Evaluation of the utility of homology models in high throughput docking

Abstract

High throughput docking (HTD) is routinely used for in silico screening of compound libraries with the aim to find novel leads in a drug discovery program. In the absence of an experimentally determined structure, a homology model can be used instead. Here we present an assessment of the utility of homology models in HTD by docking 300,000 anticipated inactive compounds along with 642 known actives into the binding site of the insulin-like growth factor 1 receptor (IGF-1R) kinase constructed by homology modeling. Twenty-one different templates were selected and the enrichment curves obtained by the homology models were compared to those obtained by three IGF-1R crystal structures. The results show a wide range of enrichments from random to as good as two of the three IGF-1R crystal structures. Nevertheless, if we consider the enrichment obtained at 2% of the database screened as a performance criterion, the best crystal structure outperforms the best homology model. Surprisingly, the sequence identity of the template to the target is not a good descriptor to predict the enrichment obtained by a homology model. The three homology models that yield the worst enrichment have the smallest binding-site volume. Based on our results, we propose ensemble docking to perform HTD with homology models.