Abstract
Proteins manifest biological functions through precisely orchestrated mechanical responses, yet classical molecular dynamics (MD) faces a profound conundrum: atomistic accuracy is intrinsically antithetical to proteomic scalability. Here we introduce FluxMD, a GPU-accelerated computational framework designed explicitly to surmount this dilemma by translating ligand-protein interactions into unique mechanical fingerprints. FluxMD systematically probes protein surfaces using brief, helically guided Brownian trajectories around rigid receptors, integrating residue-level non-covalent perturbations into a reproducible stress barcode . Benchmarking against glutathione peroxidase 4 (GPX4), an essential ferroptosis regulator, exemplifies FluxMD’s predictive sensitivity: the minimalist electrophile TMT10 elicits sharply defined mechanical flux patterns localized at critical nucleophilic residues, whereas the structurally bulky ML162 disperses mechanical signals diffusely. FluxMD enables rapid and mechanically nuanced structural analysis, and is anticipated to adeptly triage biologically salient candidates from vast AlphaFold- and diffusion-generated protein repertoires.
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Abstract
Proteins manifest biological functions through precisely orchestrated mechanical responses, yet classical molecular dynamics (MD) faces a profound conundrum: atomistic accuracy is intrinsically antithetical to proteomic scalability. Here we introduce FluxMD, a GPU-accelerated computational framework designed explicitly to surmount this dilemma by translating ligand-protein interactions into unique mechanical fingerprints. FluxMD systematically probes protein surfaces using brief, helically guided Brownian trajectories around rigid receptors, integrating residue-level non-covalent perturbations into a reproducible stress barcode. Benchmarking against glutathione peroxidase 4 (GPX4), an essential ferroptosis regulator, exemplifies FluxMD’s predictive sensitivity: the minimalist electrophile TMT10 elicits sharply defined mechanical flux patterns localized at critical nucleophilic residues, whereas the structurally bulky ML162 disperses mechanical signals diffusely. FluxMD enables rapid and mechanically nuanced structural analysis, and is anticipated to adeptly triage biologically salient candidates from vast AlphaFold- and diffusion-generated protein repertoires.
Competing Interest Statement
The authors have declared no competing interest.
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