A comparative study of family-specific protein–ligand complex affinity prediction based on random forest approach.
Abstract
The assessment of binding affinity between ligands and the target proteins plays an essential role in drug discovery and design process. As an alternative to widely used scoring approaches, machine learning methods have also been proposed for fast prediction of the binding affinity with promising results, but most of them were developed as all-purpose models despite of the specific functions of different protein families, since proteins from different function families always have different structures and physicochemical features. In this study, we proposed a random forest method to predict the protein-ligand binding affinity based on a comprehensive feature set covering protein sequence, binding pocket, ligand structure and intermolecular interaction. Feature processing and compression was respectively implemented for different protein family datasets, which indicates that different features contribute to different models, so individual representation for each protein family is necessary. Three family-specific models were constructed for three important protein target families of HIV-1 protease, trypsin and carbonic anhydrase respectively. As a comparison, two generic models including diverse protein families were also built. The evaluation results show that models on family-specific datasets have the superior performance to those on the generic datasets and the Pearson and Spearman correlation coefficients (R p and Rs) on the test sets are 0.740, 0.874, 0.735 and 0.697, 0.853, 0.723 for HIV-1 protease, trypsin and carbonic anhydrase respectively. Comparisons with the other methods further demonstrate that individual representation and model construction for each protein family is a more reasonable way in predicting the affinity of one particular protein family.
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Nonlinear scoring functions specific for protein classes/targets achieved superior performance than generic models.
“In general, nonlinear scoring functions specific for protein classes/targets also achieved superior performance than the generic models (Wang et al., 2015; Ashtawy and Mahapatra, 2018).”
From Empirical scoring functions for structure-based virtual screening: applications, critical aspects, and challenges (2018) · cites this paper as Wang et al., 2015
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- Authors
- Wang, Y., Guo, Y., Kuang, Q., Pu, X., Ji, Y., Zhang, Z., & Li, M.
- Affiliations
- Sichuan University (CN)
- Corresponding author
- Guo, Yanzhi
- Li, Menglong
- Venue
- Journal of Computer-Aided Molecular Design
- Published
- 2014-12-20
- Volume
- 29
- Issue
- 4
- Pages
- 349-360
- ISSN
- 0920-654X (ISSN-L)
- 1573-4951
- OpenAlex
- W2052460962
- Cited by
- 69
- References
- 53
- Citations by year
- 2026: 2 · 2025: 7 · 2024: 11 · 2023: 22 · 2022: 3 · 2021: 4 · 2020: 5 · 2019: 4 · 2018: 6 · 2017: 3 · 2015: 2
- FWCI
- 1.72
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Journal of Computer-Aided Molecular Design (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Science Foundation
- Topics
- 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
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Interaction Studies and Fluorescence Analysis
- Keywords
- protein-ligand binding affinity prediction, random forest, HIV-1 protease, trypsin, carbonic anhydrase, feature selection, intermolecular interactions, protein sequence features
- MeSH terms
- Artificial Intelligence, Carbonic Anhydrases, Humans, Ligands, Models, Biological, Protein Binding, Proteins, Trypsin, Drug Design, HIV Protease, Computer-Aided Design, Databases, Protein
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