Genome-Scale Metabolic Models as Platforms for Identification of Novel Genes as Antimicrobial Drug Targets

Abstract

The growing number of multidrug-resistant pathogenic bacteria is becoming a world leading challenge for the scientific community and for public health. However, advances in high-throughput technologies and whole-genome sequencing of bacterial pathogens make the construction of bacterial genome-scale metabolic models (GEMs) increasingly realistic. The use of GEMs as an alternative platforms will expedite identification of novel unconditionally essential genes and enzymes of target organisms with existing and forthcoming GEMs. This approach will follow the existing protocol for construction of high-quality GEMs, which could ultimately reduce the time, cost and labor-intensive processes involved in identification of novel antimicrobial drug targets in drug discovery pipelines. We discuss the current impact of existing GEMs of selected multidrug-resistant pathogenic bacteria for identification of novel antimicrobial drug targets and the challenges of closing the gap between genome-scale metabolic modeling and conventional experimental trial-and-error approaches in drug discovery pipelines.

Genome-Scale Metabolic Model of the Human Pathogen Candida albicans: A Promising Platform for Drug Target Prediction

“…Historically, these global mathematical descriptions of cell metabolism have mostly been linked to metabolic engineering of microbial cell factories, given their potential to simulate global metabolic behavior and provide hints to guide experimental optimization of such organisms for the production of added-value compounds [ 14 ]. However, recent examples have shown the potential of these models in the quest for novel drug targets in pathogenic organisms [ 15 , 16 , 17 , 18 , 19 ]. For example, Abdel-Haleem et al in 2018, described the reconstruction of genome-scale metabolic models for five life cycle stages of Plasmodium falciparum , enabling the identification of potential drug targets that could be used as both, anti-malarial drugs and transmission-blocking agents [ 20 ].…”

Overview of Inexpensive Production Routes of Bacterial Cellulose and Its Applications in Biomedical Engineering

“…It employs the use of in-silico approach to access and analyse the genomic data available in different databases and develop a model (using high-throughput technology and computational algorithms) that mimics the physiology and metabolism of the organism in question, so as to be able to identify the different gene annotations and their functions. [32][33][34][35][36][37] A number of successful attempts have been recorded in identifying the gene targets for antibiotic drug designing using the GEMs. [38][39][40] A similar approach can as well be used to understand the physiologic and metabolic complexity of cellulose producing bacteria and accordingly put forward a research effort aiming at high yield production of BC.…”

An integrated computational and experimental study to investigate Staphylococcus aureus metabolism

“…The increase in knowledge of macromolecular structures, availability of numerous biochemical database resources, advances in high-throughput genome sequencing, and increase in computational efficiency have accelerated the use of in silico methods for metabolic model development and analysis, strain design, therapeutic target discovery, and drug development. [12][13][14][15][16][17] There have been a number of attempts to reconstruct the metabolism of multiple strains of S. aureus using semi-automated methods. [18][19][20][21][22] However, the absence of organism-specific metabolic functions and the inclusion of genes without any specified reactions still limit the utility of these models.…”