Development of a receptor model for efficient in silico screening of HIV-1 integrase inhibitors

Abstract

Integrase (IN) is a key viral enzyme for the replication of the type-1 human immunodeficiency virus (HIV-1), and as such constitutes a relevant therapeutic target for the development of anti-HIV agents. However, the lack of crystallographic data of HIV IN complexed with the corresponding viral DNA has historically hindered the application of modern structure-based drug design techniques to the discovery of new potent IN inhibitors (INIs). Consequently, the development and validation of reliable HIV IN structural models that may be useful for the screening of large databases of chemical compounds is of particular interest. In this study, four HIV-1 IN homology models were evaluated respect to their capability to predict the inhibition potency of a training set comprising 36 previously reported INIs with IC50 values in the low nanomolar to the high micromolar range. Also, 9 inactive structurally related compounds were included in this training set. In addition, a crystallographic structure of the IN-DNA complex corresponding to the prototype foamy virus (PFV) was also evaluated as structural model for the screening of inhibitors. The applicability of high throughput screening techniques, such as blind and ligand-guided exhaustive rigid docking was assessed. The receptor models were also refined by molecular dynamics and clustering techniques to assess protein sidechain flexibility and solvent effect on inhibitor binding. Among the studied models, we conclude that the one derived from the X-ray structure of the PFV integrase exhibited the best performance to rank the potencies of the compounds in the training set, with the predictive power being further improved by explicitly modeling five water molecules within the catalytic side of IN. Also, accounting for protein sidechain flexibility enhanced the prediction of inhibition potencies among the studied compounds. Finally, an interaction fingerprint pattern was established for the fast identification of potent IN inhibitors. In conclusion, we report an exhaustively validated receptor model if IN that is useful for the efficient screening of large chemical compounds databases in the search of potent HIV-1 IN inhibitors.