Abstract
Motivation: Mutations play fundamental roles in evolution by introducing diversity into genomes. Missense mutations in structural genes may become either selectively advantageous or disadvantageous to the organism by affecting protein stability and/or interfering with interactions between partners. Thus, the ability to predict the impact of mutations on protein stability and interactions is of significant value, particularly in understanding the effects of Mendelian and somatic mutations on the progression of disease. Here, we propose a novel approach to the study of missense mutations, called mCSM, which relies on graph-based signatures. These encode distance patterns between atoms and are used to represent the protein residue environment and to train predictive models. To understand the roles of mutations in disease, we have evaluated their impacts not only on protein stability but also on protein–protein and protein–nucleic acid interactions.Results: We show that mCSM performs as well as or better than other methods that are used widely. The mCSM signatures were successfully used in different tasks demonstrating that the impact of a mutation can be correlated with the atomic-distance patterns surrounding an amino acid residue. We showed that mCSM can predict stability changes of a wide range of mutations occurring in the tumour suppressor protein p53, demonstrating the applicability of the proposed method in a challenging disease scenario.Availability and implementation: A web server is available at http://structure.bioc.cam.ac.uk/mcsm.Contact: dpires@dcc.ufmg.br; tom@cryst.bioc.cam.ac.ukSupplementary information: Supplementary data are available at Bioinformatics online.
Highlights
1.1 Background Mutations play fundamental roles in evolution by introducing diversity into genomes, most often through single nucleotide polymorphisms (SNPs)
We show that the mCSM signatures can be used successfully to tackle different tasks related to the prediction of the impacts of mutations in proteins
We have conducted a series of comparative experiments that indicate that mCSM performs as well as or better than several other widely used methods. mCSM is able to predict the direction of the change in stability of proteins and affinity of protein–protein and protein–DNA complexes and the actual numerical experimental value, with correlation coefficients up to 0.824 for a large data set of mutations
Summary
1.1 Background Mutations play fundamental roles in evolution by introducing diversity into genomes, most often through single nucleotide polymorphisms (SNPs). The advent of databases with experimental thermodynamic parameters for both wild-type and mutant proteins such as ProTherm and ProNIT (protein-nucleic acid) (Kumar et al, 2006) and more recently the SKEMPI (Moal and Fernandez-Recio, 2012), which describes protein–protein complexes, has been helpful to the study of mutations on a larger scale. These provide an experimental basis for novel in silico paradigms, models and algorithms to study more extensively missense mutations and their impacts on protein stability and function
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