Abstract
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/.
Highlights
Proteins are dynamic macromolecules, whose function is intricately linked to their biological motions [1,2]
We have shown previously that drug resistant and genetic disease mutations can both act through changes in protein conformational equilibria and dynamics [3,4,5,6,7]
The computational cost of dynamics simulation has led to most structure-based approaches for assessing mutations effects on protein structure and function relying upon static structures
Summary
Proteins are dynamic macromolecules, whose function is intricately linked to their biological motions [1,2]. We introduce DynaMut, a web server that introduces the dynamics component to mutation analysis This is achieved by implementing and integrating well established normal mode approaches with our graph-based signatures in a consensus predictor for protein stability changes upon mutation, which we show optimizes overall prediction performance. DynaMut enables rapid analysis of the impact of mutations on a protein’s dynamics and stability resulting from vibrational entropy changes Integration of these two different approaches with other well-established methods and characteristics of the wildtype residue environment into a consensus prediction enables DynaMut to provide an accurate assessment of the impact of a mutation on protein stability, and provide a comprehensive suite for protein motion and flexibility analysis and visualization via an easy-to-use web interface (http: //biosig.unimelb.edu.au/dynamut/).
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