Alternative splicing encodes functional intracellular CD59 isoforms that mediate insulin secretion and are down-regulated in diabetic islets.

Human pancreatic islets highly express CD59, which is a glycosylphosphatidylinositol (GPI)-anchored cell-surface protein and is required for insulin secretion. How cell-surface CD59 could interact with intracellular exocytotic machinery has so far not been described. We now demonstrate the existence of CD59 splice variants in human pancreatic islets, which have unique C-terminal domains replacing the GPI-anchoring signal sequence. These isoforms are found in the cytosol of ß-cells, interact with SNARE proteins VAMP2 and SNAP25, colocalize with insulin granules, and rescue insulin secretion in CD59-knockout (KO) cells. We therefore named these isoforms IRIS-1 and IRIS-2 (Isoforms Rescuing Insulin Secretion 1 and 2). Antibodies raised against each isoform revealed that expression of both IRIS-1 and IRIS-2 is significantly lower in islets isolated from human type 2 diabetes (T2D) patients, as compared to healthy controls. Further, glucotoxicity induced in primary, healthy human islets led to a significant decrease of IRIS-1 expression, suggesting that hyperglycemia (raised glucose levels) and subsequent decreased IRIS-1 expression may contribute to relative insulin deficiency in T2D patients. Similar isoforms were also identified in the mouse CD59B gene, and targeted CRISPR/Cas9-mediated knockout showed that these intracellular isoforms, but not canonical CD59B, are involved in insulin secretion from mouse ß-cells. Mouse IRIS-2 is also down-regulated in diabetic db/db mouse islets. These findings establish the endogenous existence of previously undescribed non­GPI-anchored intracellular isoforms of human CD59 and mouse CD59B, which are required for normal insulin secretion.

Biallelic variants in NOS3 and GUCY1A3, the two major genes of the nitric oxide pathway, cause moyamoya cerebral angiopathy.

BACKGROUND: Moyamoya angiopathy (MMA) is a rare cerebrovascular condition leading to stroke. Mutations in 15 genes have been identified in Mendelian forms of MMA, but they explain only a very small proportion of cases. Our aim was to investigate the genetic basis of MMA in consanguineous patients having unaffected parents in order to identify genes involved in autosomal recessive MMA. METHODS: Exome sequencing (ES) was performed in 6 consecutive consanguineous probands having MMA of unknown etiology. Functional consequences of variants were assessed using western blot and protein 3D structure analyses. RESULTS: Causative homozygous variants of NOS3, the gene encoding the endothelial nitric oxide synthase (eNOS), and GUCY1A3, the gene encoding the alpha1 subunit of the soluble guanylate cyclase (sGC) which is the major nitric oxide (NO) receptor in the vascular wall, were identified in 3 of the 6 probands. One NOS3 variant (c.1502 + 1G > C) involves a splice donor site causing a premature termination codon and leads to a total lack of eNOS in endothelial progenitor cells of the affected proband. The other NOS3 variant (c.1942 T > C) is a missense variant located into the flavodoxine reductase domain; it is predicted to be destabilizing and shown to be associated with a reduction of eNOS expression. The GUCY1A3 missense variant (c.1778G > A), located in the catalytic domain of the sGC, is predicted to disrupt the tridimensional structure of this domain and to lead to a loss of function of the enzyme. Both NOS3 mutated probands suffered from an infant-onset and severe MMA associated with posterior cerebral artery steno-occlusive lesions. The GUCY1A3 mutated proband presented an adult-onset MMA associated with an early-onset arterial hypertension and a stenosis of the superior mesenteric artery. None of the 3 probands had achalasia. CONCLUSIONS: We show for the first time that biallelic loss of function variants in NOS3 is responsible for MMA and that mutations in NOS3 and GUCY1A3 are causing fifty per cent of MMA in consanguineous patients. These data pinpoint the essential role of the NO pathway in MMA pathophysiology.

Virtual screening web servers: designing chemical probes and drug candidates in the cyberspace.

The interplay between life sciences and advancing technology drives a continuous cycle of chemical data growth; these data are most often stored in open or partially open databases. In parallel, many different types of algorithms are being developed to manipulate these chemical objects and associated bioactivity data. Virtual screening methods are among the most popular computational approaches in pharmaceutical research. Today, user-friendly web-based tools are available to help scientists perform virtual screening experiments. This article provides an overview of internet resources enabling and supporting chemical biology and early drug discovery with a main emphasis on web servers dedicated to virtual ligand screening and small-molecule docking. This survey first introduces some key concepts and then presents recent and easily accessible virtual screening and related target-fishing tools as well as briefly discusses case studies enabled by some of these web services. Notwithstanding further improvements, already available web-based tools not only contribute to the design of bioactive molecules and assist drug repositioning but also help to generate new ideas and explore different hypotheses in a timely fashion while contributing to teaching in the field of drug development.

Machine learning-driven identification of drugs inhibiting cytochrome P450 2C9.

Cytochrome P450 2C9 (CYP2C9) is a major drug-metabolizing enzyme that represents 20% of the hepatic CYPs and is responsible for the metabolism of 15% of drugs. A general concern in drug discovery is to avoid the inhibition of CYP leading to toxic drug accumulation and adverse drug-drug interactions. However, the prediction of CYP inhibition remains challenging due to its complexity. We developed an original machine learning approach for the prediction of drug-like molecules inhibiting CYP2C9. We created new predictive models by integrating CYP2C9 protein structure and dynamics knowledge, an original selection of physicochemical properties of CYP2C9 inhibitors, and machine learning modeling. We tested the machine learning models on publicly available data and demonstrated that our models successfully predicted CYP2C9 inhibitors with an accuracy, sensitivity and specificity of approximately 80%. We experimentally validated the developed approach and provided the first identification of the drugs vatalanib, piriqualone, ticagrelor and cloperidone as strong inhibitors of CYP2C9 with IC values <18 µM and sertindole, asapiprant, duvelisib and dasatinib as moderate inhibitors with IC50 values between 40 and 85 µM. Vatalanib was identified as the strongest inhibitor with an IC50 value of 0.067 µM. Metabolism assays allowed the characterization of specific metabolites of abemaciclib, cloperidone, vatalanib and tarafenacin produced by CYP2C9. The obtained results demonstrate that such a strategy could improve the prediction of drug-drug interactions in clinical practice and could be utilized to prioritize drug candidates in drug discovery pipelines.

Dominant negative mutation in oxalate transporter SLC26A6 associated with enteric hyperoxaluria and nephrolithiasis.

BACKGROUND: Nephrolithiasis (NL) is a complex multifactorial disease affecting up to 10%-20% of the human population and causing a significant burden on public health systems worldwide. It results from a combination of environmental and genetic factors. Hyperoxaluria is a major risk factor for NL. METHODS: We used a whole exome-based approach in a patient with calcium oxalate NL. The effects of the mutation were characterised using cell culture and in silico analyses. RESULTS: We identified a rare heterozygous missense mutation (c.1519C>T/p.R507W) in the SLC26A6 gene that encodes a secretory oxalate transporter. This mutation cosegregated with hyperoxaluria in the family. In vitro characterisation of mutant SLC26A6 demonstrated that Cl--dependent oxalate transport was dramatically reduced because the mutation affects both SLC26A6 transport activity and membrane surface expression. Cotransfection studies demonstrated strong dominant-negative effects of the mutant on the wild-type protein indicating that the phenotype of patients heterozygous for this mutation may be more severe than predicted by haploinsufficiency alone. CONCLUSION: Our study is in line with previous observations made in the mouse showing that SLC26A6 inactivation can cause inherited enteric hyperoxaluria with calcium oxalate NL. Consistent with an enteric form of hyperoxaluria, we observed a beneficial effect of increasing calcium in the patient's diet to reduce urinary oxalate excretion.

A Hybrid Docking and Machine Learning Approach to Enhance the Performance of Virtual Screening Carried out on Protein-Protein Interfaces.

The modulation of protein-protein interactions (PPIs) by small chemical compounds is challenging. PPIs play a critical role in most cellular processes and are involved in numerous disease pathways. As such, novel strategies that assist the design of PPI inhibitors are of major importance. We previously reported that the knowledge-based DLIGAND2 scoring tool was the best-rescoring function for improving receptor-based virtual screening (VS) performed with the Surflex docking engine applied to several PPI targets with experimentally known active and inactive compounds. Here, we extend our investigation by assessing the vs. potential of other types of scoring functions with an emphasis on docking-pose derived solvent accessible surface area (SASA) descriptors, with or without the use of machine learning (ML) classifiers. First, we explored rescoring strategies of Surflex-generated docking poses with five GOLD scoring functions (GoldScore, ChemScore, ASP, ChemPLP, ChemScore with Receptor Depth Scaling) and with consensus scoring. The top-ranked poses were post-processed to derive a set of protein and ligand SASA descriptors in the bound and unbound states, which were combined to derive descriptors of the docked protein-ligand complexes. Further, eight ML models (tree, bagged forest, random forest, Bayesian, support vector machine, logistic regression, neural network, and neural network with bagging) were trained using the derivatized SASA descriptors and validated on test sets. The results show that many SASA descriptors are better than Surflex and GOLD scoring functions in terms of overall performance and early recovery success on the used dataset. The ML models were superior to all scoring functions and rescoring approaches for most targets yielding up to a seven-fold increase in enrichment factors at 1% of the screened collections. In particular, the neural networks and random forest-based ML emerged as the best techniques for this PPI dataset, making them robust and attractive vs. tools for hit-finding efforts. The presented results suggest that exploring further docking-pose derived SASA descriptors could be valuable for structure-based virtual screening projects, and in the present case, to assist the rational design of small-molecule PPI inhibitors.

FastTargetPred: a program enabling the fast prediction of putative protein targets for input chemical databases.

SUMMARY: Several web-based tools predict the putative targets of a small molecule query compound by similarity to molecules with known bioactivity data using molecular fingerprints. In numerous situations, it would however be valuable to be able to run such computations on a local computer. We present FastTargetPred, a new program for the prediction of protein targets for small molecule queries. Structural similarity computations rely on a large collection of confirmed protein-ligand activities extracted from the curated ChEMBL 25 database. The program allows to annotate an input chemical library of ∼100k compounds within a few hours on a simple personal computer. AVAILABILITY AND IMPLEMENTATION: FastTargetPred is written in Python 3 (≥3.7) and C languages. Python code depends only on the Python Standard Library. The program can be run on Linux, MacOS and Windows operating systems. Pre-compiled versions are available at https://github.com/ludovicchaput/FastTargetPred. FastTargetPred is licensed under the GNU GPLv3. The program calls some scripts from the free chemistry toolkit MayaChemTools. CONTACT: bruno.villoutreix@inserm.fr. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Anti-Factor B Antibodies and Acute Postinfectious GN in Children.

BACKGROUND: The pathophysiology of the leading cause of pediatric acute nephritis, acute postinfectious GN, including mechanisms of the pathognomonic transient complement activation, remains uncertain. It shares clinicopathologic features with C3 glomerulopathy, a complement-mediated glomerulopathy that, unlike acute postinfectious GN, has a poor prognosis. METHODS: This retrospective study investigated mechanisms of complement activation in 34 children with acute postinfectious GN and low C3 level at onset. We screened a panel of anticomplement protein autoantibodies, carried out related functional characterization, and compared results with those of 60 children from the National French Registry who had C3 glomerulopathy and persistent hypocomplementemia. RESULTS: All children with acute postinfectious GN had activation of the alternative pathway of the complement system. At onset, autoantibodies targeting factor B (a component of the alternative pathway C3 convertase) were found in a significantly higher proportion of children with the disorder versus children with hypocomplementemic C3 glomerulopathy (31 of 34 [91%] versus 4 of 28 [14%], respectively). In acute postinfectious GN, anti-factor B autoantibodies were transient and correlated with plasma C3 and soluble C5b-9 levels. We demonstrated that anti-factor B antibodies enhance alternative pathway convertase activity in vitro, confirming their pathogenic effect. We also identified crucial antibody binding sites on factor B, including one correlated to disease severity. CONCLUSIONS: These findings elucidate the pathophysiologic mechanisms underlying acute postinfectious GN by identifying anti-factor B autoantibodies as contributing factors in alternative complement pathway activation. At onset of a nephritic syndrome with low C3 level, screening for anti-factor B antibodies might help guide indications for kidney biopsy to avoid misdiagnosed chronic glomerulopathy, such as C3 glomerulopathy, and to help determine therapy.

In Silico Investigation of the New UK (B.1.1.7) and South African (501Y.V2) SARS-CoV-2 Variants with a Focus at the ACE2-Spike RBD Interface.

SARS-CoV-2 exploits angiotensin-converting enzyme 2 (ACE2) as a receptor to invade cells. It has been reported that the UK and South African strains may have higher transmission capabilities, eventually in part due to amino acid substitutions on the SARS-CoV-2 Spike protein. The pathogenicity seems modified but is still under investigation. Here we used the experimental structure of the Spike RBD domain co-crystallized with part of the ACE2 receptor, several in silico methods and numerous experimental data reported recently to analyze the possible impacts of three amino acid replacements (Spike K417N, E484K, N501Y) with regard to ACE2 binding. We found that the N501Y replacement in this region of the interface (present in both the UK and South African strains) should be favorable for the interaction with ACE2, while the K417N and E484K substitutions (South African strain) would seem neutral or even unfavorable. It is unclear if the N501Y substitution in the South African strain could counterbalance the K417N and E484K Spike replacements with regard to ACE2 binding. Our finding suggests that the UK strain should have higher affinity toward ACE2 and therefore likely increased transmissibility and possibly pathogenicity. If indeed the South African strain has a high transmission level, this could be due to the N501Y replacement and/or to substitutions in regions located outside the direct Spike-ACE2 interface but not so much to the K417N and E484K replacements. Yet, it should be noted that amino acid changes at Spike position 484 can lead to viral escape from neutralizing antibodies. Further, these amino acid substitutions do not seem to induce major structural changes in this region of the Spike protein. This structure-function study allows us to rationalize some observations made for the UK strain but raises questions for the South African strain.

Computational Analysis of Chemical Space of Natural Compounds Interacting with Sulfotransferases.

The aim of this study was to investigate the chemical space and interactions of natural compounds with sulfotransferases (SULTs) using ligand- and structure-based in silico methods. An in-house library of natural ligands (hormones, neurotransmitters, plant-derived compounds and their metabolites) reported to interact with SULTs was created. Their chemical structures and properties were compared to those of compounds of non-natural (synthetic) origin, known to interact with SULTs. The natural ligands interacting with SULTs were further compared to other natural products for which interactions with SULTs were not known. Various descriptors of the molecular structures were calculated and analyzed. Statistical methods (ANOVA, PCA, and clustering) were used to explore the chemical space of the studied compounds. Similarity search between the compounds in the different groups was performed with the ROCS software. The interactions with SULTs were additionally analyzed by docking into different experimental and modeled conformations of SULT1A1. Natural products with potentially strong interactions with SULTs were outlined. Our results contribute to a better understanding of chemical space and interactions of natural compounds with SULT enzymes and help to outline new potential ligands of these enzymes.

Ile73Asn mutation in protein C introduces a new N-linked glycosylation site on the first EGF-domain of protein C and causes thrombosis.

Activated protein C exerts its anticoagulant activity by protein S-dependent inactivation of factors Va and VIIIa by limited proteolysis. We identified a venous thrombosis patient who has plasma protein C antigen level of 63% and activity levels of 44% and 23%, as monitored by chromogenic and clotting assays. Genetic analysis revealed the proband carries compound heterozygous mutations (c.344T>A, p.I73N and c.1181G>A, p.R352Q) in PROC We individually expressed protein C mutations and discovered that thrombin-thrombomodulin activates both variants normally and the resulting activated protein C mutants exhibit normal amidolytic and proteolytic activities. However, while protein S-dependent catalytic activity of activated protein C-R352Q toward factor Va was normal, it was significantly impaired for activated protein C-I73N. These results suggest that the Ile to Asn substitution impairs interaction of activated protein C-I73N with protein S. This conclusion was supported by a normal anticoagulant activity for activated protein C-I73N in protein S-deficient but not in normal plasma. Further analysis revealed Ile to Asn substitution introduces a new glycosylation site on first EGF-like domain of protein C, thereby adversely affecting interaction of activated protein C with protein S. Activated protein C-R352Q only exhibited reduced activity in sub-physiological concentrations of Na+ and Ca2+, suggesting that this residue contributes to metal ion-binding affinity of the protease, with no apparent adverse effect on its function in the presence of physiological levels of metal ions. These results provide insight into the mechanism by which I73N/R352Q mutations in activated protein C cause thrombosis in proband carrying this compound heterozygous mutation.

Fast Rescoring Protocols to Improve the Performance of Structure-Based Virtual Screening Performed on Protein-Protein Interfaces.

Protein-protein interactions (PPIs) are attractive targets for drug design because of their essential role in numerous cellular processes and disease pathways. However, in general, PPIs display exposed binding pockets at the interface, and as such, have been largely unexploited for therapeutic interventions with low-molecular weight compounds. Here, we used docking and various rescoring strategies in an attempt to recover PPI inhibitors from a set of active and inactive molecules for 11 targets collected in ChEMBL and PubChem. Our focus is on the screening power of the various developed protocols and on using fast approaches so as to be able to apply such a strategy to the screening of ultralarge libraries in the future. First, we docked compounds into each target using the fast "pscreen" mode of the structure-based virtual screening (VS) package Surflex. Subsequently, the docking poses were postprocessed to derive a set of 3D topological descriptors: (i) shape similarity and (ii) interaction fingerprint similarity with a co-crystallized inhibitor, (iii) solvent-accessible surface area, and (iv) extent of deviation from the geometric center of a reference inhibitor. The derivatized descriptors, together with descriptor-scaled scoring functions, were utilized to investigate possible impacts on VS performance metrics. Moreover, four standalone scoring functions, RF-Score-VS (machine-learning), DLIGAND2 (knowledge-based), Vinardo (empirical), and X-SCORE (empirical), were employed to rescore the PPI compounds. Collectively, the results indicate that the topological scoring algorithms could be valuable both at a global level, with up to 79% increase in areas under the receiver operating characteristic curve for some targets, and in early stages, with up to a 4-fold increase in enrichment factors at 1% of the screened collections. Outstandingly, DLIGAND2 emerged as the best scoring function on this data set, outperforming all rescoring techniques in terms of VS metrics. The described methodology could help in the rational design of small-molecule PPI inhibitors and has direct applications in many therapeutic areas, including cancer, CNS, and infectious diseases such as COVID-19.

AMMOS2: a web server for protein-ligand-water complexes refinement via molecular mechanics.

AMMOS2 is an interactive web server for efficient computational refinement of protein-small organic molecule complexes. The AMMOS2 protocol employs atomic-level energy minimization of a large number of experimental or modeled protein-ligand complexes. The web server is based on the previously developed standalone software AMMOS (Automatic Molecular Mechanics Optimization for in silico Screening). AMMOS utilizes the physics-based force field AMMP sp4 and performs optimization of protein-ligand interactions at five levels of flexibility of the protein receptor. The new version 2 of AMMOS implemented in the AMMOS2 web server allows the users to include explicit water molecules and individual metal ions in the protein-ligand complexes during minimization. The web server provides comprehensive analysis of computed energies and interactive visualization of refined protein-ligand complexes. The ligands are ranked by the minimized binding energies allowing the users to perform additional analysis for drug discovery or chemical biology projects. The web server has been extensively tested on 21 diverse protein-ligand complexes. AMMOS2 minimization shows consistent improvement over the initial complex structures in terms of minimized protein-ligand binding energies and water positions optimization. The AMMOS2 web server is freely available without any registration requirement at the URL: http://drugmod.rpbs.univ-paris-diderot.fr/ammosHome.php.

A Free Web-Based Protocol to Assist Structure-Based Virtual Screening Experiments.

Chemical biology and drug discovery are complex and costly processes. In silico screening approaches play a key role in the identification and optimization of original bioactive molecules and increase the performance of modern chemical biology and drug discovery endeavors. Here, we describe a free web-based protocol dedicated to small-molecule virtual screening that includes three major steps: ADME-Tox filtering (via the web service FAF-Drugs4), docking-based virtual screening (via the web service MTiOpenScreen), and molecular mechanics optimization (via the web service AMMOS2 [Automatic Molecular Mechanics Optimization for in silico Screening]). The online tools FAF-Drugs4, MTiOpenScreen, and AMMOS2 are implemented in the freely accessible RPBS (Ressource Parisienne en Bioinformatique Structurale) platform. The proposed protocol allows users to screen thousands of small molecules and to download the top 1500 docked molecules that can be further processed online. Users can then decide to purchase a small list of compounds for in vitro validation. To demonstrate the potential of this online-based protocol, we performed virtual screening experiments of 4574 approved drugs against three cancer targets. The results were analyzed in the light of published drugs that have already been repositioned on these targets. We show that our protocol is able to identify active drugs within the top-ranked compounds. The web-based protocol is user-friendly and can successfully guide the identification of new promising molecules for chemical biology and drug discovery purposes.

FAF-Drugs4: free ADME-tox filtering computations for chemical biology and early stages drug discovery.

MOTIVATION: Identification of small molecules that could be interesting starting points for drug discovery or to investigate a biological system as in chemical biology endeavours is both time consuming and costly. In silico approaches that assist the design of quality compound collections or help to prioritize molecules before synthesis or purchase are therefore valuable. Here quality refers to the selection of molecules that pass one or several selected filters that can be tuned by the users according to the project and the stage of the project. These filters can involve prediction of physicochemical properties, search for toxicophores or other unwanted chemical groups. RESULTS: FAF-Drugs4 is a novel version of our online server dedicated to the preparation and annotation of compound collections. The tool is now faster and several parameters have been optimized. In addition, a new service referred to as FAF-QED, an implementation of the quantitative estimate of drug-likeness method, is now available. AVAILABILITY AND IMPLEMENTATION: The server is available at http://fafdrugs4.mti.univ-paris-diderot.fr. CONTACT: Bruno.Villoutreix@inserm.fr. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

iPPI-DB: an online database of modulators of protein-protein interactions.

In order to boost the identification of low-molecular-weight drugs on protein-protein interactions (PPI), it is essential to properly collect and annotate experimental data about successful examples. This provides the scientific community with the necessary information to derive trends about privileged physicochemical properties and chemotypes that maximize the likelihood of promoting a given chemical probe to the most advanced stages of development. To this end we have developed iPPI-DB (freely accessible at http://www.ippidb.cdithem.fr), a database that contains the structure, some physicochemical characteristics, the pharmacological data and the profile of the PPI targets of several hundreds modulators of protein-protein interactions. iPPI-DB is accessible through a web application and can be queried according to two general approaches: using physicochemical/pharmacological criteria; or by chemical similarity to a user-defined structure input. In both cases the results are displayed as a sortable and exportable datasheet with links to external databases such as Uniprot, PubMed. Furthermore each compound in the table has a link to an individual ID card that contains its physicochemical and pharmacological profile derived from iPPI-DB data. This includes information about its binding data, ligand and lipophilic efficiencies, location in the PPI chemical space, and importantly similarity with known drugs, and links to external databases like PubChem, and ChEMBL.

FAF-Drugs3: a web server for compound property calculation and chemical library design.

Drug attrition late in preclinical or clinical development is a serious economic problem in the field of drug discovery. These problems can be linked, in part, to the quality of the compound collections used during the hit generation stage and to the selection of compounds undergoing optimization. Here, we present FAF-Drugs3, a web server that can be used for drug discovery and chemical biology projects to help in preparing compound libraries and to assist decision-making during the hit selection/lead optimization phase. Since it was first described in 2006, FAF-Drugs has been significantly modified. The tool now applies an enhanced structure curation procedure, can filter or analyze molecules with user-defined or eight predefined physicochemical filters as well as with several simple ADMET (absorption, distribution, metabolism, excretion and toxicity) rules. In addition, compounds can be filtered using an updated list of 154 hand-curated structural alerts while Pan Assay Interference compounds (PAINS) and other, generally unwanted groups are also investigated. FAF-Drugs3 offers access to user-friendly html result pages and the possibility to download all computed data. The server requires as input an SDF file of the compounds; it is open to all users and can be accessed without registration at http://fafdrugs3.mti.univ-paris-diderot.fr.

MTiOpenScreen: a web server for structure-based virtual screening.

Open screening endeavors play and will play a key role to facilitate the identification of new bioactive compounds in order to foster innovation and to improve the effectiveness of chemical biology and drug discovery processes. In this line, we developed the new web server MTiOpenScreen dedicated to small molecule docking and virtual screening. It includes two services, MTiAutoDock and MTiOpenScreen, allowing performing docking into a user-defined binding site or blind docking using AutoDock 4.2 and automated virtual screening with AutoDock Vina. MTiOpenScreen provides valuable starting collections for screening, two in-house prepared drug-like chemical libraries containing 150 000 PubChem compounds: the Diverse-lib containing diverse molecules and the iPPI-lib enriched in molecules likely to inhibit protein-protein interactions. In addition, MTiOpenScreen offers users the possibility to screen up to 5000 small molecules selected outside our two libraries. The predicted binding poses and energies of up to 1000 top ranked ligands can be downloaded. In this way, MTiOpenScreen enables researchers to apply virtual screening using different chemical libraries on traditional or more challenging protein targets such as protein-protein interactions. The MTiOpenScreen web server is free and open to all users at http://bioserv.rpbs.univ-paris-diderot.fr/services/MTiOpenScreen/.

Integrated structure- and ligand-based in silico approach to predict inhibition of cytochrome P450 2D6.

MOTIVATION: Cytochrome P450 (CYP) is a superfamily of enzymes responsible for the metabolism of drugs, xenobiotics and endogenous compounds. CYP2D6 metabolizes about 30% of drugs and predicting potential CYP2D6 inhibition is important in early-stage drug discovery. RESULTS: We developed an original in silico approach for the prediction of CYP2D6 inhibition combining the knowledge of the protein structure and its dynamic behavior in response to the binding of various ligands and machine learning modeling. This approach includes structural information for CYP2D6 based on the available crystal structures and molecular dynamic simulations (MD) that we performed to take into account conformational changes of the binding site. We performed modeling using three learning algorithms--support vector machine, RandomForest and NaiveBayesian--and we constructed combined models based on topological information of known CYP2D6 inhibitors and predicted binding energies computed by docking on both X-ray and MD protein conformations. In addition, we identified three MD-derived structures that are capable all together to better discriminate inhibitors and non-inhibitors compared with individual CYP2D6 conformations, thus ensuring complementary ligand profiles. Inhibition models based on classical molecular descriptors and predicted binding energies were able to predict CYP2D6 inhibition with an accuracy of 78% on the training set and 75% on the external validation set.

Rational design of small molecules targeting the C2 domain of coagulation factor VIII.

The C domains of coagulation factors V (FV) and VIII (FVIII) are structurally conserved domains and share a common and essential function in membrane binding. In vivo regulation of thrombin formation strongly depends on the expression and regulation of the cofactor activities of FVIII and FV. With this study, we explored the possibility of inhibition of thrombin formation in full blood with small druglike molecules. Such compounds may serve as lead molecules for the development of a new type of orally available coagulation inhibitors that act by blocking the interaction between the C domains of FVIII and the membrane surface. We identified 9 novel molecules that are able to inhibit binding of the FVIII C2 domain to a model membrane by application of a combined ligand-based and target structure-based virtual screening approach that took into account the knowledge of a set of previously identified low-molecular-weight FVIII binders that were, however, not active in full blood. The half-maximal inhibitory concentration values of our newly identified compounds varied from 2.1 to 19.9 µM, of which 7 of 9 molecules did not appreciably inhibit FV membrane binding and were thus specific for FVIII. The most active bioactive compound showed activity in both plasma and in full blood.