mCSM: predicting the effects of mutations in proteins using graph-based signatures

By Douglas E. V. Pires, David B. Ascher, Tom L. Blundell. Bioinform., 2013

1,016 citations recorded by the source corpus.

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.uk Supplementary information: Supplementary data are available at Bioinformatics online.

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/632154a7b20f96440fc421a0a64b85531fc186d4

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cancer Research

1. Frances H. Arnold
2. Kevin Kaichuang Yang
3. Zachary Wu
4. David B. Ascher
5. Douglas E. V. Pires

In Molecular Biology

1. Kevin Kaichuang Yang
2. David B. Ascher
3. Douglas E. V. Pires
4. Yang Zhang
5. Carlos H. M. Rodrigues
6. Zachary Wu
7. Arne Elofsson
8. Frances H. Arnold

Citations in the corpus

• SDM: a server for predicting effects of mutations on protein stability (2017, background): “This dataset contains 42 mutations within the DNA binding domain of the tumour suppressor protein p53 (12).”
• A structural biology community assessment of AlphaFold2 applications (2021, methodology): “These mutations were analyzed by computational predictive tools, including the sequence-based predictors I-Mutant 67 , SAAFEC-SEQ 68 and MUpro 69 , and the structure-based predictors mCSM-stability 70 , DUET 71 , SDM 72 , DynaMut 73 , MAESTRO 74 , ENCoM 75 , DynaMut2 (ref.”
• Machine-learning-guided directed evolution for protein engineering (2018, methodology): “GPs have been used to predict thermostability 11,32,43 , substrates for enzymatic reactions 44 , fluorescence 45 , membrane localization 12 , and channelrhodopsin photoproperties 13 .”
• DynaMut2: Assessing changes in stability and flexibility upon single and multiple point missense mutations (2020, background): “Our in-house graph-based signatures approach to represent molecular structures 35,59 – 61 has proven to be successful for a range of applications toward the study of protein structure and changes carried out by missense mutations, 35,37,41 – 48 including phenotypic changes.”
• Machine learning in protein engineering (2018, methodology): “…machine-learning models to predict the effects of mutations on stability (Tian et al., 2010; Li & Fang, 2012; Jia et al., 2015; Capriotti et al., 2005b;a; Cheng et al., 2006; Buske et al., 2009; Liu & Kang, 2012; Pires et al., 2013; Jokinen et al., 2018; Dehouck et al., 2009; Giollo et al., 2014).”
• Predicting changes in protein thermodynamic stability upon point mutation with deep 3D convolutional neural networks (2020, methodology): “The issue of having mutations from the same protein in both training and test sets was 171 addressed in developing the mCSM and INPS methods, where the cross-validation procedure 172 ensured that mutations of the same protein remained together in either the training or test set 173 (16, 19).”