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

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.binding motifs ͉ hydrophobic effect ͉ streptavidin ͉ dewetting T he hydrophobic interaction is considered to be an important driving force in molecular recognition, yet our understanding of hydrophobicity in enclosed regions, such as those found in protein binding sites, remains incomplete. For example, the binding affinity of biotin to streptavidin is orders of magnitude larger than expected on the basis of most current theoretical models. The inability to predict such ''super affinities'' and the absence of a molecular understanding of hydrophobic enclosure effects stands as an obstacle to rational design of potent pharmacologically active compounds. A better understanding of the nature of such enclosures is essential to further progress in the area. We show how superaffinity can arise from active sites that have two important molecular recognition motifs: hydrophobic enclosure and correlated hydrogen bonds. Using molecular dynamics, we show that these motifs can induce atypical entropic and enthalpic penalties for hydration of the apostructures of proteins that stabilize the bound state with respect to the hydrated state and, hence, lead to super affinity.It is widely believed that hydrophobic interactions constitute the principal thermodynamic driving force for the binding of small molecule ligands to their cognate protein receptors. A substantial number of empirical scoring functions aimed at computing protein-ligand binding affinities have been developed; invariably, the largest contribution in such expressions represents a measure of hydrophobic contact between the protein and ligand (1). Underlying these contributions is the idea that replacement of water molecules in the protein cavity by a ligand that is complementary to the protein groups lining the cavity (making hydrogen bonds where appropriate, and hydrophobic contacts otherwise) leads to a gain in binding affinity by releasing water molecules from a suboptimal environment into solution. Standard scoring functions aimed at describing this effect are based on pairwise atom-atom terms or bur...

An innovative replica exchange (parallel tempering) method called replica exchange with solute tempering (REST) for the efficient sampling of aqueous protein solutions is presented here. The method bypasses the poor scaling with system size of standard replica exchange and thus reduces the number of replicas (parallel processes) that must be used. This reduction is accomplished by deforming the Hamiltonian function for each replica in such a way that the acceptance probability for the exchange of replica configurations does not depend on the number of explicit water molecules in the system. For proof of concept, REST is compared with standard replica exchange for an alanine dipeptide molecule in water. The comparisons confirm that REST greatly reduces the number of CPUs required by regular replica exchange and increases the sampling efficiency. This method reduces the CPU time required for calculating thermodynamic averages and for the ab initio folding of proteins in explicit water. molecular dynamics ͉ Monte Carlo ͉ parallel tempering ͉ protein solutions ͉ rough energy landscapes S ampling the conformation space of complex systems, such as proteins, is a notoriously difficult problem in structural biology and theoretical chemistry. The difficulty arises from the infrequent crossings of high-energy barriers between local energy minima, leading to local trapping for long times and concomitant quasi-ergodicity in the sampling. Many methods have been devised to overcome the problem of quasi-ergodicity. These methods include the multicanonical ensemble method (1-3), the simulated tempering method (4-6), and the parallel tempering or replica exchange method (REM) (7-9).The first two methods require a non-Boltzmann weight factor arrived at by iteration. For systems with rough energy landscapes, such as proteins dissolved in explicit water, obtaining the weight factor is not a trivial process. Thus, the REM has been attracting more and more attention because the standard Boltzmann weight factor can be used. By using high-temperature replicas to overcome the energy barrier, the REM has proven to be a useful method for sampling phase space (10, 11).For the standard REM, the number of replicas needed increases as O(f 1/2 ), where f is the solution's total number of degrees of freedom (12). Even for a relatively small biomolecular system consisting of one ␤-hairpin protein molecule dissolved in water (4,342 atoms in all), 64 replicas were needed to cover the temperature range between 270 and 695 K with a nonvanishing acceptance ratio for replica exchange (13). This requirement severely restricts the applicability of REM to reasonably small systems, unless one has access to a massively parallel computer.The main reason that a large number of replicas are required is that the overall Hamiltonian grows with system size. The acceptance probability for the exchange of configurations between two replicas at different temperatures is exp(⌬␤⌬E), a quantity that depends exponentially on the change in energy. For a larger system, o...

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.

The accurate prediction of protein-ligand binding free energies remains a significant challenge of central importance in computational biophysics and structure-based drug design. Multiple recent advances including the development of greatly improved protein and ligand molecular mechanics force fields, more efficient enhanced sampling methods, and low-cost powerful GPU computing clusters have enabled accurate and reliable predictions of relative protein-ligand binding free energies through the free energy perturbation (FEP) methods. However, the existing FEP methods can only be used to calculate the relative binding free energies for R-group modifications or single-atom modifications and cannot be used to efficiently evaluate scaffold hopping modifications to a lead molecule. Scaffold hopping or core hopping, a very common design strategy in drug discovery projects, is critical not only in the early stages of a discovery campaign where novel active matter must be identified but also in lead optimization where the resolution of a variety of ADME/Tox problems may require identification of a novel core structure. In this paper, we introduce a method that enables theoretically rigorous, yet computationally tractable, relative protein-ligand binding free energy calculations to be pursued for scaffold hopping modifications. We apply the method to six pharmaceutically interesting cases where diverse types of scaffold hopping modifications were required to identify the drug molecules ultimately sent into the clinic. For these six diverse cases, the predicted binding affinities were in close agreement with experiment, demonstrating the wide applicability and the significant impact Core Hopping FEP may provide in drug discovery projects.