Computational design and optimization of novel d‐peptide TNFα inhibitors

Abstract

Compared to small molecule drugs, peptide therapeutics provides greater efficacy, selectivity, and safety. The intrinsic disadvantages of peptides are their sensitivity to proteases. To overcome this, we have developed a general computational strategy for de novo design of protein binding helical d‐peptides. A d‐helical fragment library was established and used in generating flexible d‐helical conformations, which were then used to generate suitable sequences with the required structural and binding properties. Using this strategy, we successfully de novo designed d‐helical peptides that bind to tumor necrosis factor‐α (TNFα), inhibit TNFα‐TNFR1 binding, reduce TNFα activity in cellular assays, and are stable against protease digestion. Our strategy of helical d‐peptide design is generally applicable for discovering d‐peptide modulators against protein–protein interactions.

L‐Glycosidase‐Cleavable Natural Glycans Facilitate the Chemical Synthesis of Correctly Folded Disulfide‐Bonded D‐Proteins

“…Although the racemic protein crystallography could fully verify the structures of D-proteins, [17] we employed the simple and direct binding affinity experiments to evaluate the bioactivity of synthetic D-protein used in mirror-image screening to discover D-peptide ligands. L-DHPT91 and its enantiomer D-DHPT91, previously reported to specifically bind to L-TNFα [18] were synthesized via SPPS (Figure S31). Surface plasmon resonance (SPR) analysis showed that L-DHPT91 bound to D-TNFα with K d = 9.66 � 2.51 μM and D-DHPT91 bound to L-TNFα with K d = 13.08 � 2.43 μM (Figures 3f, S32), while no obvious binding was detected between D-DHPT91 and D-TNFα, nor L-DHPT91 and L-TNFα (Figure S33).…”

finDr: A web server for in silico D-peptide ligand identification

“…An alternative way to identify L- and D-peptide ligands, which does not suffer from the abovementioned restrictions, is computational modelling of PPIs [ 22 ]. Recent publications have shown the feasibility of identifying biologically active D-peptide ligands by modelling the structure of short helical D-peptide segments with molecular dynamics (MD) simulations and by screening peptide libraries generated this way for ligand-target interactions via molecular docking [ 23 , 24 ]. These programs enable the high-throughput in silico screening of peptide libraries; however, with increasing peptide length, the size of peptide libraries and, consequently, the search space and computational cost for docking-based screenings increase exponentially to a point where they are simply too large to be systematically explored.…”

Discovery of novel helix binding sites at protein-protein interfaces

“…In our previous work designing TNF-binding peptides, we identified a helical binding site on TNF surface that was originally used to bind the β-structured TNF receptor. We successfully designed helical peptides that bind to the identified site and inhibit the cellular activity of TNFα [9], [13].…”