BeAtMuSiC: prediction of changes in protein–protein binding affinity on mutations
Open accessAbstract
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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FoldX and BeAtMuSic yield the lowest correlations and highest RMSE on single-point mutation datasets.
“We observed FoldX [8] and BeAtMuSic [38] yield the lowest correlations and the highest root-mean-square error (RMSE) on these datasets, indicating the difficulty of gauging the impacts of mutations via empirical energy-based methods.”
From Deep geometric representations for modeling effects of mutations on protein-protein binding affinity (2021) · cites this paper as [38]
The final dataset for building mCSM-PPI2 includes 8338 single-point mutations, an increase of up to three-fold compared to previous methods that used data from the first version of SKEMPI.
“Therefore, the final dataset for building mCSM-PPI2 predictive model includes 8338 single-point mutations (S8338), which represents an increase of up to three-fold in datapoints in comparison with previous methods that used data from the first version of SKEMPI with 2007 (S2007) (20,23), 1964 (S1964) (25), 1102 (S1102) (24) and 1327 (S1327) mutations (41).”
From mCSM-PPI2: predicting the effects of mutations on protein–protein interactions (2019) · cites this paper as [23]
Previous methods were limited either in terms of their throughput or in terms of their performance.
“Previous methods were limited either in terms of their throughput (21,22) or in terms of their performance (23).”
From mCSM-PPI2: predicting the effects of mutations on protein–protein interactions (2019) · cites this paper as [23]
mCSM-PPI2 outperformed mCSM, iSEE, FoldX, and BeAtMuSiC on the MDM2-p53 blind test.
“outperforming mCSM, iSEE, FoldX, BeAtMuSiC (23) (Table 1).”
From mCSM-PPI2: predicting the effects of mutations on protein–protein interactions (2019) · cites this paper as [23]
Empirical energy-based methods usually take linear forms, often with only energy-based terms, and fail to exploit evolution information, which can limit their ability to capture mutation effects on binding affinity.
“They usually take a form of linear functions, often with only energy‐based terms, and fail to exploit evolution information, which can limit their ability to capture mutation effects on binding affinity.”
From iSEE: Interface structure, evolution, and energy-based machine learning predictor of binding affinity changes upon mutations (2019) · cites this paper as [10]
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- Authors
- Dehouck, Y., Kwasigroch, J. M., Rooman, M., & Gilis, D.
- Affiliations
- Université Libre de Bruxelles (BE)
- Corresponding author
- Dehouck, Yves
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2013-05-30
- Volume
- 41
- Issue
- W1
- Pages
- W333-W339
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- PMC
- PMC3692068
- OpenAlex
- W2117541321
- Cited by
- 338
- References
- 51
- Citations by year
- 2026: 11 · 2025: 49 · 2024: 30 · 2023: 40 · 2022: 35 · 2021: 42 · 2020: 26 · 2019: 17 · 2018: 19 · 2017: 18 · 2016: 18 · 2015: 16 · 2014: 14 · 2013: 2
- FWCI
- 7.46
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Nucleic Acids Research (publishedVersion, cc-by-nc)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Dépôt institutionnel de l'Université libre de Bruxelles (Université Libre de Bruxelles) (submittedVersion, other-oa)
- Funders
- Fonds De La Recherche Scientifique - FNRS
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Keywords
- protein-protein binding affinity, point mutations, binding free energy prediction, statistical potentials, protein-protein complex structure, complex stability, non-synonymous single nucleotide polymorphisms
- MeSH terms
- Models, Molecular, Mutation, Protein Binding, Software, Protein Folding, Internet, Multiprotein Complexes
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