Rapid in silico Design of Potential Cyclic Peptide Binders Targeting Protein-Protein Interfaces

Abstract

Rational design of specific inhibitors of protein-protein interactions is desirable for drug design to control cellular signal transduction but also for studying protein-protein interaction networks. We have developed a rapid computational approach to rationally design cyclic peptides that potentially bind at desired regions of the interface of protein-protein complexes. The methodology is based on comparing the protein backbone structure of short peptide segments (epitopes) at the protein-protein interface with a collection of cyclic peptide backbone structures. A cyclic peptide that matches the backbone structure of the segment is used as a template for a binder by adapting the amino acid side chains to the side chains found in the target complex. For a small library of cyclic peptides with known high resolution structures we found for the majority (~82%) of 154 protein-protein complexes at least one very well fitting match for a cyclic peptide template to a protein-protein interface segment. The majority of the constructed protein-cyclic peptide complexes was very stable during Molecular Dynamics simulations and showed an interaction energy score that was typically more favorable compared to interaction scores of typical peptide-protein complexes. Our cPEPmatch approach could be a promising approach for rapid suggestion of cyclic peptide binders that could be tested experimentally and further improved by chemical modification.

Keywords: cyclo peptide design; drug design with cyclo-peptides; protein binding modulation; protein interaction inhibition; protein-protein complexes; rational cyclo peptide binders.

Figure 1

Workflow of the in silico cyclic peptide binder construction. CP indicates cyclic peptide, L and R represent the two partners of a protein-protein interaction pair.

Figure 2

(A) Interface structure of the complex (pdb4dg4) of trypsin inhibitor protein (pink) and trypsin (yellow). (B) Same view as in (A) but with a superimposed cyclic peptide (pdb3avb, atom color coded) and adapted interface side chains after 5 ns MD simulation. (C) Complex of sunflower cyclic peptide (red/blue stick model) and trypsin (pdb1sfi).

Figure 3

Representative matches and modeled structures of protein-cyclic-peptide complexes. (A) Example of a cyclic peptide with an β-hairpin motif at the interface (pdb-entries of the complex and the cyclic peptide template are indicated; the calculated MMGBSA interaction energy and final deviation from the start structure are also included). (B) Same as in (A) but for a cyclic peptide with a turn motif as binder. (C) Same as in (A,B) for a cyclic α-helix binding motif. In each case the target protein-protein complex is shown as cartoon (yellow: receptor; pink: ligand protein). The superimposed matching cyclic peptide is indicated in the second column and the cyclic peptide (with adapted interface sequence) is shown in the third column. The last column represents the final structures of the receptor protein (yellow) in complex with the cyclic peptide after 5 ns MD simulation in explicit solvent.

Figure 4

MMGBSA binding free energy calculations estimated using 250 snapshots retrieved from 2.5 to 5.0, 5.0 to 7.0, and 7.5 to 10 ns of two independent MD simulation productions for each of the studied systems (A) 1cgi, (B) 2hle, (C) 2nz8, and (D) 4dg3. The average binding free energy for each case is shown as a dashed line along the graphs.

Figure 5

Average of 3 MMGBSA binding energy calculations estimated using 250 snapshots retrieved from three intervals (2.5–5.0, 5.0–7.0, and 7.5–10 ns) of MD simulations for the 1cgi, 2nz8, 2hle, and 4dg4 PPI receptors in complex with the cPEPmatch cyclic peptides and interface residues copied from the native complex (purple bar), or using the original cyclic peptide sequence (wild type) from the data base (cyan bar) or alanine substitutions for all interface residues of the cyclic peptide (orange bar).