Woody Sherman

Protein and ligand preparation: parameters, protocols, and influence on virtual screening enrichments.

Structure-based virtual screening plays an important role in drug discovery and complements other screening approaches. In general, protein crystal structures are prepared prior to docking in order to add hydrogen atoms, optimize hydrogen bonds, remove atomic clashes, and perform other operations that are not part of the x-ray crystal structure refinement process. In addition, ligands must be prepared to create 3-dimensional geometries, assign proper bond orders, and generate accessible tautomer and ionization states prior to virtual screening. While the prerequisite for proper system preparation is generally accepted in the field, an extensive study of the preparation steps and their effect on virtual screening enrichments has not been performed. In this work, we systematically explore each of the steps involved in preparing a system for virtual screening. We first explore a large number of parameters using the Glide validation set of 36 crystal structures and 1,000 decoys. We then apply a subset of protocols to the DUD database. We show that database enrichment is improved with proper preparation and that neglecting certain steps of the preparation process produces a systematic degradation in enrichments, which can be large for some targets. We provide examples illustrating the structural changes introduced by the preparation that impact database enrichment. While the work presented here was performed with the Protein Preparation Wizard and Glide, the insights and guidance are expected to be generalizable to structure-based virtual screening with other docking methods.

Prediction of Absolute Solvation Free Energies using Molecular Dynamics Free Energy Perturbation and the OPLS Force Field

The accurate prediction of protein-ligand binding free energies is a primary objective in computer-aided drug design. The solvation free energy of a small molecule provides a surrogate to the desolvation of the ligand in the thermodynamic process of protein-ligand binding. Here, we use explicit solvent molecular dynamics free energy perturbation to predict the absolute solvation free energies of a set of 239 small molecules, spanning diverse chemical functional groups commonly found in drugs and drug-like molecules. We also compare the performance of absolute solvation free energies obtained using the OPLS_2005 force field with two other commonly used small molecule force fields-general AMBER force field (GAFF) with AM1-BCC charges and CHARMm-MSI with CHelpG charges. Using the OPLS_2005 force field, we obtain high correlation with experimental solvation free energies (R(2) = 0.94) and low average unsigned errors for a majority of the functional groups compared to AM1-BCC/GAFF or CHelpG/CHARMm-MSI. However, OPLS_2005 has errors of over 1.3 kcal/mol for certain classes of polar compounds. We show that predictions on these compound classes can be improved by using a semiempirical charge assignment method with an implicit bond charge correction.

Improved Docking of Polypeptides with Glide

Predicting the binding mode of flexible polypeptides to proteins is an important task that falls outside the domain of applicability of most small molecule and protein-protein docking tools. Here, we test the small molecule flexible ligand docking program Glide on a set of 19 non-α-helical peptides and systematically improve pose prediction accuracy by enhancing Glide sampling for flexible polypeptides. In addition, scoring of the poses was improved by post-processing with physics-based implicit solvent MM-GBSA calculations. Using the best RMSD among the top 10 scoring poses as a metric, the success rate (RMSD ≤ 2.0 Å for the interface backbone atoms) increased from 21% with default Glide SP settings to 58% with the enhanced peptide sampling and scoring protocol in the case of redocking to the native protein structure. This approaches the accuracy of the recently developed Rosetta FlexPepDock method (63% success for these 19 peptides) while being over 100 times faster. Cross-docking was performed for a subset of cases where an unbound receptor structure was available, and in that case, 40% of peptides were docked successfully. We analyze the results and find that the optimized polypeptide protocol is most accurate for extended peptides of limited size and number of formal charges, defining a domain of applicability for this approach.

Probing the α‐Helical Structural Stability of Stapled p53 Peptides: Molecular Dynamics Simulations and Analysis

Reactivation of the p53 cell apoptosis pathway through inhibition of the p53‐hDM2 interaction is a viable approach to suppress tumor growth in many human cancers and stabilization of the helical structure of synthetic p53 analogs via a hydrocarbon cross‐link (staple) has been found to lead to increased potency and inhibition of protein–protein binding (J. Am. Chem. Soc. 129: 5298). However, details of the structure and dynamic stability of the stapled peptides are not well understood. Here, we use extensive all‐atom molecular dynamics simulations to study a series of stapled α‐helical peptides over a range of temperatures in solution. The peptides are found to exhibit substantial variations in predicted α‐helical propensities that are in good agreement with the experimental observations. In addition, we find significant variation in local structural flexibility of the peptides with the position of the linker, which appears to be more closely related to the observed differences in activity than the absolute α‐helical stability. These simulations provide new insights into the design of α‐helical stapled peptides and the development of potent inhibitors of α‐helical protein–protein interfaces.

Accurate and Reliable Prediction of Relative Ligand Binding Potency in Prospective Drug Discovery by Way of a Modern Free-Energy Calculation Protocol and Force Field

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 (∼5× 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 on the basis of 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.

Use of an Induced Fit Receptor Structure in Virtual Screening

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.

Mechanism of the hydrophobic effect in the biomolecular recognition of arylsulfonamides by carbonic anhydrase

The hydrophobic effect—a rationalization of the insolubility of nonpolar molecules in water—is centrally important to biomolecular recognition. Despite extensive research devoted to the hydrophobic effect, its molecular mechanisms remain controversial, and there are still no reliably predictive models for its role in protein–ligand binding. Here we describe a particularly well-defined system of protein and ligands—carbonic anhydrase and a series of structurally homologous heterocyclic aromatic sulfonamides—that we use to characterize hydrophobic interactions thermodynamically and structurally. In binding to this structurally rigid protein, a set of ligands (also defined to be structurally rigid) shows the expected gain in binding free energy as hydrophobic surface area is added. Isothermal titration calorimetry demonstrates that enthalpy determines these increases in binding affinity, and that changes in the heat capacity of binding are negative. X-ray crystallography and molecular dynamics simulations are compatible with the proposal that the differences in binding between the homologous ligands stem from changes in the number and organization of water molecules localized in the active site in the bound complexes, rather than (or perhaps in addition to) release of structured water from the apposed hydrophobic surfaces. These results support the hypothesis that structured water molecules—including both the molecules of water displaced by the ligands and those reorganized upon ligand binding—determine the thermodynamics of binding of these ligands at the active site of the protein. Hydrophobic effects in various contexts have different structural and thermodynamic origins, although all may be manifestations of the differences in characteristics of bulk water and water close to hydrophobic surfaces.

Analysis and comparison of 2D fingerprints: Insights into database screening performance using eight fingerprint methods

Virtual screening is a widely used strategy in modern drug discovery and 2D fingerprint similarity is an important tool that has been successfully applied to retrieve active compounds from large datasets. However, it is not always straightforward to select an appropriate fingerprint method and associated settings for a given problem. Here, we applied eight different fingerprint methods, as implemented in the new cheminformatics package Canvas, on a well-validated dataset covering five targets. The fingerprint methods include Linear, Dendritic, Radial, MACCS, MOLPRINT2D, Pairwise, Triplet, and Torsion. We find that most fingerprints have similar retrieval rates on average; however, each has special characteristics that distinguish its performance on different query molecules and ligand sets. For example, some fingerprints exhibit a significant ligand size dependency whereas others are more robust with respect to variations in the query or active compounds. In cases where little information is known about the active ligands, MOLPRINT2D fingerprints produce the highest average retrieval actives. When multiple queries are available, we find that a fingerprint averaged over all query molecules is generally superior to fingerprints derived from single queries. Finally, a complementarity metric is proposed to determine which fingerprint methods can be combined to improve screening results.

Large-scale systematic analysis of 2D fingerprint methods and parameters to improve virtual screening enrichments.

A systematic virtual screening study on 11 pharmaceutically relevant targets has been conducted to investigate the interrelation between 8 two-dimensional (2D) fingerprinting methods, 13 atom-typing schemes, 13 bit scaling rules, and 12 similarity metrics using the new cheminformatics package Canvas. In total, 157 872 virtual screens were performed to assess the ability of each combination of parameters to identify actives in a database screen. In general, fingerprint methods, such as MOLPRINT2D, Radial, and Dendritic that encode information about local environment beyond simple linear paths outperformed other fingerprint methods. Atom-typing schemes with more specific information, such as Daylight, Mol2, and Carhart were generally superior to more generic atom-typing schemes. Enrichment factors across all targets were improved considerably with the best settings, although no single set of parameters performed optimally on all targets. The size of the addressable bit space for the fingerprints was also explored, and it was found to have a substantial impact on enrichments. Small bit spaces, such as 1024, resulted in many collisions and in a significant degradation in enrichments compared to larger bit spaces that avoid collisions.

Water Networks Contribute to Enthalpy/Entropy Compensation in Protein–Ligand Binding

The mechanism (or mechanisms) of enthalpy-entropy (H/S) compensation in protein-ligand binding remains controversial, and there are still no predictive models (theoretical or experimental) in which hypotheses of ligand binding can be readily tested. Here we describe a particularly well-defined system of protein and ligands--human carbonic anhydrase (HCA) and a series of benzothiazole sulfonamide ligands with different patterns of fluorination--that we use to define enthalpy/entropy (H/S) compensation in this system thermodynamically and structurally. The binding affinities of these ligands (with the exception of one ligand, in which the deviation is understood) to HCA are, despite differences in fluorination pattern, indistinguishable; they nonetheless reflect significant and compensating changes in enthalpy and entropy of binding. Analysis reveals that differences in the structure and thermodynamic properties of the waters surrounding the bound ligands are an important contributor to the observed H/S compensation. These results support the hypothesis that the molecules of water filling the active site of a protein, and surrounding the ligand, are as important as the contact interactions between the protein and the ligand for biomolecular recognition, and in determining the thermodynamics of binding.