PoPMuSiC 2.1: a web server for the estimation of protein stability changes upon mutation and sequence optimality

By Yves Dehouck, Jean Marc Kwasigroch, Dimitri Gilis, Marianne Rooman. 2011

527 citations recorded by the source corpus.

BackgroundThe rational design of modified proteins with controlled stability is of extreme importance in a whole range of applications, notably in the biotechnological and environmental areas, where proteins are used for their catalytic or other functional activities. Future breakthroughs in medical research may also be expected from an improved understanding of the effect of naturally occurring disease-causing mutations on the molecular level.ResultsPoPMuSiC-2.1 is a web server that predicts the thermodynamic stability changes caused by single site mutations in proteins, using a linear combination of statistical potentials whose coefficients depend on the solvent accessibility of the mutated residue. PoPMuSiC presents good prediction performances (correlation coefficient of 0.8 between predicted and measured stability changes, in cross validation, after exclusion of 10% outliers). It is moreover very fast, allowing the prediction of the stability changes resulting from all possible mutations in a medium size protein in less than a minute. This unique functionality is user-friendly implemented in PoPMuSiC and is particularly easy to exploit. Another new functionality of our server concerns the estimation of the optimality of each amino acid in the sequence, with respect to the stability of the structure. It may be used to detect structural weaknesses, i.e. clusters of non-optimal residues, which represent particularly interesting sites for introducing targeted mutations. This sequence optimality data is also expected to have significant implications in the prediction and the analysis of particular structural or functional protein regions. To illustrate the interest of this new functionality, we apply it to a dataset of known catalytic sites, and show that a much larger than average concentration of structural weaknesses is detected, quantifying how these sites have been optimized for function rather than stability.ConclusionThe freely available PoPMuSiC-2.1 web server is highly useful for identifying very rapidly a list of possibly relevant mutations with the desired stability properties, on which subsequent experimental studies can be focused. It can also be used to detect sequence regions corresponding to structural weaknesses, which could be functionally important or structurally delicate regions, with obvious applications in rational protein design.

Subfields: Materials Chemistry, Molecular Biology

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Citations in the corpus

Coevolutionary Landscape Inference and the Context-Dependence of Mutations in Beta-Lactamase TEM-1 (2015, background): “[28] Yves Dehouck, Jean M Kwasigroch, Dimitri Gilis, and Marianne Rooman.”
Predicting changes in protein thermodynamic stability upon point mutation with deep 3D convolutional neural networks (2020, methodology): “These 85 methods either rely on explicit biophysical modeling of amino acid interactions coupled with 86 conformational sampling of protein structures (7-11) or apply machine/statistical learning to 87 extract patterns from various types of amino acid sequence, evolutionary, and/or protein 88 struct”
Capturing the mutational landscape of the beta-lactamase TEM-1 (2013, methodology): “Setting ΔGTEM-1 = −1.73 kcal/mol as estimated before, we found that using the in vitro estimated stabilizing effect of M182T mutation (ΔΔGM182T = −2.7 kcal/mol) (21), the variance explained by PopMusic predictions, through Eq.”
BeAtMuSiC: prediction of changes in protein–protein binding affinity on mutations (2013, methodology): “For instance, the method that we previously developed for the latter purpose, on the basis of a model with the same level of coarse-graining, yielded a Pearson correlation coefﬁcient between prediction and experiments of 0.63 on a data set of 2648 mutations, and 0.79 on 90% of the data set (42,50).”
STRUM: structure-based prediction of protein stability changes upon single-point mutation (2016, methodology): “…native structure of the wild-type protein and had the DD G calculated by a linear combination of 13 knowledge-based terms on the protein structure, which resulted in a correlation of 0.8 between predicted and measured stability changes after exclusion of 10% outliers (Dehouck et al. , 2009, 2011).”
A Comprehensive, High-Resolution Map of a Gene’s Fitness Landscape (2014, background): “be/popmusic (last accessed February 17, 2014) (Dehouck et al. 2011) using 1XPB.”