2019
DOI: 10.1002/pro.3774
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Prediction of impacts of mutations on protein structure and interactions: SDM, a statistical approach, and mCSM, using machine learning

Abstract: Next-generation sequencing methods have not only allowed an understanding of genome sequence variation during the evolution of organisms but have also provided invaluable information about genetic variants in inherited disease and the emergence of resistance to drugs in cancers and infectious disease. A challenge is to distinguish mutations that are drivers of disease or drug resistance, from passengers that are neutral or even selectively advantageous to the organism.This requires an understanding of impacts … Show more

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Cited by 70 publications
(42 citation statements)
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“…A predicted tertiary structure PDB file was used as input in SDM online tool to predict variant effect on stability and relative solvent accessibility 56 . SDM was used to predict stability as it is the least biased pipeline 57 . 3D visualization was obtained using chimera 1.13.1 software package 58 .…”
Section: Methodsmentioning
confidence: 99%
“…A predicted tertiary structure PDB file was used as input in SDM online tool to predict variant effect on stability and relative solvent accessibility 56 . SDM was used to predict stability as it is the least biased pipeline 57 . 3D visualization was obtained using chimera 1.13.1 software package 58 .…”
Section: Methodsmentioning
confidence: 99%
“…Therefore, besides the gene expression and complementation experiments to understand the VISA molecular mechanisms, the importance of knowledge about protein structure mapping of mutations and predicting of the impact of the mutation on the disruption of protein function using molecular computational approaches can provide new insight into the mechanisms underlying VISA development and help to design novel therapeutic strategies 18 .…”
Section: Introductionmentioning
confidence: 99%
“…These proteins were selected owing to their curial role in host-viral interactome. Structural effects of SARS-CoV-2 mutations were studied using variant analysis module of COVID-3D suite [9, 10]. Images were created using PyMol [11].…”
Section: Figurementioning
confidence: 99%