Importance to the field-Virtual screening is a computer-based technique for identifying promising compounds to bind to a target molecule of known structure. Given the rapidly increasing number of protein and nucleic acid structures, virtual screening continues to grow as an effective method for the discovery of new inhibitors and drug molecules.Areas covered in this review-We describe virtual screening methods that are available in the AutoDock suite of programs, and several of our successes in using AutoDock virtual screening in pharmaceutical lead discovery.What the reader will gain-A general overview of the challenges of virtual screening is presented, along with the tools available in the AutoDock suite of programs for addressing these challenges.Take home message-Virtual screening is an effective tool for the discovery of compounds for use as leads in drug discovery, and the free, open source program AutoDock is an effective tool for virtual screening.

Molecular docking is a powerful tool used in drug discovery and structural biology for predicting the structures of ligand–receptor complexes. However, the accuracy of docking calculations can be limited by factors such as the neglect of protein reorganization in the scoring function; as a result, ligand screening can produce a high rate of false positive hits. Although absolute binding free energy methods still have difficulty in accurately rank-ordering binders, we believe that they can be fruitfully employed to distinguish binders from nonbinders and reduce the false positive rate. Here we study a set of ligands that dock favorably to a newly discovered, potentially allosteric site on the flap of HIV-1 protease. Fragment binding to this site stabilizes a closed form of protease, which could be exploited for the design of allosteric inhibitors. Twenty-three top-ranked protein–ligand complexes from AutoDock were subject to the free energy screening using two methods, the recently developed binding energy analysis method (BEDAM) and the standard double decoupling method (DDM). Free energy calculations correctly identified most of the false positives (≥83%) and recovered all the confirmed binders. The results show a gap averaging ≥3.7 kcal/mol, separating the binders and the false positives. We present a formula that decomposes the binding free energy into contributions from the receptor conformational macrostates, which provides insights into the roles of different binding modes. Our binding free energy component analysis further suggests that improving the treatment for the desolvation penalty associated with the unfulfilled polar groups could reduce the rate of false positive hits in docking. The current study demonstrates that the combination of docking with free energy methods can be very useful for more accurate ligand screening against valuable drug targets.

Human immunodeficiency virus type 1 (HIV-1) integrase is one of three virally encoded enzymes essential for replication and, therefore, a rational choice as a drug target for the treatment of HIV-1 infected individuals. In 2007 raltegravir became the first integrase inhibitor approved for use in the treatment of HIV infected patients, more than a decade since the approval of the first protease inhibitor (saquinavir, Hoffman La-Roche, 1995) and two decades since the approval of the first reverse transcriptase inhibitor (retrovir, Glaxo Smithkline, 1987). The slow progress towards a clinically effective HIV-1 integrase inhibitor can at least in part be attributed to a poor structural understanding of this key viral protein. Here we describe the development of a restrained molecular dynamics protocol that produces a more accurate model of the active site of this drug target. This model provides an advance on previously described models as it ensures that the catalytic DDE motif makes correct, monodentate, interactions with the two active site magnesium ions. Dynamic restraints applied to this coordination state create models with the correct solvation sphere for the metal ion complex and highlight the coordination sites available for metal binding ligands. Applying appropriate dynamic flexibility to the core domain allowed the inclusion of multiple conformational states in subsequent docking studies. These models have allowed us to (1) explore the effects of key drug resistance mutations on the dynamic flexibility and conformational preferences of HIV integrase and to (2) study raltegravir binding in the context of these dynamic models of both wild type and the G140S/Q148H drug resistant enzyme.

Ordinary least square (OLS) regression has been used widely for constructing the free scoring functions for protein-ligand interaction. However, OLS is very sensitive to the existence of outliers, and models constructed using it are easily affected by the outliers or even the choice of the dataset. On the other hand, determination of atomic charges is regarded as of central importance, because the electrostatic interaction is known to be a key contributing factor for biomolecular association. In the development of the AutoDock4 scoring function, only OLS was conducted, and the simple Gasteiger method was adopted. It is therefore of considerable interest to see whether more rigorous charge models could improve the statistical performance of the AutoDock4 scoring function. In this study, we have employed two well-established quantum chemical approaches, namely the restrained electrostatic potential (RESP) and the Austin-Model 1-Bond Charge Correction (AM1-BCC) methods, to obtain atomic partial charges, and we have compared how different charge models affect the performance of AutoDock4 scoring functions. In combination with robust regression analysis and outlier exclusion, our new protein-ligand free energy regression model with AM1-BCC charges for ligands and Amber99SB charges for proteins achieve lowest root-mean squared error of 1.637 kcal/mol for the training set of 147 complexes and 2.176 kcal/mol for the external test set of 1427 complexes. The assessment for binding pose prediction with the 100 external decoy sets indicates very high success rate of 87% with the criteria of predicted RMSD less than 2 Å. The success rates and statistical performance of our robust scoring functions are only weakly class-dependent (hydrophobic, hydrophilic, or mixed).

A chimeric feline immunodeficiency virus (FIV) protease (PR) has been engineered that supports infectivity but confers sensitivity to the human immunodeficiency virus (HIV) PR inhibitors darunavir (DRV) and lopinavir (LPV). The 6s-98S PR has five replacements mimicking homologous residues in HIV PR and a sixth which mutated from Pro to Ser during selection. Crystal structures of the 6s-98S FIV PR chimera with DRV and LPV bound have been determined at 1.7 and 1.8 Å resolution, respectively. The structures reveal the role of a flexible 90s loop and residue 98 in supporting Gag processing and infectivity and the roles of residue 37 in the active site and residues 55, 57 and 59 in the flap in conferring the ability to specifically recognize HIV PR drugs. Specifically, Ile37Val preserves tertiary structure but prevents steric clashes with DRV and LPV. Asn55Met and Val59Ile induce a distinct kink in the flap and a new hydrogen bond to DRV. Ile98Pro→Ser and Pro100Asn increase 90s loop flexibility, Gln99Val contributes hydrophobic contacts to DRV and LPV, and Pro100Asn forms compensatory hydrogen bonds. The chimeric PR exhibits a comparable number of hydrogen bonds, electrostatic interactions and hydrophobic contacts with DRV and LPV as in the corresponding HIV PR complexes, consistent with IC(50) values in the nanomolar range.