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 (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.

In Silico Investigation of the New UK (B.1.1.7) and South African (501Y.V2) SARS-CoV-2 Variants with a Focus at the ACE2–Spike RBD Interface

“…Possible effects on the folding free energy (∆∆G) due to the amino acid changes were investigated on ten representative 3D structures of the Spike RBD models after simulations performed in the absence of the ACE2 structure. These computations were performed on each 3D structure using DUET (which combines the results of two different computational approaches) [ 47 ] and DynaMut2 [ 48 ], an approach that can assess changes in stability upon single and multiple missense mutations and integrate Normal Mode Analysis during the computations. Electrostatic properties were computed using several routines implemented in the Chimera package.…”

Distinguishing between PTEN clinical phenotypes through mutation analysis

“…As a measure of residue environment geometry and physicochemical properties, graph-based signatures were also calculated [66] . We have previously shown that graph-based signatures are a powerful approach to represent a protein 3D structure in order to predict the effects of mutations on protein stability [66] , [67] , [68] , [69] , [70] and interactions [66] , [71] , [72] , [73] , [74] , [75] , [76] , [77] .…”

“…Due to the relatively small number of data points used for training, cross validation was carried out using the StratifiedKfold function within scikit-learn (v.0.23.2) [94] , which ensured that each fold retained class proportions representative of the whole dataset, and that the final metrics were representative of the whole data. A bottom-up greedy feature selection approach was employed to minimize model complexity, as prevoiusly described [67] , [68] , [71] , [72] , [77] . Best performing models were selected based on the Matthew's Correlation Coefficient (MCC), which is a well-established and balanced metric not affected by class sizes.…”

Theoretical causes of the Brazilian P.1 and P.2 lineages of the SARS-CoV-2 virus through molecular dynamics

“…Through the tool DynaMut2 (Rodrigues, Pires, & Ascher, 2021) it was possible to understand the impact of mutations on chemical bonds (see Figure 3). The K417N mutation belonging to the P.2 lineage is considered destabilizing as a result of the loss of 1 (one) Hydrogen bond, 3 (three) hydrophobic contacts in addition to 2 (two) polar interactions at the ACE2-RBD interface.…”