Abstract
Motivation: Predictive tools that model protein–ligand binding on demand are needed to promote ligand research in an innovative drug-design environment. However, it takes considerable time and effort to develop predictive tools that can be applied to individual ligands. An automated production pipeline that can rapidly and efficiently develop user-friendly protein–ligand binding predictive tools would be useful.Results: We developed a system for automatically generating protein–ligand binding predictions. Implementation of this system in a pipeline of Semantic Web technique-based web tools will allow users to specify a ligand and receive the tool within 0.5–1 day. We demonstrated high prediction accuracy for three machine learning algorithms and eight ligands.Availability and implementation: The source code and web application are freely available for download at http://utprot.net. They are implemented in Python and supported on Linux.Contact: shimizu@bi.a.u-tokyo.ac.jpSupplementary information: Supplementary data are available at Bioinformatics online.
Highlights
The identification of protein–ligand binding sites is important for understanding protein function
Using the structures of protein–ligand interaction sites extracted from Protein Data Bank (PDB) and the annotated sequences from Universal Protein Resource (UniProt) (Estrada et al, 2012; Magrane and Consortium, 2011), we developed the Protein–Ligand Binding Site Pair Residue (PLBSP Residue) database
Our predictors generally performed reasonably well when compared with the average case but the best case sensitivity was better for LigandRFs
Summary
The identification of protein–ligand binding sites is important for understanding protein function. Many bioinformatics methods have been proposed for ligand-binding site identification or prediction. Computational methods are useful because they can be applied rapidly and at low cost, compared with biochemical experiments. They have a wide range of applications in enzyme design, drug discovery, chemical genetics, and other fields. The ligand-binding site prediction methods can be classified into sequence-based and structure-based methods. With high-throughput sequencing technologies yielding large amounts of sequence data, sequence-based methods can be applied to a wide range of proteins, even those whose structures have not been determined, and can be used for genome-wide analysis.
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