Efficient GPU computation of large protein Solvent-Excluded Surface.

The Solvent-Excluded Surface (SES) is an essential representation of molecules which is massively used in molecular modeling and drug discovery since it represents the interacting surface between molecules. Based on its properties, it supports the visualization of both large scale shapes and details of molecules. While several methods targeted its computation, the ability to process large molecular structures to address the introduction of big complex analysis while leveraging the massively parallel architecture of GPUs has remained a challenge. This is mostly caused by the need for consequent memory allocation or by the complexity of the parallelization of its processing. In this paper, we leverage the last theoretical advances made for the depiction of the SES to provide fast analytical computation with low impact on memory. We show that our method is able to compute the complete surface while handling large molecular complexes with competitive computation time costs compared to previous works.

PLO3S: Protein LOcal Surficial Similarity Screening.

The study of protein molecular surfaces enables to better understand and predict protein interactions. Different methods have been developed in computer vision to compare surfaces that can be applied to protein molecular surfaces. The present work proposes a method using the Wave Kernel Signature: Protein LOcal Surficial Similarity Screening (PLO3S). The descriptor of the PLO3S method is a local surface shape descriptor projected on a unit sphere mapped onto a 2D plane and called Surface Wave Interpolated Maps (SWIM). PLO3S allows to rapidly compare protein surface shapes through local comparisons to filter large protein surfaces datasets in protein structures virtual screening protocols.

Review of in silico studies dedicated to the nuclear receptor family: Therapeutic prospects and toxicological concerns.

Being in the center of both therapeutic and toxicological concerns, NRs are widely studied for drug discovery application but also to unravel the potential toxicity of environmental compounds such as pesticides, cosmetics or additives. High throughput screening campaigns (HTS) are largely used to detect compounds able to interact with this protein family for both therapeutic and toxicological purposes. These methods lead to a large amount of data requiring the use of computational approaches for a robust and correct analysis and interpretation. The output data can be used to build predictive models to forecast the behavior of new chemicals based on their in vitro activities. This atrticle is a review of the studies published in the last decade and dedicated to NR ligands in silico prediction for both therapeutic and toxicological purposes. Over 100 articles concerning 14 NR subfamilies were carefully read and analyzed in order to retrieve the most commonly used computational methods to develop predictive models, to retrieve the databases deployed in the model building process and to pinpoint some of the limitations they faced.

Computationally driven discovery of SARS-CoV-2 Mpro inhibitors: from design to experimental validation.

We report a fast-track computationally driven discovery of new SARS-CoV-2 main protease (Mpro) inhibitors whose potency ranges from mM for the initial non-covalent ligands to sub-µM for the final covalent compound (IC50 = 830 ± 50 nM). The project extensively relied on high-resolution all-atom molecular dynamics simulations and absolute binding free energy calculations performed using the polarizable AMOEBA force field. The study is complemented by extensive adaptive sampling simulations that are used to rationalize the different ligand binding poses through the explicit reconstruction of the ligand-protein conformation space. Machine learning predictions are also performed to predict selected compound properties. While simulations extensively use high performance computing to strongly reduce the time-to-solution, they were systematically coupled to nuclear magnetic resonance experiments to drive synthesis and for in vitro characterization of compounds. Such a study highlights the power of in silico strategies that rely on structure-based approaches for drug design and allows the protein conformational multiplicity problem to be addressed. The proposed fluorinated tetrahydroquinolines open routes for further optimization of Mpro inhibitors towards low nM affinities.

Surface-based protein domains retrieval methods from a SHREC2021 challenge.

Proteins are essential to nearly all cellular mechanism and the effectors of the cells activities. As such, they often interact through their surface with other proteins or other cellular ligands such as ions or organic molecules. The evolution generates plenty of different proteins, with unique abilities, but also proteins with related functions hence similar 3D surface properties (shape, physico-chemical properties, …). The protein surfaces are therefore of primary importance for their activity. In the present work, we assess the ability of different methods to detect such similarities based on the geometry of the protein surfaces (described as 3D meshes), using either their shape only, or their shape and the electrostatic potential (a biologically relevant property of proteins surface). Five different groups participated in this contest using the shape-only dataset, and one group extended its pre-existing method to handle the electrostatic potential. Our comparative study reveals both the ability of the methods to detect related proteins and their difficulties to distinguish between highly related proteins. Our study allows also to analyze the putative influence of electrostatic information in addition to the one of protein shapes alone. Finally, the discussion permits to expose the results with respect to ones obtained in the previous contests for the extended method. The source codes of each presented method have been made available online.

UDock2: interactive real-time multi-body protein-protein docking software.

MOTIVATION: Protein-protein docking aims at predicting the geometry of protein interactions to gain insights into the mechanisms underlying these processes and develop new strategies for drug discovery. Interactive and user-oriented manipulation tools can support this task complementary to automated software. RESULTS: This article presents an interactive multi-body protein-protein docking software, UDock2, designed for research but also usable for teaching and popularization of science purposes due to its high usability. In UDock2, the users tackle the conformational space of protein interfaces using an intuitive real-time docking procedure with on-the-fly scoring. UDock2 integrates traditional computer graphics methods to facilitate the visualization and to provide better insight into protein surfaces, interfaces, and properties. AVAILABILITY AND IMPLEMENTATION: UDock2 is open-source, cross-platform (Windows and Linux), and available at http://udock.fr. The code can be accessed at https://gitlab.com/Udock/Udock2.

Comparative evaluation of shape retrieval methods on macromolecular surfaces: an application of computer vision methods in structural bioinformatics.

MOTIVATION: The investigation of the structure of biological systems at the molecular level gives insights about their functions and dynamics. Shape and surface of biomolecules are fundamental to molecular recognition events. Characterizing their geometry can lead to more adequate predictions of their interactions. In the present work, we assess the performance of reference shape retrieval methods from the computer vision community on protein shapes. RESULTS: Shape retrieval methods are efficient in identifying orthologous proteins and tracking large conformational changes. This work illustrates the interest for the protein surface shape as a higher-level representation of the protein structure that (i) abstracts the underlying protein sequence, structure or fold, (ii) allows the use of shape retrieval methods to screen large databases of protein structures to identify surficial homologs and possible interacting partners and (iii) opens an extension of the protein structure-function paradigm toward a protein structure-surface(s)-function paradigm. AVAILABILITYAND IMPLEMENTATION: All data are available online at http://datasetmachat.drugdesign.fr. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

High-resolution mining of the SARS-CoV-2 main protease conformational space: supercomputer-driven unsupervised adaptive sampling.

We provide an unsupervised adaptive sampling strategy capable of producing µs-timescale molecular dynamics (MD) simulations of large biosystems using many-body polarizable force fields (PFFs). The global exploration problem is decomposed into a set of separate MD trajectories that can be restarted within a selective process to achieve sufficient phase-space sampling. Accurate statistical properties can be obtained through reweighting. Within this highly parallel setup, the Tinker-HP package can be powered by an arbitrary large number of GPUs on supercomputers, reducing exploration time from years to days. This approach is used to tackle the urgent modeling problem of the SARS-CoV-2 Main Protease (Mpro) producing more than 38 µs of all-atom simulations of its apo (ligand-free) dimer using the high-resolution AMOEBA PFF. The first 15.14 µs simulation (physiological pH) is compared to available non-PFF long-timescale simulation data. A detailed clustering analysis exhibits striking differences between FFs, with AMOEBA showing a richer conformational space. Focusing on key structural markers related to the oxyanion hole stability, we observe an asymmetry between protomers. One of them appears less structured resembling the experimentally inactive monomer for which a 6 µs simulation was performed as a basis for comparison. Results highlight the plasticity of the Mpro active site. The C-terminal end of its less structured protomer is shown to oscillate between several states, being able to interact with the other protomer, potentially modulating its activity. Active and distal site volumes are found to be larger in the most active protomer within our AMOEBA simulations compared to non-PFFs as additional cryptic pockets are uncovered. A second 17 µs AMOEBA simulation is performed with protonated His172 residues mimicking lower pH. Data show the protonation impact on the destructuring of the oxyanion loop. We finally analyze the solvation patterns around key histidine residues. The confined AMOEBA polarizable water molecules are able to explore a wide range of dipole moments, going beyond bulk values, leading to a water molecule count consistent with experimental data. Results suggest that the use of PFFs could be critical in drug discovery to accurately model the complexity of the molecular interactions structuring Mpro.

Predicting Potential Endocrine Disrupting Chemicals Binding to Estrogen Receptor α (ERα) Using a Pipeline Combining Structure-Based and Ligand-Based in Silico Methods.

The estrogen receptors α (ERα) are transcription factors involved in several physiological processes belonging to the nuclear receptors (NRs) protein family. Besides the endogenous ligands, several other chemicals are able to bind to those receptors. Among them are endocrine disrupting chemicals (EDCs) that can trigger toxicological pathways. Many studies have focused on predicting EDCs based on their ability to bind NRs; mainly, estrogen receptors (ER), thyroid hormones receptors (TR), androgen receptors (AR), glucocorticoid receptors (GR), and peroxisome proliferator-activated receptors gamma (PPARγ). In this work, we suggest a pipeline designed for the prediction of ERα binding activity. The flagged compounds can be further explored using experimental techniques to assess their potential to be EDCs. The pipeline is a combination of structure based (docking and pharmacophore models) and ligand based (pharmacophore models) methods. The models have been constructed using the Environmental Protection Agency (EPA) data encompassing a large number of structurally diverse compounds. A validation step was then achieved using two external databases: the NR-DBIND (Nuclear Receptors DataBase Including Negative Data) and the EADB (Estrogenic Activity DataBase). Different combination protocols were explored. Results showed that the combination of models performed better than each model taken individually. The consensus protocol that reached values of 0.81 and 0.54 for sensitivity and specificity, respectively, was the best suited for our toxicological study. Insights and recommendations were drawn to alleviate the screening quality of other projects focusing on ERα binding predictions.

Neuropilin 1 and Neuropilin 2 gene invalidation or pharmacological inhibition reveals their relevance for the treatment of metastatic renal cell carcinoma.

BACKGROUND: Despite the improvement of relapse-free survival mediated by anti-angiogenic drugs like sunitinib (Sutent®), or by combinations of anti-angiogenic drugs with immunotherapy, metastatic clear cell Renal Cell Carcinoma (mccRCC) remain incurable. Hence, new relevant treatments are urgently needed. The VEGFs coreceptors, Neuropilins 1, 2 (NRP1, 2) are expressed on several tumor cells including ccRCC. We analyzed the role of the VEGFs/NRPs signaling in ccRCC aggressiveness and evaluated the relevance to target this pathway. METHODS: We correlated the NRP1, 2 levels to patients' survival using online available data base. Human and mouse ccRCC cells were knocked-out for the NRP1 and NRP2 genes by a CRISPR/Cas9 method. The number of metabolically active cells was evaluated by XTT assays. Migration ability was determined by wound closure experiments and invasion ability by using Boyden chamber coated with collagen. Production of VEGFA and VEGFC was evaluated by ELISA. Experimental ccRCC were generated in immuno-competent/deficient mice. The effects of a competitive inhibitor of NRP1, 2, NRPa-308, was tested in vitro and in vivo with the above-mentioned tests and on experimental ccRCC. NRPa-308 docking was performed on both NRPs. RESULTS: Knock-out of the NRP1 and NRP2 genes inhibited cell metabolism and migration and stimulated the expression of VEGFA or VEGFC, respectively. NRPa-308 presented a higher affinity for NRP2 than for NRP1. It decreased cell metabolism and migration/invasion more efficiently than sunitinib and the commercially available NRP inhibitor EG00229. NRPa-308 presented a robust inhibition of experimental ccRCC growth in immunocompetent and immunodeficient mice. Such inhibition was associated with decreased expression of several pro-tumoral factors. Analysis of the TCGA database showed that the NRP2 pathway, more than the NRP1 pathway correlates with tumor aggressiveness only in metastatic patients. CONCLUSIONS: Our study strongly suggests that inhibiting NRPs is a relevant treatment for mccRCC patients in therapeutic impasses and NRPa-308 represents a relevant hit.

Hits Discovery on the Androgen Receptor: In Silico Approaches to Identify Agonist Compounds.

The androgen receptor (AR) is a transcription factor that plays a key role in sexual phenotype and neuromuscular development. AR can be modulated by exogenous compounds such as pharmaceuticals or chemicals present in the environment, and particularly by AR agonist compounds that mimic the action of endogenous agonist ligands and whether restore or alter the AR endocrine system functions. The activation of AR must be correctly balanced and identifying potent AR agonist compounds is of high interest to both propose treatments for certain diseases, or to predict the risk related to agonist chemicals exposure. The development of in silico approaches and the publication of structural, affinity and activity data provide a good framework to develop rational AR hits prediction models. Herein, we present a docking and a pharmacophore modeling strategy to help identifying AR agonist compounds. All models were trained on the NR-DBIND that provides high quality binding data on AR and tested on AR-agonist activity assays from the Tox21 initiative. Both methods display high performance on the NR-DBIND set and could serve as starting point for biologists and toxicologists. Yet, the pharmacophore models still need data feeding to be used as large scope undesired effect prediction models.

New CXCR1/CXCR2 inhibitors represent an effective treatment for kidney or head and neck cancers sensitive or refractory to reference treatments.

Clear cell Renal Cell (RCC) and Head and Neck Squamous Cell Carcinomas (HNSCC) are characterized by a pro-angiogenic/pro-inflammatory context. Despite conventional or targeted therapies, metastatic RCC and HNSCC remain incurable. Alternative treatments to reference therapies (sunitinib, a multi tyrosine kinase inhibitor for RCC or cisplatin for HNSCC) are urgently needed on relapse. Here, we described the relevance of targeting the ELR+CXCL cytokines receptors, CXCR1/2, for the treatment of these two cancer types. Methods: The relevance to patient treatment was evaluated by correlating the ELR+CXCL/CXCR1/2 levels to survival using online available data. We report herein the synthesis of new pharmacological inhibitors of CXCR1/2 with anti-proliferation/survival activity. The latter was evaluated with the XTT assay with leukemic, breast, RCC and HNSCC cell lines. Their relevance as an alternative treatment was tested on sunitinib- and cisplatin- resistant cells. The most efficient compound was then tested in a mouse model of RCC and HNSCC. Results: RCC and HNSCC expressed the highest amounts of CXCR1/2 of all cancers. High levels of ELR+CXCL cytokines (CXCL1, 2, 3, 5, 6, 7, 8) correlated to shorter survival. Among the 33 synthesized and tested molecules, compound C29 reduced ELR+CXCL/CXCR1/2-dependent proliferation and migration of endothelial cells. C29 exerted an anti-proliferation/survival activity on a panel of cancer cells including naive and resistant RCC and HNSCC cells. C29 reduced the growth of experimental RCC and HNSCC tumors by decreasing tumor cell proliferation, angiogenesis and ELR+/CXCL-mediated inflammation. Conclusion: Our study highlights the relevance of new CXCR1/2 inhibitors for the treatment of RCC or HNSCC as first-line treatment or at relapse on reference therapies.

Nuclear Receptors Database Including Negative Data (NR-DBIND): A Database Dedicated to Nuclear Receptors Binding Data Including Negative Data and Pharmacological Profile.

Nuclear receptors (NRs) are transcription factors that regulate gene expression in various physiological processes through their interactions with small hydrophobic molecules. They constitute an important class of targets for drugs and endocrine disruptors and are widely studied for both health and environment concerns. Since the integration of negative data can be critical for accurate modeling of ligand activity profiles, we manually collected and annotated NRs interaction data (positive and negative) through a sharp review of the corresponding literature. 15 116 positive and negative interactions data are provided for 28 NRs together with 593 PDB structures in the freely available Nuclear Receptors Database Including Negative Data ( http://nr-dbind.drugdesign.fr ). The NR-DBIND contains the most extensive information about interaction data on NRs, which should bring valuable information to chemists, biologists, pharmacologists and toxicologists.

Decoys Selection in Benchmarking Datasets: Overview and Perspectives.

Virtual Screening (VS) is designed to prospectively help identifying potential hits, i.e., compounds capable of interacting with a given target and potentially modulate its activity, out of large compound collections. Among the variety of methodologies, it is crucial to select the protocol that is the most adapted to the query/target system under study and that yields the most reliable output. To this aim, the performance of VS methods is commonly evaluated and compared by computing their ability to retrieve active compounds in benchmarking datasets. The benchmarking datasets contain a subset of known active compounds together with a subset of decoys, i.e., assumed non-active molecules. The composition of both the active and the decoy compounds subsets is critical to limit the biases in the evaluation of the VS methods. In this review, we focus on the selection of decoy compounds that has considerably changed over the years, from randomly selected compounds to highly customized or experimentally validated negative compounds. We first outline the evolution of decoys selection in benchmarking databases as well as current benchmarking databases that tend to minimize the introduction of biases, and secondly, we propose recommendations for the selection and the design of benchmarking datasets.

New selective cyclooxygenase-2 inhibitors from cyclocoumarol: Synthesis, characterization, biological evaluation and molecular modeling.

In this work, a serie of cyclocoumarol derivatives was designed, synthesized, characterized and studied for their potentialities as selective inhibitors of COX-2. All target compounds have been screened for their anti-inflammatory activity by the assay of PGE2 production. Among them, compound 5d exhibited the most potent inhibitory activity with a PGE2 inhibition compared to NS-398 (79% and 88% respectively) and showed non-inhibitory activity towards the COX-1 enzyme. Docking studies revealed the capacity of this compound to occupy the selective COX-2 cavity establishing additional hydrogen bonds between the oxygen of the methoxy group and the His90 and Arg513 of the binding site of the enzyme.

NRPa-308, a new neuropilin-1 antagonist, exerts in vitro anti-angiogenic and anti-proliferative effects and in vivo anti-cancer effects in a mouse xenograft model.

Neuropilin-1 (NRP-1) is an extra-cellular receptor for the main Vascular Endothelial Growth Factor over-expressed in tumour tissues, VEGF-A165. Consequently, NRP-1 is involved in angiogenesis and in tumour growth, and its over-expression is related to a clinical poor prognosis. NRP-1 appears as a major target in oncology, which remains poorly exploited. Herein, we report a new series of 18 small-sized fully organic VEGF-A165/NRP-1 antagonists (NRPas). These compounds share an original scaffold, including two linkers (sulphonamide and amide) and three aromatic cores. Among them, 2a (renamed NRPa-308) emerges as a promising "hit". In vitro,2a exerts not only potent anti-angiogenic activity, but also significant effects on cell viability of large panel of human solid and haematological cancer cell lines. Importantly, 2a is less cytotoxic on healthy tissues than the marketed anti-angiogenic drug sunitinib. Lastly, in a mouse xenograft model (human MDA-MB-231 breast cancer cells), 2a improves the median survival and reduces the tumour growth, but does not exert visible acute toxicity. Altogether, these results highlight its huge potential for a further "hit-to-lead" optimization, leading to new anti-cancer drugs.

Predicting the affinity of Farnesoid X Receptor ligands through a hierarchical ranking protocol: a D3R Grand Challenge 2 case study.

The Drug Design Data Resource (D3R) Grand Challenges are blind contests organized to assess the state-of-the-art methods accuracy in predicting binding modes and relative binding free energies of experimentally validated ligands for a given target. The second stage of the D3R Grand Challenge 2 (GC2) was focused on ranking 102 compounds according to their predicted affinity for Farnesoid X Receptor. In this task, our workflow was ranked 5th out of the 77 submissions in the structure-based category. Our strategy consisted in (1) a combination of molecular docking using AutoDock 4.2 and manual edition of available structures for binding poses generation using SeeSAR, (2) the use of HYDE scoring for pose selection, and (3) a hierarchical ranking using HYDE and MM/GBSA. In this report, we detail our pose generation and ligands ranking protocols and provide guidelines to be used in a prospective computer aided drug design program.

Update on COX-2 Selective Inhibitors: Chemical Classification, Side Effects and their Use in Cancers and Neuronal Diseases.

Inflammation is a complex phenomenon necessary in human defense mechanisms but also involved in the development of some human diseases. The discovery of cyclooxygenase-2 (COX- 2) improved the pharmacology of nonsteroidal anti-inflammatory drugs (NSAID) giving a clear mechanism for prostaglandin regulation in vivo and providing a new target for the development of COX-2-selective drugs without gastrointestinal side-effects. Keeping in view the importance of this pharmacological class, several literature reports have underlined the impact of these antiinflammatory compounds in therapeutics. The present review considers the most recently published literature concerning COX-2 inhibitors until 2016. Through a wide chemical classification, the last developments concerning this therapeutic family by highlighting structure-activity relationships insights and mechanisms are presented. A summary of the principal adverse effects observed and an overview of the new potential therapeutic indications for COX-2 inhibitors are also reported.

Discriminating Agonist from Antagonist Ligands of the Nuclear Receptors Using Different Chemoinformatics Approaches.

Nuclear receptors (NRs) constitute an important class of therapeutic targets. During the last 4 years, we tackled the pharmacological profile assessment of NR ligands for which we constructed the NRLiSt BDB. We evaluated and compared the performance of different virtual screening approaches: mean of molecular descriptor distribution values, molecular docking and 3D pharmacophore models. The simple comparison of the distribution profiles of 4885 molecular descriptors between the agonist and antagonist datasets didn't provide satisfying results. We obtained an overall good performance with the docking method we used, Surflex-Dock which was able to discriminate agonist from antagonist ligands. But the availability of PDB structures in the "pharmacological-profile-to-predict-bound-state" (agonist-bound or antagonist-bound) and the availability of enough ligands of both pharmacological profiles constituted limits to generalize this protocol for all NRs. Finally, the 3D pharmacophore modeling approach, allowed us to generate selective agonist pharmacophores and selective antagonist pharmacophores that covered more than 99 % of the whole NRLiSt BDB. This study allowed a better understanding of the pharmacological modulation of NRs with small molecules and could be extended to other therapeutic classes.

Identification of an in vivo orally active dual-binding protein-protein interaction inhibitor targeting TNFα through combined in silico/in vitro/in vivo screening.

TNFα is a homotrimeric pro-inflammatory cytokine, whose direct targeting by protein biotherapies has been an undeniable success for the treatment of chronic inflammatory diseases. Despite many efforts, no orally active drug targeting TNFα has been identified so far. In the present work, we identified through combined in silico/in vitro/in vivo approaches a TNFα direct inhibitor, compound 1, displaying nanomolar and micromolar range bindings to TNFα. Compound 1 inhibits the binding of TNFα with both its receptors TNFRI and TNFRII. Compound 1 inhibits the TNFα induced apoptosis on L929 cells and the TNFα induced NF-κB activation in HEK cells. In vivo, oral administration of compound 1 displays a significant protection in a murine TNFα-dependent hepatic shock model. This work illustrates the ability of low-cost combined in silico/in vitro/in vivo screening approaches to identify orally available small-molecules targeting challenging protein-protein interactions such as homotrimeric TNFα.