Subpocket Similarity-Based Hit Identification for Challenging Targets: Application to the WDR Domain of LRRK2.

We herewith applied a priori a generic hit identification method (POEM) for difficult targets of known three-dimensional structure, relying on the simple knowledge of physicochemical and topological properties of a user-selected cavity. Searching for local similarity to a set of fragment-bound protein microenvironments of known structure, a point cloud registration algorithm is first applied to align known subpockets to the target cavity. The resulting alignment then permits us to directly pose the corresponding seed fragments in a target cavity space not typically amenable to classical docking approaches. Last, linking potentially connectable atoms by a deep generative linker enables full ligand enumeration. When applied to the WD40 repeat (WDR) central cavity of leucine-rich repeat kinase 2 (LRRK2), an unprecedented binding site, POEM was able to quickly propose 94 potential hits, five of which were subsequently confirmed to bind in vitro to LRRK2-WDR.

Targeting the Central Pocket of the Pseudomonas aeruginosa Lectin LecA.

Pseudomonas aeruginosa is an opportunistic ESKAPE pathogen that produces two lectins, LecA and LecB, as part of its large arsenal of virulence factors. Both carbohydrate-binding proteins are central to the initial and later persistent infection processes, i. e. bacterial adhesion and biofilm formation. The biofilm matrix is a major resistance determinant and protects the bacteria against external threats such as the host immune system or antibiotic treatment. Therefore, the development of drugs against the P. aeruginosa biofilm is of particular interest to restore efficacy of antimicrobials. Carbohydrate-based inhibitors for LecA and LecB were previously shown to efficiently reduce biofilm formations. Here, we report a new approach for inhibiting LecA with synthetic molecules bridging the established carbohydrate-binding site and a central cavity located between two LecA protomers of the lectin tetramer. Inspired by in silico design, we synthesized various galactosidic LecA inhibitors with aromatic moieties targeting this central pocket. These compounds reached low micromolar affinities, validated in different biophysical assays. Finally, X-ray diffraction analysis revealed the interactions of this compound class with LecA. This new mode of action paves the way to a novel route towards inhibition of P. aeruginosa biofilms.

Estimating the Similarity between Protein Pockets.

With the exponential increase in publicly available protein structures, the comparison of protein binding sites naturally emerged as a scientific topic to explain observations or generate hypotheses for ligand design, notably to predict ligand selectivity for on- and off-targets, explain polypharmacology, and design target-focused libraries. The current review summarizes the state-of-the-art computational methods applied to pocket detection and comparison as well as structural druggability estimates. The major strengths and weaknesses of current pocket descriptors, alignment methods, and similarity search algorithms are presented. Lastly, an exhaustive survey of both retrospective and prospective applications in diverse medicinal chemistry scenarios illustrates the capability of the existing methods and the hurdle that still needs to be overcome for more accurate predictions.

True Accuracy of Fast Scoring Functions to Predict High-Throughput Screening Data from Docking Poses: The Simpler the Better.

Hundreds of fast scoring functions have been developed over the last 20 years to predict binding free energies from three-dimensional structures of protein-ligand complexes. Despite numerous statistical promises, we believe that none of them has been properly validated for daily prospective high-throughput virtual screening studies, mostly because in silico screening challenges usually employ artificially built and biased datasets. We here carry out a fully unbiased evaluation of four scoring functions (Pafnucy, ΔvinaRF20, IFP, and GRIM) on an in-house developed data collection of experimental high-confidence screening data (LIT-PCBA) covering about 3 million data points on 15 diverse pharmaceutical targets. All four scoring functions were applied to rescore the docking poses of LIT-PCBA compounds in conditions mimicking exactly standard drug discovery scenarios and were compared in terms of propensity to enrich true binders in the top 1%-ranked hit lists. Interestingly, rescoring based on simple interaction fingerprints or interaction graphs outperforms state-of-the-art machine learning and deep learning scoring functions in most of the cases. The current study notably highlights the strong tendency of deep learning methods to predict affinity values within a very narrow range centered on the mean value of samples used for training. Moreover, it suggests that knowledge of pre-existing binding modes is the key to detecting the most potent binders.

Design, Synthesis and Biological Evaluation of Arylpyridin-2-yl Guanidine Derivatives and Cyclic Mimetics as Novel MSK1 Inhibitors. An Application in an Asthma Model.

Mitogen- and Stress-Activated Kinase 1 (MSK1) is a nuclear kinase, taking part in the activation pathway of the pro-inflammatory transcription factor NF-kB and is demonstrating a therapeutic target potential in inflammatory diseases such as asthma, psoriasis and atherosclerosis. To date, few MSK1 inhibitors were reported. In order to identify new MSK1 inhibitors, a screening of a library of low molecular weight compounds was performed, and the results highlighted the 6-phenylpyridin-2-yl guanidine (compound 1a, IC50~18 µM) as a starting hit for structure-activity relationship study. Derivatives, homologues and rigid mimetics of 1a were designed, and all synthesized compounds were evaluated for their inhibitory activity towards MSK1. Among them, the non-cytotoxic 2-aminobenzimidazole 49d was the most potent at inhibiting significantly: (i) MSK1 activity, (ii) the release of IL-6 in inflammatory conditions in vitro (IC50~2 µM) and (iii) the inflammatory cell recruitment to the airways in a mouse model of asthma.

Non-Carbohydrate Glycomimetics as Inhibitors of Calcium(II)-Binding Lectins.

Because of the antimicrobial resistance crisis, lectins are considered novel drug targets. Pseudomonas aeruginosa utilizes LecA and LecB in the infection process. Inhibition of both lectins with carbohydrate-derived molecules can reduce biofilm formation to restore antimicrobial susceptibility. Here, we focused on non-carbohydrate inhibitors for LecA to explore new avenues for lectin inhibition. From a screening cascade we obtained one experimentally confirmed hit, a catechol, belonging to the well-known PAINS compounds. Rigorous analyses validated electron-deficient catechols as millimolar LecA inhibitors. The first co-crystal structure of a non-carbohydrate inhibitor in complex with a bacterial lectin clearly demonstrates the catechol mimicking the binding of natural glycosides with LecA. Importantly, catechol 3 is the first non-carbohydrate lectin ligand that binds bacterial and mammalian calcium(II)-binding lectins, giving rise to this fundamentally new class of glycomimetics.

LIT-PCBA: An Unbiased Data Set for Machine Learning and Virtual Screening.

Comparative evaluation of virtual screening methods requires a rigorous benchmarking procedure on diverse, realistic, and unbiased data sets. Recent investigations from numerous research groups unambiguously demonstrate that artificially constructed ligand sets classically used by the community (e.g., DUD, DUD-E, MUV) are unfortunately biased by both obvious and hidden chemical biases, therefore overestimating the true accuracy of virtual screening methods. We herewith present a novel data set (LIT-PCBA) specifically designed for virtual screening and machine learning. LIT-PCBA relies on 149 dose-response PubChem bioassays that were additionally processed to remove false positives and assay artifacts and keep active and inactive compounds within similar molecular property ranges. To ascertain that the data set is suited to both ligand-based and structure-based virtual screening, target sets were restricted to single protein targets for which at least one X-ray structure is available in complex with ligands of the same phenotype (e.g., inhibitor, inverse agonist) as that of the PubChem active compounds. Preliminary virtual screening on the 21 remaining target sets with state-of-the-art orthogonal methods (2D fingerprint similarity, 3D shape similarity, molecular docking) enabled us to select 15 target sets for which at least one of the three screening methods is able to enrich the top 1%-ranked compounds in true actives by at least a factor of 2. The corresponding ligand sets (training, validation) were finally unbiased by the recently described asymmetric validation embedding (AVE) procedure to afford the LIT-PCBA data set, consisting of 15 targets and 7844 confirmed active and 407,381 confirmed inactive compounds. The data set mimics experimental screening decks in terms of hit rate (ratio of active to inactive compounds) and potency distribution. It is available online at http://drugdesign.unistra.fr/LIT-PCBA for download and for benchmarking novel virtual screening methods, notably those relying on machine learning.

Benchmarking Data Sets from PubChem BioAssay Data: Current Scenario and Room for Improvement.

Developing realistic data sets for evaluating virtual screening methods is a task that has been tackled by the cheminformatics community for many years. Numerous artificially constructed data collections were developed, such as DUD, DUD-E, or DEKOIS. However, they all suffer from multiple drawbacks, one of which is the absence of experimental results confirming the impotence of presumably inactive molecules, leading to possible false negatives in the ligand sets. In light of this problem, the PubChem BioAssay database, an open-access repository providing the bioactivity information of compounds that were already tested on a biological target, is now a recommended source for data set construction. Nevertheless, there exist several issues with the use of such data that need to be properly addressed. In this article, an overview of benchmarking data collections built upon experimental PubChem BioAssay input is provided, along with a thorough discussion of noteworthy issues that one must consider during the design of new ligand sets from this database. The points raised in this review are expected to guide future developments in this regard, in hopes of offering better evaluation tools for novel in silico screening procedures.

Unsupervised Classification of G-Protein Coupled Receptors and Their Conformational States Using IChem Intramolecular Interaction Patterns.

Over the past decade, the ever-growing structural information on G-protein coupled receptors (GPCRs) has revealed the three-dimensional (3D) characteristics of a receptor structure that is competent for G-protein binding. Structural markers are now commonly used to distinguish GPCR functional states, especially when analyzing molecular dynamics simulations. In particular, the position of the sixth helix within the seven transmembrane domains (TMs) is directly related to the coupling of the G-protein. Here, we show that the structural pattern defined by transmembrane intramolecular interactions (hydrogen bonds excluding backbone/backbone interactions, ionic bonds and aromatic interactions) is suitable for comparison of GPCR 3D structures and unsupervised distinction of the receptor states. First, we analyze a microsecond long molecular dynamic simulation of the human ß2-adrenergic receptor (ADRB2). Clustering of the 3D structures by pattern similarity identifies stable states which match the conformational classes defined by structural markers. Furthermore, the method directly spots the few state-specific interactions. Transforming pattern into graph, we extend the method to the comparison of different GPCRs. Clustering all GPCR experimentally determined structures by clique relative size first separates receptors, then their conformational states, thereby suggesting that the interaction patterns are specific of the receptor sequence and that the interaction signatures of conformational states are not shared across distant homologues.

All in One: Cavity Detection, Druggability Estimate, Cavity-Based Pharmacophore Perception, and Virtual Screening.

Discovering the very first ligands of pharmacologically important targets in a fast and cost-efficient manner is an important issue in drug discovery. In the absence of structural information on either endogenous or synthetic ligands, computational chemists classically identify the very first hits by docking compound libraries to a binding site of interest, with well-known biases arising from the usage of scoring functions. We herewith propose a novel computational method tailored to ligand-free protein structures and consisting in the generation of simple cavity-based pharmacophores to which potential ligands could be aligned by the use of a smooth Gaussian function. The method, embedded in the IChem toolkit, automatically detects ligand-binding cavities, then predicts their structural druggability, and last creates a structure-based pharmacophore for predicted druggable binding sites. A companion tool (Shaper2) was designed to align ligands to cavity-derived pharmacophoric features. The proposed method is as efficient as state-of-the-art virtual screening methods (ROCS, Surflex-Dock) in both posing and virtual screening challenges. Interestingly, IChem-Shaper2 is clearly orthogonal to these latter methods in retrieving unique chemotypes from high-throughput virtual screening data.

Local Interaction Density (LID), a Fast and Efficient Tool to Prioritize Docking Poses.

Ligand docking at a protein site can be improved by prioritizing poses by similarity to validated binding modes found in the crystal structures of ligand/protein complexes. The interactions formed in the predicted model are searched in each of the reference 3D structures, taken individually. We propose to merge the information provided by all references, creating a single representation of all known binding modes. The method is called LID, an acronym for Local Interaction Density. LID was benchmarked in a pose prediction exercise on 19 proteins and 1382 ligands using PLANTS as docking software. It was also tested in a virtual screening challenge on eight proteins, with a dataset of 140,000 compounds from DUD-E and PubChem. LID significantly improved the performance of the docking program in both pose prediction and virtual screening. The gain is comparable to that obtained with a rescoring approach based on the individual comparison of reference binding modes (the GRIM method). Importantly, LID is effective with a small number of references. LID calculation time is negligible compared to the docking time.

Ranking docking poses by graph matching of protein-ligand interactions: lessons learned from the D3R Grand Challenge 2.

A novel docking challenge has been set by the Drug Design Data Resource (D3R) in order to predict the pose and affinity ranking of a set of Farnesoid X receptor (FXR) agonists, prior to the public release of their bound X-ray structures and potencies. In a first phase, 36 agonists were docked to 26 Protein Data Bank (PDB) structures of the FXR receptor, and next rescored using the in-house developed GRIM method. GRIM aligns protein-ligand interaction patterns of docked poses to those of available PDB templates for the target protein, and rescore poses by a graph matching method. In agreement with results obtained during the previous 2015 docking challenge, we clearly show that GRIM rescoring improves the overall quality of top-ranked poses by prioritizing interaction patterns already visited in the PDB. Importantly, this challenge enables us to refine the applicability domain of the method by better defining the conditions of its success. We notably show that rescoring apolar ligands in hydrophobic pockets leads to frequent GRIM failures. In the second phase, 102 FXR agonists were ranked by decreasing affinity according to the Gibbs free energy of the corresponding GRIM-selected poses, computed by the HYDE scoring function. Interestingly, this fast and simple rescoring scheme provided the third most accurate ranking method among 57 contributions. Although the obtained ranking is still unsuitable for hit to lead optimization, the GRIM-HYDE scoring scheme is accurate and fast enough to post-process virtual screening data.

sc-PDB: a 3D-database of ligandable binding sites--10 years on.

The sc-PDB database (available at http://bioinfo-pharma.u-strasbg.fr/scPDB/) is a comprehensive and up-to-date selection of ligandable binding sites of the Protein Data Bank. Sites are defined from complexes between a protein and a pharmacological ligand. The database provides the all-atom description of the protein, its ligand, their binding site and their binding mode. Currently, the sc-PDB archive registers 9283 binding sites from 3678 unique proteins and 5608 unique ligands. The sc-PDB database was publicly launched in 2004 with the aim of providing structure files suitable for computational approaches to drug design, such as docking. During the last 10 years we have improved and standardized the processes for (i) identifying binding sites, (ii) correcting structures, (iii) annotating protein function and ligand properties and (iv) characterizing their binding mode. This paper presents the latest enhancements in the database, specifically pertaining to the representation of molecular interaction and to the similarity between ligand/protein binding patterns. The new website puts emphasis in pictorial analysis of data.

MRT-92 inhibits Hedgehog signaling by blocking overlapping binding sites in the transmembrane domain of the Smoothened receptor.

The Smoothened (Smo) receptor, a member of class F G protein-coupled receptors, is the main transducer of the Hedgehog (Hh) signaling pathway implicated in a wide range of developmental and adult processes. Smo is the target of anticancer drugs that bind to a long and narrow cavity in the 7-transmembrane (7TM) domain. X-ray structures of human Smo (hSmo) bound to several ligands have revealed 2 types of 7TM-directed antagonists: those binding mostly to extracellular loops (site 1, e.g., LY2940680) and those penetrating deeply in the 7TM cavity (site 2, e.g., SANT-1). Here we report the development of the acylguanidine MRT-92, which displays subnanomolar antagonist activity against Smo in various Hh cell-based assays. MRT-92 inhibits rodent cerebellar granule cell proliferation induced by Hh pathway activation through pharmacologic (half maximal inhibitory concentration [IC50] = 0.4 nM) or genetic manipulation. Using [(3)H]MRT-92 (Kd = 0.3 nM for hSmo), we created a comprehensive framework for the interaction of small molecule modulators with hSmo and for understanding chemoresistance linked to hSmo mutations. Guided by molecular docking and site-directed mutagenesis data, our work convincingly confirms that MRT-92 simultaneously recognized and occupied both sites 1 and 2. Our data demonstrate the existence of a third type of Smo antagonists, those entirely filling the Smo binding cavity from the upper extracellular part to the lower cytoplasmic-proximal subpocket. Our studies should help design novel potent Smo antagonists and more effective therapeutic strategies for treating Hh-linked cancers and associated chemoresistance.

TRAF4 is a novel phosphoinositide-binding protein modulating tight junctions and favoring cell migration.

Tumor necrosis factor (TNF) receptor-associated factor 4 (TRAF4) is frequently overexpressed in carcinomas, suggesting a specific role in cancer. Although TRAF4 protein is predominantly found at tight junctions (TJs) in normal mammary epithelial cells (MECs), it accumulates in the cytoplasm of malignant MECs. How TRAF4 is recruited and functions at TJs is unclear. Here we show that TRAF4 possesses a novel phosphoinositide (PIP)-binding domain crucial for its recruitment to TJs. Of interest, this property is shared by the other members of the TRAF protein family. Indeed, the TRAF domain of all TRAF proteins (TRAF1 to TRAF6) is a bona fide PIP-binding domain. Molecular and structural analyses revealed that the TRAF domain of TRAF4 exists as a trimer that binds up to three lipids using basic residues exposed at its surface. Cellular studies indicated that TRAF4 acts as a negative regulator of TJ and increases cell migration. These functions are dependent from its ability to interact with PIPs. Our results suggest that TRAF4 overexpression might contribute to breast cancer progression by destabilizing TJs and favoring cell migration.

A step-economical multicomponent synthesis of 3D-shaped aza-diketopiperazines and their drug-like chemical space analysis.

A rapid and atom economical multicomponent synthesis of complex aza-diketopiperazines (aza-DKPs) driven by Rh(i)-catalyzed hydroformylation of alkenylsemicarbazides is described. Combined with catalytic amounts of acid and the presence of nucleophilic species, this unprecedented multicomponent reaction (MCR) enabled the formation of six bonds and a controlled stereocenter from simple substrates. The efficacy of the strategy was demonstrated with a series of various allyl-substituted semicarbazides and nucleophiles leading to the preparation of 3D-shaped bicyclic aza-DKPs. Moreover, an analysis of their 3D molecular descriptors and "drug-likeness" properties highlights not only their originality in the chemical space of aza-heterocycles but also their great potential for medicinal chemistry.

Docking pose selection by interaction pattern graph similarity: application to the D3R grand challenge 2015.

High affinity ligands for a given target tend to share key molecular interactions with important anchoring amino acids and therefore often present quite conserved interaction patterns. This simple concept was formalized in a topological knowledge-based scoring function (GRIM) for selecting the most appropriate docking poses from previously X-rayed interaction patterns. GRIM first converts protein-ligand atomic coordinates (docking poses) into a simple 3D graph describing the corresponding interaction pattern. In a second step, proposed graphs are compared to that found from template structures in the Protein Data Bank. Last, all docking poses are rescored according to an empirical score (GRIMscore) accounting for overlap of maximum common subgraphs. Taking the opportunity of the public D3R Grand Challenge 2015, GRIM was used to rescore docking poses for 36 ligands (6 HSP90α inhibitors, 30 MAP4K4 inhibitors) prior to the release of the corresponding protein-ligand X-ray structures. When applied to the HSP90α dataset, for which many protein-ligand X-ray structures are already available, GRIM provided very high quality solutions (mean rmsd = 1.06 Å, n = 6) as top-ranked poses, and significantly outperformed a state-of-the-art scoring function. In the case of MAP4K4 inhibitors, for which preexisting 3D knowledge is scarce and chemical diversity is much larger, the accuracy of GRIM poses decays (mean rmsd = 3.18 Å, n = 30) although GRIM still outperforms an energy-based scoring function. GRIM rescoring appears to be quite robust with comparison to the other approaches competing for the same challenge (42 submissions for the HSP90 dataset, 27 for the MAP4K4 dataset) as it ranked 3rd and 2nd respectively, for the two investigated datasets. The rescoring method is quite simple to implement, independent on a docking engine, and applicable to any target for which at least one holo X-ray structure is available.

Single cell tracking assay reveals an opposite effect of selective small non-peptidic α5ß1 or αvß3/ß5 integrin antagonists in U87MG glioma cells.

BACKGROUND: Integrins are extracellular matrix receptors involved in several pathologies. Despite homologies between the RGD-binding α5ß1 and αvß3 integrins, selective small antagonists for each heterodimer have been proposed. Herein, we evaluated the effects of such small antagonists in a cellular context, the U87MG cell line, which express both integrins. The aim of the study was to determine if fibronectin-binding integrin antagonists are able to impact on cell adhesion and migration in relationships with their defined affinity and selectivity for α5ß1 and αvß3/ß5 purified integrins. METHODS: Small antagonists were either selective for α5ß1 integrin, for αvß3/ß5 integrin or non-selective. U87MG cell adhesion was evaluated on fibronectin or vitronectin. Migration assays included wound healing recovery and single cell tracking experiments. U87MG cells stably manipulated for the expression of α5 integrin subunit were used to explore the impact of α5ß1 integrin in the biological assays. RESULTS: U87MG cell adhesion on fibronectin or vitronectin was respectively dependent on α5ß1 or αvß3/ß5 integrin. Wound healing migration was dependent on both integrins. However U87MG single cell migration was highly dependent on α5ß1 integrin and was inhibited selectively by α5ß1 integrin antagonists but increased by αvß3/ß5 integrin antagonists. CONCLUSIONS: We provide a rationale for testing new integrin ligands in a cell-based assay to characterize more directly their potential inhibitory effects on integrin cellular functions. GENERAL SIGNIFICANCE: Our data highlight a single cell tracking assay as a powerful cell-based test which may help to characterize true functional integrin antagonists that block α5ß1 integrin-dependent cell migration.

IChemPIC: A Random Forest Classifier of Biological and Crystallographic Protein-Protein Interfaces.

Protein-protein interactions are becoming a major focus of academic and pharmaceutical research to identify low molecular weight compounds able to modulate oligomeric signaling complexes. As the number of protein complexes of known three-dimensional structure is constantly increasing, there is a need to discard biologically irrelevant interfaces and prioritize those of high value for potential druggability assessment. A Random Forest model has been trained on a set of 300 protein-protein interfaces using 45 molecular interaction descriptors as input. It is able to predict the nature of external test interfaces (crystallographic vs biological) with accuracy at least equal to that of the best state-of-the-art methods. However, our method presents unique advantages in the early prioritization of potentially ligandable protein-protein interfaces: (i) it is equally robust in predicting either crystallographic or biological contacts and (ii) it can be applied to a wide array of oligomeric complexes ranging from small-sized biological interfaces to large crystallographic contacts.

Synthesis and biological properties of thiazole-analogues of pyochelin, a siderophore of Pseudomonas aeruginosa.

Pyochelin is a siderophore common to all strains of Pseudomonas aeruginosa utilized by this Gram-negative bacterium to acquire iron(III). FptA is the outer membrane transporter responsible of ferric-pyochelin uptake in P. aeruginosa. We describe in this Letter the synthesis and the biological properties ((55)Fe uptake, binding to FptA) of several thiazole analogues of pyochelin. Among them we report in this Letter the two first pyochelin analogues able to bind FptA without promoting any iron uptake in P. aeruginosa.