Abstract
Antimicrobial resistance (AMR) is a major global threat, contributing to an estimated 700,000 deaths annually. One of the main ways that AMR genes spread among bacteria is through conjugation. Therefore, it is crucial to develop conjugation inhibitors (COINs) to combat AMR. Shared characteristics between known COIN molecules are a hydrophobic tail, unsaturations, and a polar head. Considering these characteristics and accounting for the efficacy of 2-hexadecynoic acid and Tanzawaic Acids (TZAs) low toxicity, we designed a new set of TZA analogs. The predicted protein target of previously identified COINs is TrwD, an ATPase in the type IV secretion system (T4SS). This multi-protein complex plays a crucial role in bacterial conjugation. In this work, we set up a computational pipeline to screen our TZA analogs. This pipeline involves predicting the structure of the experimentally uncharacterized protein TrwD structure using homology-based protein structure prediction via Protein Homology/analogy Recognition Engine V 2.0 (Phyre2), and identifying binding pockets and potential binding poses are identified using a combined SwissDock and MELD (Modeling Employing Limited Data) accelerated molecular dynamics (MD) simulations (MELDxMD) approach. Preliminary results with ATP, the natural ligand for TrwD (the predicted target of COINs), demonstrate the effectiveness of our pipeline. SwissDock identified over 30 potential binding poses, which were grouped into five distinct clusters. The system was then optimized in AMBER under unbiased conditions to further refine the ligand binding predictions. These results validate the pipeline’s capacity to predict and refine binding poses. However, integrating the system into MELDxMD has proven challenging due to the use of a ligand and the GAFF force field. Despite efforts, difficulties arose in refining the binding poses and conducting detailed competitive simulations. Additional work is needed to fully integrate the system into MELDxMD, refine the binding poses, and evaluate the relative binding affinities of the novel COIN molecules. This study provides a robust framework for assessing COIN binding affinities and identifying promising candidates for synthesis and biological evaluation. Ultimately, this pipeline may aid in the development of novel therapeutics to combat AMR by inhibiting bacterial conjugation and curbing the spread of resistance genes.
Library of Congress Subject Headings
Drug resistance in microorganisms--Prevention--Data processing; Conjugation (Biology)--Prevention--Data processing; Computational biology; Drug development
Publication Date
4-10-2025
Document Type
Thesis
Student Type
Graduate
Degree Name
Bioinformatics (MS)
Department, Program, or Center
Thomas H. Gosnell School of Life Sciences
College
College of Science
Advisor
Andre Hudson
Advisor/Committee Member
Renata Rezende Miranda
Advisor/Committee Member
Emiliano Brini
Recommended Citation
Tuytschaevers, Serena, "A Computational Framework for Investigating Novel Bacterial Conjugation Inhibitors" (2025). Thesis. Rochester Institute of Technology. Accessed from
https://repository.rit.edu/theses/12080
Campus
RIT – Main Campus
Plan Codes
BIOINFO-MS
