49 research outputs found
Algorithmes d'apprentissage automatique pour la conception de composés pharmaceutiques et de vaccins
La dĂ©couverte de composĂ©s pharmaceutiques est actuellement trop longue et trop coĂ»teuse, et le taux dâĂ©chec, trop Ă©levĂ©. Les bases de donnĂ©es biochimiques et gĂ©nomiques ne cessent de grossir et il est maintenant impraticable dâinterprĂ©ter ces donnĂ©es. Un changement radical est nĂ©cessaire ; certaines Ă©tapes de ce processus doivent ĂȘtre automatisĂ©es. Les peptides jouent un rĂŽle important dans le systĂšme immunitaire et dans la signalisation cellulaire. Leurs propriĂ©tĂ©s favorables en font des candidats de choix pour initier la conception de nouveaux mĂ©dicaments et assister la production de nouveaux vaccins. De plus, les techniques de synthĂšse modernes permettent de rapidement synthĂ©tiser ces molĂ©cules Ă faible coĂ»t. Les algorithmes dâapprentissage statistique sont particuliĂšrement bien adaptĂ©s pour apprendre de façon automatisĂ©e des modĂšles, possiblement biochimiques, Ă partir des donnĂ©es existantes. Ces mĂ©thodes et les peptides offrent donc une solution de choix aux dĂ©fis auxquels fait face la recherche pharmaceutique. Nous proposons un noyau permettant lâapprentissage de modĂšles statistiques de phĂ©nomĂšnes biochimiques impliquant des peptides. Celui-ci permet, entre autres, lâapprentissage dâun modĂšle universel pouvant raisonnablement quantifier lâĂ©nergie de liaison entre toute sĂ©quence peptidique et tout site de liaison dâune protĂ©ine cristallisĂ©e. De plus, il unifie la thĂ©orie de plusieurs noyaux existants tout en conservant une faible complexitĂ© algorithmique. Ce noyau sâavĂšre particuliĂšrement adaptĂ© pour quantifier lâinteraction entre les antigĂšnes et les complexes majeurs dâhistocompatibilitĂ©. Nous proposons un outil pour prĂ©dire les peptides qui survivront au processus de prĂ©sentation antigĂ©nique. Cet outil a gagnĂ© une compĂ©tition internationale et aura plusieurs applications en immunologie, dont la conception de vaccins. Ultimement, un peptide doit maximiser lâinteraction avec une protĂ©ine cible ou maximiser la bioactivitĂ© chez lâhĂŽte. Nous formalisons ce problĂšme comme un problĂšme de prĂ©diction de structures. Puis, nous proposons un algorithme exploitant les plus longs chemins dans un graphe pour dĂ©terminer les peptides maximisant la bioactivitĂ© prĂ©dite par un modĂšle prĂ©alablement appris. Nous validons cette nouvelle approche en laboratoire par la dĂ©couverte de peptides antimicrobiens. Finalement, nous fournissons des garanties de performance de type PAC-Bayes pour deux algorithmes de prĂ©diction 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 monofluorogalactosideâLecA 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 monoâ 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 relaxationâoptimized 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 19Fâ
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 Câ3 and Câ4 showed weaker affinities with LecA as compared to those with the fluorine atom at Câ2 or Câ6. This research has focused on the chemical synthesis of âdrugâlikeâ lowâmolecularâweight inhibitors that circumvent drawbacks typically associated with natural oligosaccharides
Diversityâoriented synthesis of diolâbased 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 33âa, 35âa, and 35âb 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