Implementation of homology based and non-homology based computational methods for the identification and annotation of orphan enzymes: using Mycobacterium tuberculosis H37Rv as a case study

Abstract

Background Homology based methods are one of the most important and widely used approaches for functional annotation of high-throughput microbial genome data. A major limitation of these methods is the absence of well-characterized sequences for certain functions. The non-homology methods based on the context and the interactions of a protein are very useful for identifying missing metabolic activities and functional annotation in the absence of significant sequence similarity. In the current work, we employ both homology and context-based methods, incrementally, to identify local holes and chokepoints, whose presence in the Mycobacterium tuberculosis genome is indicated based on its interaction with known proteins in a metabolic network context, but have not been annotated. We have developed two computational procedures using network theory to identify orphan enzymes (‘Hole finding protocol’) coupled with the identification of candidate proteins for the predicted orphan enzyme (‘Hole filling protocol’). We propose an integrated interaction score based on scores from the STRING database to identify candidate protein sequences for the orphan enzymes from M. tuberculosis, as a case study, which are most likely to perform the missing function. Results The application of an automated homology-based enzyme identification protocol, ModEnzA, on M. tuberculosis genome yielded 56 novel enzyme predictions. We further predicted 74 putative local holes, 6 choke points, and 3 high confidence local holes in the genome using ‘Hole finding protocol’. The ‘Hole-filling protocol’ was validated on the E. coli genome using artificial in-silico enzyme knockouts where our method showed 25% increased accuracy, compared to other methods, in assigning the correct sequence for the knocked-out enzyme amongst the top 10 ranks. The method was further validated on 8 additional genomes. Conclusions We have developed methods that can be generalized to augment homology-based annotation to identify missing enzyme coding genes and to predict a candidate protein for them. For pathogens such as M. tuberculosis, this work holds significance in terms of increasing the protein repertoire and thereby, the potential for identifying novel drug targets.

Identification of a novel gene required for competitive growth at high temperature in the thermotolerant yeast Kluyveromyces marxianus

“…Of the 12 genes, one shows evidence of having been acquired by horizontal gene transfer, and it was only possible to predict two putative functional protein domains in the others, illustrating the challenge of working with novel genes. The overall lack of functional annotation for the identified genes reflects the limitation of homology-based algorithms for genome annotation [54], especially when it comes to species phylogenetically distant from the extensively annotated S. cerevisiae [55]. This matches the experience of previous studies with K. marxianus, Y. lipolytica and Lachancea kluyveri [37,56].…”

An informatic workflow for the enhanced annotation of excretory/secretory proteins of Haemonchus contortus

“… [34] ). Nonetheless, given the challenges associated with the annotation of “unknown” or “orphan” molecules, ML and structure-based tools (established recently; [89] , [90] , [91] , [92] ) appeared to provide a useful alternative to sequence-based methods, although often their performance had not been thoroughly evaluated individually or in combination [93] . To address this issue, we evaluated individual tools and optimised the cut-off values for select algorithms (cf.…”

Molecular Insight into Mycobacterium tuberculosis Resistance to Nitrofuranyl Amides Gained through Metagenomics-like Analysis of Spontaneous Mutants

“…We additionally applied gene/protein enrichment and network analysis to the set of the above six mutated genes by making use of the STRING tool. This kind of analysis was shown to be useful in recent studies aimed to decipher the emergence of drug resistance, and to elucidate the potential gene network and signaling pathways in pulmonary TB through gene/protein network analysis [22][23][24].…”