PepDRED: De Novo Peptide Design with Strong Binding Affinity for Target Protein

Denovo design of peptides that bind specifically to functionalproteins is beneficial for diagnostics and therapeutics. However,complex permutations and combinations of amino acids pose significantchallenges to the rational design of peptides with desirable stabilityand affinity. Herein, we develop a computational-based evolution method,namely, peptidomimetics-driven recognition elements design (PepDRED),to derive hemoglobin-inspired peptidomimetics. PepDRED mimics thenatural evolutionism pipeline to generate stable apovariant (AVs)structures for wild-type counterparts via automated point mutationsand validates their efficiency through free binding energy analysisand per residue energy decomposition analysis. For application demonstration,we applied PepDRED to design de novo peptides to bind FhuA, a typicalTonB-dependent transporter (TBDT). TBDTs are Gram-negative bacterialouter membrane proteins responsible for iron transport and vital forbacterial resistance. PepDRED generated a pool of AVs and proceededto reach an optimized peptide, AV440, with a remarkable binding affinityof -21 kcal/mol. AV440 is & SIM;2.5-fold stronger than theexisting FhuA inhibitor Microcin J25. Network energy analysis furtherunveils that incorporating methionine (M42) in the N-terminal regionsignificantly enhances inter-residue contacts and binding affinity.PepDRED offers a prompt and efficient in silico approach to developpotent peptide candidates for target proteins.