Can self-inhibitory peptides be derived from the interfaces of globular protein-protein interactions?

Abstract

In this study, we assess on a large scale the possibility of deriving self-inhibitory peptides from protein domains with globular architectures. Such inhibitory peptides would inhibit interactions of their origin domain by mimicking its mode of binding to cognate partners, and could serve as promising leads for rational design of inhibitory drugs. For our large-scale analysis, we analyzed short linear segments that were cut out of protein interfaces in silico in complex structures of protein-protein docking Benchmark 3.0 and CAPRI targets from rounds 1-19. Our results suggest that more than 50% of these globular interactions are dominated by one short linear segment at the domain interface, which provides more than half of the original interaction energy. Importantly, in many cases the derived peptides show strong energetic preference for their original binding mode independently of the context of their original domain, as we demonstrate by extensive computational peptide docking experiments. As an in depth case study, we computationally design a candidate peptide to inhibit the EphB4-EphrinB2 interaction based on a short peptide derived from the G-H loop in EphrinB2. Altogether, we provide an elaborate framework for the in silico selection of candidate inhibitory molecules for protein-protein interactions. Such candidate molecules can be readily subjected to wet-laboratory experiments and provide highly promising starting points for subsequent drug design.

Figure 1. Most protein-protein interactions are mediated by one dominant linear peptide at the interface

A.The distribution of the relative interaction energy contribution (% of total interaction energy) of the highest-affinity decamer at the interface, for complexes from the docking Benchmark 3.0 (squares) and CAPRI (triangles) data sets. For roughly 50% of the complexes, a single linear segment contributes more than half of the interaction energy. See Methods for more details about the estimation of interaction energies. B. The corresponding distribution of absolute interaction energy values of the highest-affinity decamers (in Rosetta energy units; REU). For reference, the distribution of absolute interaction energy is given for a dataset of solved structures of peptide-protein interactions (peptiDB; diamonds). A large number of the proposed peptides have energetic values that are comparable to known peptide interactions. C and D. Examples of protein interactions that are dominated by a single linear peptide, vs. interactions that are not. C. In the complex of Ephrin type-b receptor 4 kinase (EphB4; dark blue) and EphrinB2 (white) (pdb: 2HLE30), most of the interaction energy is contributed by a linear segment at the interface of EphrinB2 (cartoon, widened). D. In the complex of b. Anthracis protective antigen (dark blue) and human anthrax toxin receptor (white) (pdb: 1T6B33), the interface is composed of several joined loops. No single linear segment contributes a significant fraction of the interaction energy.

Figure 2. Derived peptides in different types of interfaces

A. The distribution of relative binding energies of the highest affinity decamer at the interface (see Figure 1) is shown for different classes of interactions (see legend). Dominating linear peptides at the interface appear in particular in the subclass of Trypsin- and Subtilisin enzyme-inhibitor complexes (EI-proteases). Nonetheless, significant contributions by one dominant interface peptide are also observed for all other classes of protein interactions. B. Which side contributes the highest-affinity decamer? High-affinity linear segments show an asymmetric distribution in the Antibody-Antigen and Enzyme-Inhibitor classes of interactions, where in 80% of the complexes the high-affinity peptide is derived from the Antigen or Inhibitor, respectively. In the ‘Other’ class in turn, no particular preference for any of the partners is observed.

Figure 3. Derived peptides favor the native binding conformation observed in the full protein

A. Sampling of a wide range of conformations allows accurate mapping of the energy landscape. Example of sampled range of peptide conformations for CAPRI target 20, showing 100 representative models of a docked peptide derived from RF1 (ribbon presentation in different colors; out of the total 9000) bound to HemK (surface representation). B. Energy funnel for RF1-derived peptide - HemK docking. Each model is presented as a dot, indicating its total energy (y-axis) vs. the backbone RMSD of the peptide interface residues to the peptide in its native protein context (x-axis). For clearness, only top-scoring models out of the total 9000 are shown. See Supp. Figure S2 for energy funnel plots of peptides derived from 13 additional CAPRI targets. C. Example of successful docking simulations on peptides derived from the Plasminogen receptor antibody/Plasminogen activator receptor complex (pdb: 2FD6). In this example, docking starts from the unbound conformation of the derived peptide in its native context. More examples can be found in Supp. Figure S3. The native peptide is colored green, the peptide in its unbound form (as part of the unbound monomer) is colored red, and the model created with the FlexPepDock protocol is colored magenta. D. Mapping the energy landscapes of peptides derived from CAPRI targets indicates that derived peptides favor the original binding conformation for a large number of targets. For each interaction, we show the interface backbone RMSD of the lowest-energy model created for the derived peptide (dark blue bar), as well as for the best out of the 10 top scoring models (light blue bar). Red diamonds indicate for each target the fraction of binding energy that the derived peptide contributes (a dotted line is drawn at 50%). In 72% of the interactions (21 out of 29), a near-native conformation (below 2Å backbone-RMSD over peptide interface residues) is found among the top-10 models. In 35%, of the interactions, this is the top-scoring model. We note that we also calculated all-atom RMSD values for top-scoring models: these exceed backbone RMSD values on the average by 0.55±0.40Å. Thus, top-scoring models well below 2Å all-atom RMSD were created for a large fraction of the targets.

Figure 4. Design of an inhibitory peptide derived from the EphB4-EphrinB2 interaction

A-B. The interaction of the EphrinB2 native G-H loop (residues 116-128; magenta) and of a proposed inhibitory designed peptide (blue) with EphB4 (gray). A. A docking model of the suggested S-S cyclized inhibitory peptide (blue) suggests that the stabilizing S-S bond can be introduced into the peptide without affecting the general binding mode or compromising the major binding determinants (0.75Å RMSD over all heavy atoms of residues 120-126). B. Models of the three proposed mutations (Q3R, F5H, L9K). C. The energy landscape of the designed EphrinB2 derived peptide shows a pronounced energy funnel leading towards the native binding mode. See also Supp. Figure S6.