Ligand Pose and Orientational Sampling in Molecular Docking

By Ryan G. Coleman, Michael Carchia, Teague Sterling, John J. Irwin, Brian K. Shoichet. 2013

198 citations recorded by the source corpus.

Molecular docking remains an important tool for structure-based screening to find new ligands and chemical probes. As docking ambitions grow to include new scoring function terms, and to address ever more targets, the reliability and extendability of the orientation sampling, and the throughput of the method, become pressing. Here we explore sampling techniques that eliminate stochastic behavior in DOCK3.6, allowing us to optimize the method for regularly variable sampling of orientations. This also enabled a focused effort to optimize the code for efficiency, with a three-fold increase in the speed of the program. This, in turn, facilitated extensive testing of the method on the 102 targets, 22,805 ligands and 1,411,214 decoys of the Directory of Useful Decoys - Enhanced (DUD-E) benchmarking set, at multiple levels of sampling. Encouragingly, we observe that as sampling increases from 50 to 500 to 2000 to 5000 to 20000 molecular orientations in the binding site (and so from about 1×1010 to 4×1010 to 1×1011 to 2×1011 to 5×1011 mean atoms scored per target, since multiple conformations are sampled per orientation), the enrichment of ligands over decoys monotonically increases for most DUD-E targets. Meanwhile, including internal electrostatics in the evaluation ligand conformational energies, and restricting aromatic hydroxyls to low energy rotamers, further improved enrichment values. Several of the strategies used here to improve the efficiency of the code are broadly applicable in the field.

Subfields: Computational Theory and Mathematics, Materials Chemistry, Molecular Biology

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Citations in the corpus

ZINC 15 – Ligand Discovery for Everyone (2015, methodology): “We have added PDBQT to SDF, mol2, and our own ﬂ exibase formats, for both DOCK37 70 and previous versions.”
AtomNet: A Deep Convolutional Neural Network for Bioactivity Prediction in Structure-based Drug Discovery (2015, background): “7 [42] in a fully automated manner over the whole DUDE benchmark.”
A practical guide to large-scale docking (2021, methodology): “2) 17,77,79 – 81 metric to monitor the performance of a virtual screen by a single number 17 (Fig.”
DARC 2.0: Improved Docking and Virtual Screening at Protein Interaction Sites (2015, methodology): “Broadly speaking, this is strategy utilized in both AutoDock4 (through AutoGrid) [33, 43] and the DOCK suite [34, 44].”
The incorporation of protein flexibility and conformational energy penalties in docking screens to improve ligand discovery (2014, methodology): “Experimental affinities were measured by fluorescence monitoring of the heme Soret band shift as before 37,39 Flexible Receptor Preparation DOCK 3.7 41 uses physics-based scoring consisting of van der Waals 42 and ligand desolvation terms 49 , combined with interaction electrostatic using a…”