Applications of Model-Based Target Pharmacology Assessment in Defining Drug Design and DMPK Strategies: GSK Experiences.

Many critical decisions faced in early discovery require a thorough understanding of the dynamic behavior of pharmacological pathways following target engagement. From fundamental decisions on the optimal target to pursue and the ultimate drug product profile (combination of modality, potency, and compound properties) expected to elicit the desired clinical outcome to tactical program decisions such as what chemical series to pursue, what chemical properties require optimization, and what compounds to synthesize and progress, all demand detailed consideration of pharmacodynamics. Model-based target pharmacology assessment (mTPA) is a computational approach centered around large-scale virtual exploration of pharmacokinetic and pharmacodynamic models built early in discovery to guide these decisions. The present work summarizes several examples (use cases) from programs at GlaxoSmithKline that demonstrate the utility of mTPA throughout the drug discovery lifecycle.

Structural understanding of non-nucleoside inhibition in an elongating herpesvirus polymerase.

All herpesviruses encode a conserved DNA polymerase that is required for viral genome replication and serves as an important therapeutic target. Currently available herpesvirus therapies include nucleoside and non-nucleoside inhibitors (NNI) that target the DNA-bound state of herpesvirus polymerase and block replication. Here we report the ternary complex crystal structure of Herpes Simplex Virus 1 DNA polymerase bound to DNA and a 4-oxo-dihydroquinoline NNI, PNU-183792 (PNU), at 3.5 Å resolution. PNU bound at the polymerase active site, displacing the template strand and inducing a conformational shift of the fingers domain into an open state. These results demonstrate that PNU inhibits replication by blocking association of dNTP and stalling the enzyme in a catalytically incompetent conformation, ultimately acting as a nucleotide competing inhibitor (NCI). Sequence conservation of the NCI binding pocket further explains broad-spectrum activity while a direct interaction between PNU and residue V823 rationalizes why mutations at this position result in loss of inhibition.

Structures of active-state orexin receptor 2 rationalize peptide and small-molecule agonist recognition and receptor activation.

Narcolepsy type 1 (NT1) is a chronic neurological disorder that impairs the brain's ability to control sleep-wake cycles. Current therapies are limited to the management of symptoms with modest effectiveness and substantial adverse effects. Agonists of the orexin receptor 2 (OX2R) have shown promise as novel therapeutics that directly target the pathophysiology of the disease. However, identification of drug-like OX2R agonists has proven difficult. Here we report cryo-electron microscopy structures of active-state OX2R bound to an endogenous peptide agonist and a small-molecule agonist. The extended carboxy-terminal segment of the peptide reaches into the core of OX2R to stabilize an active conformation, while the small-molecule agonist binds deep inside the orthosteric pocket, making similar key interactions. Comparison with antagonist-bound OX2R suggests a molecular mechanism that rationalizes both receptor activation and inhibition. Our results enable structure-based discovery of therapeutic orexin agonists for the treatment of NT1 and other hypersomnia disorders.

Women Make COMP: Mentoring the Next Generation of Women in Computational Chemistry.

The Women Make COMP symposium (258th American Chemical Society Meeting) aims at inspiring, motivating, and supporting young women in computational and theoretical chemistry. As a role model of the event, Ada Lovelace (1815-1852) was an English mathematician and writer, known for having founded computing science.

Predicting Binding Free Energies in a Large Combinatorial Chemical Space Using Multisite λ Dynamics.

In this study, we demonstrate the extensive scalability of the biasing potential replica exchange multisite λ dynamics (BP-REX MSλD) free energy method by calculating binding affinities for 512 inhibitors to HIV Reverse Transcriptase (HIV-RT). This is the largest exploration of chemical space using free energy methods known to date, requires only a few simulations, and identifies 55 new inhibitor designs against HIV-RT predicted to be at least as potent as a tight binding reference compound (i.e., as potent as 56 nM). We highlight that BP-REX MSλD requires an order of magnitude less computational resources than conventional free energy methods while maintaining a similar level of precision, overcomes the inherent poor scalability of conventional free energy methods, and enables the exploration of combinatorially large chemical spaces in the context of in silico drug discovery.

The Transition from Academia: Overcoming the Barrier to a Career in the Drug Discovery Industry.

This perspective describes the transition from academic training (specifically graduate school and a postdoctoral fellowship) to a career in the pharmaceutical industry as a computational chemist. My personal journey from childhood to senior scientist is described, along with suggestions and insights into a career in the pharmaceutical industry.

Performance of multiple docking and refinement methods in the pose prediction D3R prospective Grand Challenge 2016.

We describe the performance of multiple pose prediction methods for the D3R 2016 Grand Challenge. The pose prediction challenge includes 36 ligands, which represent 4 chemotypes and some miscellaneous structures against the FXR ligand binding domain. In this study we use a mix of fully automated methods as well as human-guided methods with considerations of both the challenge data and publicly available data. The methods include ensemble docking, colony entropy pose prediction, target selection by molecular similarity, molecular dynamics guided pose refinement, and pose selection by visual inspection. We evaluated the success of our predictions by method, chemotype, and relevance of publicly available data. For the overall data set, ensemble docking, visual inspection, and molecular dynamics guided pose prediction performed the best with overall mean RMSDs of 2.4, 2.2, and 2.2 Å respectively. For several individual challenge molecules, the best performing method is evaluated in light of that particular ligand. We also describe the protein, ligand, and public information data preparations that are typical of our binding mode prediction workflow.

Workflows and performances in the ranking prediction of 2016 D3R Grand Challenge 2: lessons learned from a collaborative effort.

The 2016 D3R Grand Challenge 2 includes both pose and affinity or ranking predictions. This article is focused exclusively on affinity predictions submitted to the D3R challenge from a collaborative effort of the modeling and informatics group. Our submissions include ranking of 102 ligands covering 4 different chemotypes against the FXR ligand binding domain structure, and the relative binding affinity predictions of the two designated free energy subsets of 15 and 18 compounds. Using all the complex structures prepared in the same way allowed us to cover many types of workflows and compare their performances effectively. We evaluated typical workflows used in our daily structure-based design modeling support, which include docking scores, force field-based scores, QM/MM, MMGBSA, MD-MMGBSA, and MacroModel interaction energy estimations. The best performing methods for the two free energy subsets are discussed. Our results suggest that affinity ranking still remains very challenging; that the knowledge of more structural information does not necessarily yield more accurate predictions; and that visual inspection and human intervention are considerably important for ranking. Knowledge of the mode of action and protein flexibility along with visualization tools that depict polar and hydrophobic maps are very useful for visual inspection. QM/MM-based workflows were found to be powerful in affinity ranking and are encouraged to be applied more often. The standardized input and output enable systematic analysis and support methodology development and improvement for high level blinded predictions.

Adaptive Landscape Flattening Accelerates Sampling of Alchemical Space in Multisite λ Dynamics.

Multisite λ dynamics (MSλD) is a powerful emerging method in free energy calculation that allows prediction of relative free energies for a large set of compounds from very few simulations. Calculating free energy differences between substituents that constitute large volume or flexibility jumps in chemical space is difficult for free energy methods in general, and for MSλD in particular, due to large free energy barriers in alchemical space. This study demonstrates that a simple biasing potential can flatten these barriers and introduces an algorithm that determines system specific biasing potential coefficients. Two sources of error, deep traps at the end points and solvent disruption by hard-core potentials, are identified. Both scale with the size of the perturbed substituent and are removed by sharp biasing potentials and a new soft-core implementation, respectively. MSλD with landscape flattening is demonstrated on two sets of molecules: derivatives of the heat shock protein 90 inhibitor geldanamycin and derivatives of benzoquinone. In the benzoquinone system, landscape flattening leads to 2 orders of magnitude improvement in transition rates between substituents and robust solvation free energies. Landscape flattening opens up new applications for MSλD by enabling larger chemical perturbations to be sampled with improved precision and accuracy.

Molecular Mechanism for Isoform-Selective Inhibition of Acyl Protein Thioesterases 1 and 2 (APT1 and APT2).

Post-translational S-palmitoylation directs the trafficking and membrane localization of hundreds of cellular proteins, often involving a coordinated palmitoylation cycle that requires both protein acyl transferases (PATs) and acyl protein thioesterases (APTs) to actively redistribute S-palmitoylated proteins toward different cellular membrane compartments. This process is necessary for the trafficking and oncogenic signaling of S-palmitoylated Ras isoforms, and potentially many peripheral membrane proteins. The depalmitoylating enzymes APT1 and APT2 are separately conserved in all vertebrates, suggesting unique functional roles for each enzyme. The recent discovery of the APT isoform-selective inhibitors ML348 and ML349 has opened new possibilities to probe the function of each enzyme, yet it remains unclear how each inhibitor achieves orthogonal inhibition. Herein, we report the high-resolution structure of human APT2 in complex with ML349 (1.64 Å), as well as the complementary structure of human APT1 bound to ML348 (1.55 Å). Although the overall peptide backbone structures are nearly identical, each inhibitor adopts a distinct conformation within each active site. In APT1, the trifluoromethyl group of ML348 is positioned above the catalytic triad, but in APT2, the sulfonyl group of ML349 forms hydrogen bonds with active site resident waters to indirectly engage the catalytic triad and oxyanion hole. Reciprocal mutagenesis and activity profiling revealed several differing residues surrounding the active site that serve as critical gatekeepers for isoform accessibility and dynamics. Structural and biochemical analysis suggests the inhibitors occupy a putative acyl-binding region, establishing the mechanism for isoform-specific inhibition, hydrolysis of acyl substrates, and structural orthogonality important for future probe development.

Parametrization of halogen bonds in the CHARMM general force field: Improved treatment of ligand-protein interactions.

A halogen bond is a highly directional, non-covalent interaction between a halogen atom and another electronegative atom. It arises due to the formation of a small region of positive electrostatic potential opposite the covalent bond to the halogen, called the 'sigma hole.' Empirical force fields in which the electrostatic interactions are represented by atom-centered point charges cannot capture this effect because halogen atoms usually carry a negative charge and therefore interact unfavorably with other electronegative atoms. A strategy to overcome this problem is to attach a positively charged virtual particle to the halogen. In this work, we extend the additive CHARMM General Force Field (CGenFF) to include such interactions in model systems of phenyl-X, with X being Cl, Br or I including di- and trihalogenated species. The charges, Lennard-Jones parameters, and halogen-virtual particle distances were optimized to reproduce the orientation dependence of quantum mechanical interaction energies with water, acetone, and N-methylacetamide as well as experimental pure liquid properties and relative hydration free energies with respect to benzene. The resulting parameters were validated in molecular dynamics simulations on small-molecule crystals and on solvated protein-ligand complexes containing halogenated compounds. The inclusion of positive virtual sites leads to better agreement across experimental observables, including preservation of ligand binding poses as a direct result of the improved representation of halogen bonding.

Biasing Potential Replica Exchange Multisite λ-Dynamics for Efficient Free Energy Calculations.

Traditional free energy calculation methods are well-known for their drawbacks in scalability and speed in converging results particularly for calculations with large perturbations. In the present work, we report on the development of biasing potential replica exchange multisite λ-dynamics (BP-REX MSλD), which is a free energy method that is capable of performing simultaneous alchemical free energy transformations, including perturbations between flexible moieties. BP-REX MSλD and the original MSλD are applied to a series of symmetrical 2,5-benzoquinone derivatives covering a diverse chemical space and range of conformational flexibility. Improved λ-space sampling is observed for the BP-REX MSλD simulations, yielding a 2-5-fold increase in the number of transitions between substituents compared to traditional MSλD. We also demonstrate the efficacy of varying the value of c, the parameter that controls the ruggedness of the landscape mediating the sampling of λ-states, based on the flexibility of the fragment. Finally, we developed a protocol for maximizing the transition frequency between fragments. This protocol reduces the "kinetic barrier" for alchemically transforming fragments by grouping and ordering based on volume. These findings are applied to a challenging test set involving a series of geldanamycin-based inhibitors of heat shock protein 90 (Hsp90). Even though the perturbations span volume changes by as large as 60 Å(3), the values for the free energy change achieve an average unsigned error (AUE) of 1.5 kcal/mol relative to experimental Kd measurements with a reasonable correlation (R = 0.56). Our results suggest that the BP-REX MSλD algorithm is a highly efficient and scalable free energy method, which when utilized will enable routine calculations on the order of hundreds of compounds using only a few simulations.