deleterious) of mutations.

Main Results:

• Identified sixty-one mutations across SARS-CoV-2 genomes, with a concentration in nsp3, RdRp, and Nucleocapsid (N) genes.
• Four mutations (L37H in E, R203K and P344S in N, D614G in S) were predicted as neutral, while three (P13L, S197L, G204R in N) were predicted as deleterious.
• Molecular dynamics simulations indicated that some mutations (P13L, S197L, R203K in N) have stabilizing effects, while others destabilize the mutant proteins.

Conclusions:

• Significant SARS-CoV-2 mutations were identified and computationally characterized.
• Specific mutations can impact the stability and functionality of crucial viral structural proteins (E, N, S).
• This study provides a foundation for understanding mutation-driven viral evolution and pathogenicity.