Project Decrypthon II

Transcription

1 Project Decrypthon II Informatics and theoretical approaches for the analysis of the relationships between splicing and human diseases ) development of SVM for the identification of constitutive and alternative splice sites: application to the detection of splicing defects ) modeling of the RNA D structures of CH(H)G repeats and their interaction with splicing protein factors: application to repeats responsible for DM UMR 7567 CNRS-UHP (MAEM) & l UMR 750 CNRS-INPL- INRIA-UHP (LORIA)

2 Pre-mRNA splicing and human disease Human Pathology: 5% of human genetic disease are caused by mutations that destroy functional splice sites or generate new ones (skip the mutant s) Diagnosis: mutations difficult to detect (nonsense, missense, translationally silent, etc): they can be intronic (ISE,ISS) or ic (ESE,ESS), they can affect constitutive splice sites or alternate splice sites Mechanisms: - cis effects (constitutive or alternative splicing sites) - trans effects (basal splicing machinery, regulation of alternative splicing) >> 5% of human genetic disease related to splicing defects

3 Splicing and its Regulation DNA spliceosome cis-acting mutations on: constitutive splicing sites, alternative splice sites pre-mrna (+/-) (+/-) branch ' 5' regulation elements ESE,ESS ISE,ISS splicing elements trans-acting mutations on: basal splicing machinery, splicing regulation splicing regulator DNA

4 Loss-of-function Models pre-mrna mrna intron intron normal protein loss of normal function pre-mrna mrna intron intron protein isoforms

5 Development of SVM for the identification of constitutive and alternative splice sites: application to the detection of splicing defects Machine Learning: a learning algorithm receives a set of training examples, each labelled as belonging to a particular class. The algorithm s goal is to produce a classification rule for correctly assigning new examples to theses classes. Application: predictions of non-pathological and pathological splicing profiles, examples: SMA: spinal muscular atrophy, DM: myotonic dystrophy, BMD/DMD: Becker or Duchenne muscular dystrophy.

6 Gain-of-function Models (+/-) mrna (CH(H)G) n, 50<n<,000 AAA pre-mrna mrna - + protein isoform A (foetal) protein isoform B (adult)

7 Modeling of the RNA D structures of CH(H)G repeats and their interaction with splicing protein factors: application to RNA repeats responsible for DM RNA D Modeling (Mc-Sym) of C(H)HG repeats: - (CUG)n repeats (DM) - (CCUG)n repeats (DM) Homology Modeling of: - ssrna binding protein CUG-BP (CELF family, RRM domain) - dsrna binding protein PKR (DRBP family, dsrbd domain) Development of a docking method for modeling RNA/protein interactions & Application to RNA/protein complexes: - (CUG)n/CUG-BP(RRM & RRM), - (CCUG)n/PKR(dsRBM & dsrbm)

8 RNA D Structures of (CUG)n repeats and recognition by RRM proteins D Structure Probing (Sobczak et al., 00) (CUG)n+ (CUG)n D Structure of RNA/RRM Complex (NRE/nucleolin) (PDB ID: FJE) loop stem

9 RNA D Structures of (CCUG)n repeats and recognition by dsrbd proteins D Structure Probing (Sobczak et al., 00) (CCUG)n+ (CCUG)n D Structure of RNA/dsRBM Complex (dsrna/protein A) (PDB ID: DI) loop stem

10 Modeling and Experimental Approaches structure probing Constraint Satisfaction (Mc-Sym) RNA D Models (CUG)n repeats (CCUG)n repeats MC conformational sampling Monte-Carlo Docking modeling approaches experimental approaches RNA/protein Models protein D Models Homology Modeling CUG-BP MM + NLPB scoring (CCUG)n repeats/pkr (CUG)n/CUG-BP structure probing structure probing, -ray PKR

11 People MAEM: Petar Mitrasinovic (postdoc), Nathalie Gourrier (IR) LORIA: Yann Guermeur (CR), (+postdocs) AFM (funding), IBM, CNRS.