Prediction of hot spot residues at protein-protein interfaces by combining machine learning and energy-based methods
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BACKGROUND: Alanine scanning mutagenesis is a powerful experimental methodology for investigating the structural and energetic characteristics of protein complexes. Individual amino-acids are systematically mutated to alanine and changes in free energy of binding (DeltaDeltaG) measured. Several experiments have shown that protein-protein interactions are critically dependent on just a few residues ("hot spots") at the interface. Hot spots make a dominant contribution to the free energy of binding and if mutated they can disrupt the interaction. As mutagenesis studies require significant experimental efforts, there is a need for accurate and reliable computational methods. Such methods would also add to our understanding of the determinants of affinity and specificity in protein-protein recognition. RESULTS: We present a novel computational strategy to identify hot spot residues, given the structure of a complex. We consider the basic energetic terms that contribute to hot spot interactions, i.e. van der Waals potentials, solvation energy, hydrogen bonds and Coulomb electrostatics. We treat them as input features and use machine learning algorithms such as Support Vector Machines and Gaussian Processes to optimally combine and integrate them, based on a set of training examples of alanine mutations. We show that our approach is effective in predicting hot spots and it compares favourably to other available methods. In particular we find the best performances using Transductive Support Vector Machines, a semi-supervised learning scheme. When hot spots are defined as those residues for which DeltaDeltaG >or= 2 kcal/mol, our method achieves a precision and a recall respectively of 56% and 65%. CONCLUSION: We have developed an hybrid scheme in which energy terms are used as input features of machine learning models. This strategy combines the strengths of machine learning and energy-based methods. Although so far these two types of approaches have mainly been applied separately to biomolecular problems, the results of our investigation indicate that there are substantial benefits to be gained by their integration.
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Protein-protein interfaces are characterized by hot spots or hot regions rather than even contribution across all contact areas.
“Protein-protein interfaces are characterized by the existence of hot spots or ‘hot regions’ in interacting regions instead of an even contribution across all contact areas.371,476,487,492–495”
From Protein Ensembles: How Does Nature Harness Thermodynamic Fluctuations for Life? The Diverse Functional Roles of Conformational Ensembles in the Cell (2016) · cites this paper as [492]
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- Authors
- Lise, S., Archambeau, C., Pontil, M., & Jones, D. T.
- Affiliations
- University College London (GB)
- Corresponding author
- Lise, Stefano
- Venue
- BMC Bioinformatics
- Published
- 2009-10-30
- Volume
- 10
- Issue
- 1
- Pages
- 365-365
- ISSN
- 1471-2105 (ISSN-L)
- PMC
- PMC2777894
- OpenAlex
- W2162402626
- Cited by
- 107
- References
- 53
- Citations by year
- 2026: 3 · 2025: 1 · 2024: 4 · 2023: 1 · 2022: 8 · 2021: 5 · 2020: 5 · 2019: 2 · 2018: 10 · 2017: 5 · 2016: 7 · 2015: 7 · 2014: 8 · 2013: 12 · 2012: 8
- FWCI
- 3.90
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- BMC Bioinformatics (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Funders
- Biotechnology and Biological Sciences Research Council (BB/E017452/1)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Keywords
- protein-protein interfaces, hot spot residues, alanine scanning, binding free energy, protein-protein interaction, energy-based features, van der Waals interactions, solvation energy, hydrogen bonding, electrostatics, support vector machine, Gaussian processes, semi-supervised learning
- MeSH terms
- Alanine, Artificial Intelligence, Binding Sites, Hydrogen Bonding, Proteins, Thermodynamics, Computational Biology, Protein Interaction Mapping, Databases, Protein, Static Electricity
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