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DynaMut: predicting the impact of mutations on protein conformation, flexibility and stability
Author(s) -
Carlos H. M. Rodrigues,
Douglas E. V. Pires,
David B. Ascher
Publication year - 2018
Publication title -
nucleic acids research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 9.008
H-Index - 537
eISSN - 1362-4954
pISSN - 0305-1048
DOI - 10.1093/nar/gky300
Subject(s) - biology , web server , protein structure , flexibility (engineering) , molecular dynamics , computational biology , stability (learning theory) , visualization , mutation , protein dynamics , protein function , biological system , computer science , bioinformatics , the internet , genetics , data mining , physics , machine learning , mathematics , gene , statistics , biochemistry , quantum mechanics , world wide web
Proteins are highly dynamic molecules, whose function is intrinsically linked to their molecular motions. Despite the pivotal role of protein dynamics, their computational simulation cost has led to most structure-based approaches for assessing the impact of mutations on protein structure and function relying upon static structures. Here we present DynaMut, a web server implementing two distinct, well established normal mode approaches, which can be used to analyze and visualize protein dynamics by sampling conformations and assess the impact of mutations on protein dynamics and stability resulting from vibrational entropy changes. DynaMut integrates our graph-based signatures along with normal mode dynamics to generate a consensus prediction of the impact of a mutation on protein stability. We demonstrate our approach outperforms alternative approaches to predict the effects of mutations on protein stability and flexibility (P-value < 0.001), achieving a correlation of up to 0.70 on blind tests. DynaMut also provides a comprehensive suite for protein motion and flexibility analysis and visualization via a freely available, user friendly web server at http://biosig.unimelb.edu.au/dynamut/.

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