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Integrated approaches for the discovery of novel enzymatic activities
1.
Orphan and non-orphan
EC number distribution across superkingdoms Integrated approaches for the discovery of novel enzymatic activities Guillaume REBOUL, Maria SOROKINA, Jonathan MERCIER, Karine BASTARD, Mark STAM, David VALLENET, Claudine MEDIGUE â CEA, Genoscope, LABGeM Annotation Rules Functional annotation rules UniRule: HAMAP + PIRSF + RuleBase Pathway Rules Consistency against biological processes Knowledge Base Rule Engine Enzyme Activity Discovery workflow Biological Facts rule "Missing state" when $fact: Fact( present == "no", require == "yes", avoid == "no" ) then modify( $fact.setState("missing") ); end rule "require Pathway" when $org: Organism() $path: Pathway(org == $org) then Fact fact = new Fact($org, $path); fact.setRequire("yes"); insert(fact); end The âNovel Enzymatic Activitiesâ group âą Group of the LABGeM: Laboratory of Bioinformatics Analyses for Genomics and Metabolism âą Part of the CEA (French Alternative Energies and Atomic Energy Commission) âą 3 Researchers âą 2 PhD students âą 1 sandwich placement Master Student âą 1 undergraduate student âą 1 post-doctoral placement available Pool of information able to pull up Novel Enzymatic Activities Protein or Domain Families Protein Annotation and Sequences Literature Enzymatic Reactions Own database: NEADB Summary ? ï§ iso-functional groups ï§ multi-functional group ï§ non-âactivityâ groups ï§ Motifs and key residues for function assignment 3 Promiscuity & Specificity Modeling of compounds in active sites + Full family ï§ new metabolic functions and associated pathways 4 Metabolic Role Biochemical validation + Genomic contextRepresentants enzymes family 1 Define one reaction +Multiple alignment one generic reaction A +B <-> Aâ + Bâ ï§ A family of unknown function ï§ with experimental evidences ï§ with one available structure 17 substrats new reactions 2 Selection & Screening +Enzymatic screening Statistical analysis + Family partitioning Potential metabolites Set of sequences BLAST PDB Homology Modeling - MODELLER 3D Models Cavity Detection - FPOCKET 3D-Active Sites Structural Alignment - MULTALIGN Hierarchical Clustering - WEKA Specificity Determining Residues are determined by a log-likelihood analysis Pfam unknown family (DUF 846) Next Generation Sequencing technology has dramatically increased the number of available sequences in public databases. At the same time, many enzymatic activities (~22%) are orphans of protein sequence (Sorokina et al., 2014). The large amount of available protein sequences is an opportunity to discover enzymes associated to new reactions. We present here an integrated bioinformatics approach to reduce this lack of knowledge in metabolism and to propose new activity/protein associations for experimental validation. With this objective, the âNew Enzymatic Activityâ group of the LABGeM team is developing several methods. The CanOE method combines genomic and metabolic contexts to predict candidate genes for orphan enzymes (Smith et al., 2012). Currently, this approach is extended to the detection of conserved chemical transformation motifs in the metabolism (Sorokina et al., submitted). From a structural point of view, the ASMC (Active Site Modeling and Clustering) method finds and compares active site pockets to classify enzymes of a family and detects important residues for substrate specificity (de Melo-Minardi et al., 2010). These methods were successful applied to elucidate the enzymatic diversity of a protein family of unknown function (Bastard et al., 2014). Their results, associated with present knowledge, must be unified in a database allowing the elaboration of strategies for the selection of enzymatic families of interest. This work is supported by genomic and metabolic network data from MicroScope, a platform for microbial genome analyses (Vallenet et al., 2013). Exploration of archaeal enzyme activities: ARCHAEOACTOME research project Literature references Bastard, K. et al. Revealing the hidden functional diversity of an enzyme family. Nat. Chem. Biol. 10, 42â9 (2014). de Melo-Minardi, R. C., Bastard, K. & Artiguenave, F. Identification of subfamily- specific sites based on active sites modeling and clustering. Bioinformatics 26, 3075â82 (2010). Smith, A. A. T., Belda, E., Viari, A., Medigue, C. & Vallenet, D. The CanOE strategy: Integrating genomic and metabolic contexts across multiple prokaryote genomes to find candidate genes for orphan enzymes. PLoS Comput. Biol. 8, (2012). Sorokina, M., Stam, M., MĂ©digue, C., Lespinet, O. & Vallenet, D. Profiling the orphan enzymes. Biol. Direct 9, 10 (2014). Vallenet, D. et al. MicroScope--an integrated microbial resource for the curation and comparative analysis of genomic and metabolic data. Nucleic Acids Res. 41, D636â 47 (2013). Sorokina et al. A novel metabolic network representation for the discovery of conserved modules of chemical transformations. Submitted. Mercier, J., Vallenet, D. GROOLS: Reactive Graph Reasoning for Genome Annotation. RuleML 2015 Conference Active Sites Classification (ASMC) The CanOE strategy Reaction Molecular Signature Network The dynamics of enzyme discoveryMicroScope From genomes to biological systems Reactions sharing a same RMS Reaction Network reduction into a RMS Network Microbial genome analysis Metabolic network >3,900 genomes 1-10 Mb ASMC method Classification of a family into groups of similar active sites NEA team Workbench Data Integration Orphan Enzymes Grools Structural Analysis Metabolic Network MicroScope
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