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J Chem Inf Model. 2016 Jun 27;56(6):1088-99. doi: 10.1021/acs.jcim.5b00625. Epub 2015 Dec 30.

Combined Approach of Patch-Surfer and PL-PatchSurfer for Protein-Ligand Binding Prediction in CSAR 2013 and 2014.

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School of Life Science, Anhui University , Hefei, Anhui 230601, China.
Department of Biology Science, Purdue University , West Lafayette, Indiana 47907, United States.
Department of Computer Science, Purdue University , West Lafayette, Indiana 47907, United States.


The Community Structure-Activity Resource (CSAR) benchmark exercise provides a unique opportunity for researchers to objectively evaluate the performance of protein-ligand docking methods. Patch-Surfer and PL-PatchSurfer, molecular surface-based methods for predicting binding ligands of proteins developed in our group, were tested on both CSAR 2013 and 2014 benchmark exercises in combination with an empirical scoring function-based method, AutoDock, while we only participated in CSAR 2013 using Patch-Surfer. The prediction results for Phase 1 task in CSAR 2013 showed that Patch-Surfer was able to rank all the four designed binding proteins within top ranks, outperforming AutoDock Vina. In Phase 2 of 2013, PL-PatchSurfer correctly selected the correct ligand pose for two target proteins. PL-PatchSurfer performed reasonably well in ranking ligands according to their binding affinity and in selecting near-native ligand poses in 2013 Phase 3 and 2014 Phase 1, respectively, although AutoDock Vina showed better performance. Lastly, in the 2014 Phase 2 exercise, the PL-PatchSurfer scores computed for ligands to target protein pairs correlated well with their pIC50 values, which was better or comparable to results by other participants. Overall, our methods showed fairly good performance in CSAR 2013 and 2014. Unique characteristics of the methods are discussed in comparison with AutoDock.

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