Benchmarking AlphaFold-Generated Structures of Chemokine-Chemokine Receptor Complexes.

AlphaFold and AlphaFold-Multimer have become two essential tools for the modeling of unknown structures of proteins and protein complexes. In this work, we extensively benchmarked the quality of chemokine-chemokine receptor structures generated by AlphaFold-Multimer against experimentally determined structures. Our analysis considered both the global quality of the model, as well as key structural features for chemokine recognition. To study the effects of template and multiple sequence alignment parameters on the results, a new prediction pipeline called LIT-AlphaFold (https://github.com/LIT-CCM-lab/LIT-AlphaFold) was developed, allowing extensive input customization. AlphaFold-Multimer correctly predicted differences in chemokine binding orientation and accurately reproduced the unique binding orientation of the CXCL12-ACKR3 complex. Further, the predictions of the full receptor N-terminus provided insights into a putative chemokine recognition site 0.5. The accuracy of chemokine N-terminus binding mode prediction varied between complexes, but the confidence score permitted the distinguishing of residues that were very likely well positioned. Finally, we generated a high-confidence model of the unsolved CXCL12-CXCR4 complex, which agreed with experimental mutagenesis and cross-linking data.

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.

Comparing transmembrane protein structures with ATOLL.

SUMMARY: The 3D structure of transmembrane helices plays a key role in the function of membrane proteins. While visual inspection can usually discern the distinctive features of a helix bundle, simply translating them into a 2D diagram can be difficult. ATOLL (Aligned Transmembrane dOmains Layout fLattening) projects the helix bundle onto the lipid bilayer plane, thereby facilitating the comparison of different structures of the same membrane protein or structures of different membrane proteins. AVAILABILITY AND IMPLEMENTATION: ATOLL is a program written in Python3. The source code is freely available on the web at https://github.com/LIT-CCM-lab/ATOLL. ATOLL is implemented into a web server (https://atoll.drugdesign.unistra.fr/). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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.

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.

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.

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.

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.

Comprehensive analysis of commercial fragment libraries.

Screening of fragment libraries is a valuable approach to the drug discovery process. The quality of the library is one of the keys to success, and more particularly the design or choice of a library has to meet the specificities of the research program. In this study, we made an inventory of the commercial fragment libraries and we established a methodology which allows any library to be positioned in relation to the complete offer currently on the market, by addressing the following questions: does this chemical library look like another chemical library? What is the coverage of the current chemical space by this chemical library? What are the characteristic structural features of the fragments of this chemical library? We based our analysis on 2D and 3D chemical descriptors, framework class generation and the generative topographic map. We identified 59 270 scaffolds, which can be searched in a dedicated web site (https://gtmfrag.drugdesign.unistra.fr) and developed a model which accounts for fragment diversity while being easy to interpret (download at 10.5281/zenodo.5534434).

High-Throughput Screening for Extracellular Inhibitors of the FLT3 Receptor Tyrosine Kinase Reveals Chemically Diverse and Druggable Negative Allosteric Modulators.

Inhibiting receptor tyrosine kinases is commonly achieved by two main strategies targeting either the intracellular kinase domain by low molecular weight compounds or the extracellular ligand-binding domain by monoclonal antibodies. Identifying small molecules able to inhibit RTKs at the extracellular level would be highly desirable to gain exquisite selectivity but is believed to be challenging owing to the size of RTK endogenous ligands (cytokines, growth factors) and the topology of RTK extracellular domains. We here report the high-throughput screening of the French Chemical Library (48K compounds) for extracellular inhibitors of the Fms-like tyrosine kinase 3 (FLT3) receptor tyrosine kinase, by a homogeneous time-resolved fluorescence competition assay. A total of 679 small molecular weight ligands (1.4%) were confirmed to strongly inhibit (>75%) the binding of the fluorescent labeled FLT3 ligand (FL cytokine) to FLT3 overexpressed in HEK-293 cells, at two different concentrations (5 and 20 µM). Concentration-response curves, obtained for 111 lead-like molecules, confirmed the unexpected tolerance of the FLT3 extracellular domain for low molecular weight druggable inhibitors exhibiting submicromolar potencies, chemical diversity, and promising pharmacokinetic properties. Further investigation of one hit confirmed inhibitory properties in dorsal root ganglia neurons and in a mouse model of neuropathic pain.

A meteorological dataset of the West African monsoon during the 2016 DACCIWA campaign.

As part of the Dynamics-Aerosol-Chemistry-Cloud Interactions in West Africa (DACCIWA) project, extensive in-situ measurements of the southern West African atmospheric boundary layer (ABL) have been performed at three supersites Kumasi (Ghana), Savè (Benin) and Ile-Ife (Nigeria) during the 2016 monsoon period (June and July). The measurements were designed to provide data for advancing our understanding of the relevant processes governing the formation, persistence and dissolution of nocturnal low-level stratus clouds and their influence on the daytime ABL in southern West Africa. An extensive low-level cloud deck often forms during the night and persists long into the following day strongly influencing the ABL diurnal cycle. Although the clouds are of a high significance for the regional climate, the dearth of observations in this region has hindered process understanding. Here, an overview of the measurements ranging from near-surface observations, cloud characteristics, aerosol and precipitation to the dynamics and thermodynamics in the ABL and above, and data processing is given. So-far achieved scientific findings, based on the dataset analyses, are briefly overviewed.

Exhaustive Repertoire of Druggable Cavities at Protein-Protein Interfaces of Known Three-Dimensional Structure.

Protein-protein interactions (PPIs) offer the unique opportunity to tailor ligands aimed at specifically stabilizing or disrupting the corresponding interfaces and providing a safer alternative to conventional ligands targeting monomeric macromolecules. Selecting biologically relevant protein-protein interfaces for either stabilization or disruption by small molecules is usually biology-driven on a case-by-case basis and does not follow a structural rationale that could be applied to an entire interactome. We herewith provide a first step to the latter goal by using a fully automated and structure-based workflow, applicable to any PPI of known three-dimensional (3D) structure, to identify and prioritize druggable cavities at and nearby PPIs of pharmacological interest. When applied to the entire Protein Data Bank, 164 514 druggable cavities were identified and classified in four groups (interfacial, rim, allosteric, orthosteric) according to their properties and spatial locations. Systematic comparison of PPI cavities with pockets deduced from druggable protein-ligand complexes shows almost no overlap in property space, suggesting that even the most druggable PPI cavities are unlikely to be addressed with conventional drug-like compound libraries. The archive is freely accessible at http://drugdesign.unistra.fr/ppiome .

Structural Insights on Fragment Binding Mode Conservation.

Aiming at a deep understanding of fragment binding to ligandable targets, we performed a large scale analysis of the Protein Data Bank. Binding modes of 1832 drug-like ligands and 1079 fragments to 235 proteins were compared. We observed that the binding modes of fragments and their drug-like superstructures binding to the same protein are mostly conserved, thereby providing experimental evidence for the preservation of fragment binding modes during molecular growing. Furthermore, small chemical changes in the fragment are tolerated without alteration of the fragment binding mode. The exceptions to this observation generally involve conformational variability of the molecules. Our data analysis also suggests that, provided enough fragments have been crystallized within a protein, good interaction coverage of the binding pocket is achieved. Last, we extended our study to 126 crystallization additives and discuss in which cases they provide information relevant to structure-based drug design.

Multi-target Fragments Display Versatile Binding Modes.

Promiscuity is an interesting concept in fragment-based drug design as fragments with low specificity can be advantageous for finding many screening hits. We present a PDB-wide analysis of multi-target fragments and their binding mode conservation. Focussing on multi-target fragments, we found that the majority shows non-conserved binding modes, even if they bind in a similar conformation or similar protein targets. Surprisingly, fragment properties alone are not able to predict whether a fragment will exhibit a versatile or conserved binding mode, emphasizing the interplay between protein and fragment features during a binding event and the importance of structure-based modelling.

Recovering the true targets of specific ligands by virtual screening of the protein data bank.

The Protein Data Bank (PDB) has been processed to extract a screening protein library (sc-PDB) of 2148 entries. A knowledge-based detection algorithm has been applied to 18,000 PDB files to find regular expressions corresponding to either protein, ions, co-factors, solvent, or ligand atoms. The sc-PDB database comprises high-resolution X-ray structures of proteins for which (i) a well-defined active site exists, (ii) the bound-ligand is a small molecular weight molecule. The database has been screened by an inverse docking tool derived from the GOLD program to recover the known target of four unrelated ligands. Both the database and the inverse screening procedures are accurate enough to rank the true target of the four investigated ligands among the top 1% scorers, with 70-100 fold enrichment with respect to random screening. Applying the proposed screening procedure to a small-sized generic ligand was much less accurate suggesting that inverse screening shall be reserved to rather selective compounds.

Assessing the scaffold diversity of screening libraries.

Medicinal chemists have traditionally realized assessments of chemical diversity and subsequent compound acquisition, although a recent study suggests that experts are usually inconsistent in reviewing large data sets. To analyze the scaffold diversity of commercially available screening collections, we have developed a general workflow aimed at (1) identifying druglike compounds, (2) clustering them by maximum common substructures (scaffolds), (3) measuring the scaffold diversity encoded by each screening collection independently of its size, and finally (4) merging all common substructures in a nonredundant scaffold library that can easily be browsed by structural and topological queries. Starting from 2.4 million compounds out of 12 commercial sources, four categories of libraries could be identified: large- and medium-sized combinatorial libraries (low scaffold diversity), diverse libraries (medium diversity, medium size), and highly diverse libraries (high diversity, low size). The chemical space covered by the scaffold library can be searched to prioritize scaffold-focused libraries.

sc-PDB: an annotated database of druggable binding sites from the Protein Data Bank.

The sc-PDB is a collection of 6 415 three-dimensional structures of binding sites found in the Protein Data Bank (PDB). Binding sites were extracted from all high-resolution crystal structures in which a complex between a protein cavity and a small-molecular-weight ligand could be identified. Importantly, ligands are considered from a pharmacological and not a structural point of view. Therefore, solvents, detergents, and most metal ions are not stored in the sc-PDB. Ligands are classified into four main categories: nucleotides (< 4-mer), peptides (< 9-mer), cofactors, and organic compounds. The corresponding binding site is formed by all protein residues (including amino acids, cofactors, and important metal ions) with at least one atom within 6.5 angstroms of any ligand atom. The database was carefully annotated by browsing several protein databases (PDB, UniProt, and GO) and storing, for every sc-PDB entry, the following features: protein name, function, source, domain and mutations, ligand name, and structure. The repository of ligands has also been archived by diversity analysis of molecular scaffolds, and several chemoinformatics descriptors were computed to better understand the chemical space covered by stored ligands. The sc-PDB may be used for several purposes: (i) screening a collection of binding sites for predicting the most likely target(s) of any ligand, (ii) analyzing the molecular similarity between different cavities, and (iii) deriving rules that describe the relationship between ligand pharmacophoric points and active-site properties. The database is periodically updated and accessible on the web at http://bioinfo-pharma.u-strasbg.fr/scPDB/.