Exploration of the proteome of Olsenella uli DSM 7084 to identify potential drug targets against oral infections

The oral microbiome is central to the maintenance of oral health, and disruptions in its composition contribute to persistent infections, including endodontic pathologies. Among bacteria and other members of the microbial community, Olsenella uli is a concerning pathogen responsible for several endodontic infections. Despite its clinical significance, organism-specific therapeutic targets for O. uli remain largely unexplored. In this study, a systematic comparative subtractive proteomics pipeline was employed to identify novel drug targets unique to O. uli DSM 7084. Comparative KEGG pathway mapping revealed 21 pathogen-specific metabolic pathways, from which 14 non-homologous essential proteins were identified. Physicochemical characterization further priortized these to eight stable proteins prioritized for druggability screening. Druggability analysis using DrugBank identified corresponding FDA-approved compounds, and three-dimensional structures generated through I-TASSER enabled downstream molecular docking. Docking simulations highlighted three high-affinity protein–drug complexes—UDP-N-acetylmuramoylalanine–D-glutamate ligase, alanine racemase, and UDP-N-acetylglucosamine-1-carboxyvinyltransferase—with strong inhibitory potential. These findings provide a comprehensive in silico foundation for the rapid development of targeted antimicrobial strategies against O. uli, supporting future experimental validation for improved management of persistent oral and endodontic infections.

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Publication Details

Journal
Discover Bacteria.
Published
2026-01-08
DOI
https://doi.org/10.1007/s44351-025-00038-2
Primary Topic
Machine Learning in Bioinformatics
Type
article
Field-Weighted Citation Impact
0.00

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article

Exploration of the proteome of Olsenella uli DSM 7084 to identify potential drug targets against oral infections

Mansoor Alsahag, Ali Alisaac, Abrar Ahmad, Mazin A. Zamzami et al.
Discover Bacteria.
Machine Learning in Bioinformatics
article

Exploration of the proteome of Olsenella uli DSM 7084 to identify potential drug targets against oral infections

Mansoor Alsahag, Ali Alisaac, Abrar Ahmad, Mazin A. Zamzami, Tulika Bhardwaj, Hani Choudhry, Ishtiaq Qadri, Arshi Talat, Mohammad Salman Akhtar, Nada M. Ali
article en

Abstract

The oral microbiome is central to the maintenance of oral health, and disruptions in its composition contribute to persistent infections, including endodontic pathologies. Among bacteria and other members of the microbial community, Olsenella uli is a concerning pathogen responsible for several endodontic infections. Despite its clinical significance, organism-specific therapeutic targets for O. uli remain largely unexplored. In this study, a systematic comparative subtractive proteomics pipeline was employed to identify novel drug targets unique to O. uli DSM 7084. Comparative KEGG pathway mapping revealed 21 pathogen-specific metabolic pathways, from which 14 non-homologous essential proteins were identified. Physicochemical characterization further priortized these to eight stable proteins prioritized for druggability screening. Druggability analysis using DrugBank identified corresponding FDA-approved compounds, and three-dimensional structures generated through I-TASSER enabled downstream molecular docking. Docking simulations highlighted three high-affinity protein–drug complexes—UDP-N-acetylmuramoylalanine–D-glutamate ligase, alanine racemase, and UDP-N-acetylglucosamine-1-carboxyvinyltransferase—with strong inhibitory potential. These findings provide a comprehensive in silico foundation for the rapid development of targeted antimicrobial strategies against O. uli, supporting future experimental validation for improved management of persistent oral and endodontic infections.

Discover Bacteria.Vol. 3(1)
University of Calgary (CA), King Abdulaziz University (SA), ESIC Hospital (IN), Al Baha University (SA)
Albaha University, King Abdulaziz University
Good health and well-being
Openalex Percentile: Top 94%
Machine Learning in Bioinformatics
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