Abstract
Prediction of the bound configuration of small-molecule ligands that differ substantially from the cognate ligand of a protein co-crystal structure is much more challenging than re-docking the cognate ligand. Success rates for cross-docking in the range of 20–30 % are common. We present an approach that uses structural information known prior to a particular cutoff-date to make predictions on ligands whose bounds structures were determined later. The knowledge-guided docking protocol was tested on a set of ten protein targets using a total of 949 ligands. The benchmark data set, called PINC (“PINC Is Not Cognate”), is publicly available. Protein pocket similarity was used to choose representative structures for ensemble-docking. The docking protocol made use of known ligand poses prior to the cutoff-date, both to help guide the configurational search and to adjust the rank of predicted poses. Overall, the top-scoring pose family was correct over 60 % of the time, with the top-two pose families approaching a 75 % success rate. Correct poses among all those predicted were identified nearly 90 % of the time. The largest improvements came from the use of molecular similarity to improve ligand pose rankings and the strategy for identifying representative protein structures. With the exception of a single outlier target, the knowledge-guided docking protocol produced results matching the quality of cognate-ligand re-docking, but it did so on a very challenging temporally-segregated cross-docking benchmark.
Highlights
Docking of small molecules to protein binding sites by computational means is a mature field, having been established on rigid ligands in the 1980s [1]
On benchmark sets containing dozens of diverse targets, success rates in the late 1990s to early 2000s for top-scoring pose prediction were roughly 70–80 % [4, 10, 11]. Success rates at this level for cognate ligand re-docking have persisted across different data sets [4, 12], though lower success rates have been reported for challenging cognate-docking benchmarks [13, 14]
In contrast to the results reported by Warren et al, only in the case of one target and one docking algorithm was the success rate over 50 %
Summary
Docking of small molecules to protein binding sites by computational means is a mature field, having been established on rigid ligands in the 1980s [1]. The first practical methods that addressed ligand flexibility in an automatic fashion appeared in the 1990s, with AutoDock [2], GOLD [3, 4], Hammerhead [5,6,7], and FlexX [8, 9]. These early reports shared a common validation strategy: re-docking of ligands into their cognate protein binding pockets, with success rates typically defined as symmetrycorrected RMSD B2.0 A. Among the more widely used methods (DOCK, FlexX, Glide, GOLD, and SurflexDock), using agnostic procedures for complex preparation that favored no method in particular, success rates in the low to mid 70 % range were typical
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