De Novo Structure-Based Design of TEM-171 β-Lactamase Protein Inhibitors Using Integrated Deep Learning and Multi-Scale Simulations to Combat Bacterial Resistance

The emergence of TEM-171 {beta}-lactamase represents a significant threat to modern antimicrobial therapy due to its ability to hydrolyze an extended spectrum of {beta}-lactam antibiotics. While traditional {beta}-lactamase inhibitors like tazobactam show diminishing efficacy against this enzyme, no true systematic approach exists for developing targeted protein-based inhibitors. Here, I present an integrated computational pipeline for de novo protein design targeting TEM-171, combining quantum-inspired diffusion models with evolutionary optimization. This dual-platform approach employs RFDiffusion for scaffold generation (n=2048) and Bind-Craft for interface refinement (n=67), guided by AlphaFold2 structural predictions (mean pLDDT score: 93.2) and Protein-MPNN sequence optimization. The designed inhibitor demonstrates exceptional structural stability (RMSD: 1.2[A]-1.5[A])) and binding affinity ({Delta}G:-12.3 kcal/mol) in microsecond-scale molecular dynamics simulations, with force-displacement profiles revealing peak unbinding forces of 1500-1700 kN/mol. The designed inhibitor maintains stable contacts with critical catalytic residues including Ser70 and maintains conformational integrity across varied physiological conditions (pH 6.5-8.0, 298-310,K). Beyond the immediate therapeutic application, the generalizable framework demonstrates 38.4,s average computation time per design and 94% success rate in generating stable protein-protein interfaces (i_ptm > 0.8), establishing an efficacious pipeline for accelerated therapeutic protein development. These findings not only present a promising candidate for combating TEM-171-mediated resistance, but also provide a wider-scale methodology for addressing emerging therapeutic challenges through rational protein design.