Jeremy R. Greenwood

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.

Towards the comprehensive, rapid, and accurate prediction of the favorable tautomeric states of drug-like molecules in aqueous solution

Generating the appropriate protonation states of drug-like molecules in solution is important for success in both ligand- and structure-based virtual screening. Screening collections of millions of compounds requires a method for determining tautomers and their energies that is sufficiently rapid, accurate, and comprehensive. To maximise enrichment, the lowest energy tautomers must be determined from heterogeneous input, without over-enumerating unfavourable states. While computationally expensive, the density functional theory (DFT) method M06-2X/aug-cc-pVTZ(-f) [PB-SCRF] provides accurate energies for enumerated model tautomeric systems. The empirical Hammett–Taft methodology can very rapidly extrapolate substituent effects from model systems to drug-like molecules via the relationship between pKT and pKa. Combining the two complementary approaches transforms the tautomer problem from a scientific challenge to one of engineering scale-up, and avoids issues that arise due to the very limited number of measured pKT values, especially for the complicated heterocycles often favoured by medicinal chemists for their novelty and versatility. Several hundreds of pre-calculated tautomer energies and substituent pKa effects are tabulated in databases for use in structural adjustment by the program Epik, which treats tautomers as a subset of the larger problem of the protonation states in aqueous ensembles and their energy penalties. Accuracy and coverage is continually improved and expanded by parameterizing new systems of interest using DFT and experimental data. Recommendations are made for how to best incorporate tautomers in molecular design and virtual screening workflows.

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.

Inhibition of acetyl-CoA carboxylase suppresses fatty acid synthesis and tumor growth of non-small-cell lung cancer in preclinical models

Continuous de novo fatty acid synthesis is a common feature of cancer that is required to meet the biosynthetic demands of a growing tumor. This process is controlled by the rate-limiting enzyme acetyl-CoA carboxylase (ACC), an attractive but traditionally intractable drug target. Here we provide genetic and pharmacological evidence that in preclinical models ACC is required to maintain the de novo fatty acid synthesis needed for growth and viability of non-small-cell lung cancer (NSCLC) cells. We describe the ability of ND-646—an allosteric inhibitor of the ACC enzymes ACC1 and ACC2 that prevents ACC subunit dimerization—to suppress fatty acid synthesis in vitro and in vivo. Chronic ND-646 treatment of xenograft and genetically engineered mouse models of NSCLC inhibited tumor growth. When administered as a single agent or in combination with the standard-of-care drug carboplatin, ND-646 markedly suppressed lung tumor growth in the Kras;Trp53−/− (also known as KRAS p53) and Kras;Stk11−/− (also known as KRAS Lkb1) mouse models of NSCLC. These findings demonstrate that ACC mediates a metabolic liability of NSCLC and that ACC inhibition by ND-646 is detrimental to NSCLC growth, supporting further examination of the use of ACC inhibitors in oncology.

Acetyl-CoA carboxylase inhibition by ND-630 reduces hepatic steatosis, improves insulin sensitivity, and modulates dyslipidemia in rats

Significance Using structure-based drug design, we have identified a series of potent allosteric protein–protein interaction acetyl-CoA carboxylase inhibitors, exemplified by ND-630, that interact within the acetyl-CoA carboxylase subunit phosphopeptide acceptor and dimerization site to prevent dimerization and inhibit enzymatic activity. ND-630 reduces fatty acid synthesis and stimulates fatty acid oxidation in cultured cells and experimental animals, reduces hepatic steatosis, improves insulin sensitivity, reduces weight gain without affecting food intake, and favorably affects dyslipidemia in diet-induced obese rats and reduces hepatic steatosis, improves glucose-stimulated insulin secretion, and reduces hemoglobin A1c in Zucker diabetic fatty rats. These data suggest that ND-630 may be useful in treating a variety of metabolic disorders, including metabolic syndrome, type 2 diabetes, and fatty liver disease. Simultaneous inhibition of the acetyl-CoA carboxylase (ACC) isozymes ACC1 and ACC2 results in concomitant inhibition of fatty acid synthesis and stimulation of fatty acid oxidation and may favorably affect the morbidity and mortality associated with obesity, diabetes, and fatty liver disease. Using structure-based drug design, we have identified a series of potent allosteric protein–protein interaction inhibitors, exemplified by ND-630, that interact within the ACC phosphopeptide acceptor and dimerization site to prevent dimerization and inhibit the enzymatic activity of both ACC isozymes, reduce fatty acid synthesis and stimulate fatty acid oxidation in cultured cells and in animals, and exhibit favorable drug-like properties. When administered chronically to rats with diet-induced obesity, ND-630 reduces hepatic steatosis, improves insulin sensitivity, reduces weight gain without affecting food intake, and favorably affects dyslipidemia. When administered chronically to Zucker diabetic fatty rats, ND-630 reduces hepatic steatosis, improves glucose-stimulated insulin secretion, and reduces hemoglobin A1c (0.9% reduction). Together, these data suggest that ACC inhibition by representatives of this series may be useful in treating a variety of metabolic disorders, including metabolic syndrome, type 2 diabetes mellitus, and fatty liver disease.

Structure-Activity Relationships of Selective GABA Uptake Inhibitors

For more than four decades there has been a search for selective inhibitors of GABA transporters. This has led to potent and selective inhibitors of the cloned GABA transporter subtype GAT1, which is responsible for a majority of neuronal GABA transport. The only clinically approved compound with this mechanism of action is Tiagabine. Other GABA transporter subtypes have not been targeted with comparable selectivity and potency. We here review a comprehensive series of competitive inhibitors that provide information about the GABA recognition site and summarise the structure-activity relations in a ligand-based pharmacophore model that suggests how future compounds could be designed. Finally, some of the recent results on subtype-characterised competitive inhibitors and recent lipophilic aromatic GABA uptake inhibitors are reviewed.

Improving database enrichment through ensemble docking

While it may seem intuitive that using an ensemble of multiple conformations of a receptor in structure-based virtual screening experiments would necessarily yield improved enrichment of actives relative to using just a single receptor, it turns out that at least in the p38 MAP kinase model system studied here, a very large majority of all possible ensembles do not yield improved enrichment of actives. However, there are combinations of receptor structures that do lead to improved enrichment results. We present here a method to select the ensembles that produce the best enrichments that does not rely on knowledge of active compounds or sophisticated analyses of the 3D receptor structures. In the system studied here, the small fraction of ensembles of up to 3 receptors that do yield good enrichments of actives were identified by selecting ensembles that have the best mean GlideScore for the top 1% of the docked ligands in a database screen of actives and drug-like “decoy” ligands. Ensembles of two receptors identified using this mean GlideScore metric generally outperform single receptors, while ensembles of three receptors identified using this metric consistently give optimal enrichment factors in which, for example, 40% of the known actives outrank all the other ligands in the database.

The dance of the clams: twists and turns in the family C GPCR homodimer

Abstract The recently published high-resolution crystal structures of the amino-terminal domains (ATDs) of the metabotropic glutamate 1 (mglu 1 ) receptor homodimer present an exciting milestone in the study of the molecular pharmacology of family C G-protein-coupled receptors (GPCRs). In this article, we outline recent developments in the understanding of signal transduction by family C GPCR homodimers, with particular emphasis on the conformational movements of the two ATDs, in addition to allosteric modulation and competitive and noncompetitive antagonism of these processes.

WScore: A Flexible and Accurate Treatment of Explicit Water Molecules in Ligand-Receptor Docking

We have developed a new methodology for protein-ligand docking and scoring, WScore, incorporating a flexible description of explicit water molecules. The locations and thermodynamics of the waters are derived from a WaterMap molecular dynamics simulation. The water structure is employed to provide an atomic level description of ligand and protein desolvation. WScore also contains a detailed model for localized ligand and protein strain energy and integrates an MM-GBSA scoring component with these terms to assess delocalized strain of the complex. Ensemble docking is used to take into account induced fit effects on the receptor conformation, and protein reorganization free energies are assigned via fitting to experimental data. The performance of the method is evaluated for pose prediction, rank ordering of self-docked complexes, and enrichment in virtual screening, using a large data set of PDB complexes and compared with the Glide SP and Glide XP models; significant improvements are obtained.

Accelerating drug discovery through tight integration of expert molecular design and predictive scoring

Modeling protein-ligand interactions has been a central goal of computational chemistry for many years. We here review recent progress toward this goal, and highlight the role free energy calculation methods and computational solvent analysis techniques are now having in drug discovery. We further describe recent use of these methodologies to advance two separate drug discovery programs targeting acetyl-CoA carboxylase and tyrosine kinase 2. These examples suggest that tight integration of sophisticated chemistry teams with state-of-the-art computational methods can dramatically improve the efficiency of small molecule drug discovery.