Algorithmes d'apprentissage automatique pour la conception de composés pharmaceutiques et de vaccins

La découverte de composés pharmaceutiques est actuellement trop longue et trop coûteuse, et le taux d’échec, trop élevé. Les bases de données biochimiques et génomiques ne cessent de grossir et il est maintenant impraticable d’interpréter ces données. Un changement radical est nécessaire ; certaines étapes de ce processus doivent être automatisées. Les peptides jouent un rôle important dans le système immunitaire et dans la signalisation cellulaire. Leurs propriétés favorables en font des candidats de choix pour initier la conception de nouveaux médicaments et assister la production de nouveaux vaccins. De plus, les techniques de synthèse modernes permettent de rapidement synthétiser ces molécules à faible coût. Les algorithmes d’apprentissage statistique sont particulièrement bien adaptés pour apprendre de façon automatisée des modèles, possiblement biochimiques, à partir des données existantes. Ces méthodes et les peptides offrent donc une solution de choix aux défis auxquels fait face la recherche pharmaceutique. Nous proposons un noyau permettant l’apprentissage de modèles statistiques de phénomènes biochimiques impliquant des peptides. Celui-ci permet, entre autres, l’apprentissage d’un modèle universel pouvant raisonnablement quantifier l’énergie de liaison entre toute séquence peptidique et tout site de liaison d’une protéine cristallisée. De plus, il unifie la théorie de plusieurs noyaux existants tout en conservant une faible complexité algorithmique. Ce noyau s’avère particulièrement adapté pour quantifier l’interaction entre les antigènes et les complexes majeurs d’histocompatibilité. Nous proposons un outil pour prédire les peptides qui survivront au processus de présentation antigénique. Cet outil a gagné une compétition internationale et aura plusieurs applications en immunologie, dont la conception de vaccins. Ultimement, un peptide doit maximiser l’interaction avec une protéine cible ou maximiser la bioactivité chez l’hôte. Nous formalisons ce problème comme un problème de prédiction de structures. Puis, nous proposons un algorithme exploitant les plus longs chemins dans un graphe pour déterminer les peptides maximisant la bioactivité prédite par un modèle préalablement appris. Nous validons cette nouvelle approche en laboratoire par la découverte de peptides antimicrobiens. Finalement, nous fournissons des garanties de performance de type PAC-Bayes pour deux algorithmes de prédiction de structure dont un est nouveau.The discovery of pharmaceutical compounds is currently too time-consuming, too expensive, and the failure rate is too high. Biochemical and genomic databases continue to grow and it is now impracticable to interpret these data. A radical change is needed; some steps in this process must be automated. Peptides are molecules that play an important role in the immune system and in cell signaling. Their favorable properties make them prime candidates for initiating the design of new drugs and assist in the design of vaccines. In addition, modern synthesis techniques can quickly generate these molecules at low cost. Statistical learning algorithms are well suited to manage large amount of data and to learn models in an automated fashion. These methods and peptides thus offer a solution of choice to the challenges facing pharmaceutical research. We propose a kernel for learning statistical models of biochemical phenomena involving peptides. This allows, among other things, to learn a universal model that can reasonably quantify the binding energy between any peptide sequence and any binding site of a protein. In addition, it unifies the theory of many existing string kernels while maintaining a low computational complexity. This kernel is particularly suitable for quantifying the interaction between antigens and proteins of the major histocompatibility complex. We provide a tool to predict peptides that are likely to be processed by the antigen presentation pathway. This tool has won an international competition and has several applications in immunology, including vaccine design. Ultimately, a peptide should maximize the interaction with a target protein or maximize bioactivity in the host. We formalize this problem as a structured prediction problem. Then, we propose an algorithm exploiting the longest paths in a graph to identify peptides maximizing the predicted bioactivity of a previously learned model. We validate this new approach in the laboratory with the discovery of new antimicrobial peptides. Finally, we provide PAC-Bayes bound for two structured prediction algorithms, one of which is new

Modular organization of the white spruce (Picea glauca) transcriptome reveals functional organization and evolutionary signatures

Transcript profiling has shown the molecular bases of several biological processes in plants but few studies have developed an understanding of overall transcriptome variation. We investigated transcriptome structure in white spruce (Picea glauca), aiming to delineate its modular organization and associated functional and evolutionary attributes. Microarray analyses were used to: identify and functionally characterize groups of co-expressed genes; investigate expressional and functional diversity of vascular tissue preferential genes which were conserved among Picea species, and identify expression networks underlying wood formation. We classified 22 857 genes as variable (79%; 22 coexpression groups) or invariant (21%) by profiling across several vegetative tissues. Modular organization and complex transcriptome restructuring among vascular tissue preferential genes was revealed by their assignment to coexpression groups with partially overlapping profiles and partially distinct functions. Integrated analyses of tissue-based and temporally variable profiles identified secondary xylem gene networks, showed their remodelling over a growing season and identified PgNAC-7 (no apical meristerm (NAM), Arabidopsis transcription activation factor (ATAF) and cup-shaped cotyledon (CUC) transcription factor 007 in Picea glauca) as a major hub gene specific to earlywood formation. Reference profiling identified comprehensive, statistically robust coexpressed groups, revealing that modular organization underpins the evolutionary conservation of the transcriptome structure. © 2015 The Authors

Stereoselective synthesis of fluorinated galactopyranosides as potential molecular probes for galactophilic proteins : assessment of monofluorogalactoside–LecA interactions

The replacement of hydroxyl groups by fluorine atoms on hexopyranoside scaffolds may allow access to invaluable tools for studying various biochemical processes. As part of ongoing activities toward the preparation of fluorinated carbohydrates, a systematic investigation involving the synthesis and biological evaluation of a series of mono‐ and polyfluorinated galactopyranosides is described. Various monofluorogalactopyranosides, a trifluorinated, and a tetrafluorinated galactopyranoside have been prepared using a Chiron approach. Given the scarcity of these compounds in the literature, in addition to their synthesis, their biological profiles were evaluated. Firstly, the fluorinated compounds were investigated as antiproliferative agents using normal human and mouse cells in comparison with cancerous cells. Most of the fluorinated compounds showed no antiproliferative activity. Secondly, these carbohydrate probes were used as potential inhibitors of galactophilic lectins. The first transverse relaxation‐optimized spectroscopy (TROSY) NMR experiments were performed on these interactions, examining chemical shift perturbations of the backbone resonances of LecA, a virulence factor from Pseudomonas aeruginosa. Moreover, taking advantage of the fluorine atom, the 19F NMR resonances of the monofluorogalactopyranosides were directly monitored in the presence and absence of LecA to assess ligand binding. Lastly, these results were corroborated with the binding potencies of the monofluorinated galactopyranoside derivatives by isothermal titration calorimetry experiments. Analogues with fluorine atoms at C‐3 and C‐4 showed weaker affinities with LecA as compared to those with the fluorine atom at C‐2 or C‐6. This research has focused on the chemical synthesis of “drug‐like” low‐molecular‐weight inhibitors that circumvent drawbacks typically associated with natural oligosaccharides

Diversity‐oriented synthesis of diol‐based peptidomimetics as potential HIV protease inhibitors and antitumor agents

Peptidomimetic HIV protease inhibitors are an important class of drugs used in the treatment of AIDS. The synthesis of a new type of diol-based peptidomimetics is described. Our route is flexible, uses d-glucal as an inexpensive starting material, and makes minimal use of protection/deprotection cycles. Binding affinities from molecular docking simulations suggest that these compounds are potential inhibitors of HIV protease. Moreover, the antiproliferative activities of compounds 33 a, 35 a, and 35 b on HT-29, M21, and MCF7 cancer cell lines are in the low micromolar range. The results provide a platform that could facilitate the development of medically relevant asymmetrical diol-based peptidomimetic

Predictive computational phenotyping and biomarker discovery using reference-free genome comparisons

The antibiotic susceptibility data for the Clostridium difficile genomes. (XLSX 20.2 kb