title: Application of a hierarchical enzyme classification method reveals the role of gut microbiome in human metabolism.
abstract: BACKGROUND
  Enzymes are known as the molecular machines that drive the metabolism of an organism; hence identification of the full enzyme complement of an organism is essential to build the metabolic blueprint of that species as well as to understand the interplay of multiple species in an ecosystem. Experimental characterization of the enzymatic reactions of all enzymes in a genome is a tedious and expensive task. The problem is more pronounced in the metagenomic samples where even the species are not adequately cultured or characterized. Enzymes encoded by the gut microbiota play an essential role in the host metabolism; thus, warranting the need to accurately identify and annotate the full enzyme complements of species in the genomic and metagenomic projects. To fulfill this need, we develop and apply a method called ECemble, an ensemble approach to identify enzymes and enzyme classes and study the human gut metabolic pathways.
  
  
  RESULTS
  ECemble method uses an ensemble of machine-learning methods to accurately model and predict enzymes from protein sequences and also identifies the enzyme classes and subclasses at the finest resolution. A tenfold cross-validation result shows accuracy between 97 and 99% at different levels in the hierarchy of enzyme classification, which is superior to comparable methods. We applied ECemble to predict the entire complements of enzymes from ten sequenced proteomes including the human proteome. We also applied this method to predict enzymes encoded by the human gut microbiome from gut metagenomic samples, and to study the role played by the microbe-derived enzymes in the human metabolism. After mapping the known and predicted enzymes to canonical human pathways, we identified 48 pathways that have at least one bacteria-encoded enzyme, which demonstrates the complementary role of gut microbiome in human gut metabolism. These pathways are primarily involved in metabolizing dietary nutrients such as carbohydrates, amino acids, lipids, cofactors and vitamins.
  
  
  CONCLUSIONS
  The ECemble method is able to hierarchically assign high quality enzyme annotations to genomic and metagenomic data. This study demonstrated the real application of ECemble to understand the indispensable role played by microbe-encoded enzymes in the healthy functioning of human metabolic systems.
authors.forename: Akram
authors.initials: A
authors.lastname: Mohammed
authors.forename: Chittibabu
authors.initials: C
authors.lastname: Guda
mesh_terms: D001419:Bacteria / Q000201:enzymology*; D001426:Bacterial Proteins / Q000145:classification*; D019295:Computational Biology / Q000379:methods*; D000069196:Gastrointestinal Microbiome / Q000235:genetics*; D015894:Genome, Human; D006801:Humans; D000069550:Machine Learning; D053858:Metabolic Networks and Pathways; D040901:Proteomics / Q000379:methods
chemical_list: D001426:Bacterial Proteins