Induced-Fit Docking Enables Accurate Free Energy Perturbation Calculations in Homology Models.

Homology models have been used for virtual screening and to understand the binding mode of a known active compound; however, rarely have the models been shown to be of sufficient accuracy, comparable to crystal structures, to support free-energy perturbation (FEP) calculations. We demonstrate here that the use of an advanced induced-fit docking methodology reliably enables predictive FEP calculations on congeneric series across homology models ≥30% sequence identity. Furthermore, we show that retrospective FEP calculations on a congeneric series of drug-like ligands are sufficient to discriminate between predicted binding modes. Results are presented for a total of 29 homology models for 14 protein targets, showing FEP results comparable to those obtained using experimentally determined crystal structures for 86% of homology models with template structure sequence identities ranging from 30 to 50%. Implications for the use and validation of homology models in drug discovery projects are discussed.

Plasticity in ligand recognition at somatostatin receptors.

Somatostatin is a signaling peptide that plays a pivotal role in physiologic processes relating to metabolism and growth through its actions at somatostatin receptors (SSTRs). Members of the SSTR subfamily, particularly SSTR2, are key drug targets for neuroendocrine neoplasms, with synthetic peptide agonists currently in clinical use. Here, we show the cryogenic-electron microscopy structures of active-state SSTR2 in complex with heterotrimeric Gi3 and either the endogenous ligand SST14 or the FDA-approved drug octreotide. Complemented by biochemical assays and molecular dynamics simulations, these structures reveal key details of ligand recognition and receptor activation at SSTRs. We find that SSTR ligand recognition is highly diverse, as demonstrated by ligand-induced conformational changes in ECL2 and substantial sequence divergence across subtypes in extracellular regions. Despite this complexity, we rationalize several known sources of SSTR subtype selectivity and identify an additional interaction for specific binding. These results provide valuable insights for structure-based drug discovery at SSTRs.

Structural basis for isoform-specific inhibition of human CTPS1.

Cytidine triphosphate synthase 1 (CTPS1) is necessary for an effective immune response, as revealed by severe immunodeficiency in CTPS1-deficient individuals [E. Martin et al], [Nature] [510], [288-292] ([2014]). CTPS1 expression is up-regulated in activated lymphocytes to expand CTP pools [E. Martin et al], [Nature] [510], [288-292] ([2014]), satisfying increased demand for nucleic acid and lipid synthesis [L. D. Fairbanks, M. Bofill, K. Ruckemann, H. A. Simmonds], [J. Biol. Chem. ] [270], [29682-29689] ([1995]). Demand for CTP in other tissues is met by the CTPS2 isoform and nucleoside salvage pathways [E. Martin et al], [Nature] [510], [288-292] ([2014]). Selective inhibition of the proliferative CTPS1 isoform is therefore desirable in the treatment of immune disorders and lymphocyte cancers, but little is known about differences in regulation of the isoforms or mechanisms of known inhibitors. We show that CTP regulates both isoforms by binding in two sites that clash with substrates. CTPS1 is less sensitive to CTP feedback inhibition, consistent with its role in increasing CTP levels in proliferation. We also characterize recently reported small-molecule inhibitors, both CTPS1 selective and nonselective. Cryo-electron microscopy (cryo-EM) structures reveal these inhibitors mimic CTP binding in one inhibitory site, where a single amino acid substitution explains selectivity for CTPS1. The inhibitors bind to CTPS assembled into large-scale filaments, which for CTPS1 normally represents a hyperactive form of the enzyme [E. M. Lynch et al], [Nat. Struct. Mol. Biol.] [24], [507-514] ([2017]). This highlights the utility of cryo-EM in drug discovery, particularly for cases in which targets form large multimeric assemblies not amenable to structure determination by other techniques. Both inhibitors also inhibit the proliferation of human primary T cells. The mechanisms of selective inhibition of CTPS1 lay the foundation for the design of immunosuppressive therapies.

Macromolecular refinement of X-ray and cryoelectron microscopy structures with Phenix/OPLS3e for improved structure and ligand quality.

With the advent of the resolution revolution in cryoelectron microscopy (cryo-EM), low-resolution refinement is common, and likewise increases the need for a reliable force field. Here, we report on the incorporation of the OPLS3e force field with the VSGB2.1 solvation model in the structure determination package Phenix. Our results show significantly improved structure quality and reduced ligand strain at lower resolution for X-ray refinement. For refinement of cryo-EM-based structures, we find comparable quality structures, goodness-of-fit, and reduced ligand strain. We also show how structure quality and ligand strain are related to the map-model cross-correlation as a function of data weight, and how that can detect overfitting. Signs of overfitting are found in over half of our cryo-EM dataset, which can be remedied by a re-refinement at a lower data weight. Finally, a start-to-end script for refining structures with Phenix/OPLS3e is available in the Schrödinger 2020-3 distribution.

Reliable and Accurate Solution to the Induced Fit Docking Problem for Protein-Ligand Binding.

We present a reliable and accurate solution to the induced fit docking problem for protein-ligand binding by combining ligand-based pharmacophore docking, rigid receptor docking, and protein structure prediction with explicit solvent molecular dynamics simulations. This novel methodology in detailed retrospective and prospective testing succeeded to determine protein-ligand binding modes with a root-mean-square deviation within 2.5 Å in over 90% of cross-docking cases. We further demonstrate these predicted ligand-receptor structures were sufficiently accurate to prospectively enable predictive structure-based drug discovery for challenging targets, substantially expanding the domain of applicability for such methods.

Synthetic group A streptogramin antibiotics that overcome Vat resistance.

Natural products serve as chemical blueprints for most antibiotics in clinical use. The evolutionary process by which these molecules arise is inherently accompanied by the co-evolution of resistance mechanisms that shorten the clinical lifetime of any given class of antibiotics1. Virginiamycin acetyltransferase (Vat) enzymes are resistance proteins that provide protection against streptogramins2, potent antibiotics against Gram-positive bacteria that inhibit the bacterial ribosome3. Owing to the challenge of selectively modifying the chemically complex, 23-membered macrocyclic scaffold of group A streptogramins, analogues that overcome the resistance conferred by Vat enzymes have not been previously developed2. Here we report the design, synthesis, and antibacterial evaluation of group A streptogramin antibiotics with extensive structural variability. Using cryo-electron microscopy and forcefield-based refinement, we characterize the binding of eight analogues to the bacterial ribosome at high resolution, revealing binding interactions that extend into the peptidyl tRNA-binding site and towards synergistic binders that occupy the nascent peptide exit tunnel. One of these analogues has excellent activity against several streptogramin-resistant strains of Staphylococcus aureus, exhibits decreased rates of acetylation in vitro, and is effective at lowering bacterial load in a mouse model of infection. Our results demonstrate that the combination of rational design and modular chemical synthesis can revitalize classes of antibiotics that are limited by naturally arising resistance mechanisms.

GemSpot: A Pipeline for Robust Modeling of Ligands into Cryo-EM Maps.

Producing an accurate atomic model of biomolecule-ligand interactions from maps generated by cryoelectron microscopy (cryo-EM) often presents challenges inherent to the methodology and the dynamic nature of ligand binding. Here, we present GemSpot, an automated pipeline of computational chemistry methods that take into account EM map potentials, quantum mechanics energy calculations, and water molecule site prediction to generate candidate poses and provide a measure of the degree of confidence. The pipeline is validated through several published cryo-EM structures of complexes in different resolution ranges and various types of ligands. In all cases, at least one identified pose produced both excellent interactions with the target and agreement with the map. GemSpot will be valuable for the robust identification of ligand poses and drug discovery efforts through cryo-EM.

qFit-ligand Reveals Widespread Conformational Heterogeneity of Drug-Like Molecules in X-Ray Electron Density Maps.

Proteins and ligands sample a conformational ensemble that governs molecular recognition, activity, and dissociation. In structure-based drug design, access to this conformational ensemble is critical to understand the balance between entropy and enthalpy in lead optimization. However, ligand conformational heterogeneity is currently severely underreported in crystal structures in the Protein Data Bank, owing in part to a lack of automated and unbiased procedures to model an ensemble of protein-ligand states into X-ray data. Here, we designed a computational method, qFit-ligand, to automatically resolve conformationally averaged ligand heterogeneity in crystal structures, and applied it to a large set of protein receptor-ligand complexes. In an analysis of the cancer related BRD4 domain, we found that up to 29% of protein crystal structures bound with drug-like molecules present evidence of unmodeled, averaged, relatively isoenergetic conformations in ligand-receptor interactions. In many retrospective cases, these alternate conformations were adventitiously exploited to guide compound design, resulting in improved potency or selectivity. Combining qFit-ligand with high-throughput screening or multitemperature crystallography could therefore augment the structure-based drug design toolbox.

Aromatic stacking interactions govern catalysis in aryl-alcohol oxidase.

Aryl-alcohol oxidase (AAO, EC 1.1.3.7) generates H2 O2 for lignin degradation at the expense of benzylic and other π system-containing primary alcohols, which are oxidized to the corresponding aldehydes. Ligand diffusion studies on Pleurotus eryngii AAO showed a T-shaped stacking interaction between the Tyr92 side chain and the alcohol substrate at the catalytically competent position for concerted hydride and proton transfers. Bi-substrate kinetics analysis revealed that reactions with 3-chloro- or 3-fluorobenzyl alcohols (halogen substituents) proceed via a ping-pong mechanism. However, mono- and dimethoxylated substituents (in 4-methoxybenzyl and 3,4-dimethoxybenzyl alcohols) altered the mechanism and a ternary complex was formed. Electron-withdrawing substituents resulted in lower quantum mechanics stacking energies between aldehyde and the tyrosine side chain, contributing to product release, in agreement with the ping-pong mechanism observed in 3-chloro- and 3-fluorobenzyl alcohol kinetics analysis. In contrast, the higher stacking energies when electron donor substituents are present result in reaction of O2 with the flavin through a ternary complex, in agreement with the kinetics of methoxylated alcohols. The contribution of Tyr92 to the AAO reaction mechanism was investigated by calculation of stacking interaction energies and site-directed mutagenesis. Replacement of Tyr92 by phenylalanine does not alter the AAO kinetic constants (on 4-methoxybenzyl alcohol), most probably because the stacking interaction is still possible. However, introduction of a tryptophan residue at this position strongly reduced the affinity for the substrate (i.e. the pre-steady state Kd and steady-state Km increase by 150-fold and 75-fold, respectively), and therefore the steady-state catalytic efficiency, suggesting that proper stacking is impossible with this bulky residue. The above results confirm the role of Tyr92 in substrate binding, thus governing the kinetic mechanism in AAO.

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.

Structure-based virtual screening approach for discovery of covalently bound ligands.

We present a fast and effective covalent docking approach suitable for large-scale virtual screening (VS). We applied this method to four targets (HCV NS3 protease, Cathepsin K, EGFR, and XPO1) with known crystal structures and known covalent inhibitors. We implemented a customized "VS mode" of the Schrödinger Covalent Docking algorithm (CovDock), which we refer to as CovDock-VS. Known actives and target-specific sets of decoys were docked to selected X-ray structures, and poses were filtered based on noncovalent protein-ligand interactions known to be important for activity. We were able to retrieve 71%, 72%, and 77% of the known actives for Cathepsin K, HCV NS3 protease, and EGFR within 5% of the decoy library, respectively. With the more challenging XPO1 target, where no specific interactions with the protein could be used for postprocessing of the docking results, we were able to retrieve 95% of the actives within 30% of the decoy library and achieved an early enrichment factor (EF1%) of 33. The poses of the known actives bound to existing crystal structures of 4 targets were predicted with an average RMSD of 1.9 Å. To the best of our knowledge, CovDock-VS is the first fully automated tool for efficient virtual screening of covalent inhibitors. Importantly, CovDock-VS can handle multiple chemical reactions within the same library, only requiring a generic SMARTS-based predefinition of the reaction. CovDock-VS provides a fast and accurate way of differentiating actives from decoys without significantly deteriorating the accuracy of the predicted poses for covalent protein-ligand complexes. Therefore, we propose CovDock-VS as an efficient structure-based virtual screening method for discovery of novel and diverse covalent ligands.

Leveraging Data Fusion Strategies in Multireceptor Lead Optimization MM/GBSA End-Point Methods.

Accurate and efficient affinity calculations are critical to enhancing the contribution of in silico modeling during the lead optimization phase of a drug discovery campaign. Here, we present a large-scale study of the efficacy of data fusion strategies to leverage results from end-point MM/GBSA calculations in multiple receptors to identify potent inhibitors among an ensemble of congeneric ligands. The retrospective analysis of 13 congeneric ligand series curated from publicly available data across seven biological targets demonstrates that in 90% of the individual receptor structures MM/GBSA scores successfully identify subsets of inhibitors that are more potent than a random selection, and data fusion strategies that combine MM/GBSA scores from each of the receptors significantly increase the robustness of the predictions. Among nine different data fusion metrics based on consensus scores or receptor rankings, the SumZScore (i.e., converting MM/GBSA scores into standardized Z-Scores within a receptor and computing the sum of the Z-Scores for a given ligand across the ensemble of receptors) is found to be a robust and physically meaningful metric for combining results across multiple receptors. Perhaps most surprisingly, even with relatively low to modest overall correlations between SumZScore and experimental binding affinities, SumZScore tends to reliably prioritize subsets of inhibitors that are at least as potent as those that are prioritized from a "best" single receptor identified from known compounds within the congeneric series.

Testing physical models of passive membrane permeation.

The biophysical basis of passive membrane permeability is well-understood, but most methods for predicting membrane permeability in the context of drug design are based on statistical relationships that indirectly capture the key physical aspects. Here, we investigate molecular mechanics-based models of passive membrane permeability and evaluate their performance against different types of experimental data, including parallel artificial membrane permeability assays (PAMPA), cell-based assays, in vivo measurements, and other in silico predictions. The experimental data sets we use in these tests are diverse, including peptidomimetics, congeneric series, and diverse FDA approved drugs. The physical models are not specifically trained for any of these data sets; rather, input parameters are based on standard molecular mechanics force fields, such as partial charges, and an implicit solvent model. A systematic approach is taken to analyze the contribution from each component in the physics-based permeability model. A primary factor in determining rates of passive membrane permeation is the conformation-dependent free energy of desolvating the molecule, and this measure alone provides good agreement with experimental permeability measurements in many cases. Other factors that improve agreement with experimental data include deionization and estimates of entropy losses of the ligand and the membrane, which lead to size-dependence of the permeation rate.

Substrate diffusion and oxidation in GMC oxidoreductases: an experimental and computational study on fungal aryl-alcohol oxidase.

AAO (aryl-alcohol oxidase) provides H2O2 in fungal degradation of lignin, a process of high biotechnological interest. The crystal structure of AAO does not show open access to the active site, where different aromatic alcohols are oxidized. In the present study we investigated substrate diffusion and oxidation in AAO compared with the structurally related CHO (choline oxidase). Cavity finder and ligand diffusion simulations indicate the substrate-entrance channel, requiring side-chain displacements and involving a stacking interaction with Tyr9². Mixed QM (quantum mechanics)/MM (molecular mechanics) studies combined with site-directed mutagenesis showed two active-site catalytic histidine residues, whose substitution strongly decreased both catalytic and transient-state reduction constants for p-anisyl alcohol in the H502A (over 1800-fold) and H546A (over 35-fold) variants. Combination of QM/MM energy profiles, protonation predictors, molecular dynamics, mutagenesis and pH profiles provide a robust answer regarding the nature of the catalytic base. The histidine residue in front of the FAD ring, AAO His5°² (and CHO His466), acts as a base. For the two substrates assayed, it was shown that proton transfer preceded hydride transfer, although both processes are highly coupled. No stable intermediate was observed in the energy profiles, in contrast with that observed for CHO. QM/MM, together with solvent KIE (kinetic isotope effect) results, suggest a non-synchronous concerted mechanism for alcohol oxidation by AAO.

Exploring hierarchical refinement techniques for induced fit docking with protein and ligand flexibility.

We present a series of molecular-mechanics-based protein refinement methods, including two novel ones, applied as part of an induced fit docking procedure. The methods used include minimization; protein and ligand sidechain prediction; a hierarchical ligand placement procedure similar to a-priori protein loop predictions; and a minimized Monte Carlo approach using normal mode analysis as a move step. The results clearly indicate the importance of a proper opening of the active site backbone, which might not be accomplished when the ligand degrees of freedom are prioritized. The most accurate method consisted of the minimized Monte Carlo procedure designed to open the active site followed by a hierarchical optimization of the sidechain packing around a mobile flexible ligand. The methods have been used on a series of 88 protein-ligand complexes including both cross-docking and apo-docking members resulting in complex conformations determined to within 2.0 A heavy-atom RMSD in 75% of cases where the protein backbone rearrangement upon binding is less than 1.0 A alpha-carbon RMSD. We also demonstrate that physics-based all-atom potentials can be more accurate than docking-style potentials when complexes are sufficiently refined.

Ligand migration in the truncated hemoglobin-II from Mycobacterium tuberculosis: the role of G8 tryptophan.

Resonance Raman studies show that the heme-bound CO in trHbO, a truncated-II hemoglobin from Mycobacterium tuberculosis, is exposed to an environment with a positive electrostatic potential. The mutation of Trp(G8), an absolutely conserved residue in group II and III truncated hemoglobins, to Phe introduces two new Fe-CO conformers, both of which exhibit reduced electrostatic potentials. Computer simulations reveal that the structural perturbation is a result of the increased flexibility of the Tyr(CD1) and Leu(E11) side chains due to the reduction of the size of the G8 residue. Laser flash photolysis studies show that the G8 mutation induces 1) the presence of two new geminate recombination phases, one with a rate faster than the time resolution of our instrument and the other with a rate 13-fold slower than that of the wild type protein, and 2) the reduction of the total geminate recombination yield from 86 to 62% and the increase in the bimolecular recombination rate by a factor of 530. Computer simulations uncover that the photodissociated ligand migrates between three distal temporary docking sites before it subsequently rebinds to the heme iron or ultimately escapes into the solvent via a hydrophobic tunnel. The calculated energy profiles associated with the ligand migration processes are in good agreement with the experimental observations. The results highlight the importance of the Trp(G8) in regulating ligand migration in trHbO, underscoring its pivotal role in the structural and functional properties of the group II and III truncated hemoglobins.

A binding mechanism in protein-nucleotide interactions: implication for U1A RNA binding.

We present a close electronic view of the protein-base interface for the N-terminal domain of the human protein U1A. Combining accurate mixed quantum mechanics/molecular mechanics techniques and protein structure prediction methods, we provide a detailed electronic structure description of the protein-RNA stacking interactions. Our analysis indicates the evolution of the protein structure optimizing the interaction between Asp-92 and the RNA bases. The results show a direct coupling of the C-terminal tail and Asp-92, providing a direct rationalization of the experimentally determined role of the C-terminal domain in RNA binding. Here, we propose a mechanism where a protein side chain, with a delocalized electronic pi system, assists in the nucleotide binding. The binding mechanism involves a short-range interaction of the side chain with the nucleotide base and an electronic long-range interaction through a sandwich-stacking motif. The structural motif of the binding mechanism is observed in similar protein-RNA interactions and in various protein-ATP-binding sites.

PELE: Protein Energy Landscape Exploration. A Novel Monte Carlo Based Technique.

Combining protein structure prediction algorithms and Metropolis Monte Carlo techniques, we provide a novel method to explore all-atom energy landscapes. The core of the technique is based on a steered localized perturbation followed by side-chain sampling as well as minimization cycles. The algorithm and its application to ligand diffusion are presented here. Ligand exit pathways are successfully modeled for different systems containing ligands of various sizes: carbon monoxide in myoglobin, camphor in cytochrome P450cam, and palmitic acid in the intestinal fatty-acid-binding protein. These initial applications reveal the potential of this new technique in mapping millisecond-time-scale processes. The computational cost associated with the exploration is significantly less than that of conventional MD simulations.