ArticleScientific reports2016
Improving the performance of the PLB index for ligand-binding site prediction using dihedral angles and the solvent-accessible surface area.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- ViralBindPredict: empowering viral protein-ligand binding sites through deep learning and protein sequence-derived insights.GigaScience · 2026Article
- Unraveling Hydration Shell Dynamics and Viscosity Effects Around Cyanamide Probes via 2D IR Spectroscopy.Journal of the American Chemical Society · 2025Article
- Design of rigid protein-protein interaction inhibitors enables targeting of undruggable Mcl-1.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Recognizing Ion Ligand-Binding Residues by Random Forest Algorithm Based on Optimized Dihedral Angle.Frontiers in bioengineering and biotechnology · 2020Article
- Fragment-Based Ligand-Protein Contact Statistics: Application to Docking Simulations.International journal of molecular sciences · 2019Article
- RaptorX-Angle: real-value prediction of protein backbone dihedral angles through a hybrid method of clustering and deep learning.BMC bioinformatics · 2018Article
- Systematic Identification of Machine-Learning Models Aimed to Classify Critical Residues for Protein Function from Protein Structure.Molecules (Basel, Switzerland) · 2017Article
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2 authors.
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Abstract
Protein ligand-binding site prediction is highly important for protein function determination and structure-based drug design. Over the past twenty years, dozens of computational methods have been developed to address this problem. Soga et al. identified ligand cavities based on the preferences of amino acids for the ligand-binding site (RA) and proposed the propensity for ligand binding (PLB) index to rank the cavities on the protein surface. However, we found that residues exhibit different RAs in response to changes in solvent exposure. Furthermore, previous studies have suggested that some dihedral angles of amino acids in specific regions of the Ramachandran plot are preferred at the functional sites of proteins. Based on these discoveries, the amino acid solvent-accessible surface area and dihedral angles were combined with the RA and PLB to obtain two new indexes, multi-factor RA (MF-RA) and multi-factor PLB (MF-PLB). MF-PLB, PLB and other methods were tested using two benchmark databases and two particular ligand-binding sites. The results show that MF-PLB can improve the success rate of PLB for both ligand-bound and ligand-unbound structures, particularly for top choice prediction.
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