Glide's ability to identify active compounds in a database screen is characterized by applying Glide to a diverse set of nine protein receptors. In many cases, two, or even three, protein sites are employed to probe the sensitivity of the results to the site geometry. To make the database screens as realistic as possible, the screens use sets of "druglike" decoy ligands that have been selected to be representative of what we believe is likely to be found in the compound collection of a pharmaceutical or biotechnology company. Results are presented for releases 1.8, 2.0, and 2.5 of Glide. The comparisons show that average measures for both "early" and "global" enrichment for Glide 2.5 are 3 times higher than for Glide 1.8 and more than 2 times higher than for Glide 2.0 because of better results for the least well-handled screens. This improvement in enrichment stems largely from the better balance of the more widely parametrized GlideScore 2.5 function and the inclusion of terms that penalize ligand-protein interactions that violate established principles of physical chemistry, particularly as it concerns the exposure to solvent of charged protein and ligand groups. Comparisons to results for the thymidine kinase and estrogen receptors published by Rognan and co-workers (J. Med. Chem. 2000, 43, 4759-4767) show that Glide 2.5 performs better than GOLD 1.1, FlexX 1.8, or DOCK 4.01.

We provide an overview of the IMPACT molecular mechanics program with an emphasis on recent developments and a description of its current functionality. With respect to core molecular mechanics technologies we include a status report for the fixed charge and polarizable force fields that can be used with the program and illustrate how the force fields, when used together with new atom typing and parameter assignment modules, have greatly expanded the coverage of organic compounds and medicinally relevant ligands. As we discuss in this review, explicit solvent simulations have been used to guide our design of implicit solvent models based on the generalized Born framework and a novel nonpolar estimator that have recently been incorporated into the program. With IMPACT it is possible to use several different advanced conformational sampling algorithms based on combining features of molecular dynamics and Monte Carlo simulations. The program includes two specialized molecular mechanics modules: Glide, a high-throughput docking program, and QSite, a mixed quantum mechanics/molecular mechanics module. These modules employ the IMPACT infrastructure as a starting point for the construction of the protein model and assignment of molecular mechanics parameters, but have then been developed to meet specialized objectives with respect to sampling and the energy function.

Structured-based drug design has traditionally relied on a single receptor structure as a target for docking and screening studies. However, it has become increasingly clear that in many cases where protein flexibility is an issue, it is critical to accurately model ligand-induced receptor movement in order to obtain high enrichment factors. We present a novel protein-ligand docking method that accounts for both ligand and receptor flexibility and accurately predicts the conformation of protein-ligand binding complexes. This method can generate viable receptor ensembles that can be used in virtual database screens. Induced Fit DockingSchrçdinger has developed technology that accounts for receptor flexibility in ligand-receptor docking by iteratively combining rigid receptor docking [using Glide (1,2)] with protein structure prediction and refinement [using Prime (3-5)] (6). While traditional rigid-receptor docking methods work well when the receptor structure does not change substantially upon ligand binding, success is limited when the protein conformation must change in order to accommodate the correct binding conformation of the ligand. Schrçdinger's induced fit docking (IFD) protocol accounts for both small backbone relaxations in the receptor structure as well as significant side-chain conformational changes. This IFD protocol has been validated on a large set of pharmaceutically relevant examples with surprisingly good results (6). In a study of 21 cases requiring a wide range of receptor movements to properly accommodate particular ligands, traditional rigid-receptor docking yields an average ligand root-mean-square deviation (RMSD) of 5.5 , while the average ligand RMSD for IFD is 1.4 , and in 18 cases the RMSD is less than 1.8 (6). As seen in Figure 1, over 95% of the cases from IFD have an RMSD less than 2 , when compared with less than 20% for rigid-receptor docking.The IFD protocol described above has recently been extended to allow for full flexibility in loop regions. In a study of the activation loop in p38 MAP kinase, the automated IFD protocol was successfully used to generate the DFG-out conformation starting from a DFG-in structure (1a9u) and the ligand from 1kv1 (BMU). The high degree of similarity between the IFD structure and 1kv1 (ligand RMSD ¼ 1.15 ) is striking given the significant difference between the DFG-out structure and the starting DFG-in structure (Figure 2).In a retrospective virtual screening study of 25K decoy ligands and 46 known actives, using an ensemble consisting of the IFD structure (DFG-out) and the 1a9u crystal structure (DFG-in), 14 actives were identified in the top 1% of the database, including BMU and BIRB 796. This is compared to only three actives when 1a9u was used alone. In summary, we have produced, using a fully automated protocol, the DFG-out conformation of p38 MAPK starting from the DFG-in conformation and an inhibitor that binds to DFG-out. Combining the induced-fit structure in an ensemble with the original DFG-in structure dramatically increased enrich...

Predicting changes in protein binding affinity due to single amino acid mutations helps us better understand the driving forces underlying protein-protein interactions and design improved biotherapeutics. Here, we use the MM-GBSA approach with the OPLS2005 force field and the VSGB2.0 solvent model to calculate differences in binding free energy between wild type and mutant proteins. Crucially, we made no changes to the scoring model as part of this work on protein-protein binding affinity—the energy model has been developed for structure prediction and has previously been validated only for calculating the energetics of small molecule binding. Here, we compare predictions to experimental data for a set of 418 single residue mutations in 21 targets and find that the MM-GBSA model, on average, performs well at scoring these single protein residue mutations. Correlation between the predicted and experimental change in binding affinity is statistically significant and the model performs well at picking “hotspots,” or mutations that change binding affinity by more than 1 kcal/mol. The promising performance of this physics-based method with no tuned parameters for predicting binding energies suggests that it can be transferred to other protein engineering problems.