An iterative knowledge‐based scoring function to predict protein–ligand interactions: II. Validation of the scoring function

Abstract

We have developed an iterative knowledge-based scoring function (ITScore) to describe protein-ligand interactions. Here, we assess ITScore through extensive tests on native structure identification, binding affinity prediction, and virtual database screening. Specifically, ITScore was first applied to a test set of 100 protein-ligand complexes constructed by Wang et al. (J Med Chem 2003, 46, 2287), and compared with 14 other scoring functions. The results show that ITScore yielded a high success rate of 82% on identifying native-like binding modes under the criterion of rmsd < or = 2 A for each top-ranked ligand conformation. The success rate increased to 98% if the top five conformations were considered for each ligand. In the case of binding affinity prediction, ITScore also obtained a good correlation for this test set (R = 0.65). Next, ITScore was used to predict binding affinities of a second diverse test set of 77 protein-ligand complexes prepared by Muegge and Martin (J Med Chem 1999, 42, 791), and compared with four other widely used knowledge-based scoring functions. ITScore yielded a high correlation of R2 = 0.65 (or R = 0.81) in the affinity prediction. Finally, enrichment tests were performed with ITScore against four target proteins using the compound databases constructed by Jacobsson et al. (J Med Chem 2003, 46, 5781). The results were compared with those of eight other scoring functions. ITScore yielded high enrichments in all four database screening tests. ITScore can be easily combined with the existing docking programs for the use of structure-based drug design.

Discovery of novel FabF ligands inspired by platensimycin by integrating structure-based design with diversity-oriented synthetic accessibility

“…His303 and His340 then catalyse the decarboxylation of the malonyl-ACP, and carbon-carbon bond formation to extend the fatty acid chain (Figure 1). 20,25,26 Platensimycin 6 (Figure 2) is a natural product, known to target FabF, that displays good activity against a range of Gram positive bacteria including those that have developed resistance to common antibacterials. 24 Platensimycin inhibits wild type FabF with an IC 50 of 160 nM for native E. coli.…”

Classification of Current Scoring Functions

“…[63,64] A rapid spread of such methods has been witnessed in the following ten years or so. A few more examples, ranked by their first publication date, include Muegge's PMF, [65][66][67] DrugScore, [68][69][70] IT-Score, [71][72][73] and KECSA. [74] Although differing in technical aspects, these scoring functions follow the same principle.…”

Windock: Structure‐based drug discovery on windows‐based PCs

“…However, for the screening efficiency, we would suggested, according to our experiences in several projects, to divide the compounds libraries into sub libraries including 100,000 or 200,000 entries, and set the docking poses from 1000 to 5000 for consensus scoring followed since it is usually thought that the top 5-10% of the library screened is of interest in virtual screening. 28,29 We have tested the screening efficiency of WinDock on a typical currently available Windows-based PC (Table 2). It can be seen that it takes <2 s to process a compound.…”