Phenotypic similarity-based approach for variant prioritization for unsolved rare disease: a preliminary methodological report.

Rare diseases (RD) have a prevalence of not more than 1/2000 persons in the European population, and are characterised by the difficulty experienced in obtaining a correct and timely diagnosis. According to Orphanet, 72.5% of RD have a genetic origin although 35% of them do not yet have an identified causative gene. A significant proportion of patients suspected to have a genetic RD receive an inconclusive exome/genome sequencing. Working towards the International Rare Diseases Research Consortium (IRDiRC)'s goal for 2027 to ensure that all people living with a RD receive a diagnosis within one year of coming to medical attention, the Solve-RD project aims to identify the molecular causes underlying undiagnosed RD. As part of this strategy, we developed a phenotypic similarity-based variant prioritization methodology comparing submitted cases with other submitted cases and with known RD in Orphanet. Three complementary approaches based on phenotypic similarity calculations using the Human Phenotype Ontology (HPO), the Orphanet Rare Diseases Ontology (ORDO) and the HPO-ORDO Ontological Module (HOOM) were developed; genomic data reanalysis was performed by the RD-Connect Genome-Phenome Analysis Platform (GPAP). The methodology was tested in 4 exemplary cases discussed with experts from European Reference Networks. Variants of interest (pathogenic or likely pathogenic) were detected in 8.8% of the 725 cases clustered by similarity calculations. Diagnostic hypotheses were validated in 42.1% of them and needed further exploration in another 10.9%. Based on the promising results, we are devising an automated standardized phenotypic-based re-analysis pipeline to be applied to the entire unsolved cases cohort.

The Human Phenotype Ontology in 2021.

The Human Phenotype Ontology (HPO, https://hpo.jax.org) was launched in 2008 to provide a comprehensive logical standard to describe and computationally analyze phenotypic abnormalities found in human disease. The HPO is now a worldwide standard for phenotype exchange. The HPO has grown steadily since its inception due to considerable contributions from clinical experts and researchers from a diverse range of disciplines. Here, we present recent major extensions of the HPO for neurology, nephrology, immunology, pulmonology, newborn screening, and other areas. For example, the seizure subontology now reflects the International League Against Epilepsy (ILAE) guidelines and these enhancements have already shown clinical validity. We present new efforts to harmonize computational definitions of phenotypic abnormalities across the HPO and multiple phenotype ontologies used for animal models of disease. These efforts will benefit software such as Exomiser by improving the accuracy and scope of cross-species phenotype matching. The computational modeling strategy used by the HPO to define disease entities and phenotypic features and distinguish between them is explained in detail.We also report on recent efforts to translate the HPO into indigenous languages. Finally, we summarize recent advances in the use of HPO in electronic health record systems.

Fr-PPIChem: An Academic Compound Library Dedicated to Protein-Protein Interactions.

Protein-protein interactions (PPIs) mediate nearly every cellular process and represent attractive targets for modulating disease states but are challenging to target with small molecules. Despite this, several PPI inhibitors (iPPIs) have entered clinical trials, and a growing number of PPIs have become validated drug targets. However, high-throughput screening efforts still endure low hit rates mainly because of the use of unsuitable screening libraries. Here, we describe the collective effort of a French consortium to build, select, and store in plates a unique chemical library dedicated to the inhibition of PPIs. Using two independent predictive models and two updated databases of experimentally confirmed PPI inhibitors developed by members of the consortium, we built models based on different training sets, molecular descriptors, and machine learning methods. Independent statistical models were used to select putative PPI inhibitors from large commercial compound collections showing great complementarity. Medicinal chemistry filters were applied to remove undesirable structures from this set (such as PAINS, frequent hitters, and toxic compounds) and to improve drug likeness. The remaining compounds were subjected to a clustering procedure to reduce the final size of the library while maintaining its chemical diversity. In practice, the library showed a 46-fold activity rate enhancement when compared to a non-iPPI-enriched diversity library in high-throughput screening against the CD47-SIRPα PPI. The Fr-PPIChem library is plated in 384-well plates and will be distributed on demand to the scientific community as a powerful tool for discovering new chemical probes and early hits for the development of potential therapeutic drugs.

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.

In silico model of the human ClC-Kb chloride channel: pore mapping, biostructural pathology and drug screening.

The human ClC-Kb channel plays a key role in exporting chloride ions from the cytosol and is known to be involved in Bartter syndrome type 3 when its permeation capacity is decreased. The ClC-Kb channel has been recently proposed as a potential therapeutic target to treat hypertension. In order to gain new insights into the sequence-structure-function relationships of this channel, to investigate possible impacts of amino-acid substitutions, and to design novel inhibitors, we first built a structural model of the human ClC-Kb channel using comparative modeling strategies. We combined in silico and in vitro techniques to analyze amino acids involved in the chloride ion pathway as well as to rationalize the possible role of several clinically observed mutations leading to the Bartter syndrome type 3. Virtual screening and drug repositioning computations were then carried out. We identified six novel molecules, including 2 approved drugs, diflusinal and loperamide, with Kd values in the low micromolar range, that block the human ClC-Kb channel and that could be used as starting point to design novel chemical probes for this potential therapeutic target.

Computational analysis of calculated physicochemical and ADMET properties of protein-protein interaction inhibitors.

The modulation of PPIs by low molecular weight chemical compounds, particularly by orally bioavailable molecules, would be very valuable in numerous disease indications. However, it is known that PPI inhibitors (iPPIs) tend to have properties that are linked to poor Absorption, Distribution, Metabolism, Excretion and Toxicity (ADMET) and in some cases to poor clinical outcomes. Previously reported in silico analyses of iPPIs have essentially focused on physicochemical properties but several other ADMET parameters would be important to assess. In order to gain new insights into the ADMET properties of iPPIs, computations were carried out on eight datasets collected from several databases. These datasets involve compounds targeting enzymes, GPCRs, ion channels, nuclear receptors, allosteric modulators, oral marketed drugs, oral natural product-derived marketed drugs and iPPIs. Several trends are reported that should assist the design and optimization of future PPI inhibitors, either for drug discovery endeavors or for chemical biology projects.

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.

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/.

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.

In silico design of low molecular weight protein-protein interaction inhibitors: Overall concept and recent advances.

Protein-protein interactions (PPIs) are carrying out diverse functions in living systems and are playing a major role in the health and disease states. Low molecular weight (LMW) "drug-like" inhibitors of PPIs would be very valuable not only to enhance our understanding over physiological processes but also for drug discovery endeavors. However, PPIs were deemed intractable by LMW chemicals during many years. But today, with the new experimental and in silico technologies that have been developed, about 50 PPIs have already been inhibited by LMW molecules. Here, we first focus on general concepts about protein-protein interactions, present a consensual view about ligandable pockets at the protein interfaces and the possibilities of using fast and cost effective structure-based virtual screening methods to identify PPI hits. We then discuss the design of compound collections dedicated to PPIs. Recent financial analyses of the field suggest that LMW PPI modulators could be gaining momentum over biologics in the coming years supporting further research in this area.

Rational design of small-molecule stabilizers of spermine synthase dimer by virtual screening and free energy-based approach.

Snyder-Robinson Syndrome (SRS) is a rare mental retardation disorder which is caused by the malfunctioning of an enzyme, the spermine synthase (SMS), which functions as a homo-dimer. The malfunctioning of SMS in SRS patients is associated with several identified missense mutations that occur away from the active site. This investigation deals with a particular SRS-causing mutation, the G56S mutation, which was shown computationally and experimentally to destabilize the SMS homo-dimer and thus to abolish SMS enzymatic activity. As a proof-of-concept, we explore the possibility to restore the enzymatic activity of the malfunctioning SMS mutant G56S by stabilizing the dimer through small molecule binding at the mutant homo-dimer interface. For this purpose, we designed an in silico protocol that couples virtual screening and a free binding energy-based approach to identify potential small-molecule binders on the destabilized G56S dimer, with the goal to stabilize it and thus to increase SMS G56S mutant activity. The protocol resulted in extensive list of plausible stabilizers, among which we selected and tested 51 compounds experimentally for their capability to increase SMS G56S mutant enzymatic activity. In silico analysis of the experimentally identified stabilizers suggested five distinctive chemical scaffolds. This investigation suggests that druggable pockets exist in the vicinity of the mutation sites at protein-protein interfaces which can be used to alter the disease-causing effects by small molecule binding. The identified chemical scaffolds are drug-like and can serve as original starting points for development of lead molecules to further rescue the disease-causing effects of the Snyder-Robinson syndrome for which no efficient treatment exists up to now.

Drug-Like Protein-Protein Interaction Modulators: Challenges and Opportunities for Drug Discovery and Chemical Biology.

[Formula: see text] Fundamental processes in living cells are largely controlled by macromolecular interactions and among them, protein-protein interactions (PPIs) have a critical role while their dysregulations can contribute to the pathogenesis of numerous diseases. Although PPIs were considered as attractive pharmaceutical targets already some years ago, they have been thus far largely unexploited for therapeutic interventions with low molecular weight compounds. Several limiting factors, from technological hurdles to conceptual barriers, are known, which, taken together, explain why research in this area has been relatively slow. However, this last decade, the scientific community has challenged the dogma and became more enthusiastic about the modulation of PPIs with small drug-like molecules. In fact, several success stories were reported both, at the preclinical and clinical stages. In this review article, written for the 2014 International Summer School in Chemoinformatics (Strasbourg, France), we discuss in silico tools (essentially post 2012) and databases that can assist the design of low molecular weight PPI modulators (these tools can be found at www.vls3d.com). We first introduce the field of protein-protein interaction research, discuss key challenges and comment recently reported in silico packages, protocols and databases dedicated to PPIs. Then, we illustrate how in silico methods can be used and combined with experimental work to identify PPI modulators.

In silico mechanistic profiling to probe small molecule binding to sulfotransferases.

Drug metabolizing enzymes play a key role in the metabolism, elimination and detoxification of xenobiotics, drugs and endogenous molecules. While their principal role is to detoxify organisms by modifying compounds, such as pollutants or drugs, for a rapid excretion, in some cases they render their substrates more toxic thereby inducing severe side effects and adverse drug reactions, or their inhibition can lead to drug-drug interactions. We focus on sulfotransferases (SULTs), a family of phase II metabolizing enzymes, acting on a large number of drugs and hormones and showing important structural flexibility. Here we report a novel in silico structure-based approach to probe ligand binding to SULTs. We explored the flexibility of SULTs by molecular dynamics (MD) simulations in order to identify the most suitable multiple receptor conformations for ligand binding prediction. Then, we employed structure-based docking-scoring approach to predict ligand binding and finally we combined the predicted interaction energies by using a QSAR methodology. The results showed that our protocol successfully prioritizes potent binders for the studied here SULT1 isoforms, and give new insights on specific molecular mechanisms for diverse ligands' binding related to their binding sites plasticity. Our best QSAR models, introducing predicted protein-ligand interaction energy by using docking, showed accuracy of 67.28%, 78.00% and 75.46%, for the isoforms SULT1A1, SULT1A3 and SULT1E1, respectively. To the best of our knowledge our protocol is the first in silico structure-based approach consisting of a protein-ligand interaction analysis at atomic level that considers both ligand and enzyme flexibility, along with a QSAR approach, to identify small molecules that can interact with II phase dug metabolizing enzymes.

One hundred thousand mouse clicks down the road: selected online resources supporting drug discovery collected over a decade.

Online resources enabling and supporting drug discovery have blossomed during the past ten years. However, drug hunters commonly find themselves overwhelmed by the proliferation of these computer-based resources. Ten years ago, we, the authors of this review, felt that a comprehensive list of in silico resources relating to drug discovery was needed. Especially because the internet provides a wealth of inspiring tools that, if fully exploited, could greatly assist the process. We present here a compilation of online tools and databases collected over the past decade. The tools were essentially found through literature and internet searches and, currently, our list contains over 1500 URLs. We also briefly highlight some recently reported services and comment about ongoing and future efforts in the field.

In silico prediction of aqueous solubility: a multimodel protocol based on chemical similarity.

Aqueous solubility is one of the most important ADMET properties to assess and to optimize during the drug discovery process. At present, accurate prediction of solubility remains very challenging and there is an important need of independent benchmarking of the existing in silico models such as to suggest solutions for their improvement. In this study, we developed a new protocol for improved solubility prediction by combining several existing models available in commercial or free software packages. We first performed an evaluation of ten in silico models for aqueous solubility prediction on several data sets in order to assess the reliability of the methods, and we proposed a new diverse data set of 150 molecules as relevant test set, SolDiv150. We developed a random forest protocol to evaluate the performance of different fingerprints for aqueous solubility prediction based on molecular structure similarity. Our protocol, called a "multimodel protocol", allows selecting the most accurate model for a compound of interest among the employed models or software packages, achieving r(2) of 0.84 when applied to SolDiv150. We also found that all models assessed here performed better on druglike molecules than on real drugs, thus additional improvement is needed in this direction. Overall, our approach enlarges the applicability domain as demonstrated by the more accurate results for solubility prediction obtained using our protocol in comparison to using individual models.

The FAF-Drugs2 server: a multistep engine to prepare electronic chemical compound collections.

SUMMARY: The FAF-Drugs2 server is a web application that prepares chemical compound libraries prior to virtual screening or that assists hit selection/lead optimization before chemical synthesis or ordering. The FAF-Drugs2 web server is an enhanced version of the FAF-Drugs2 package that now includes Pan Assay Interference Compounds detection. This online toolkit has been designed through a user-centered approach with emphasis on user-friendliness. This is a unique online tool allowing to prepare large compound libraries with in house or user-defined filtering parameters. AVAILABILITY: The FAF-Drugs2 server is freely available at http://bioserv.rpbs.univ-paris-diderot.fr/FAF-Drugs/.

Tyrosine kinase syk non-enzymatic inhibitors and potential anti-allergic drug-like compounds discovered by virtual and in vitro screening.

In the past decade, the spleen tyrosine kinase (Syk) has shown a high potential for the discovery of new treatments for inflammatory and autoimmune disorders. Pharmacological inhibitors of Syk catalytic site bearing therapeutic potential have been developed, with however limited specificity towards Syk. To address this topic, we opted for the design of drug-like compounds that could impede the interaction of Syk with its cellular partners while maintaining an active kinase protein. To achieve this challenging task, we used the powerful potential of intracellular antibodies for the modulation of cellular functions in vivo, combined to structure-based in silico screening. In our previous studies, we reported the anti-allergic properties of the intracellular antibody G4G11. With the aim of finding functional mimics of G4G11, we developed an Antibody Displacement Assay and we isolated the drug-like compound C-13, with promising in vivo anti-allergic activity. The likely binding cavity of this compound is located at the close vicinity of G4G11 epitope, far away from the catalytic site of Syk. Here we report the virtual screen of a collection of 500,000 molecules against this new cavity, which led to the isolation of 1000 compounds subsequently evaluated for their in vitro inhibitory effects using the Antibody Displacement Assay. Eighty five compounds were selected and evaluated for their ability to inhibit the liberation of allergic mediators from mast cells. Among them, 10 compounds inhibited degranulation with IC50 values ≤ 10 µM. The most bioactive compounds combine biological activity, significant inhibition of antibody binding and strong affinity for Syk. Moreover, these molecules show a good potential for oral bioavailability and are not kinase catalytic site inhibitors. These bioactive compounds could be used as starting points for the development of new classes of non-enzymatic inhibitors of Syk and for drug discovery endeavour in the field of inflammation related disorders.

Three-dimensional structure generators of drug-like compounds: DG-AMMOS, an open-source package.

INTRODUCTION: Drug discovery is a time consuming and costly process. Thus, a trend towards the use of in silico approaches such as structure- and ligand-based virtual screening methods to speed up the process has gained significant momentum in recent years. Most of these in silico applications require a good quality 3D structure of the small drug-like molecules as input. AREAS COVERED: This article reviews the algorithm and validation of the open-source software DG-AMMOS, a tool that generates the 3D conformation of small molecules using distance geometry construction and molecular mechanics optimization comparing its performance with some related free and commercial packages. EXPERT OPINION: The number of chemo/bioinformatics free and/or open-source tools assisting drug discovery projects is increasing, and many successful contributions making use of these computer programs have been reported. DG-AMMOS is an efficient 3D structure generator engine that provides fast and reliable generation of 3D structures and contributes to the preparation of a compound collection. DG-AMMOS can still be improved and an increased speed and user-friendly interface in addition to the implementation of workflow engines will increase its effectiveness.

DG-AMMOS: a new tool to generate 3d conformation of small molecules using distance geometry and automated molecular mechanics optimization for in silico screening.

BACKGROUND: Discovery of new bioactive molecules that could enter drug discovery programs or that could serve as chemical probes is a very complex and costly endeavor. Structure-based and ligand-based in silico screening approaches are nowadays extensively used to complement experimental screening approaches in order to increase the effectiveness of the process and facilitating the screening of thousands or millions of small molecules against a biomolecular target. Both in silico screening methods require as input a suitable chemical compound collection and most often the 3D structure of the small molecules has to be generated since compounds are usually delivered in 1D SMILES, CANSMILES or in 2D SDF formats. RESULTS: Here, we describe the new open source program DG-AMMOS which allows the generation of the 3D conformation of small molecules using Distance Geometry and their energy minimization via Automated Molecular Mechanics Optimization. The program is validated on the Astex dataset, the ChemBridge Diversity database and on a number of small molecules with known crystal structures extracted from the Cambridge Structural Database. A comparison with the free program Balloon and the well-known commercial program Omega generating the 3D of small molecules is carried out. The results show that the new free program DG-AMMOS is a very efficient 3D structure generator engine. CONCLUSION: DG-AMMOS provides fast, automated and reliable access to the generation of 3D conformation of small molecules and facilitates the preparation of a compound collection prior to high-throughput virtual screening computations. The validation of DG-AMMOS on several different datasets proves that generated structures are generally of equal quality or sometimes better than structures obtained by other tested methods.