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

Abstract

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.

CSAR 2014: A Benchmark Exercise Using Unpublished Data from Pharma

“…Their papers properly describe the unique features of their methods and what they have learned from the CSAR 2014 exercise. 11,13,16,72,76–82 …”

“…For more information about the various methods, the reader is encouraged to read the manuscripts that the participants have submitted to this special issue. Their papers properly describe the unique features of their methods and what they have learned from the CSAR 2014 exercise. ,,,,− …”

One-Pot Biocatalytic Preparation of Enantiopure Unusual α-Amino Acids from α-Hydroxy Acids via a Hydrogen-Borrowing Dual-Enzyme Cascade

“…The L243M and L243I mutations also changed the size of entrance channel making it accessible by different substrates. [33]. As showed in Figure 6, residue 243 was located at the substrate entrance channel and there were no inter-molecular hydrogen bonds between residue 243 and other residues.…”

“…Structure analysis of LbMDH and the variants was carried out to study the reason for the different specific activities and affinities towards different substrates. Molecular docking of LbMDH and its variants together with D-mandelic acid and cofactor NAD were carried out by Autodock vina [33]. As showed in Figure 6, residue 243 was located at the substrate entrance channel and there were no inter-molecular hydrogen bonds between residue 243 and other residues.…”

Docking of small molecules to farnesoid X receptors using AutoDock Vina with the Convex-PL potential: lessons learned from D3R Grand Challenge 2

“…Recently several major docking competitions were held, namely CSAR 2013 [5], CSAR 2014 [6] and D3R 2015-2016 [7], which became an opportunity for many teams to assess their protein-ligand pose and binding affinity prediction algorithms and protocols. In the course of these competitions various methods were used including classical docking methods [8][9][10][11][12][13][14][15][16][17][18][19][20][21], QSAR models [15,22,23], targetspecific scoring functions [23][24][25], and sometimes combinations of these with more computationally expensive molecular dynamics-based methods [26][27][28][29]. The CSAR 2013 exercise also involved homol-ogy modeling to obtain proper receptors from the given sequences, while in CSAR 2014 protein structures were provided.…”