DUET: a server for predicting effects of mutations on protein stability using an integrated computational approach

By Douglas E. V. Pires, David B. Ascher, Tom L. Blundell. Nucleic Acids Res., 2014

867 citations recorded by the source corpus.

Cancer genome and other sequencing initiatives are generating extensive data on non-synonymous single nucleotide polymorphisms (nsSNPs) in human and other genomes. In order to understand the impacts of nsSNPs on the structure and function of the proteome, as well as to guide protein engineering, accurate in silicomethodologies are required to study and predict their effects on protein stability. Despite the diversity of available computational methods in the literature, none has proven accurate and dependable on its own under all scenarios where mutation analysis is required. Here we present DUET, a web server for an integrated computational approach to study missense mutations in proteins. DUET consolidates two complementary approaches (mCSM and SDM) in a consensus prediction, obtained by combining the results of the separate methods in an optimized predictor using Support Vector Machines (SVM). We demonstrate that the proposed method improves overall accuracy of the predictions in comparison with either method individually and performs as well as or better than similar methods. The DUET web server is freely and openly available at http://structure.bioc.cam.ac.uk/duet.

Subfields: Computational Theory and Mathematics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/0bfe3eb9b9e3f276dee5c59a43cf111b0c8e0b9b

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Citations in the corpus

mCSM-PPI2: predicting the effects of mutations on protein–protein interactions (2019, methodology): “Our previously described graph-based signatures concept has proven to be a powerful approach and has been widely applied to the study of protein structure, including how mutations alter protein stability (20,26), dynamics (27) and interactions with other molecules (20,28–34).”
DynaMut: predicting the impact of mutations on protein conformation, flexibility and stability (2018, methodology): “The consensus method DUET highlighted that these approaches were complimentary, and that their integration provided more accurate and reliable predictions (17).”
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.”
Systematic evaluation of computational tools to predict the effects of mutations on protein stability in the absence of experimental structures (2022, methodology): “DynaMut2, mCSMStability and DUET showed a steadier performance on mainly alpha proteins, with larger drops for mainly beta and mixed alpha/beta.”
TP53 copy number expansion is associated with the evolution of increased body size and an enhanced DNA damage response in elephants (2015, methodology): “Indeed, three methods (Pires et al., 2014) inferred that the 388 W23G substitution is destabilizing on the TP53RTG12/MDM2 interaction (mCSM ΔΔG=-2.”
Variation Interpretation Predictors: Principles, Types, Performance, and Choice (2016, neutral): “They use features derived from tertiary protein structure, evolutionary conservation, sequence environment, network topology, and properties of amino acid residues to describe each variation [Capriotti et al., 2005; Cheng et al., 2006; Capriotti et al., 2008; Teng et al., 2010; Chen et al., 2013; Ya”
SDM: a server for predicting effects of mutations on protein stability (2017, methodology): “SDM has also been successfully used in combination with machine learning techniques to predict better the impact of mutations on protein stability (15).”
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.”