Automated benchmarking of combined protein structure and ligand conformation prediction.

The prediction of protein-ligand complexes (PLC), using both experimental and predicted structures, is an active and important area of research, underscored by the inclusion of the Protein-Ligand Interaction category in the latest round of the Critical Assessment of Protein Structure Prediction experiment CASP15. The prediction task in CASP15 consisted of predicting both the three-dimensional structure of the receptor protein as well as the position and conformation of the ligand. This paper addresses the challenges and proposed solutions for devising automated benchmarking techniques for PLC prediction. The reliability of experimentally solved PLC as ground truth reference structures is assessed using various validation criteria. Similarity of PLC to previously released complexes are employed to judge PLC diversity and the difficulty of a PLC as a prediction target. We show that the commonly used PDBBind time-split test-set is inappropriate for comprehensive PLC evaluation, with state-of-the-art tools showing conflicting results on a more representative and high quality dataset constructed for benchmarking purposes. We also show that redocking on crystal structures is a much simpler task than docking into predicted protein models, demonstrated by the two PLC-prediction-specific scoring metrics created. Finally, we introduce a fully automated pipeline that predicts PLC and evaluates the accuracy of the protein structure, ligand pose, and protein-ligand interactions.

Assessment of protein-ligand complexes in CASP15.

CASP15 introduced a new category, ligand prediction, where participants were provided with a protein or nucleic acid sequence, SMILES line notation, and stoichiometry for ligands and tasked with generating computational models for the three-dimensional structure of the corresponding protein-ligand complex. These models were subsequently compared with experimental structures determined by x-ray crystallography or cryoEM. To assess these predictions, two novel scores were developed. The Binding-Site Superposed, Symmetry-Corrected Pose Root Mean Square Deviation (BiSyRMSD) evaluated the absolute deviations of the models from the experimental structures. At the same time, the Local Distance Difference Test for Protein-Ligand Interactions (lDDT-PLI) assessed the ability of models to reproduce the protein-ligand interactions in the experimental structures. The ligands evaluated in this challenge range from single-atom ions to large flexible organic molecules. More than 1800 submissions were evaluated for their ability to predict 23 different protein-ligand complexes. Overall, the best models could faithfully reproduce the geometries of more than half of the prediction targets. The ligands' size and flexibility were the primary factors influencing the predictions' quality. Small ions and organic molecules with limited flexibility were predicted with high fidelity, while reproducing the binding poses of larger, flexible ligands proved more challenging.

New prediction categories in CASP15.

Prediction categories in the Critical Assessment of Structure Prediction (CASP) experiments change with the need to address specific problems in structure modeling. In CASP15, four new prediction categories were introduced: RNA structure, ligand-protein complexes, accuracy of oligomeric structures and their interfaces, and ensembles of alternative conformations. This paper lists technical specifications for these categories and describes their integration in the CASP data management system.

WaterKit: Thermodynamic Profiling of Protein Hydration Sites.

Water desolvation is one of the key components of the free energy of binding of small molecules to their receptors. Thus, understanding the energetic balance of solvation and desolvation resulting from individual water molecules can be crucial when estimating ligand binding, especially when evaluating different molecules and poses as done in High-Throughput Virtual Screening (HTVS). Over the most recent decades, several methods were developed to tackle this problem, ranging from fast approximate methods (usually empirical functions using either discrete atom-atom pairwise interactions or continuum solvent models) to more computationally expensive and accurate ones, mostly based on Molecular Dynamics (MD) simulations, such as Grid Inhomogeneous Solvation Theory (GIST) or Double Decoupling. On one hand, MD-based methods are prohibitive to use in HTVS to estimate the role of waters on the fly for each ligand. On the other hand, fast and approximate methods show an unsatisfactory level of accuracy, with low agreement with results obtained with the more expensive methods. Here we introduce WaterKit, a new grid-based sampling method with explicit water molecules to calculate thermodynamic properties using the GIST method. Our results show that the discrete placement of water molecules is successful in reproducing the position of crystallographic waters with very high accuracy, as well as providing thermodynamic estimates with accuracy comparable to more expensive MD simulations. Unlike these methods, WaterKit can be used to analyze specific regions on the protein surface, (such as the binding site of a receptor), without having to hydrate and simulate the whole receptor structure. The results show the feasibility of a general and fast method to compute thermodynamic properties of water molecules, making it well-suited to be integrated in high-throughput pipelines such as molecular docking.

AutoDock Vina 1.2.0: New Docking Methods, Expanded Force Field, and Python Bindings.

AutoDock Vina is arguably one of the fastest and most widely used open-source programs for molecular docking. However, compared to other programs in the AutoDock Suite, it lacks support for modeling specific features such as macrocycles or explicit water molecules. Here, we describe the implementation of this functionality in AutoDock Vina 1.2.0. Additionally, AutoDock Vina 1.2.0 supports the AutoDock4.2 scoring function, simultaneous docking of multiple ligands, and a batch mode for docking a large number of ligands. Furthermore, we implemented Python bindings to facilitate scripting and the development of docking workflows. This work is an effort toward the unification of the features of the AutoDock4 and AutoDock Vina programs. The source code is available at https://github.com/ccsb-scripps/AutoDock-Vina.

Discovery of small-molecule enzyme activators by activity-based protein profiling.

Activity-based protein profiling (ABPP) has been used extensively to discover and optimize selective inhibitors of enzymes. Here, we show that ABPP can also be implemented to identify the converse-small-molecule enzyme activators. Using a kinetically controlled, fluorescence polarization-ABPP assay, we identify compounds that stimulate the activity of LYPLAL1-a poorly characterized serine hydrolase with complex genetic links to human metabolic traits. We apply ABPP-guided medicinal chemistry to advance a lead into a selective LYPLAL1 activator suitable for use in vivo. Structural simulations coupled to mutational, biochemical and biophysical analyses indicate that this compound increases LYPLAL1's catalytic activity likely by enhancing the efficiency of the catalytic triad charge-relay system. Treatment with this LYPLAL1 activator confers beneficial effects in a mouse model of diet-induced obesity. These findings reveal a new mode of pharmacological regulation for this large enzyme family and suggest that ABPP may aid discovery of activators for additional enzyme classes.

Integrative X-ray Structure and Molecular Modeling for the Rationalization of Procaspase-8 Inhibitor Potency and Selectivity.

Caspases are a critical class of proteases involved in regulating programmed cell death and other biological processes. Selective inhibitors of individual caspases, however, are lacking, due in large part to the high structural similarity found in the active sites of these enzymes. We recently discovered a small-molecule inhibitor, 63-R, that covalently binds the zymogen, or inactive precursor (pro-form), of caspase-8, but not other caspases, pointing to an untapped potential of procaspases as targets for chemical probes. Realizing this goal would benefit from a structural understanding of how small molecules bind to and inhibit caspase zymogens. There have, however, been very few reported procaspase structures. Here, we employ X-ray crystallography to elucidate a procaspase-8 crystal structure in complex with 63-R, which reveals large conformational changes in active-site loops that accommodate the intramolecular cleavage events required for protease activation. Combining these structural insights with molecular modeling and mutagenesis-based biochemical assays, we elucidate key interactions required for 63-R inhibition of procaspase-8. Our findings inform the mechanism of caspase activation and its disruption by small molecules and, more generally, have implications for the development of small molecule inhibitors and/or activators that target alternative (e.g., inactive precursor) protein states to ultimately expand the druggable proteome.

D3R Grand Challenge 4: prospective pose prediction of BACE1 ligands with AutoDock-GPU.

In this paper we describe our approaches to predict the binding mode of twenty BACE1 ligands as part of Grand Challenge 4 (GC4), organized by the Drug Design Data Resource. Calculations for all submissions (except for one, which used AutoDock4.2) were performed using AutoDock-GPU, the new GPU-accelerated version of AutoDock4 implemented in OpenCL, which features a gradient-based local search. The pose prediction challenge was organized in two stages. In Stage 1a, the protein conformations associated with each of the ligands were undisclosed, so we docked each ligand to a set of eleven receptor conformations, chosen to maximize the diversity of binding pocket topography. Protein conformations were made available in Stage 1b, making it a re-docking task. For all calculations, macrocyclic conformations were sampled on the fly during docking, taking the target structure into account. To leverage information from existing structures containing BACE1 bound to ligands available in the PDB, we tested biased docking and pose filter protocols to facilitate poses resembling those experimentally determined. Both pose filters and biased docking resulted in more accurate docked poses, enabling us to predict for both Stages 1a and 1b ligand poses within 2 Å RMSD from the crystallographic pose. Nevertheless, many of the ligands could be correctly docked without using existing structural information, demonstrating the usefulness of physics-based scoring functions, such as the one used in AutoDock4, for structure based drug design.

Comparison of affinity ranking using AutoDock-GPU and MM-GBSA scores for BACE-1 inhibitors in the D3R Grand Challenge 4.

Molecular docking has been successfully used in computer-aided molecular design projects for the identification of ligand poses within protein binding sites. However, relying on docking scores to rank different ligands with respect to their experimental affinities might not be sufficient. It is believed that the binding scores calculated using molecular mechanics combined with the Poisson-Boltzman surface area (MM-PBSA) or generalized Born surface area (MM-GBSA) can predict binding affinities more accurately. In this perspective, we decided to take part in Stage 2 of the Drug Design Data Resource (D3R) Grand Challenge 4 (GC4) to compare the performance of a quick scoring function, AutoDock4, to that of MM-GBSA in predicting the binding affinities of a set of [Formula: see text]-Amyloid Cleaving Enzyme 1 (BACE-1) ligands. Our results show that re-scoring docking poses using MM-GBSA did not improve the correlation with experimental affinities. We further did a retrospective analysis of the results and found that our MM-GBSA protocol is sensitive to details in the protein-ligand system: (i) neutral ligands are more adapted to MM-GBSA calculations than charged ligands, (ii) predicted binding affinities depend on the initial conformation of the BACE-1 receptor, (iii) protonating the aspartyl dyad of BACE-1 correctly results in more accurate binding affinity predictions.

A revisited version of the apo structure of the ligand-binding domain of the human nuclear receptor retinoic X receptor α.

The retinoic X receptor (RXR) plays a crucial role in the superfamily of nuclear receptors (NRs) by acting as an obligatory partner of several nuclear receptors; its role as a transcription factor is thus critical in many signalling pathways, such as metabolism, cell development, differentiation and cellular death. The first published structure of the apo ligand-binding domain (LBD) of RXRα, which is still used as a reference today, contained inaccuracies. In the present work, these inaccuracies were corrected using modern crystallographic tools. The most important correction concerns the presence of a π-bulge in helix H7, which was originally built as a regular α-helix. The presence of several CHAPS molecules, which are visible for the first time in the electron-density map and which stabilize the H1-H3 loop, which contains helix H2, are also revealed. The apo RXR structure has played an essential role in deciphering the molecular mode of action of NR ligands and is still used in numerous biophysical studies. This refined structure should be used preferentially in the future in interpreting experiments as well as for modelling and structural dynamics studies of the apo RXRα LBD.

Allostery in Its Many Disguises: From Theory to Applications.

Allosteric regulation plays an important role in many biological processes, such as signal transduction, transcriptional regulation, and metabolism. Allostery is rooted in the fundamental physical properties of macromolecular systems, but its underlying mechanisms are still poorly understood. A collection of contributions to a recent interdisciplinary CECAM (Center Européen de Calcul Atomique et Moléculaire) workshop is used here to provide an overview of the progress and remaining limitations in the understanding of the mechanistic foundations of allostery gained from computational and experimental analyses of real protein systems and model systems. The main conceptual frameworks instrumental in driving the field are discussed. We illustrate the role of these frameworks in illuminating molecular mechanisms and explaining cellular processes, and describe some of their promising practical applications in engineering molecular sensors and informing drug design efforts.

Structural Basis of Altered Potency and Efficacy Displayed by a Major in Vivo Metabolite of the Antidiabetic PPARγ Drug Pioglitazone.

Pioglitazone (Pio) is a Food and Drug Administration-approved drug for type-2 diabetes that binds and activates the nuclear receptor peroxisome proliferator-activated receptor γ (PPARγ), yet it remains unclear how in vivo Pio metabolites affect PPARγ structure and function. Here, we present a structure-function comparison of Pio and its most abundant in vivo metabolite, 1-hydroxypioglitazone (PioOH). PioOH displayed a lower binding affinity and reduced potency in co-regulator recruitment assays. X-ray crystallography and molecular docking analysis of PioOH-bound PPARγ ligand-binding domain revealed an altered hydrogen bonding network, including the formation of water-mediated bonds, which could underlie its altered biochemical phenotype. NMR spectroscopy and hydrogen/deuterium exchange mass spectrometry analysis coupled to activity assays revealed that PioOH better stabilizes the PPARγ activation function-2 (AF-2) co-activator binding surface and better enhances co-activator binding, affording slightly better transcriptional efficacy. These results indicating that Pio hydroxylation affects its potency and efficacy as a PPARγ agonist contributes to our understanding of PPARγ-drug metabolite interactions.

Unrolr: Structural analysis of protein conformations using stochastic proximity embedding.

Molecular dynamics (MD) simulations are widely used to explore the conformational space of biological macromolecules. Advances in hardware, as well as in methods, make the generation of large and complex MD datasets much more common. Although different clustering and dimensionality reduction methods have been applied to MD simulations, there remains a need for improved strategies that handle nonlinear data and/or can be applied to very large datasets. We present an original implementation of the pivot-based version of the stochastic proximity embedding method aimed at large MD datasets using the dihedral distance as a metric. The advantages of the algorithm in terms of data storage and computational efficiency are presented, as well as the implementation realized. Application and testing through the analysis of a 200 ns accelerated MD simulation of a 35-residue villin headpiece is discussed. Analysis of the simulation shows the promise of this method to organize large conformational ensembles. © 2018 Wiley Periodicals, Inc.

Humanized GPIbα-von Willebrand factor interaction in the mouse.

The interaction of platelet glycoprotein Ibα (GPIbα) with von Willebrand factor (VWF) initiates hemostasis after vascular injury and also contributes to pathological thrombosis. GPIbα binding to the VWF A1 domain (VWFA1) is a target for antithrombotic intervention, but attempts to develop pharmacologic inhibitors have been hindered by the lack of animal models because of the species specificity of the interaction. To address this problem, we generated a knockin mouse with Vwf exon 28-encoding domains A1 and A2 replaced by the human homolog (VWFh28). VWFh28 mice (M1HA) were crossbred with a transgenic mouse strain expressing human GPIbα on platelets (mGPIbαnull;hGPIbαTg; H1MA) to generate a new strain (H1HA) with humanized GPIbα-VWFA1 binding. Plasma VWF levels in the latter 3 strains were similar to those of wild-type mice (M1MA). Compared with the strains that had homospecific GPIbα-VWF pairing (M1MA and H1HA), M1HA mice of those with heterospecific pairing had a markedly greater prolongation of tail bleeding time and attenuation of thrombogenesis after injury to the carotid artery than H1MA mice. Measurements of GPIbα-VWFA1 binding affinity by surface plasmon resonance agreed with the extent of observed functional defects. Ristocetin-induced platelet aggregation was similar in H1HA mouse and human platelet-rich plasma, and it was comparably inhibited by monoclonal antibody NMC-4, which is known to block human GPIbα-VWFA1 binding, which also inhibited FeCl3-induced mouse carotid artery thrombosis. Thus, the H1HA mouse strain is a fully humanized model of platelet GPIbα-VWFA1 binding that provides mechanistic and pharmacologic information relevant to human hemostatic and thrombotic disorders.

Structural insights into the molecular mechanism of vitamin D receptor activation by lithocholic acid involving a new mode of ligand recognition.

The vitamin D receptor (VDR), an endocrine nuclear receptor for 1α,25-dihydroxyvitamin D3, acts also as a bile acid sensor by binding lithocholic acid (LCA). The crystal structure of the zebrafish VDR ligand binding domain in complex with LCA and the SRC-2 coactivator peptide reveals the binding of two LCA molecules by VDR. One LCA binds to the canonical ligand-binding pocket, and the second one, which is not fully buried, is anchored to a site located on the VDR surface. Despite the low affinity of the alternative site, the binding of the second molecule promotes stabilization of the active receptor conformation. Biological activity assays, structural analysis, and molecular dynamics simulations indicate that the recognition of two ligand molecules is crucial for VDR agonism by LCA. The unique binding mode of LCA provides clues for the development of new chemical compounds that target alternative binding sites for therapeutic applications.