DynaMut: predicting the impact of mutations on protein conformation, flexibility and stability

Clicks: 1
ID: 299405
2018
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal

Ranked #1,015 of 1,214 articles by views in Nucleic Acids Research

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 1,214 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
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/.
Reference Key
openalex_W2801491392 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Carlos H. M. Rodrigues, Douglas E. V. Pires, David B. Ascher
Journal Nucleic Acids Research
Year 2018
DOI
10.1093/nar/gky300
URL
https://academic.oup.com/nar/article-pdf/46/W1/W350/25110500/gky300.pdf
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.