International Journal of Biological MacromoleculesBiophysicsPeer-reviewed journal article
Mapping DHPS Evolvability: Identification of Novel Evolutionarily Critical Sub-Structure through Evolutionary and Structural Analyses of DHPS
Dwipanjan SanyaliD, Arti Shivram, Deeptanshu PandeyiD, Suharto BanerjeeiD, Vladimir Nikolaevich UverskyiD, Danny MuzataiD, Aneesh Sreevallabh ChivukulaiD, Ravi JasujaiD, Krishnananda ChattopadhyayiD, Sourav ChowdhuryiD
Abstract
Protein evolution shapes pathogen adaptation-landscape, particularly in developing drug resistance. The rapid evolution of target proteins under antibiotic pressure leads to escape mutations leading to the problem of antibiotic resistance. A deep understanding of the evolutionary dynamics of antibiotic target proteins presents a plausible intervention strategy for disrupting the evolutionary trajectory of resistance. Mutations in Dihydropteroate synthase (DHPS), an essential folate pathway protein and a key target for sulfonamide antibiotics, result in reduced antibiotic binding, leading to resistance. Deploying an array of statistical analyses on the DHPS sequence-space and integrating those with deep mutational analysis and structure-based network-topology models we identified critical DHPS-subsequences. Our analysis of the frustration landscape of DHPS predicts how conformational and mutational changes shift the energy distributions within the DHPS substructures. Combining dimensionality reduction and optimality analysis we identified a substructure critical to DHPS evolvability, and computed its druggablity. Our integrated evolution and structure-informed framework identified a DHPS-substructure with significant evolutionary and structural impact. Targeting this region could constrain DHPS evolvability and disrupt the resistome, presenting a new avenue for antibiotic development and contributing to the broader effort to address the problem of antibiotic resistance.
- assay: protein structure
- assay: protein sequence
- research focus: protein folding & dynamics
- research focus: drug response / drug discovery
- approach: statistical model
- approach: deep learning
- Citations
- 4
- FWCI
- 2.09
- Field percentile
- top 14%
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