Updates to Binding MOAD (Mother of All Databases): Polypharmacology Tools and Their Utility in Drug Repurposing.

The goal of Binding MOAD is to provide users with a data set focused on high-quality x-ray crystal structures that have been solved with biologically relevant ligands bound. Where available, experimental binding affinities (Ka, Kd, Ki, IC50) are provided from the primary literature of the crystal structure. The database has been updated regularly since 2005, and this most recent update has added nearly 7000 new structures (growth of 21%). MOAD currently contains 32,747 structures, composed of 9117 protein families and 16,044 unique ligands. The data are freely available on www.BindingMOAD.org. This paper outlines updates to the data in Binding MOAD as well as improvements made to both the website and its contents. The NGL viewer has been added to improve visualization of the ligands and protein structures. MarvinJS has been implemented, over the outdated MarvinView, to work with JChem for small molecule searching in the database. To add tools for predicting polypharmacology, we have added information about sequence, binding-site, and ligand similarity between entries in the database. A main premise behind polypharmacology is that similar binding sites will bind similar ligands. The large amount of protein-ligand information available in Binding MOAD allows us to compute pairwise ligand and binding-site similarities. Lists of similar ligands and similar binding sites have been added to allow users to identify potential polypharmacology pairs. To show the utility of the polypharmacology data, we detail a few examples from Binding MOAD of drug repurposing targets with their respective similarities.

D3R grand challenge 2015: Evaluation of protein-ligand pose and affinity predictions.

The Drug Design Data Resource (D3R) ran Grand Challenge 2015 between September 2015 and February 2016. Two targets served as the framework to test community docking and scoring methods: (1) HSP90, donated by AbbVie and the Community Structure Activity Resource (CSAR), and (2) MAP4K4, donated by Genentech. The challenges for both target datasets were conducted in two stages, with the first stage testing pose predictions and the capacity to rank compounds by affinity with minimal structural data; and the second stage testing methods for ranking compounds with knowledge of at least a subset of the ligand-protein poses. An additional sub-challenge provided small groups of chemically similar HSP90 compounds amenable to alchemical calculations of relative binding free energy. Unlike previous blinded Challenges, we did not provide cognate receptors or receptors prepared with hydrogens and likewise did not require a specified crystal structure to be used for pose or affinity prediction in Stage 1. Given the freedom to select from over 200 crystal structures of HSP90 in the PDB, participants employed workflows that tested not only core docking and scoring technologies, but also methods for addressing water-mediated ligand-protein interactions, binding pocket flexibility, and the optimal selection of protein structures for use in docking calculations. Nearly 40 participating groups submitted over 350 prediction sets for Grand Challenge 2015. This overview describes the datasets and the organization of the challenge components, summarizes the results across all submitted predictions, and considers broad conclusions that may be drawn from this collaborative community endeavor.

CSAR 2014: A Benchmark Exercise Using Unpublished Data from Pharma.

The 2014 CSAR Benchmark Exercise was the last community-wide exercise that was conducted by the group at the University of Michigan, Ann Arbor. For this event, GlaxoSmithKline (GSK) donated unpublished crystal structures and affinity data from in-house projects. Three targets were used: tRNA (m1G37) methyltransferase (TrmD), Spleen Tyrosine Kinase (SYK), and Factor Xa (FXa). A particularly strong feature of the GSK data is its large size, which lends greater statistical significance to comparisons between different methods. In Phase 1 of the CSAR 2014 Exercise, participants were given several protein-ligand complexes and asked to identify the one near-native pose from among 200 decoys provided by CSAR. Though decoys were requested by the community, we found that they complicated our analysis. We could not discern whether poor predictions were failures of the chosen method or an incompatibility between the participant's method and the setup protocol we used. This problem is inherent to decoys, and we strongly advise against their use. In Phase 2, participants had to dock and rank/score a set of small molecules given only the SMILES strings of the ligands and a protein structure with a different ligand bound. Overall, docking was a success for most participants, much better in Phase 2 than in Phase 1. However, scoring was a greater challenge. No particular approach to docking and scoring had an edge, and successful methods included empirical, knowledge-based, machine-learning, shape-fitting, and even those with solvation and entropy terms. Several groups were successful in ranking TrmD and/or SYK, but ranking FXa ligands was intractable for all participants. Methods that were able to dock well across all submitted systems include MDock,1 Glide-XP,2 PLANTS,3 Wilma,4 Gold,5 SMINA,6 Glide-XP2/PELE,7 FlexX,8 and MedusaDock.9 In fact, the submission based on Glide-XP2/PELE7 cross-docked all ligands to many crystal structures, and it was particularly impressive to see success across an ensemble of protein structures for multiple targets. For scoring/ranking, submissions that showed statistically significant achievement include MDock1 using ITScore1,10 with a flexible-ligand term,11 SMINA6 using Autodock-Vina,12,13 FlexX8 using HYDE,14 and Glide-XP2 using XP DockScore2 with and without ROCS15 shape similarity.16 Of course, these results are for only three protein targets, and many more systems need to be investigated to truly identify which approaches are more successful than others. Furthermore, our exercise is not a competition.

CSAR Benchmark Exercise 2013: Evaluation of Results from a Combined Computational Protein Design, Docking, and Scoring/Ranking Challenge.

Community Structure-Activity Resource (CSAR) conducted a benchmark exercise to evaluate the current computational methods for protein design, ligand docking, and scoring/ranking. The exercise consisted of three phases. The first phase required the participants to identify and rank order which designed sequences were able to bind the small molecule digoxigenin. The second phase challenged the community to select a near-native pose of digoxigenin from a set of decoy poses for two of the designed proteins. The third phase investigated the ability of current methods to rank/score the binding affinity of 10 related steroids to one of the designed proteins (pKd = 4.1 to 6.7). We found that 11 of 13 groups were able to correctly select the sequence that bound digoxigenin, with most groups providing the correct three-dimensional structure for the backbone of the protein as well as all atoms of the active-site residues. Eleven of the 14 groups were able to select the appropriate pose from a set of plausible decoy poses. The ability to predict absolute binding affinities is still a difficult task, as 8 of 14 groups were able to correlate scores to affinity (Pearson-r > 0.7) of the designed protein for congeneric steroids and only 5 of 14 groups were able to correlate the ranks of the 10 related ligands (Spearman-ρ > 0.7).

Recent improvements to Binding MOAD: a resource for protein-ligand binding affinities and structures.

For over 10 years, Binding MOAD (Mother of All Databases; http://www.BindingMOAD.org) has been one of the largest resources for high-quality protein-ligand complexes and associated binding affinity data. Binding MOAD has grown at the rate of 1994 complexes per year, on average. Currently, it contains 23,269 complexes and 8156 binding affinities. Our annual updates curate the data using a semi-automated literature search of the references cited within the PDB file, and we have recently upgraded our website and added new features and functionalities to better serve Binding MOAD users. In order to eliminate the legacy application server of the old platform and to accommodate new changes, the website has been completely rewritten in the LAMP (Linux, Apache, MySQL and PHP) environment. The improved user interface incorporates current third-party plugins for better visualization of protein and ligand molecules, and it provides features like sorting, filtering and filtered downloads. In addition to the field-based searching, Binding MOAD now can be searched by structural queries based on the ligand. In order to remove redundancy, Binding MOAD records are clustered in different families based on 90% sequence identity. The new Binding MOAD, with the upgraded platform, features and functionalities, is now equipped to better serve its users.

An allosteric modulator of HIV-1 protease shows equipotent inhibition of wild-type and drug-resistant proteases.

NMR and MD simulations have demonstrated that the flaps of HIV-1 protease (HIV-1p) adopt a range of conformations that are coupled with its enzymatic activity. Previously, a model was created for an allosteric site located between the flap and the core of HIV-1p, called the Eye site (Biopolymers 2008, 89, 643-652). Here, results from our first study were combined with a ligand-based, lead-hopping method to identify a novel compound (NIT). NIT inhibits HIV-1p, independent of the presence of an active-site inhibitor such as pepstatin A. Assays showed that NIT acts on an allosteric site other than the dimerization interface. MD simulations of the ligand-protein complex show that NIT stably binds in the Eye site and restricts the flaps. That bound state of NIT is consistent with a crystal structure of similar fragments bound in the Eye site (Chem. Biol. Drug Des. 2010, 75, 257-268). Most importantly, NIT is equally potent against wild-type and a multidrug-resistant mutant of HIV-1p, which highlights the promise of allosteric inhibitors circumventing existing clinical resistance.

CSAR data set release 2012: ligands, affinities, complexes, and docking decoys.

A major goal in drug design is the improvement of computational methods for docking and scoring. The Community Structure Activity Resource (CSAR) has collected several data sets from industry and added in-house data sets that may be used for this purpose ( www.csardock.org). CSAR has currently obtained data from Abbott, GlaxoSmithKline, and Vertex and is working on obtaining data from several others. Combined with our in-house projects, we are providing a data set consisting of 6 protein targets, 647 compounds with biological affinities, and 82 crystal structures. Multiple congeneric series are available for several targets with a few representative crystal structures of each of the series. These series generally contain a few inactive compounds, usually not available in the literature, to provide an upper bound to the affinity range. The affinity ranges are typically 3-4 orders of magnitude per series. For our in-house projects, we have had compounds synthesized for biological testing. Affinities were measured by Thermofluor, Octet RED, and isothermal titration calorimetry for the most soluble. This allows the direct comparison of the biological affinities for those compounds, providing a measure of the variance in the experimental affinity. It appears that there can be considerable variance in the absolute value of the affinity, making the prediction of the absolute value ill-defined. However, the relative rankings within the methods are much better, and this fits with the observation that predicting relative ranking is a more tractable problem computationally. For those in-house compounds, we also have measured the following physical properties: logD, logP, thermodynamic solubility, and pK(a). This data set also provides a substantial decoy set for each target consisting of diverse conformations covering the entire active site for all of the 58 CSAR-quality crystal structures. The CSAR data sets (CSAR-NRC HiQ and the 2012 release) provide substantial, publically available, curated data sets for use in parametrizing and validating docking and scoring methods.

CSAR benchmark exercise 2011-2012: evaluation of results from docking and relative ranking of blinded congeneric series.

The Community Structure-Activity Resource (CSAR) recently held its first blinded exercise based on data provided by Abbott, Vertex, and colleagues at the University of Michigan, Ann Arbor. A total of 20 research groups submitted results for the benchmark exercise where the goal was to compare different improvements for pose prediction, enrichment, and relative ranking of congeneric series of compounds. The exercise was built around blinded high-quality experimental data from four protein targets: LpxC, Urokinase, Chk1, and Erk2. Pose prediction proved to be the most straightforward task, and most methods were able to successfully reproduce binding poses when the crystal structure employed was co-crystallized with a ligand from the same chemical series. Multiple evaluation metrics were examined, and we found that RMSD and native contact metrics together provide a robust evaluation of the predicted poses. It was notable that most scoring functions underpredicted contacts between the hetero atoms (i.e., N, O, S, etc.) of the protein and ligand. Relative ranking was found to be the most difficult area for the methods, but many of the scoring functions were able to properly identify Urokinase actives from the inactives in the series. Lastly, we found that minimizing the protein and correcting histidine tautomeric states positively trended with low RMSD for pose prediction but minimizing the ligand negatively trended. Pregenerated ligand conformations performed better than those that were generated on the fly. Optimizing docking parameters and pretraining with the native ligand had a positive effect on the docking performance as did using restraints, substructure fitting, and shape fitting. Lastly, for both sampling and ranking scoring functions, the use of the empirical scoring function appeared to trend positively with the RMSD. Here, by combining the results of many methods, we hope to provide a statistically relevant evaluation and elucidate specific shortcomings of docking methodology for the community.

Biophysical limits of protein-ligand binding.

In classic work, Kuntz et al. (Proc. Nat. Acad. Sci. USA 1999, 96, 9997-10002) introduced the concept of ligand efficiency. Though that study focused primarily on drug-like molecules, it also showed that metal binding led to the greatest ligand efficiencies. Here, the physical limits of binding are examined across the wide variety of small molecules in the Binding MOAD database. The complexes with the greatest ligand efficiencies share the trait of being small, charged ligands bound in highly charged, well buried binding sites. The limit of ligand efficiency is -1.75 kcal/mol·atom for the protein-ligand complexes within Binding MOAD, and 95% of the set have efficiencies below a "soft limit" of -0.83 kcal/mol·atom. On the basis of buried molecular surface area, the hard limit of ligand efficiency is -117 cal/mol·Å(2), which is in surprising agreement with the limit of macromolecule-protein binding. Close examination of the most efficient systems reveals their incredibly high efficiency is dictated by tight contacts between the charged groups of the ligand and the pocket. In fact, a misfit of 0.24 Å in the average contacts inherently decreases the maximum possible efficiency by at least 0.1 kcal/mol·atom.

CSAR benchmark exercise of 2010: combined evaluation across all submitted scoring functions.

As part of the Community Structure-Activity Resource (CSAR) center, a set of 343 high-quality, protein-ligand crystal structures were assembled with experimentally determined K(d) or K(i) information from the literature. We encouraged the community to score the crystallographic poses of the complexes by any method of their choice. The goal of the exercise was to (1) evaluate the current ability of the field to predict activity from structure and (2) investigate the properties of the complexes and methods that appear to hinder scoring. A total of 19 different methods were submitted with numerous parameter variations for a total of 64 sets of scores from 16 participating groups. Linear regression and nonparametric tests were used to correlate scores to the experimental values. Correlation to experiment for the various methods ranged R(2) = 0.58-0.12, Spearman ρ = 0.74-0.37, Kendall τ = 0.55-0.25, and median unsigned error = 1.00-1.68 pK(d) units. All types of scoring functions-force field based, knowledge based, and empirical-had examples with high and low correlation, showing no bias/advantage for any particular approach. The data across all the participants were combined to identify 63 complexes that were poorly scored across the majority of the scoring methods and 123 complexes that were scored well across the majority. The two sets were compared using a Wilcoxon rank-sum test to assess any significant difference in the distributions of >400 physicochemical properties of the ligands and the proteins. Poorly scored complexes were found to have ligands that were the same size as those in well-scored complexes, but hydrogen bonding and torsional strain were significantly different. These comparisons point to a need for CSAR to develop data sets of congeneric series with a range of hydrogen-bonding and hydrophobic characteristics and a range of rotatable bonds.

CSAR benchmark exercise of 2010: selection of the protein-ligand complexes.

A major goal in drug design is the improvement of computational methods for docking and scoring. The Community Structure Activity Resource (CSAR) aims to collect available data from industry and academia which may be used for this purpose ( www.csardock.org ). Also, CSAR is charged with organizing community-wide exercises based on the collected data. The first of these exercises was aimed to gauge the overall state of docking and scoring, using a large and diverse data set of protein-ligand complexes. Participants were asked to calculate the affinity of the complexes as provided and then recalculate with changes which may improve their specific method. This first data set was selected from existing PDB entries which had binding data (K(d) or K(i)) in Binding MOAD, augmented with entries from PDB bind. The final data set contains 343 diverse protein-ligand complexes and spans 14 pK(d). Sixteen proteins have three or more complexes in the data set, from which a user could start an inspection of congeneric series. Inherent experimental error limits the possible correlation between scores and measured affinity; Pearson R is limited to ~ 0.91 (Pearson R2 0.83) when fitting to the data set without over parameterizing. Pearson R is limited to ~ 0.83(Pearson R2 ~ 0.70) when scoring the data set with a method trained on outside data [corrected]. The details of how the data set was initially selected, and the process by which it matured to better fit the needs of the community are presented. Many groups generously participated in improving the data set, and this underscores the value of a supportive, collaborative effort in moving our field forward.

Benzothiophene piperazine and piperidine urea inhibitors of fatty acid amide hydrolase (FAAH).

The synthesis and structure-activity relationships (SAR) of a series of benzothiophene piperazine and piperidine urea FAAH inhibitors is described. These compounds inhibit FAAH by covalently modifying the enzyme's active site serine nucleophile. Activity-based protein profiling (ABPP) revealed that these urea inhibitors were completely selective for FAAH relative to other mammalian serine hydrolases. Several compounds showed in vivo activity in a rat complete Freund's adjuvant (CFA) model of inflammatory pain.

Differences between high- and low-affinity complexes of enzymes and nonenzymes.

Physical differences in small molecule binding between enzymes and nonenzymes were found through mining the protein-ligand database, Binding MOAD (Mother of All Databases). The data suggest that divergent approaches may be more productive for improving the affinity of ligands for the two classes of proteins. High-affinity ligands of enzymes are much larger than those with low affinity, indicating that the addition of complementary functional groups is likely to improve the affinity of an enzyme inhibitor. However, this process may not be as fruitful for ligands of nonenzymes. High- and low-affinity ligands of nonenzymes are nearly the same size, so modest modifications and isosteric replacement might be most productive. The inherent differences between enzymes and nonenzymes have significant ramifications for scoring functions and structure-based drug design. In particular, nonenzymes were found to have greater ligand efficiencies than enzymes. Ligand efficiencies are often used to indicate druggability of a target, and this finding supports the feasibility of nonenzymes as drug targets. The differences in ligand efficiencies do not appear to come from the ligands; instead, the pockets yield different amino acid compositions despite very similar distributions of amino acids in the overall protein sequences.

Thermodynamic and structure guided design of statin based inhibitors of 3-hydroxy-3-methylglutaryl coenzyme A reductase.

Clinical studies have demonstrated that statins, 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGR) inhibitors, are effective at lowering mortality levels associated with cardiovascular disease; however, 2-7% of patients may experience statin-induced myalgia that limits compliance with a treatment regimen. High resolution crystal structures, thermodynamic binding parameters, and biochemical data were used to design statin inhibitors with improved HMGR affinity and therapeutic index relative to statin-induced myalgia. These studies facilitated the identification of imidazole 1 as a potent (IC 50 = 7.9 nM) inhibitor with excellent hepatoselectivity (>1000-fold) and good in vivo efficacy. The binding of 1 to HMGR was found to be enthalpically driven with a Delta H of -17.7 kcal/M. Additionally, a second novel series of bicyclic pyrrole-based inhibitors was identified that induced order in a protein flap of HMGR. Similar ordering was detected in a substrate complex, but has not been reported in previous statin inhibitor complexes with HMGR.

CLIP: similarity searching of 3D databases using clique detection.

This paper describes a program for 3D similarity searching, called CLIP (for Candidate Ligand Identification Program), that uses the Bron-Kerbosch clique detection algorithm to find those structures in a file that have large structures in common with a target structure. Structures are characterized by the geometric arrangement of pharmacophore points and the similarity between two structures calculated using modifications of the Simpson and Tanimoto association coefficients. This modification takes into account the fact that a distance tolerance is required to ensure that pairs of interatomic distances can be regarded as equivalent during the clique-construction stage of the matching algorithm. Experiments with HIV assay data demonstrate the effectiveness and the efficiency of this approach to virtual screening.