Using Consensus-Shape Clustering To Identify Promiscuous Ligands and Protein Targets and To Choose the Right Query for Shape-Based Virtual Screening

International audienceLigand-based shape matching approaches have become established as important and popular virtual screening (VS) techniques. However, despite their relative success, many authors have discussed how best to choose the initial query compounds and which of their conformations should be used. Furthermore, it is increasingly the case that pharmaceutical companies have multiple ligands for a given target and these may bind in different ways to the same pocket. Conversely, a given ligand can sometimes bind to multiple targets, and this is clearly of great importance when considering drug side-effects. We recently introduced the notion of spherical harmonic-based "consensus shapes" to help deal with these questions. Here, we apply a consensus shape clustering approach to the 40 protein-ligand targets in the DUD data set using PARASURF/PARAFIT. Results from clustering show that in some cases the ligands for a given target are split into two subgroups which could suggest they bind to different subsites of the same target. In other cases, our clustering approach sometimes groups together ligands from different targets, and this suggests that those ligands could bind to the same targets. Hence spherical harmonic-based clustering can rapidly give cross-docking information while avoiding the expense of performing all-against-all docking calculations. We also report on the effect of the query conformation on the performance of shape-based screening of the DUD data set and the potential gain in screening performance by using consensus shapes calculated in different ways. We provide details of our analysis of shape-based screening using both PARASURF/PARAFIT and ROCS, and we compare the results obtained with shape-based and conventional docking approaches using MSSH/SHEF and GOLD. The utility of each type of query is analyzed using commonly reported statistics such as enrichment factors (EF) and receiver-operator-characteristic (ROC) plots as well as other early performance metrics

Advances in the Development of Shape Similarity Methods and Their Application in Drug Discovery

Molecular similarity is a key concept in drug discovery. It is based on the assumption that structurally similar molecules frequently have similar properties. Assessment of similarity between small molecules has been highly effective in the discovery and development of various drugs. Especially, two-dimensional (2D) similarity approaches have been quite popular due to their simplicity, accuracy and efficiency. Recently, the focus has been shifted toward the development of methods involving the representation and comparison of three-dimensional (3D) conformation of small molecules. Among the 3D similarity methods, evaluation of shape similarity is now gaining attention for its application not only in virtual screening but also in molecular target prediction, drug repurposing and scaffold hopping. A wide range of methods have been developed to describe molecular shape and to determine the shape similarity between small molecules. The most widely used methods include atom distance-based methods, surface-based approaches such as spherical harmonics and 3D Zernike descriptors, atom-centered Gaussian overlay based representations. Several of these methods demonstrated excellent virtual screening performance not only retrospectively but also prospectively. In addition to methods assessing the similarity between small molecules, shape similarity approaches have been developed to compare shapes of protein structures and binding pockets. Additionally, shape comparisons between atomic models and 3D density maps allowed the fitting of atomic models into cryo-electron microscopy maps. This review aims to summarize the methodological advances in shape similarity assessment highlighting advantages, disadvantages and their application in drug discovery

Computational prediction of metabolism: sites, products, SAR, P450 enzyme dynamics, and mechanisms.

Metabolism of xenobiotics remains a central challenge for the discovery and development of drugs, cosmetics, nutritional supplements, and agrochemicals. Metabolic transformations are frequently related to the incidence of toxic effects that may result from the emergence of reactive species, the systemic accumulation of metabolites, or by induction of metabolic pathways. Experimental investigation of the metabolism of small organic molecules is particularly resource demanding; hence, computational methods are of considerable interest to complement experimental approaches. This review provides a broad overview of structure- and ligand-based computational methods for the prediction of xenobiotic metabolism. Current computational approaches to address xenobiotic metabolism are discussed from three major perspectives: (i) prediction of sites of metabolism (SOMs), (ii) elucidation of potential metabolites and their chemical structures, and (iii) prediction of direct and indirect effects of xenobiotics on metabolizing enzymes, where the focus is on the cytochrome P450 (CYP) superfamily of enzymes, the cardinal xenobiotics metabolizing enzymes. For each of these domains, a variety of approaches and their applications are systematically reviewed, including expert systems, data mining approaches, quantitative structure-activity relationships (QSARs), and machine learning-based methods, pharmacophore-based algorithms, shape-focused techniques, molecular interaction fields (MIFs), reactivity-focused techniques, protein-ligand docking, molecular dynamics (MD) simulations, and combinations of methods. Predictive metabolism is a developing area, and there is still enormous potential for improvement. However, it is clear that the combination of rapidly increasing amounts of available ligand- and structure-related experimental data (in particular, quantitative data) with novel and diverse simulation and modeling approaches is accelerating the development of effective tools for prediction of in vivo metabolism, which is reflected by the diverse and comprehensive data sources and methods for metabolism prediction reviewed here. This review attempts to survey the range and scope of computational methods applied to metabolism prediction and also to compare and contrast their applicability and performance.JK, MJW, JT, PJB, AB and RCG thank Unilever for funding

Estudio y cribado virtual de compuestos químicos antivirales (VIH). Estudio de la modulación alostérica de agonistas y antagonistas del receptor celular CXCR4

Els mètodes de descobriment de nous fàrmacs han evolucionat recentment gràcies a la resolució de les estructures proteiques que actuen com a dianes terapèutiques responsables de malalties o desregulacions biològiques. Aquestes estructures proteiques tridimensionals, juntament amb el desenvolupament de noves tècniques computacionals permeten el desenvolupament accelerat de nous compostos candidats a esdevenir fàrmacs. El present treball s’inicia proposant un nou mètode que millora l’elecció de compostos candidats a ser inhibidors d’una “diana difícil”, però ben coneguda com és el receptor VEGFR-2, a partir de la seva estructura tridimensional cristal·litzada, així com de compostos inhibidors coneguts de l’esmentada diana. La resolució tridimensional de l’estructura CXCR4 mitjançant cristal•lografia de raigs X a l’any 2010, ha esdevingut un avenç important a l’hora de millorar el disseny de compostos inhibidors del VIH, així com compostos antitumorals, malalties en les que intervé de forma determinant el receptor CXCR4. Així doncs, els models de cribratge virtual desenvolupats abans del 2010 dins el laboratori de disseny molecular de l’IQS (GEM) han estat generats a partir de models creats per homologia vers a altres proteïnes GPCRs i/o s’han basat solament en la forma de lligands coneguts. D’aquesta forma, a partir de les diferents estructures proteiques publicades de CXCR4, s’ha avaluat quina d’aquestes estructures presenta la conformació que distingeix millor els compostos antagonistes actius dels compostos inactius. A més, s’han avaluat múltiples mètodes de cribratge virtual de CXCR4 basats en l’estructura, en la forma del lligand i mitjançant models farmacofòrics. Una vegada obtinguda la millor estructura de CXCR4 i els millors mètodes de cribratge virtual retrospectiu, es realitzen cribratges virtuals prospectius d’una nova quimioteca generada de forma combinatòria, basada en estructures anàlogues prèviament desenvolupades al laboratori de disseny molecular de l’IQS. Addicionalment, s’ha estudiat el comportament al·lostèric del receptor CXCR4 davant de moduladors antagonistes petits i moduladors al·lostèrics agonistes de naturalesa pèptica. CXCR4 és qualificada com a una “diana difícil” per la gran mida del seu lloc actiu ortostèric, així com per l’ampli número de funcions reguladores en les que intervé el receptor. Per això la modulació al·lostèrica en CXCR4 s’ha estudiat utilitzant diferents aproximacions com són el docking cec, docking proteïna-proteïna, docking per subllocs d’unió i dinàmica molecular.Los métodos de descubrimiento de nuevos fármacos han evolucionado recientemente gracias a la resolución de las estructuras proteicas las cuales actúan como dianas terapéuticas responsables de enfermedades o desregulaciones biológicas. Estas estructuras proteicas tridimensionales, conjuntamente con el desarrollo de nuevas técnicas computacionales están permitiendo el desarrollo acelerado de nuevos compuestos candidatos a convertirse en fármacos. El presente trabajo se inicia proponiendo un nuevo método que permita mejorar la elección de compuestos candidatos a ser inhibidores de una “diana difícil” aunque bien conocida, como es el receptor VEGFR-2, partiendo de su estructura tridimensional cristalizada y de compuestos inhibidores conocidos de dicha diana. La resolución tridimensional de la estructura del receptor CXCR4 mediante cristalografía de rayos X, en el año 2010, ha supuesto un avance importante de cara a mejorar el diseño de compuestos inhibidores del VIH, así como de compuestos antitumorales, enfermedades en las que interviene de forma determinante el receptor CXCR4. Así pues, los modelos de cribado virtual desarrollados anteriormente al 2010 en el laboratorio de diseño molecular del IQS (GEM) han sido generados a partir de modelos creados por homología a otras proteínas GPCRs y/o basados únicamente en la forma de ligandos conocidos. De este modo, partiendo de las diferentes estructuras proteicas publicadas de CXCR4, se ha evaluado cuál de dichas estructuras presenta la conformación que distingue mejor los compuestos antagonistas activos de compuestos inactivos. Además, se han evaluado múltiples métodos de cribado virtual de CXCR4 basados en la estructura, en la forma del ligando y mediante modelos farmacofóricos. Una vez obtenida la mejor estructura de CXCR4 y los mejores métodos de cribado virtual retrospectivo, se realizan cribados virtuales prospectivos de una nueva quimioteca generada combinatoriamente, basada en análogos de estructuras previamente desarrolladas en el laboratorio de diseño molecular del IQS. Adicionalmente se ha estudiado el comportamiento alostérico del receptor CXCR4 frente a moduladores antagonistas de pequeño tamaño y moduladores alostéricos agonistas de naturaleza peptídica. CXCR4 se califica como “diana difícil” debido al gran tamaño del sitio activo ortostérico, juntamente con el amplio número de funciones reguladoras en las que interviene el receptor CXCR4. Por ello la modulación alostérica en CXCR4 se ha estudiado utilizando diferentes aproximaciones, como son: docking ciego, docking proteína-proteína, docking por subsitios y dinámica molecular.: Drug discovery methods have recently emerged thanks to the resolution of protein structures which act as therapeutic targets responsible for diseases or biological deregulations. These three dimensional structures in combination with the development of new computational techniques are accelerating the development of new candidates to become drug compounds. This work starts with the proposal of a new method that improves the selection of candidates to become inhibitors of a well-known “difficult target” such us VEGFR-2 receptor. This method is based on the crystal structure of the receptor and also by a number of inhibitors known for this target. CXCR4 crystal structure was solved in 2010 by X-ray crystallography and this has been an important event in order to improve the molecular design of HIV inhibitors, as well as anticancer compounds, diseases where CXCR4 receptor is involved. Therefore, virtual screening models developed in the laboratory of molecular design of IQS (GEM) were generated using homology models from other GPCRs and/or based on ligand shape techniques. In this sense, taking into consideration all published CXCR4 crystal structures, it has been evaluated which of them shows the most suitable conformation to distinguish antagonists actives from inactives. Moreover, different virtual screening methods have also been evaluated such us structure based methods, ligand based methods and pharmacophoric models. Once obtained the most suitable structure and the best retrospective virtual screening methods, a prospective virtual screening has been carried out using a new combinatorial library of chemical structures. This new library is based on analogous structures previously generated in the laboratory of molecular design of IQS (GEM). In addition, the allosteric behaviour of CXCR4 receptor has been studied versus small antagonist modulators and versus peptidomimetic agonist modulators. CXCR4 is classified as a “difficult target” due to the large size of its extracellular pocket that the orthosteric binding site is placed as well as the diverse number of biochemical regulations where the receptor mediates. Thus, the allosteric modulation of CXCR4 has been studied using different approaches such as blind docking, protein-protein docking, docking by subsites and molecular dynamics