EFICAz: a comprehensive approach for accurate genome-scale enzyme function inference. EFICAz (Enzyme Function Inference by Combined Approach) is an automatic engine for large-scale enzyme function inference that combines predictions from four different methods developed and optimized to achieve high prediction accuracy: (i) recognition of functionally discriminating residues (FDRs) in enzyme families obtained by a Conservation-controlled HMM Iterative procedure for Enzyme Family classification (CHIEFc), (ii) pairwise sequence comparison using a family specific Sequence Identity Threshold, (iii) recognition of FDRs in Multiple Pfam enzyme families, and (iv) recognition of multiple Prosite patterns of high specificity. For FDR (i.e. conserved positions in an enzyme family that discriminate between true and false members of the family) identification, we have developed an Evolutionary Footprinting method that uses evolutionary information from homofunctional and heterofunctional multiple sequence alignments associated with an enzyme family. The FDRs show a significant correlation with annotated active site residues. In a jackknife test, EFICAz shows high accuracy (92%) and sensitivity (82%) for predicting four EC digits in testing sequences that are <40% identical to any member of the corresponding training set. Applied to Escherichia coli genome, EFICAz assigns more detailed enzymatic function than KEGG, and generates numerous novel predictions.

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  1. Desai, Dhwani K.; Nandi, Soumyadeep; Srivastava, Prashant K.; Lynn, Andrew M.: ModEnzA: accurate identification of metabolic enzymes using function specific profile HMMs with optimised discrimination threshold and modified emission probabilities (2011)
  2. Arakaki, Adrian K.; Huang, Ying; Skolnick, Jeffrey: (EFICAz^2): enzyme function inference by a combined approach enhanced by machine learning (2009) ioport
  3. Mistry, Jaina; Bateman, Alex; Finn, Robert D.: Predicting active site residue annotations in the pfam database (2007) ioport