DynaMut2: Assessing changes in stability and flexibility upon single and multiple point missense mutations
By Carlos H. M. Rodrigues, Douglas E. V. Pires, David B. Ascher. Protein Science, 2020
556 citations recorded by the source corpus.
Predicting the effect of missense variations on protein stability and dynamics is important for understanding their role in diseases, and the link between protein structure and function. Approaches to estimate these changes have been proposed, but most only consider single‐point missense variants and a static state of the protein, with those that incorporate dynamics are computationally expensive. Here we present DynaMut2, a web server that combines Normal Mode Analysis (NMA) methods to capture protein motion and our graph‐based signatures to represent the wildtype environment to investigate the effects of single and multiple point mutations on protein stability and dynamics. DynaMut2 was able to accurately predict the effects of missense mutations on protein stability, achieving Pearson's correlation of up to 0.72 (RMSE: 1.02 kcal/mol) on a single point and 0.64 (RMSE: 1.80 kcal/mol) on multiple‐point missense mutations across 10‐fold cross‐validation and independent blind tests. For single‐point mutations, DynaMut2 achieved comparable performance with other methods when predicting variations in Gibbs Free Energy (ΔΔG) and in melting temperature (ΔTm). We anticipate our tool to be a valuable suite for the study of protein flexibility analysis and the study of the role of variants in disease. DynaMut2 is freely available as a web server and API at http://biosig.unimelb.edu.au/dynamut2.
Subfields: Materials Chemistry, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/a2f60e30baff15f69a740540bd888789d5da6928
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In Materials Chemistry
In Molecular Biology
- Douglas E. V. Pires
- David B. Ascher
- Thanh-Binh Nguyen
- Qisheng Pan
- Carlos H. M. Rodrigues
- Kevin Kaichuang Yang
- Zachary Wu
- Frances H. Arnold
Citations in the corpus
- Systematic evaluation of computational tools to predict the effects of mutations on protein stability in the absence of experimental structures (2022, background): “DynaMut2 [50] is an optimized version that also considers the global environment of the wild-type residues to estimate the conformational change upon residue substitution and train supervised learning methods.”