DynaMut2: Assessing changes in stability and flexibility upon single and multiple point missense mutations

Abstract

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 (ΔT_m ). 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.

Keywords: dynamics; graph-based signatures; missense mutations; stability changes.

FIGURE 1

DynaMut2 workflow. The methodology for this work can be summarized into four steps: (1) data collection and curation of single and multiple mutations, (2) feature engineering to model the effects of mutations, (3) supervised machine learning, and (4) the predicted effects on stability and dynamics

FIGURE 2

Predictive performance of DynaMut2 on 10‐fold cross‐validation (a) and non‐redundant test sets (b) for single point mutations. 10% of outliers are shown as pink crosses

FIGURE 3

Predictive performance of DynaMut2 on 10‐fold cross‐validation (a) and non‐redundant test sets (b) for multiple point mutations. 10% of outliers are shown as pink crosses

FIGURE 4

DynaMut2 results page. The figure depicts the prediction results page for single‐point mutations. The predicted effect of a mutation in stability and dynamics is given as the variation in Gibbs Free Energy (in Kcal/mol) (1), together with complementary information about the mutation provided (2). Users can inspect the wild‐type residue environment via an interactive viewer (3), which also allows the visualization of non‐covalent interactions established by the mutated residue