De Novo Design of Highly Stable Binders Targeting Dihydrofolate Reductase in Klebsiella pneumoniae

ABSTRACT The study aims to design novel therapeutic inhibitors targeting the DHFR protein of Klebsiella pneumoniae . However, challenges like bacterial resistance to peptides and the limitations of computational models in predicting in vivo behavior must be addressed to refine the design process and improve therapeutic efficacy. This study employed deep learning‐based bioinformatics techniques to tackle these issues. The study involved retrieving DHFR protein sequences from Klebsiella strains, aligning them to identify conserved regions, and using deep learning models (OmegaFold, ProteinMPNN) to design de novo inhibitors. Cell‐penetrating peptide (CPP) motifs were added to enhance delivery, followed by allergenicity and thermal stability assessments. Molecular docking and dynamics simulations evaluated the binding affinity and stability of the inhibitors with DHFR. A conserved 60‐residue region was identified, and 60 de novo binders were generated, resulting in 7200 sequences. After allergenicity prediction and stability testing, 10 sequences with melting points near 70°C were shortlisted. Strong binding affinities were observed, especially for complexes 4OR7‐1787 and 4OR7‐1811, which remained stable in molecular dynamics simulations, indicating their potential as therapeutic agents. This study designed stable de novo peptides with cell‐penetrating properties and strong binding affinity to DHFR. Future steps include in vitro validation to assess their effectiveness in inhibiting DHFR, followed by in vivo studies to evaluate their therapeutic potential and stability. These peptides offer a promising strategy against Klebsiella pneumoniae infections, providing potential alternatives to current antibiotics. Experimental validation will be key to assessing their clinical relevance.