SDM: a server for predicting effects of mutations on protein stability

By Arun Prasad Pandurangan, Bernardo Ochoa-Montaño, David B. Ascher, Tom L. Blundell. Nucleic Acids Res., 2017

459 citations recorded by the source corpus.

Abstract Here, we report a webserver for the improved SDM, used for predicting the effects of mutations on protein stability. As a pioneering knowledge-based approach, SDM has been highlighted as the most appropriate method to use in combination with many other approaches. We have updated the environment-specific amino-acid substitution tables based on the current expanded PDB (a 5-fold increase in information), and introduced new residue-conformation and interaction parameters, including packing density and residue depth. The updated server has been extensively tested using a benchmark containing 2690 point mutations from 132 different protein structures. The revised method correlates well against the hypothetical reverse mutations, better than comparable methods built using machine-learning approaches, highlighting the strength of our knowledge-based approach for identifying stabilising mutations. Given a PDB file (a Protein Data Bank file format containing the 3D coordinates of the protein atoms), and a point mutation, the server calculates the stability difference score between the wildtype and mutant protein. The server is available at http://structure.bioc.cam.ac.uk/sdm2

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/d11946288c8f1b9dc420130b50731782d06b63f6

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Citations in the corpus

Systematic evaluation of computational tools to predict the effects of mutations on protein stability in the absence of experimental structures (2022, background): “Little performance variation until 40% identity was observed for DynaMut1, Maestro, SDM, ENCoM and DDGun.”
DynaMut: predicting the impact of mutations on protein conformation, flexibility and stability (2018, methodology): “Previous studies have highlighted that many machine learning based structural approaches are unbalanced, and can less accurately identify stabilizing mutations (16).”
DynaMut2: Assessing changes in stability and flexibility upon single and multiple point missense mutations (2020, unknown): “To minimize the imbalanced nature of our dataset (Figure S1) and as a sanity check evidenced by other studies, here we use hypothetical reverse mutations.(36,54) However, differently from our previous implementation of DynaMut, hypothetical reverse mutations with more drastic changes on Gibbs free e”