MutaBind estimates and interprets the effects of sequence variants on protein–protein interactions
Open accessAbstract
Proteins engage in highly selective interactions with their macromolecular partners. Sequence variants that alter protein binding affinity may cause significant perturbations or complete abolishment of function, potentially leading to diseases. There exists a persistent need to develop a mechanistic understanding of impacts of variants on proteins. To address this need we introduce a new computational method MutaBind to evaluate the effects of sequence variants and disease mutations on protein interactions and calculate the quantitative changes in binding affinity. The MutaBind method uses molecular mechanics force fields, statistical potentials and fast side-chain optimization algorithms. The MutaBind server maps mutations on a structural protein complex, calculates the associated changes in binding affinity, determines the deleterious effect of a mutation, estimates the confidence of this prediction and produces a mutant structural model for download. MutaBind can be applied to a large number of problems, including determination of potential driver mutations in cancer and other diseases, elucidation of the effects of sequence variants on protein fitness in evolution and protein design. MutaBind is available at http://www.ncbi.nlm.nih.gov/projects/mutabind/.
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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 [25]
Significant efforts have been devoted to computationally study the effects of mutations on protein complexes, but their poor predictive performance on new variants, particularly mutations that lead to increased binding affinity, has limited their use.
“Since then, significant efforts have been devoted to computationally study the effects of mutations on protein complexes (24,25) but their poor predictive performance on new variants, particularly mutations that lead to increased binding affinity of the complex, has limited their use.”
From mCSM-PPI2: predicting the effects of mutations on protein–protein interactions (2019) · cites this paper as [25]
mCSM-PPI2 outperformed MutaBind on leave-one-complex-out cross-validation (MutaBind ρ = 0.68 and RMSE = 1.57 kcal/mol).
“outperforming MutaBind (25) (ρ = 0.68 and RMSE = 1.57 kcal/mol).”
From mCSM-PPI2: predicting the effects of mutations on protein–protein interactions (2019) · cites this paper as [25]
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- Authors
- Li, M., Simonetti, F. L., Goncearenco, A., & Panchenko, A. R.
- Affiliations
- National Institutes of Health (US)
- Fundación Instituto Leloir (AR)
- Corresponding author
- Panchenko, Anna R.
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2016-05-05
- Volume
- 44
- Issue
- W1
- Pages
- W494-W501
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- PMC
- PMC4987923
- OpenAlex
- W2345331290
- Cited by
- 118
- References
- 32
- Citations by year
- 2026: 3 · 2025: 5 · 2024: 7 · 2023: 15 · 2022: 10 · 2021: 18 · 2020: 16 · 2019: 16 · 2018: 14 · 2017: 10 · 2016: 4
- FWCI
- 7.62
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- public-domain
- Locations
- Nucleic Acids Research (publishedVersion, public-domain)
- Europe PMC (PubMed Central) (publishedVersion, public-domain)
- PubMed Central (publishedVersion, public-domain)
- PubMed (publishedVersion)
- Americanae (AECID Library) (submittedVersion, cc-by-nc-sa)
- HighWire Press Open Archive (submittedVersion)
- LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (submittedVersion, cc-by-nc-sa)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Keywords
- protein-protein interaction, genetic variants, pathogenic variants, molecular mechanics force field, statistical potentials, side-chain optimization, protein design
- MeSH terms
- Algorithms, Binding Sites, Humans, Neoplasms, Protein Binding, Proteins, Software, Evolution, Molecular, Internet, Protein Interaction Mapping, Molecular Dynamics Simulation, Genetic Fitness, Protein Interaction Maps, Datasets as Topic
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