The pharmacophore kernel for virtual screening with support vector machines

We introduce a family of positive definite kernels specifically optimized for the manipulation of 3D structures of molecules with kernel methods. The kernels are based on the comparison of the three-points pharmacophores present in the 3D structures of molecul es, a set of molecular features known to be particularly relevant for virtual screening applications. We present a computationally demanding exact implementation of these kernels, as well as fast approximations related to the classical fingerprint-based approa ches. Experimental results suggest that this new approach outperforms state-of-the-art algorithms based on the 2D structure of mol ecules for the detection of inhibitors of several drug targets

Predicting drug side-effect profiles: a chemical fragment-based approach

<p>Abstract</p> <p>Background</p> <p>Drug side-effects, or adverse drug reactions, have become a major public health concern. It is one of the main causes of failure in the process of drug development, and of drug withdrawal once they have reached the market. Therefore, <it>in silico </it>prediction of potential side-effects early in the drug discovery process, before reaching the clinical stages, is of great interest to improve this long and expensive process and to provide new efficient and safe therapies for patients.</p> <p>Results</p> <p>In the present work, we propose a new method to predict potential side-effects of drug candidate molecules based on their chemical structures, applicable on large molecular databanks. A unique feature of the proposed method is its ability to extract correlated sets of chemical substructures (or chemical fragments) and side-effects. This is made possible using sparse canonical correlation analysis (SCCA). In the results, we show the usefulness of the proposed method by predicting 1385 side-effects in the SIDER database from the chemical structures of 888 approved drugs. These predictions are performed with simultaneous extraction of correlated ensembles formed by a set of chemical substructures shared by drugs that are likely to have a set of side-effects. We also conduct a comprehensive side-effect prediction for many uncharacterized drug molecules stored in DrugBank, and were able to confirm interesting predictions using independent source of information.</p> <p>Conclusions</p> <p>The proposed method is expected to be useful in various stages of the drug development process.</p

Virtual screening of GPCRs: An in silico chemogenomics approach

International audienceThe G-protein coupled receptor (GPCR) superfamily is currently the largest class of therapeutic targets. In silico prediction of interactions between GPCRs and small molecules in the transmembrane ligand-binding site is therefore a crucial step in the drug discovery process, which remains a daunting task due to the difficulty to characterize the 3D structure of most GPCRs, and to the limited amount of known ligands for some members of the superfamily. Chemogenomics, which attempts to characterize interactions between all members of a target class and all small molecules simultaneously, has recently been proposed as an interesting alternative to traditional docking or ligand-based virtual screening strategies

A new protein binding pocket similarity measure based on comparison of clouds of atoms in 3D: application to ligand prediction

<p>Abstract</p> <p>Background</p> <p>Predicting which molecules can bind to a given binding site of a protein with known 3D structure is important to decipher the protein function, and useful in drug design. A classical assumption in structural biology is that proteins with similar 3D structures have related molecular functions, and therefore may bind similar ligands. However, proteins that do not display any overall sequence or structure similarity may also bind similar ligands if they contain similar binding sites. Quantitatively assessing the similarity between binding sites may therefore be useful to propose new ligands for a given pocket, based on those known for similar pockets.</p> <p>Results</p> <p>We propose a new method to quantify the similarity between binding pockets, and explore its relevance for ligand prediction. We represent each pocket by a cloud of atoms, and assess the similarity between two pockets by aligning their atoms in the 3D space and comparing the resulting configurations with a convolution kernel. Pocket alignment and comparison is possible even when the corresponding proteins share no sequence or overall structure similarities. In order to predict ligands for a given target pocket, we compare it to an ensemble of pockets with known ligands to identify the most similar pockets. We discuss two criteria to evaluate the performance of a binding pocket similarity measure in the context of ligand prediction, namely, area under ROC curve (AUC scores) and classification based scores. We show that the latter is better suited to evaluate the methods with respect to ligand prediction, and demonstrate the relevance of our new binding site similarity compared to existing similarity measures.</p> <p>Conclusions</p> <p>This study demonstrates the relevance of the proposed method to identify ligands binding to known binding pockets. We also provide a new benchmark for future work in this field. The new method and the benchmark are available at <url>http://cbio.ensmp.fr/paris/</url>.</p

Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis

Correction: vol 7, 13205, 2016, doi:10.1038/ncomms13205Rheumatoid arthritis (RA) affects millions world-wide. While anti-TNF treatment is widely used to reduce disease progression, treatment fails in Bone-third of patients. No biomarker currently exists that identifies non-responders before treatment. A rigorous community-based assessment of the utility of SNP data for predicting anti-TNF treatment efficacy in RA patients was performed in the context of a DREAM Challenge (http://www.synapse.org/RA_Challenge). An open challenge framework enabled the comparative evaluation of predictions developed by 73 research groups using the most comprehensive available data and covering a wide range of state-of-the-art modelling methodologies. Despite a significant genetic heritability estimate of treatment non-response trait (h(2) = 0.18, P value = 0.02), no significant genetic contribution to prediction accuracy is observed. Results formally confirm the expectations of the rheumatology community that SNP information does not significantly improve predictive performance relative to standard clinical traits, thereby justifying a refocusing of future efforts on collection of other data.Peer reviewe

Développement d'approches de chémogénomique pour la prédiction des interactions protéine - ligand

Cette thèse porte sur le développement de méthodes bioinformatiques permettant la prédiction des interactions protéine - ligand. L'approche employée est d'utiliser le partage entre protéines, des informations connues, à la fois sur les protéines et sur les ligands, afin d'améliorer la prédiction de ces interactions. Les méthodes proposées appartiennent aux méthodes dites de chémogénomique. La première contribution de cette thèse est le développement d'une méthode d'apprentissage statistique pour la prédiction des interactions protéines - ligands par famille. Elle est illustrée dans le cas des GPCRs. Cette méthode comprend la proposition de noyaux pour les protéines qui permettent de prendre en compte la similarité globale des GPCRs par l'utilisation de la hiérarchie issue de l'alignement des séquences de cette famille, et la similarité locale au niveau des sites de fixation des ligands de ces GPCRs grâce à l'utilisation des structures 3D connues des membres de cette famille. Pour cela un jeu de données a été créé afin d'évaluer la capacité de cette méthode à prédire correctement les interactions connues. La deuxième contribution est le développement d'une mesure de similarité entre deux sites de fixation de ligands provenant de deux protéines différentes représentés par des nuages d'atomes en 3D. Cette mesure implique la superposition des poches par rotation et la translation, avec pour but la recherche du meilleur alignement possible en maximisant le regroupement d'atomes ayant des propriétés similaires dans des régions proches de l'espace. Les performances de cette méthodes ont été mesurées à l'aide d'un premier jeu de donnés provenant de la littérature et de deux autres qui ont été créé à cet effet. L'ensemble des résultats de cette thèse montre que les approches de chémogénomique présentent de meilleures performances de prédiction que les approches classique par protéine.PARIS-MINES ParisTech (751062310) / SudocSudocFranceF

Etudes structurales et criblages in silico de chimiothèques sur des cibles thérapeutiques de plasmodium falciparum et mycobacterium tuberculosis

PARIS7-Bibliothèque centrale (751132105) / SudocPARIS-BIUP (751062107) / SudocSudocFranceF

Evaluation of deep and shallow learning methods in chemogenomics for the prediction of drugs specificity

International audienc

Relating drug-protein interaction network with drug side effects.

[Motivation]: Identifying the emergence and underlying mechanisms of drug side effects is a challenging task in the drug development process. This underscores the importance of system–wide approaches for linking different scales of drug actions; namely drug-protein interactions (molecular scale) and side effects (phenotypic scale) toward side effect prediction for uncharacterized drugs. [Results]: We performed a large-scale analysis to extract correlated sets of targeted proteins and side effects, based on the co-occurrence of drugs in protein-binding profiles and side effect profiles, using sparse canonical correlation analysis. The analysis of 658 drugs with the two profiles for 1368 proteins and 1339 side effects led to the extraction of 80 correlated sets. Enrichment analyses using KEGG and Gene Ontology showed that most of the correlated sets were significantly enriched with proteins that are involved in the same biological pathways, even if their molecular functions are different. This allowed for a biologically relevant interpretation regarding the relationship between drug–targeted proteins and side effects. The extracted side effects can be regarded as possible phenotypic outcomes by drugs targeting the proteins that appear in the same correlated set. The proposed method is expected to be useful for predicting potential side effects of new drug candidate compounds based on their protein-binding profiles