Microbial volatile organic compounds (VOCs) have gained prominence in the recent past for their potential use as disease markers. The discovery of microbial VOCs has benefited 'difficult to detect' diseases such as tuberculosis (TB). Few of the identified VOCs of Mycobacterium tuberculosis (Mtb) are currently being explored for their diagnostic potential. However, very little is known about the biosynthesis of these small lipophilic molecules. Here, we propose putative biosynthetic pathways in Mycobacterium tuberculosis for three VOCs, namely methyl nicotinate, methyl phenylacetate and methyl p-anisate, using computational approaches. In particular, we identify S-adenosyl methionine (SAM) transferases that play a crucial role in esterification of the acids to the final product. Our results provide important insights into the specificity of these pathways to Mtb species. © 2017 The Royal Society of Chemistry.

Karthik Raman

Department Of Biotechnology

Janakiraman Vani

Purva D. Bhatter

biological marker

volatile organic compound

biological model

Biosynthesis

chemistry

gene expression regulation

genetics

metabolism

Mycobacterium tuberculosis

biomarkers

Biosynthetic Pathways

Gene Expression Regulation, Bacterial

Gene Expression Regulation, Enzymologic

Models, Biological

Volatile organic compounds

Fulltext

IIT Madras is a public technical and research university located in Chennai, Tamil Nadu. Founded in 1959, it is recognised as an Institute of National Importance.

IIT Madras has been ranked as the top engineering institute in India for four years in a row by the National Institutional Ranking Framework of the MHRD

It currently offers undergraduate, postgraduate and research degrees across 16 disciplines in Engineering, Sciences, Humanities and Management. About 596 faculty belonging to science and engineering departments and centres of the Institute are engaged in teaching, research and industrial consultancy.

IIT Madras

Molecular BioSystems

Motivation: The ability to predict pathways for biosynthesis of metabolites is very important in metabolic engineering. It is possible to mine the repertoire of biochemical transformations from reaction databases, and apply the knowledge to predict reactions to synthesize new molecules. However, this usually involves a careful understanding of the mechanism and the knowledge of the exact bonds being created and broken. There is a need for a method to rapidly predict reactions for synthesizing new molecules, which relies only on the structures of the molecules, without demanding additional information such as thermodynamics or hand-curated reactant mapping, which are often hard to obtain accurately. Results: We here describe a robust method based on subgraph mining, to predict a series of biochemical transformations, which can convert between two (even previously unseen) molecules. We first describe a reliable method based on subgraph edit distance to map reactants and products, using only their chemical structures. Having mapped reactants and products, we identify the reaction centre and its neighbourhood, the reaction signature, and store this in a reaction rule network. This novel representation enables us to rapidly predict pathways, even between previously unseen molecules. We demonstrate this ability by predicting pathways to molecules not present in the KEGG database. We also propose a heuristic that predominantly recovers natural biosynthetic pathways from amongst hundreds of possible alternatives, through a directed search of the reaction rule network, enabling us to provide a reliable ranking of the different pathways. Our approach scales well, even to databases with &gt;100 000 reactions. © The Author 2017. Published by Oxford University Press. All rights reserved.

Bioinformatics

Predicting novel metabolic pathways through subgraph mining

Nucleic Acids Research

The STRING database in 2017: quality-controlled protein–protein association networks, made broadly accessible

The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases

Molecular & Cellular Proteomics

Profiling the Proteome of Mycobacterium tuberculosis during Dormancy and Reactivation

Natural Product Communications

Polyphenolic Compounds and Antioxidant Activities of the Leaves of Glochidion hypoleucum

BMC Infectious Diseases

Choosing algorithms for TB screening: a modelling study to compare yield, predictive value and diagnostic burden

Elucidating the biosynthetic pathways of volatile organic compounds in: Mycobacterium tuberculosis through a computational approach

Journal	Data powered by TypesetMolecular BioSystems
Publisher	Data powered by TypesetRoyal Society of Chemistry
ISSN	1742206X
Open Access	No