Predicting Protein-Ligand Binding Site Using Support Vector Machine with Protein Properties
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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Motivation: Protein–ligand binding sites are the active sites on protein surface that perform protein functions. Thus, the identification of those binding sites is often the first step to study protein functions and structure-based drug design. There are many computational algorithms and tools developed in recent decades, such as LIGSITEcs/c, PASS, Q-SiteFinder, SURFNET, and so on. In our previous work, MetaPocket, we have proved that it is possible to combine the results of many methods together to improve the prediction result. Results: Here, we continue our previous work by adding four more methods Fpocket, GHECOM, ConCavity and POCASA to further improve the prediction success rate. The new method MetaPocket 2.0 and the individual approaches are all tested on two datasets of 48 unbound/bound and 210 bound structures as used before. The results show that the average success rate has been raised 5% at the top 1 prediction compared with previous work. Moreover, we construct a non-redundant dataset of drug–target complexes with known structure from DrugBank, DrugPort and PDB database and apply MetaPocket 2.0 to this dataset to predict drug binding sites. As a result, 74% drug binding sites on protein target are correctly identified at the top 3 prediction, and it is 12% better than the best individual approach. Availability: The web service of MetaPocket 2.0 and all the test datasets are freely available at http://projects.biotec.tu-dresden.de/metapocket/ and http://sysbio.zju.edu.cn/metapocket. Contact: bhuang@biotec.tu-dresden.de Supplementary Information:Supplementary data are available at Bioinformatics online.