BeAtMuSiC: prediction of changes in protein–protein binding affinity on mutations
By Yves Dehouck, Jean Marc Kwasigroch, Marianne Rooman, Dimitri Gilis. 2013
327 citations recorded by the source corpus.
The ability of proteins to establish highly selective interactions with a variety of (macro)molecular partners is a crucial prerequisite to the realization of their biological functions. The availability of computational tools to evaluate the impact of mutations on protein–protein binding can therefore be valuable in a wide range of industrial and biomedical applications, and help rationalize the consequences of non-synonymous single-nucleotide polymorphisms. BeAtMuSiC (http://babylone.ulb.ac.be/beatmusic) is a coarse-grained predictor of the changes in binding free energy induced by point mutations. It relies on a set of statistical potentials derived from known protein structures, and combines the effect of the mutation on the strength of the interactions at the interface, and on the overall stability of the complex. The BeAtMuSiC server requires as input the structure of the protein–protein complex, and gives the possibility to assess rapidly all possible mutations in a protein chain or at the interface, with predictive performances that are in line with the best current methodologies.
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Citations in the corpus
- Better prediction of functional effects for sequence variants (2015, neutral): “Some tools are focused on non-coding regions [2-4]; others focus on coding regions and predict the effects of single amino acid variants (non-synonymous single-nucleotide polymorphisms, nsSNPs, or single amino acid substitutions, SAAS) on aspects such as protein structure [5], stability [6-8], bindi”
- Predicting the Effect of Mutations on Protein-Protein Binding Interactions through Structure-Based Interface Profiles (2015, methodology): “We show that a scoring function based on interface structure profiles collected from analogous protein-protein interactions in the PDB is a powerful predictor of protein binding affinity changes upon mutation.”
- mCSM-PPI2: predicting the effects of mutations on protein–protein interactions (2019, methodology): “Previous methods were limited either in terms of their throughput (21,22) or in terms of their performance (23).”
- mCSM: predicting the effects of mutations in proteins using graph-based signatures (2013, background): “Received on April 18, 2013; revised on November 12, 2013; accepted on November 21, 2013”