Toward Fully Automated High Performance Computing Drug Discovery: A Massively Parallel Virtual Screening Pipeline for Docking and Molecular Mechanics/Generalized Born Surface Area Rescoring to Improve Enrichment

Abstract

In this work we announce and evaluate a high throughput virtual screening pipeline for in-silico screening of virtual compound databases using high performance computing (HPC). Notable features of this pipeline are an automated receptor preparation scheme with unsupervised binding site identification. The pipeline includes receptor/target preparation, ligand preparation, VinaLC docking calculation, and molecular mechanics/generalized Born surface area (MM/GBSA) rescoring using the GB model by Onufriev and co-workers [J. Chem. Theory Comput. 2007, 3, 156-169]. Furthermore, we leverage HPC resources to perform an unprecedented, comprehensive evaluation of MM/GBSA rescoring when applied to the DUD-E data set (Directory of Useful Decoys: Enhanced), in which we selected 38 protein targets and a total of ∼0.7 million actives and decoys. The computer wall time for virtual screening has been reduced drastically on HPC machines, which increases the feasibility of extremely large ligand database screening with more accurate methods. HPC resources allowed us to rescore 20 poses per compound and evaluate the optimal number of poses to rescore. We find that keeping 5-10 poses is a good compromise between accuracy and computational expense. Overall the results demonstrate that MM/GBSA rescoring has higher average receiver operating characteristic (ROC) area under curve (AUC) values and consistently better early recovery of actives than Vina docking alone. Specifically, the enrichment performance is target-dependent. MM/GBSA rescoring significantly out performs Vina docking for the folate enzymes, kinases, and several other enzymes. The more accurate energy function and solvation terms of the MM/GBSA method allow MM/GBSA to achieve better enrichment, but the rescoring is still limited by the docking method to generate the poses with the correct binding modes.

Ultrafast protein structure-based virtual screening with Panther

“…In enzymes, the results are usually improved by the MM/ GBSA. However, COX2 is one of the rare cases in which post-processing is disadvantageous [66]. This supports the assumption that in the virtual screening of large molecular databases, it is best to use fast and relatively accurate methods, such as Panther.…”

Assessing the performance of MM/PBSA and MM/GBSA methods. 5. Improved docking performance using high solute dielectric constant MM/GBSA and MM/PBSA rescoring

“…In some cases, based on e = 1, the time-consuming rescoring even performs worse than the original scoring function. 18 As shown in Table 1, based on the dielectric constant of 1, the accuracies (AUC and p-value) of the MM/PBSA and MM/ GBSA rescoring are even worse than those of Autodock for ABL (AUC = 0.781 for MM/PBSA versus 0.859 for Autodock) and ALK (AUC = 0.830 for MM/PBSA versus 0.898 for Autodock). However, this situation could be substantially improved by using an interior dielectric constant of 2 or 4.…”

“…17 Zhang et al have done a comprehensive study on the DUD dataset by using MM/GBSA, and summarized that using the top-5 originally scored docking poses for the MM/GBSA rescoring may be a good balance between computational burden and prediction accuracy. 18 However, we will show below that the influence of using the best scored docking pose or the best of the top-3 docking poses is not significant when a higher interior dielectric constant (i.e. e = 2 or 4) was used.…”

Benchmarking methods and data sets for ligand enrichment assessment in virtual screening

“…The optimization included: 1) More targets (102), including 5 GPCRs and 2 ion channels, were added to increase target diversity; 2) More annotated ligands (100–600) were collected from ChEMBL09 (https://www.ebi.ac.uk/chembl/) [101] and clustering based on Bemis-Murcko atomic frameworks [102] was performed on those ligands to ensure structural diversity; 3) The net FC was also added as an important descriptor to improve property matching between ligands and decoys; 4) The topology (extended-connectivity fingerprints of maximum diameter 4, ECFP_4) related criteria were used to select the most 25% dissimilar decoys so as to further lower the chance of “false negatives”; 5) The ratio of decoys per ligand was adjusted to 50; 6) An online tool of decoy-maker for user-supplied ligands was also provided at http://dude.docking.org/generate to broaden its application domain. In spite of its recent release, applications of DUD-E have already been reported [69, 70, 103]. …”

“…A detailed introduction of each data set is given in Table 1. To date, DUD and DUD-E have been intensively employed as gold standard data sets among the community [38, 69–74], while much fewer citations of DUD LIB VS 1.0 [56, 75] and MUV [76, 77] have been reported. In order to broaden the application domain of currently available LBVS-specific benchmarking sets, we recently proposed an unbiased method to build LBVS-specific benchmarking sets [78].…”