Biology subjects

Bannerman, B. P.

Publications and source records attributed to Bannerman, B. P..

3 recordsLinked to original sources

bioRxiv2023

Mycobacterial Metabolic Model Development for Drug Target Identification

Antibiotic resistance is increasing at an alarming rate, and three related mycobacteria are the source of widespread infections in humans. According to the World Health Organization, Mycobacterium leprae, which causes leprosy, is still endemic in tropical countries; Mycobacterium tuberculosis is the second largest infectious disease killer worldwide after COVID-19; Mycobacteroides abscessus, a group of non-tuberculous mycobacteria, causes lung infections and other health-care-associated infections in humans. Due to the rise in resistance to common antibacterial drugs, it is critical to develop alternatives to traditional treatment procedures. Furthermore, an understanding of the biochemical mechanisms underlying pathogenic evolution is important for the treatment and management of these diseased conditions. In this study, metabolic models have been developed for two bacterial pathogens, M. leprae, and M. abscessus, and a new computational tool has been used to identify potential drug targets, which are referred to as bottleneck reactions. The genes, reactions, and pathways in each of these organisms have been highlighted; the potential drug targets can be further explored as broad-spectrum antibacterials and the unique drug targets to each pathogen are significant for precision medicine initiatives. The models and associating datasets are available in GigaScience and the following repositories: O_LIM. abscessus O_LIBiomodels: https://www.ebi.ac.uk/biomodels/MODEL2203300002 C_LIO_LIhttps://www.patmedb.org/Bacteria/Mabscessus C_LI C_LIO_LIM. leprae O_LIBiomodels: https://www.ebi.ac.uk/biomodels/MODEL2203300001 C_LIO_LIhttps://www.patmedb.org/Bacteria/Mleprae C_LI C_LI

systems biology↗

bioRxiv2021

Predicted structural mimicry of spike receptor-binding motifs from highly pathogenic human coronaviruses

Viruses often encode proteins that mimic host proteins in order to facilitate infection. Little work has been done to understand the potential mimicry of the SARS-CoV-2, SARS-CoV, and MERS-CoV spike proteins, particularly the receptor-binding motifs, which could be important in determining tropism of the virus. Here, we use structural bioinformatics software to characterize potential mimicry of the three coronavirus spike protein receptor-binding motifs. We utilize sequence-independent alignment tools to compare structurally known or predicted three-dimensional protein models with the receptor-binding motifs and verify potential mimicry with protein docking simulations. Both human and non-human proteins were found to be similar to all three receptor-binding motifs. Similarity to human proteins may reveal which pathways the spike protein is co-opting, while analogous non-human proteins may indicate shared host interaction partners and overlapping antibody cross-reactivity. These findings can help guide experimental efforts to further understand potential interactions between human and coronavirus proteins. HighlightsO_LIPotential coronavirus spike protein mimicry revealed by structural comparison C_LIO_LIHuman and non-human protein potential interactions with virus identified C_LIO_LIPredicted structural mimicry corroborated by protein-protein docking C_LIO_LIEpitope-based alignments may help guide vaccine efforts C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=111 SRC="FIGDIR/small/441187v1_ufig1.gif" ALT="Figure 1"> View larger version (22K): org.highwire.dtl.DTLVardef@1f09454org.highwire.dtl.DTLVardef@19a5557org.highwire.dtl.DTLVardef@158d3fdorg.highwire.dtl.DTLVardef@c59511_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2019

Comparative analyses of metabolic pathways in mycobacteria: Insights into the identification and characterisation of new drug targets in M. tuberculosis, M. leprae and M. abscessus

Three related mycobacteria are the cause of widespread infections in man and are the focus of intense research and drug-discovery efforts in the face of growing antimicrobial resistance. Mycobacterium tuberculosis, the causative agent of tuberculosis, is currently one of the top ten causes of death in the world according to WHO; M. abscessus, a group of non-tuberculous mycobacteria causes lung infections and other opportunistic infections in humans; and M. leprae, the causative agent of leprosy, remains endemic in tropical countries. There is an urgent need to design alternatives to conventional treatment strategies, due to the increase in resistance to standard antibacterials. In this study, we present a comparative analysis of chokepoint and essentiality datasets that will provide insight into the development of new treatment regimes. We illustrate the key metabolic pathways shared between these three organisms and identify drug targets with a wide metabolic impact that are common to the three species. We demonstrate that 72% of the chokepoint enzymes are proteins essential to Mycobacterium tuberculosis. We show also that 78% of the drug targets, prioritized based on their presence in multiple paths on the metabolic network, are present in pathways shared by M. tuberculosis, M. leprae and M. abscessus, including biosynthesis of amino acids, carbohydrates, cell structures, fatty acid and lipid biosynthesis. A further 17% is found in the prioritised pathways shared between M. tuberculosis and M. abscessus. We have performed comparative structure modelling of potential drug targets identified using our analysis in order to assess druggability and demonstrate the importance of chokepoint analysis in terms of drug target identification. AUTHOR SUMMARYComputer simulation studies to design new drugs against mycobacteria

bioinformatics↗