A-P.30: Computational Mutational Scanning of DHFR for Mutation-Dependent Inhibitor Preference
Tandac Furkan Guclu
Istinye University
Ebru Cetin
Sabanci University
Muhammed Sadik Yildiz
University of Texas Southwestern Medical Center
Erdal Toprak
University of Texas Southwestern Medical Center
Ali Rana Atilgan
Sabanci University
Canan Atilgan
Sabanci University
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Antibiotic resistance is a public health challenge driven by mutations that enable bacteria to maintain function while escaping drug pressure. One enzyme extensively studied in this context is dihydrofolate reductase (DHFR), an essential catalyst in folate metabolism and nucleotide biosynthesis. This protein is suitable for studying antibiotic resistance because resistance often arises from single amino-acid substitutions that preserve function while altering inhibitor binding. In this work, we investigated the mutational landscape of E. coli DHFR in the presence of competitive inhibitors, trimethoprim(TMP) and its derivative 4'-deoxytrimethoprim(4'DTMP). Because TMP and 4'DTMP are closely related chemically, comparing them allows us to assess whether a structural modification can reshape mutation-dependent inhibitor preference and alter high-impact resistance trajectories. To characterize mutation-dependent energetic effects, we generated a deep mutational scanning library. Large-scale free-energy perturbation(FEP) simulations were performed for apo, TMP-bound, and 4'DTMP-bound DHFR to estimate mutational free energies and relative binding preferences. Single amino-acid substitutions were examined across DHFR positions with triplicate FEP calculations. Although TMP and 4'DTMP showed similar behavior across much of the scanned landscape, the strongest differences were concentrated at important DHFR regions, including cryptic-site and allosteric regions, demonstrating that even a subtle chemical modification can reshape inhibitor preference at sites most relevant to resistance dynamics and function. These findings provide a structural-energetic map of mutation-dependent inhibitor preference and highlight the potential of alchemical calculations for mutation-resolved studies of antibiotic resistance. Results were interpreted alongside experimental relative fitness data, with explicit incorporation of uncertainty from both computational and experimental sources.
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