journal article

Journal Article

Text

Journal of Chemical Information and Modeling. 2016, 56(6), p. 974-987

Integration of Ligand and Structure Based Approaches for CSAR-2014

American Chemical Society

English

Text

2016-06-27

https://doi.org/10.1021/acs.jcim.5b00477

Philip Prathipati and Kenji Mizuguchi, Integration of Ligand and Structure Based Approaches for CSAR-2014. Journal of Chemical Information and Modeling 56 (6), 974-987, 2016, © 2015 American Chemical Society, DOI: 10.1021/acs.jcim.5b0047

The prediction of binding poses and affinities is an area of active interest in computer-aided drug design (CADD). Given the documented limitations with either ligand or structure based approaches, we employed an integrated approach and developed a rapid protocol for binding mode and affinity predictions. This workflow was applied to the three protein targets of Community Structure-Activity Resource-2014 (CSAR-2014) exercise: Factor Xa (FXa), Spleen Tyrosine Kinase (SYK), and tRNA (guanine-N(1))-methyltransferase (TrmD). Our docking and scoring workflow incorporates compound clustering and ligand and protein structure based pharmacophore modeling, followed by local docking, minimization, and scoring. While the former part of the protocol ensures high-quality ligand alignments and mapping, the subsequent minimization and scoring provides the predicted binding modes and affinities. We made blind predictions of docking pose for 1, 5, and 14 ligands docked into 1, 2, and 12 crystal structures of FXa, SYK, and TrmD, respectively. The resulting 174 poses were compared with cocrystallized structures (1, 5, and 14 complexes) made available at the end of CSAR. Our predicted poses were related to the experimentally determined structures with a mean root-mean-square deviation value of 3.4 Å. Further, we were able to classify high and low affinity ligands with the area under the curve values of 0.47, 0.60, and 0.69 for FXa, SYK, and TrmD, respectively, indicating the validity of our approach in at least two of the three systems. Detailed critical analysis of the results and CSAR methodology ranking procedures suggested that a straightforward application of our workflow has limitations, as some of the performance measures do not reflect the actual utility of pose and affinity predictions in the biological context of individual systems.

This document is the Accepted Manuscript version of a Published Work that appeared in final form in Journal of Chemical Information and Modeling, © American Chemical Society after peer review and technical editing by the publisher. To access the final edited and published work see https://doi.org/10.1021/acs.jcim.5b00477

79143

Integration of Ligand and Structure Based Approaches for CSAR-2014

American Chemical Society

English

2016-06-27

Journal of Chemical Information and Modeling

56

6

974

987

https://doi.org/10.1021/acs.jcim.5b00477

26492437

https://hdl.handle.net/11094/79143