Advances in bioinformatics tools for genomic variant analysis, protein structure prediction, and molecular simulations

Abstract

Advances in bioinformatics resources and tools have greatly enhanced the capabilities of protein structure prediction, molecular dynamics simulations, pathway analysis, and genomic variant annotation. These tools are essential for figuring out drug targets, comprehending disease mechanisms, and assisting with customized treatments. In this study, we point out the key bioinformatics tools involved in the prediction of pathogenicity, the annotation of variations, the modelling of protein structures, the simulation of molecular structures, and the analysis of pathways. Clinical databases and population rates are employed to classify genetic variants in variant annotation systems. Pathogenicity predictors rely on structure context of the protein, evolutionary conservation, or machine learning to predict the functional effect. Near-experimental accuracy is now achieved in the modeling of 3D protein structures using deep learning, and the ability of such platforms to reveal the effects of mutations on protein folding and function is impressive. The molecular dynamics simulation software packages GROMACS, AMBER, CHARMM, and NAMD are used for investigating conformational changes at the atomic level or drug interactions. Pathway resources like KEGG, Reactome, STRING, and BioCyc provide information on signaling cascades, metabolic pathways and networks of protein-protein interactions. These methods can be used together to provide a complete approach for advancing biomedical research, which is enhanced with visualization programs such as PyMOL, ChimeraX, VMD, Jmol, and Mol*. Bioinformatics software and computational methods were comprehensively evaluated in this review, by using ANNOVAR, VEP and SIFT, PolyPhen-2, CADD, REVEL, MutationTaster, RoseTTAFold, I-TASSER, AlphaFold, GROMACS, AMBER, CHARMM, NAMD, KEGG, Reactome, STRING, and BioCyc for variant annotation, pathogenicity