Development of Computational Methods to Predict Protein Pocket Druggability and Profile Ligands using Structural Data

This thesis presents the development of computational methods and tools using as input three-dimensional structures data of protein-ligand complexes. The tools are useful to mine, profile and predict data from protein-ligand complexes to improve the modeling and the understanding of the protein-ligand recognition. This thesis is divided into five sub-projects. In addition, unpublished results about positioning water molecules in binding pockets are also presented. I developed a statistical model, PockDrug, which combines three properties (hydrophobicity, geometry and aromaticity) to predict the druggability of protein pockets, with results that are not dependent on the pocket estimation methods. The performance of pockets estimated on apo or holo proteins is better than that previously reported in the literature (Publication I). PockDrug is made available through a web server, PockDrug-Server (http://pockdrug.rpbs.univ-paris-diderot.fr), which additionally includes many tools for protein pocket analysis and characterization (Publication II). I developed a customizable computational workflow based on the superimposition of homologous proteins to mine the structural replacements of functional groups in the Protein Data Bank (PDB). Applied to phosphate groups, we identified a surprisingly high number of phosphate non-polar replacements as well as some mechanisms allowing positively charged replacements. In addition, we observed that ligands adopted a U-shape conformation at nucleotide binding pockets across phylogenetically unrelated proteins (Publication III). I investigated the prevalence of salt bridges at protein-ligand complexes in the PDB for five basic functional groups. The prevalence ranges from around 70% for guanidinium to 16% for tertiary ammonium cations, in this latter case appearing to be connected to a smaller volume available for interacting groups. In the absence of strong carboxylate-mediated salt bridges, the environment around the basic functional groups studied appeared enriched in functional groups with acidic properties such as hydroxyl, phenol groups or water molecules (Publication IV). I developed a tool that allows the analysis of binding poses obtained by docking. The tool compares a set of docked ligands to a reference bound ligand (may be different molecule) and provides a graphic output that plots the shape overlap and a Jaccard score based on comparison of molecular interaction fingerprints. The tool was applied to analyse the docking poses of active ligands at the orexin-1 and orexin-2 receptors found as a result of a combined virtual and experimental screen (Publication V). The review of literature focusses on protein-ligand recognition, presenting different concepts and current challenges in drug discovery.Tässä väitöskirjassa esitetään tietokoneavusteisia menetelmiä ja työkaluja, jotka perustuvat proteiini-ligandikompleksien kolmiulotteisiin rakenteisiin. Ne soveltuvat proteiini-ligandikompleksien rakennetiedon louhimiseen, optimointiin ja ennustamiseen. Tavoitteena on parantaa sekä mallinnusta että käsitystä proteiini-liganditunnistuksesta. Väitöskirjassa työkalut kuvataan viitenä eri alahankkeena. Lisäksi esitetään toistaiseksi julkaisemattomia tuloksia vesimolekyylien asemoinnista proteiinien sitoutumistaskuihin. Kehitin PockDrugiksi kutsumani tilastollisen mallin, joka yhdistää kolme ominaisuutta – hydrofobisuuden, geometrian ja aromaattisuuden – proteiinitaskujen lääkekehityskohteeksi soveltuvuuden ennustamista varten siten, että tulokset ovat riippumattomia sitoutumistaskun sijoitusmenetelmästä. Apo- ja holoproteiinien taskujen ennustaminen toimii paremmin kuin alan kirjallisuudessa on aiemmin kuvattu (Julkaisu I). PockDrug on vapaasti käyttäjien saatavilla PockDrug-verkkopalvelimelta (http://pockdrug.rpbs.univ-paris-diderot.fr), jossa on lisäksi useita työkaluja proteiinin sitoutumiskohdan analyysiin ja karakterisointiin (Julkaisu II). Kehitin myös muokattavissa olevan tietokoneavusteisen prosessin, joka perustuu samankaltaisten proteiinien päällekkäin asetteluun, louhiakseni Protein Data Bankista (PDB) toiminnallisten ryhmien rakenteellisia korvikkeita. Tätä fosfaattiryhmiin soveltaessani tunnistin yllättävän paljon poolittomia fosfaattiryhmän korvikkeita ja joitakin positiivisesti varautuneita korvikkeita mahdollistavia mekanismeja. Lisäksi havaitsin, että ligandit omaksuivat U muotoisen konformaation fylogeneettisesti riippumattomien proteiinien nukleotidien sitoutumistaskuissa (Julkaisu III). Tutkin PDB:n proteiini-ligandikompleksien suolasiltojen yleisyyttä viidelle emäksiselle toiminnalliselle ryhmälle. Suolasiltojen yleisyys vaihteli guanidinium-ionin 70 prosentista tertiääristen ammoniumkationien 16 prosenttiin. Jälkimmäisessä tapauksessa suolasiltojen vähäisyys vaikuttaa riippuvan siitä, että vuorovaikuttaville ryhmille on vähemmän tilaa. Mikäli tarkastellut emäksiset ryhmät eivät osallistuneet vahvoihin karboksylaattivälitteisiin suolasiltoihin, niiden ympäristössä vaikutti olevan runsaasti happamia toiminnallisia ryhmiä, kuten hydroksi- ja fenoliryhmiä sekä vesimolekyylejä (Julkaisu IV). Lopuksi kehitin työkalun, joka mahdollistaa telakoinnista saatujen sitoutumisasentojen analyysin. Työkalu vertaa telakoitua ligandisarjaa sitoutuneeseen vertailuligandiin, joka voi olla eri molekyyli. Graafisena tulosteena saadaan diagrammi ligandien muotojen samankaltaisuudesta ja molekyylivuorovaikutusten sormenjälkiin perustuvasta Jaccard-pistemäärästä. Työkalua sovellettiin oreksiini-1- ja oreksiini-2-reseptoreille yhdistetyllä virtuaalisella ja kokeellisella seulonnalla löydettyjen aktiivisten ligandien sitoutumisasentojen analyysiin (Julkaisu V).Cette thèse présente le développement de méthodes et d’outils informatiques basés sur la structure tridimensionnelle des complexes protéine-ligand. Ces différentes méthodes sont utilisées pour extraire, optimiser et prédire des données à partir de la structure des complexes afin d’améliorer la modélisation et la compréhension de la reconnaissance entre une protéine et un ligand. Ce travail de thèse est divisé en cinq projets. En complément, une étude sur le positionnement des molécules d’eau dans les sites de liaisons a aussi été développée et est présentée. Dans une première partie un modèle statistique, PockDrug, a été mis en place. Il combine trois propriétés de poches protéiques (l’hydrophobicité, la géométrie et l’aromaticité) pour prédire la druggabilité des poches protéiques, si une poche protéique peut lier une molécule drug-like. Le modèle est optimisé pour s’affranchir des différentes méthodes d’estimation de poches protéiques. La qualité des prédictions, est meilleure à la fois sur des poches estimées à partir de protéines apo et holo et est supérieure aux autres modèles de la littérature (Publication I). Le modèle PockDrug est disponible sur un serveur web, PockDrug-Server (http://pockdrug.rpbs.univ-paris-diderot.fr) qui inclus d’autres outils pour l’analyse et la caractérisation des poches protéiques. Dans un second temps un protocole, basé sur la superposition de protéines homologues a été développé pour extraire des replacements structuraux de groupements chimiques fonctionnels à partir de la Protein Data Bank (PDB). Appliqué aux phosphates, un grand nombre de remplacements non-polaires ont été identifié pouvant notamment être chargés positivement. Quelques mécanismes de remplacements ont ainsi pu être analysé. Nous avons, par exemple, observé que le ligand adopte une configuration en forme U dans les sites de liaison des nucléotides indépendamment de la phylogénétique des protéines (Publication III). Dans une quatrième partie, la prévalence des ponts salins de cinq groupements chimiques basiques a été étudié dans les complexes protéine-ligand. Ainsi le pourcentage de pont salin fluctue de 70% pour le guanidinium à 16% pour l’amine tertiaire qui a le plus faible volume disponible autour de lui pour accueillir un group pouvant interagir. L’absence d’acide fort comme l’acide carboxylique pour former un pont salin est remplacé par un milieu enrichis en groupement chimiques fonctionnels avec des propriétés acides comme l’hydroxyle, le phénol ou encore les molécules d’eau (Publication IV). Dans un dernier temps un outil permettant l’analyse des poses de ligand obtenues par une méthode d’ancrage moléculaire a été développé. Cet outil compare ces poses à un ligand de référence, qui peut être une molécule différente en combinant l’information du chevauchement de forme de la pose et du ligand de référence et un score de Jaccard basé sur une comparaison des empreintes d’interaction moléculaires du ligand de référence et de la pose. Cette méthode a été utilisé dans l’analyse des résultats d’ancrage moléculaires pour des ligands actifs pour les récepteurs aux orexine 1 et 2. Ces ligands actifs ont été trouvés à partir de résultats combinant un criblage virtuel et expérimental. La revue de la littérature associée est focalisée sur la reconnaissance moléculaire d’un ligand pour une protéine et présente diffèrent concepts et challenges pour la recherche de nouveaux médicaments

Experimental validation of a Bulk Built-In Current Sensor for detecting laser-induced currents

International audience—Bulk Built-In Current Sensors (BBICS) were developed to detect the transient bulk currents induced in the bulk of integrated circuits when hit by ionizing particles or pulsed laser. This paper reports the experimental evaluation of a complete BBICS architecture, designed to simultaneously monitor PMOS and NMOS transistors, under Photoelectric Laser Stimulation (PLS). The obtained results are the first experimental proof of the efficiency of BBICS in laser fault injection detection attempts. Furthermore, this paper highlights the importance of BBICS tapping in a sensitive area (logical gates) for improved laser detection. It studies the performances of this BBICS architecture and suggests modifications for its future implementation

SEU sensitivity and modeling using picosecond pulsed laser stimulation of a D Flip-Flop in 40 nm CMOS technology

International audience—This paper presents the design of a CMOS 40 nm D Flip-Flop cell and reports the laser fault sensitivity mapping both with experiments and simulation results. Theses studies are driven by the need to propose a simulation methodology based on laser/silicon interactions with a complex integrated circuit. In the security field, it is therefore mandatory to understand the behavior of sensitive devices like D Flip-Flops to laser stimulation. In previous works, Roscian et al., Sarafianos et al., Lacruche et al. or Courbon et al. studied the relations between the layout of cells, its different laser-sensitive areas and their associated fault model using laser pulse duration in the nanosecond range. In this paper, we report similar experiments carried out using a shorter laser pulse duration (30 ps instead of 50 ns). We also propose an upgrade of the simulation model they used to take into account laser pulse durations in the picosecond range on a logic gate composed of a large number of transistors for a recent CMOS technology (40 nm)

PockDrug-Server : a new web server for predicting pocket druggability on holo and apo proteins

Predicting protein pocket's ability to bind drug-like molecules with high affinity, i.e. druggability, is of major interest in the target identification phase of drug discovery. Therefore, pocket druggability investigations represent a key step of compound clinical progression projects. Currently computational druggability prediction models are attached to one unique pocket estimation method despite pocket estimation uncertainties. In this paper, we propose 'PockDrug-Server' to predict pocket druggability, efficient on both (i) estimated pockets guided by the ligand proximity (extracted by proximity to a ligand from a holo protein structure) and (ii) estimated pockets based solely on protein structure information (based on amino atoms that form the surface of potential binding cavities). PockDrug-Server provides consistent druggability results using different pocket estimation methods. It is robust with respect to pocket boundary and estimation uncertainties, thus efficient using apo pockets that are challenging to estimate. It clearly distinguishes druggable from less druggable pockets using different estimation methods and outperformed recent druggability models for apo pockets. It can be carried out from one or a set of apo/holo proteins using different pocket estimation methods proposed by our web server or from any pocket previously estimated by the user. PockDrug-Server is publicly available at: http://pockdrug.rpbs.univ-paris-diderot.fr.Peer reviewe

Influence of triple-well technology on laser fault injection and laser sensor efficiency

International audienceThis study is driven by the need to understand the influence of a Deep-Nwell implant on the sensitivity of integrated circuits to laser-induced fault injections. CMOS technologies can be either dual-well or triple-well. Triple-well technology has several advantages compared to dual-well technology in terms of electrical performances. Single-event responses have been widely studied in dual-well whereas SEE (single event effects) in triple-well is not well understood. This paper presents a comparative analysis of soft error rate and countermeasures sensors with for these two techniques in 40 nm and 90 nm CMOS technology. First, laser fault injection on registers were investigated, showing that triple-well technology is more vulnerable. Similarly, we studied the efficiency of Bulk Built-In Current Sensors (BBICS) in detecting laser induced fault injection attempts for both techniques. This sensor was found less effective in triple-well. Finally, a new BBICS compliant with body-biasing adjustments is proposed in order to improve its detection efficiency

Methane Clumped Isotopes: Progress and Potential for a New Isotopic Tracer

The isotopic composition of methane is of longstanding geochemical interest, with important implications for understanding petroleum systems, atmospheric greenhouse gas concentrations, the global carbon cycle, and life in extreme environments. Recent analytical developments focusing on multiply substituted isotopologues (‘clumped isotopes’) are opening a valuable new window into methane geochemistry. When methane forms in internal isotopic equilibrium, clumped isotopes can provide a direct record of formation temperature, making this property particularly valuable for identifying different methane origins. However, it has also become clear that in certain settings methane clumped isotope measurements record kinetic rather than equilibrium isotope effects. Here we present a substantially expanded dataset of methane clumped isotope analyses, and provide a synthesis of the current interpretive framework for this parameter. In general, clumped isotope measurements indicate plausible formation temperatures for abiotic, thermogenic, and microbial methane in many geological environments, which is encouraging for the further development of this measurement as a geothermometer, and as a tracer for the source of natural gas reservoirs and emissions. We also highlight, however, instances where clumped isotope derived temperatures are higher than expected, and discuss possible factors that could distort equilibrium formation temperature signals. In microbial methane from freshwater ecosystems, in particular, clumped isotope values appear to be controlled by kinetic effects, and may ultimately be useful to study methanogen metabolism

Cheminformatics analysis of chemicals that increase estrogen and progesterone synthesis for a breast cancer hazard assessment

Abstract Factors that increase estrogen or progesterone (P4) action are well-established as increasing breast cancer risk, and many first-line treatments to prevent breast cancer recurrence work by blocking estrogen synthesis or action. In previous work, using data from an in vitro steroidogenesis assay developed for the U.S. Environmental Protection Agency (EPA) ToxCast program, we identified 182 chemicals that increased estradiol (E2up) and 185 that increased progesterone (P4up) in human H295R adrenocortical carcinoma cells, an OECD validated assay for steroidogenesis. Chemicals known to induce mammary effects in vivo were very likely to increase E2 or P4 synthesis, further supporting the importance of these pathways for breast cancer. To identify additional chemical exposures that may increase breast cancer risk through E2 or P4 steroidogenesis, we developed a cheminformatics approach to identify structural features associated with these activities and to predict other E2 or P4 steroidogens from their chemical structures. First, we used molecular descriptors and physicochemical properties to cluster the 2,012 chemicals screened in the steroidogenesis assay using a self-organizing map (SOM). Structural features such as triazine, phenol, or more broadly benzene ramified with halide, amine or alcohol, are enriched for E2 or P4up chemicals. Among E2up chemicals, phenol and benzenone are found as significant substructures, along with nitrogen-containing biphenyls. For P4up chemicals, phenol and complex aromatic systems ramified with oxygen-based groups such as flavone or phenolphthalein are significant substructures. Chemicals that are active for both E2up and P4up are enriched with substructures such as dihydroxy phosphanedithione or are small chemicals that contain one benzene ramified with chlorine, alcohol, methyl or primary amine. These results are confirmed with a chemotype ToxPrint analysis. Then, we used machine learning and artificial intelligence algorithms to develop and validate predictive classification QSAR models for E2up and P4up chemicals. These models gave reasonable external prediction performances (balanced accuracy ~ 0.8 and Matthews Coefficient Correlation ~ 0.5) on an external validation. The QSAR models were enriched by adding a confidence score that considers the chemical applicability domain and a ToxPrint assessment of the chemical. This profiling and these models may be useful to direct future testing and risk assessments for chemicals related to breast cancer and other hormonally-mediated outcomes

La campagne présidentielle de 2017 au prisme des électeurs. Entre sensibilité au traitement médiatique et perplexité politique

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High-Throughput Chemical Screening and Structure-Based Models to Predict hERG Inhibition

Chemical inhibition of the human ether-a -go-go-related gene (hERG) potassium channel leads to a prolonged QT interval that can contribute to severe cardiotoxicity. The adverse effects of hERG inhibition are one of the principal causes of drug attrition in clinical and pre-clinical development. Preliminary studies have demonstrated that a wide range of environmental chemicals and toxicants may also inhibit the hERG channel and contribute to the pathophysiology of cardiovascular (CV) diseases. As part of the US federal Tox21 program, the National Center for Advancing Translational Science (NCATS) applied a quantitative high throughput screening (qHTS) approach to screen the Tox21 library of 10,000 compounds (~7871 unique chemicals) at 14 concentrations in triplicate to identify chemicals perturbing hERG activity in the U2OS cell line thallium flux assay platform. The qHTS cell-based thallium influx assay provided a robust and reliable dataset to evaluate the ability of thousands of drugs and environmental chemicals to inhibit hERG channel protein, and the use of chemical structure-based clustering and chemotype enrichment analysis facilitated the identification of molecular features that are likely responsible for the observed hERG activity. We employed several machine-learning approaches to develop QSAR prediction models for the assessment of hERG liabilities for drug-like and environmental chemicals. The training set was compiled by integrating hERG bioactivity data from the ChEMBL database with the Tox21 qHTS thallium flux assay data. The best results were obtained with the random forest method (~92.6% balanced accuracy). The data and scripts used to generate hERG prediction models are provided in an open-access format as key in vitro and in silico tools that can be applied in a translational toxicology pipeline for drug development and environmental chemical screening