Robert Abel

Motifs for molecular recognition exploiting hydrophobic enclosure in protein–ligand binding

The thermodynamic properties and phase behavior of water in confined regions can vary significantly from that observed in the bulk. This is particularly true for systems in which the confinement is on the molecular-length scale. In this study, we use molecular dynamics simulations and a powerful solvent analysis technique based on inhomogenous solvation theory to investigate the properties of water molecules that solvate the confined regions of protein active sites. Our simulations and analysis indicate that the solvation of protein active sites that are characterized by hydrophobic enclosure and correlated hydrogen bonds induce atypical entropic and enthalpic penalties of hydration. These penalties apparently stabilize the protein–ligand complex with respect to the independently solvated ligand and protein, which leads to enhanced binding affinities. Our analysis elucidates several challenging cases, including the super affinity of the streptavidin–biotin system.

Role of the active-site solvent in the thermodynamics of factor Xa ligand binding.

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.

OPLS3: A Force Field Providing Broad Coverage of Drug-like Small Molecules and Proteins

The parametrization and validation of the OPLS3 force field for small molecules and proteins are reported. Enhancements with respect to the previous version (OPLS2.1) include the addition of off-atom charge sites to represent halogen bonding and aryl nitrogen lone pairs as well as a complete refit of peptide dihedral parameters to better model the native structure of proteins. To adequately cover medicinal chemical space, OPLS3 employs over an order of magnitude more reference data and associated parameter types relative to other commonly used small molecule force fields (e.g., MMFF and OPLS_2005). As a consequence, OPLS3 achieves a high level of accuracy across performance benchmarks that assess small molecule conformational propensities and solvation. The newly fitted peptide dihedrals lead to significant improvements in the representation of secondary structure elements in simulated peptides and native structure stability over a number of proteins. Together, the improvements made to both the small molecule and protein force field lead to a high level of accuracy in predicting protein-ligand binding measured over a wide range of targets and ligands (less than 1 kcal/mol RMS error) representing a 30% improvement over earlier variants of the OPLS force field.

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.

The VSGB 2.0 Model: A Next Generation Energy Model for High Resolution Protein Structure Modeling

A novel energy model (VSGB 2.0) for high resolution protein structure modeling is described, which features an optimized implicit solvent model as well as physics‐based corrections for hydrogen bonding, π–π interactions, self‐contact interactions, and hydrophobic interactions. Parameters of the VSGB 2.0 model were fit to a crystallographic database of 2239 single side chain and 100 11–13 residue loop predictions. Combined with an advanced method of sampling and a robust algorithm for protonation state assignment, the VSGB 2.0 model was validated by predicting 115 super long loops up to 20 residues. Despite the dramatically increasing difficulty in reconstructing longer loops, a high accuracy was achieved: all of the lowest energy conformations have global backbone RMSDs better than 2.0 Å from the native conformations. Average global backbone RMSDs of the predictions are 0.51, 0.63, 0.70, 0.62, 0.80, 1.41, and 1.59 Å for 14, 15, 16, 17, 18, 19, and 20 residue loop predictions, respectively. When these results are corrected for possible statistical bias as explained in the text, the average global backbone RMSDs are 0.61, 0.71, 0.86, 0.62, 1.06, 1.67, and 1.59 Å. Given the precision and robustness of the calculations, we believe that the VSGB 2.0 model is suitable to tackle “real” problems, such as biological function modeling and structure‐based drug discovery. Proteins 2011;

Contribution of explicit solvent effects to the binding affinity of small-molecule inhibitors in blood coagulation factor serine proteases.

The prevention of blood coagulation is important in treating thromboembolic disorders, and several serine proteases involved in the coagulation cascade have been classified as pharmaceutically relevant. Whereas structure‐based drug design has contributed to the development of some serine protease inhibitors, traditional computational methods have not been able to fully describe structure–activity relationships (SAR). Here, we study the SAR for a number of serine proteases by using a method that calculates the thermodynamic properties (enthalpy and entropy) of the water that solvates the active site. We show that the displacement of water from specific subpockets (such as S1–4 and the ester binding pocket) of the active site by the ligand can govern potency, especially for cases in which small chemical changes (i.e., a methyl group or halogen) result in a substantial increase in potency. Furthermore, we describe how relative binding free energies can be estimated by combining the water displacement energy with complementary terms from an implicit solvent molecular mechanics description binding.

Thermodynamic analysis of water molecules at the surface of proteins and applications to binding site prediction and characterization

Water plays an essential role in determining the structure and function of all biological systems. Recent methodological advances allow for an accurate and efficient estimation of the thermodynamic properties of water molecules at the surface of proteins. In this work, we characterize these thermodynamic properties and relate them to various structural and functional characteristics of the protein. We find that high‐energy hydration sites often exist near protein motifs typically characterized as hydrophilic, such as backbone amide groups. We also find that waters around alpha helices and beta sheets tend to be less stable than waters around loops. Furthermore, we find no significant correlation between the hydration site‐free energy and the solvent accessible surface area of the site. In addition, we find that the distribution of high‐energy hydration sites on the protein surface can be used to identify the location of binding sites and that binding sites of druggable targets tend to have a greater density of thermodynamically unstable hydration sites. Using this information, we characterize the FKBP12 protein and show good agreement between fragment screening hit rates from NMR spectroscopy and hydration site energetics. Finally, we show that water molecules observed in crystal structures are less stable on average than bulk water as a consequence of the high degree of spatial localization, thereby resulting in a significant loss in entropy. These findings should help to better understand the characteristics of waters at the surface of proteins and are expected to lead to insights that can guide structure‐based drug design efforts. Proteins 2011.

Modeling Local Structural Rearrangements Using FEP/REST: Application to Relative Binding Affinity Predictions of CDK2 Inhibitors.

Accurate and reliable calculation of protein-ligand binding affinities remains a hotbed of computer-aided drug design research. Despite the potentially large impact FEP (free energy perturbation) may have in drug design projects, practical applications of FEP in industrial contexts have been limited. In this work, we use a recently developed method, FEP/REST (free energy perturbation/replica exchange with solute tempering), to calculate the relative binding affinities for a set of congeneric ligands binding to the CDK2 receptor. We compare the FEP/REST results with traditional FEP/MD (molecular dynamics) results and MM/GBSA (molecular mechanics/Generalized Born Surface Area model) results and examine why FEP/REST performed notably better than these other methods, as well as why certain ligand mutations lead to large increases of the binding affinity while others do not. We also introduce a mathematical framework for assessing the consistency and reliability of the calculations using cycle closures in FEP mutation paths.

Docking Covalent Inhibitors: A Parameter Free Approach To Pose Prediction and Scoring

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.

Protein Side-Chain Dynamics and Residual Conformational Entropy

Changes in residual conformational entropy of proteins can be significant components of the thermodynamics of folding and binding. Nuclear magnetic resonance (NMR) spin relaxation is the only experimental technique capable of probing local protein entropy, by inference from local internal conformational dynamics. To assess the validity of this approach, the picosecond-to-nanosecond dynamics of the arginine side-chain N(epsilon)-H(epsilon) bond vectors of Escherichia coli ribonuclease H (RNase H) were determined by NMR spin relaxation and compared to the mechanistic detail provided by molecular dynamics (MD) simulations. The results indicate that arginine N(epsilon) spin relaxation primarily reflects persistence of guanidinium salt bridges and correlates well with simulated side-chain conformational entropy. In particular cases, the simulations show that the aliphatic part of the arginine side chain can retain substantial disorder while the guanidinium group maintains its salt bridges; thus, the N(epsilon)-H(epsilon) bond-vector orientation is conserved and side-chain flexibility is concealed from N(epsilon) spin relaxation. The MD simulations and an analysis of a rotamer library suggest that dynamic decoupling of the terminal moiety from the remainder of the side chain occurs for all five amino acids with more than two side-chain dihedral angles (R, K, E, Q, and M). Dynamic decoupling thus may represent a general biophysical strategy for minimizing the entropic penalties of folding and binding.