Abstract
BackgroundProtein-ligand binding is important for some proteins to perform their functions. Protein-ligand binding sites are the residues of proteins that physically bind to ligands. Despite of the recent advances in computational prediction for protein-ligand binding sites, the state-of-the-art methods search for similar, known structures of the query and predict the binding sites based on the solved structures. However, such structural information is not commonly available.ResultsIn this paper, we propose a sequence-based approach to identify protein-ligand binding residues. We propose a combination technique to reduce the effects of different sliding residue windows in the process of encoding input feature vectors. Moreover, due to the highly imbalanced samples between the ligand-binding sites and non ligand-binding sites, we construct several balanced data sets, for each of which a random forest (RF)-based classifier is trained. The ensemble of these RF classifiers forms a sequence-based protein-ligand binding site predictor.ConclusionsExperimental results on CASP9 and CASP8 data sets demonstrate that our method compares favorably with the state-of-the-art protein-ligand binding site prediction methods.
Highlights
Protein-ligand binding is important for some proteins to perform their functions
We propose a sequence-based approach, LigandRFs (Ligand binding site prediction by the ensemble of Random Forest classifiers), to identify protein-ligand binding residues based on the co-evolutionary context of amino acid residues
Comparison with other binding site prediction methods Previous experiments showed that template-based prediction methods will perform much better than de novo methods in the context [1], but our method provides a comparative prediction on protein ligand binding sites, especially for the CASP8 data set
Summary
Protein-ligand binding is important for some proteins to perform their functions. Protein-ligand binding sites are the residues of proteins that physically bind to ligands. Despite of the recent advances in computational prediction for protein-ligand binding sites, the state-of-the-art methods search for similar, known structures of the query and predict the binding sites based on the solved structures. Such structural information is not commonly available. Protein-ligand binding is important for some proteins to perform their functions. A ligand is a signal triggering molecule, binding to a site on a target protein. None of the amino acid side chains in proteins is suited for the reversible binding of oxygen molecules.
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