Designing tight binding ligands is a primary objective of small molecule drug discovery.Over the past few decades, free energy calculations have benefited from improved force fields and sampling algorithms, as well as the advent of low cost parallel computing.However, it has proven to be challenging to reliably achieve the level of accuracy that would be needed to guide lead optimization (~5X in binding affinity) for a wide range of ligands and protein targets. Not surprisingly, widespread commercial application of free energy simulations has been limited due to the lack of large-scale validation coupled with the technical challenges traditionally associated with running these types of calculations.Here, we report an approach that achieves an unprecedented level of accuracy across a broad range of target classes and ligands, with retrospective results encompassing 200 ligands and a wide variety of chemical perturbations, many of which involve significant changes in ligand chemical structures. In addition, we have applied the method in prospective drug discovery projects and found a significant improvement in the quality of the compounds synthesized that have been predicted to be potent. Compounds predicted to be potent by this approach have a substantial reduction in false positives relative to compounds synthesized based on other computational or medicinal chemistry approaches. Furthermore, the results are consistent with those obtained from our retrospective studies, demonstrating the robustness and broad range of applicability of this approach, which can be used to drive decisions in lead optimization.3

We present a novel protein-ligand docking method that accurately accounts for both ligand and receptor flexibility by iteratively combining rigid receptor docking (Glide) with protein structure prediction (Prime) techniques. While traditional rigid-receptor docking methods are useful when the receptor structure does not change substantially upon ligand binding, success is limited when the protein must be "induced" into the correct binding conformation for a given ligand. We provide an in-depth description of our novel methodology and present results for 21 pharmaceutically relevant examples. Traditional rigid-receptor docking for these 21 cases yields an average RMSD of 5.5 A. The average ligand RMSD for docking to a flexible receptor for the 21 pairs is 1.4 A; the RMSD is < or =1.8 A for 18 of the cases. For the three cases with RMSDs greater than 1.8 A, the core of the ligand is properly docked and all key protein/ligand interactions are captured.

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.

Understanding the underlying physics of the binding of small-molecule ligands to protein active sites is a key objective of computational chemistry and biology. It is widely believed that displacement of water molecules from the active site by the ligand is a principal (if not the dominant) source of binding free energy. Although continuum theories of hydration are routinely used to describe the contributions of the solvent to the binding affinity of the complex, it is still an unsettled question as to whether or not these continuum solvation theories describe the underlying molecular physics with sufficient accuracy to reliably rank the binding affinities of a set of ligands for a given protein. Here we develop a novel, computationally efficient descriptor of the contribution of the solvent to the binding free energy of a small molecule and its associated receptor that captures the effects of the ligand displacing the solvent from the protein active site with atomic detail. This descriptor quantitatively predicts (R(2) = 0.81) the binding free energy differences between congeneric ligand pairs for the test system factor Xa, elucidates physical properties of the active-site solvent that appear to be missing in most continuum theories of hydration, and identifies several features of the hydration of the factor Xa active site relevant to the structure-activity relationship of its inhibitors.

A water-soluble, 62-residue, di-alpha-helical peptide has been synthesized which accommodates two bis-histidyl haem groups. The peptide assembles into a four-helix dimer with 2-fold symmetry and four parallel haems that closely resemble native haems in their spectral and electrochemical properties, including haem-haem redox interaction. This protein is an essential intermediate in the synthesis of molecular 'maquettes', a novel class of simplified versions of the metalloproteins involved in redox catalysis and in energy conversion in respiratory and photosynthetic electron transfer.

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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...

Although many popular docking programs include a facility to account for covalent ligands, large-scale systematic docking validation studies of covalent inhibitors have been sparse. In this paper, we present the development and validation of a novel approach for docking and scoring covalent inhibitors, which consists of conventional noncovalent docking, heuristic formation of the covalent attachment point, and structural refinement of the protein-ligand complex. This approach combines the strengths of the docking program Glide and the protein structure modeling program Prime and does not require any parameter fitting for the study of additional covalent reaction types. We first test this method by predicting the native binding geometry of 38 covalently bound complexes. The average RMSD of the predicted poses is 1.52 Å, and 76% of test set inhibitors have an RMSD of less than 2.0 Å. In addition, the apparent affinity score constructed herein is tested on a virtual screening study and the characterization of the SAR properties of two different series of congeneric compounds with satisfactory success.