Scaffold Hopping and Bioisosteric Replacements Based on Binding Site Alignments

Bioisosteric replacements and scaffold hopping play an important role in modern drug discovery and design, as they enable the change of either a core scaffold or substitutes in a drug structure, thereby facilitating optimization of pharmacokinetic properties and patenting, while the drug retains its activity. A new knowledge-based method was developed to obtain bioisosteric or scaffold replacements based on the extensive data existing in the Protein Data Bank. The method uses all-against-all ProBiS-based protein superimposition to identify ligand fragments that overlap in similar binding sites and could therefore be considered as bioisosteric replacements. The method was demonstrated on a specific example of drug candidate – a nanomolar butyrylcholinesterase inhibitor, on which bioisosteric replacements of the three ring fragments were performed. The new molecule containing bioisosteric replacements was evaluated virtually using AutoDock Vina; a similar score for the original and the compound with replacements was obtained, suggesting that the newly designed bioisostere compound might retain the potency of the original inhibitor. This work is licensed under a Creative Commons Attribution 4.0 International License

Computational Analysis of Structure-Activity Relationships : From Prediction to Visualization Methods

Understanding how structural modifications affect the biological activity of small molecules is one of the central themes in medicinal chemistry. By no means is structure-activity relationship (SAR) analysis a priori dependent on computational methods. However, as molecular data sets grow in size, we quickly approach our limits to access and compare structures and associated biological properties so that computational data processing and analysis often become essential. Here, different types of approaches of varying complexity for the analysis of SAR information are presented, which can be applied in the context of screening and chemical optimization projects. The first part of this thesis is dedicated to machine-learning strategies that aim at de novo ligand prediction and the preferential detection of potent hits in virtual screening. High emphasis is put on benchmarking of different strategies and a thorough evaluation of their utility in practical applications. However, an often claimed disadvantage of these prediction methods is their "black box" character because they do not necessarily reveal which structural features are associated with biological activity. Therefore, these methods are complemented by more descriptive SAR analysis approaches showing a higher degree of interpretability. Concepts from information theory are adapted to identify activity-relevant structure-derived descriptors. Furthermore, compound data mining methods exploring prespecified properties of available bioactive compounds on a large scale are designed to systematically relate molecular transformations to activity changes. Finally, these approaches are complemented by graphical methods that primarily help to access and visualize SAR data in congeneric series of compounds and allow the formulation of intuitive SAR rules applicable to the design of new compounds. The compendium of SAR analysis tools introduced in this thesis investigates SARs from different perspectives

On the origins of enzyme inhibitor selectivity and promiscuity: a case study of protein kinase binding to staurosporine

Protein kinases are important regulatory enzymes in signal transduction and in cell regulation. Understanding inhibition mechanisms of kinases is important for the further development of new therapies for cancer and inflammatory diseases. I have developed a statistical approach based on the Mantel test to find the relationship between the shapes of ATP binding sites and their affinities for inhibitors. My shape-based dendrogram shows clustering of the kinases based on similarity in shape. I investigate the pocket in terms of conservation of surrounding amino acids and atoms in order to identify the key determinants of ligand binding. I find that the most conserved regions are the main chain atoms in the hinge region and I show that the tetrahydropyran ring of staurosporine causes induced-fit of the glycine rich loop. I apply multiple linear regression to select distances measured between the distinctive parts of residues which correlate with the binding constants. This method allows me to understand the importance of the size of the gatekeeper residue and the closure between the first glycine of the GXGXXG motif and the aspartate of the DFG loop, which act together to promote tight binding to staurosporine. I also find that the greater the number of hydrogen bonds made by the kinase around the methylamine group of staurosporine, the tighter the binding to staurosporine. The website I have developed allows a better understanding of cross reactivity and may be useful for narrowing down the options for a synthetic strategy to design kinase inhibitors.This work was supported by the Royal Thai Government

Investigating phosphate structural replacements through computational and experimental approaches

Bioisosteric replacements are used in drug design during lead generation and optimization processes with the aim to replace one functional group of a known molecule by another while retaining biological activity. The reason to use bioisosteric replacements are typically to optimize bioavailability or reducing toxicity. Phosphate groups represent a paradigm to study bioisosteric replacements. Protein-phosphate interaction plays a critical role during molecular recognition processes, and for example kinases represent one of the largest families of drug targets. However, some challenges exclude phosphate as a promising lead-like building block: i) charged phosphates do not cross molecular membranes; ii) some widely expressed proteins such as phosphatases easily hydrolyze phosphoric acid esters, which lead phosphate-containing ligands to lose their binding affinities before reaching their biological targets; iii) introduction of phosphate groups to parent scaffold is not easy. In the first part of the thesis work, I designed and implemented a computational protocol to mine information about phosphate structural replacements deposited in the Protein Data Bank. I constructed 116, 314, 271, and 42 sets of superimposed proteins where each set contains a reference protein to either POP, AMP, ADP, or ATP as well as a certain number of non-nucleotide ligands. 929 of such ligands are under study. The chemotypes that came out as structural replacements are diverse, ranging from common phosphate isosteres such as carboxyl, amide and squaramide to more surprising moieties such as benzoxaborole and aromatic ring systems. I exemplified some novel examples and interpreted the mechanism behind them. Local structural replacements are circumstance dependent: one chemical group valid in certain set-up cannot necessarily guarantee the success of another. The data from the study is available at http://86.50.168.121/phosphates_LSR.php. In the second part, I synthesized fifteen compounds retaining the adenosine moieties and bearing bioisosteric replacements of the phosphate at the ribose 5'-oxygen to test their stability toward human macro domain protein 1. These compounds are composed of either a squaryldiamide or an amide group as the bioisosteric replacement and/or as a linker. To these groups a variety of substituents were attached: phenyl, benzyl, pyridyl, carboxyl, hydroxy and tetrazolyl. Biological evaluation using differential scanning fluorimetry showed that four compounds stabilized human MDO1 at levels comparable to ADP and one at level comparable to AMP. Virtual screening was also run to identify MDO1 binding ligands. Among 20,000 FIMM database lead-like molecules, 39 compounds were selected for testing and eleven compounds found active based on ADPr and Poly-ADPr competition binding assay. The assay is however not well validated and a second confirmatory assay was conducted using calorimetry. To the best of my knowledge, this is the first report of non-endogenous ligands of the human MDO1. Altogether, this thesis highlights the versatility of molecular recognition processes that accompanies chemical replacements in compounds; this in turns shows the limits of the concepts of molecular similarity and classical bioisosterism that are based on the conservation of molecular interactions.Bioisosteeristä korvausta käytetään lääkeainekehityksessä johtolankamolekyylien tuottamisessa ja optimoinnissa. Tarkoitus on vaihtaa molekyylin funktionaalinen ryhmä toiseksi biologisen aktiivisuuden muuttumatta. Yleensä tavoitteena on parantaa biologista hyötyosuutta tai vähentää toksisuutta. Fosfaattiryhmää on tässä työssä käytetty esimerkkiryhmänä bioisosteerisiä korvauksia tutkittaessa. Väitöskirjatyön ensimmäisessä osassa suunnittelin ja toteutin tiedonlouhintaprotokollan etsiäkseni Protein Data Bank -tietokannasta korvaavia rakenteita fosfaattiryhmälle. Kokosin 116, 314, 271 ja 42 proteiiniryhmää, joissa kussakin on vertailumolekyylinä fosfaattiryhmän sisältävä POP, AMP, ADP tai ATP, ja lisäksi ei-nukleotidisiä ligandeja. Yhteensä 929 ei-nukleotidistä ligandia tutkittiin. Niistä löydettiin monipuolisesti fosfaattiryhmän korvaavia rakenteita, muun muassa yleisesti tunnettuja fosfaatin bioisosteerejä kuten karboksyyli, amidi ja squaramidi, mutta myös erikoisempia ryhmiä kuten bentsoksaboroli ja aromaattisia rengasrakenteita. Työssäni esittelen muutamia uusia rakenteita ja tulkitsen niiden vaikutusmekanismeja. Rakenteiden korvaaminen riippuu tilanteesta; yhteen tapaukseen sopiva korvaava ryhmä ei välttämättä toimi toisessa. Työn toisessa osassa syntetisoin 15 adenosiiniyhdistettä, joiden riboosiosan 5'-hapessa oleva fosfaattiryhmä on korvattu vaihtelevalla bioisosteerisellä ryhmällä. Bioisosteerisenä ryhmänä tai linkkerinä oli joko squaramidi- tai amidiryhmä. Yhdisteiden vakaus testattiin ihmisen MDO1-makrodomeeniproteiinin kanssa.Julkaisussa virheellinen verkkoaineiston ISBN 978-951-51-0045-0

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

Design and selection of novel C1s inhibitors by in silico and in vitro approaches

The complement system is associated with various diseases such as inflammation or autoimmune diseases. Complement-targeted drugs could provide novel therapeutic intervention against the above diseases. C1s, a serine protease, plays an important role in the CS and could be an attractive target since it blocks the system at an early stage of the complement cascade. Designing C1 inhibitors is particularly challenging since known inhibitors are restricted to a narrow bioactive chemical space in addition selectivity over other serine proteases is an important requirement. The typical architecture of a small molecule inhibitor of C1s contains an amidine (or guanidine) residue, however, the discovery of non-amidine inhibitors might have high value, particularly if novel chemotypes and/or compounds displaying improved selectivity are identified. We applied various virtual screening approaches to identify C1s focused libraries that lack the amidine/guanidine functionalities, then the in silico generated libraries were evaluated by in vitro biological assays. While 3D structure-based methods were not suitable for virtual screening of C1s inhibitors, and a 2D similarity search did not lead to novel chemotypes, pharmacophore model generation allowed us to identify two novel chemotypes with submicromolar activities. In three screening rounds we tested altogether 89 compounds and identified 20 hit compounds (<10 μM activities; overall hit rate: 22.5%). The highest activity determined was 12 nM (1,2,4-triazole), while for the newly identified chemotypes (1,3-benzoxazin-4-one and thieno[2,3-d][1,3]oxazin-4-one) it was 241 nM and 549 nM, respectively. © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/)

Quantitative surface field analysis: learning causal models to predict ligand binding affinity and pose.

We introduce the QuanSA method for inducing physically meaningful field-based models of ligand binding pockets based on structure-activity data alone. The method is closely related to the QMOD approach, substituting a learned scoring field for a pocket constructed of molecular fragments. The problem of mutual ligand alignment is addressed in a general way, and optimal model parameters and ligand poses are identified through multiple-instance machine learning. We provide algorithmic details along with performance results on sixteen structure-activity data sets covering many pharmaceutically relevant targets. In particular, we show how models initially induced from small data sets can extrapolatively identify potent new ligands with novel underlying scaffolds with very high specificity. Further, we show that combining predictions from QuanSA models with those from physics-based simulation approaches is synergistic. QuanSA predictions yield binding affinities, explicit estimates of ligand strain, associated ligand pose families, and estimates of structural novelty and confidence. The method is applicable for fine-grained lead optimization as well as potent new lead identification