Structural and dynamic determinants of protein-peptide recognition

Abstract

Protein-peptide interactions play important roles in many cellular processes, including signal transduction, trafficking, and immune recognition. Protein conformational changes upon binding, an ill-defined peptide binding surface, and the large number of peptide degrees of freedom make the prediction of protein-peptide interactions particularly challenging. To address these challenges, we perform rapid molecular dynamics simulations in order to examine the energetic and dynamic aspects of protein-peptide binding. We find that, in most cases, we recapitulate the native binding sites and native-like poses of protein-peptide complexes. Inclusion of electrostatic interactions in simulations significantly improves the prediction accuracy. Our results also highlight the importance of protein conformational flexibility, especially side-chain movement, which allows the peptide to optimize its conformation. Our findings not only demonstrate the importance of sufficient sampling of the protein and peptide conformations, but also reveal the possible effects of electrostatics and conformational flexibility on peptide recognition.

Figure 1. Analysis of flexible side-chain simulation of PDZ-peptide complex

(A) RMSD values of peptide conformations with respect to the crystallographic pose of the peptide for peptide-bound (black) and peptide-unbound (gray) states from a representative replica. If any atom of the peptide is within 5.5 Å of any atom of the protein in the trajectory, then that snapshot is considered as a peptide-bound conformation. (B) The backbone of PDZ domain is fixed during simulation, and we reconstruct all peptide-bound states from the simulation trajectories. The positions of the peptide in each peptide-bound frame are displayed in ribbon diagrams. The hit map of peptide interactions with the protein corresponds to the frequency with which the peptide atoms interact with the protein atoms, and these interactions range from very frequent (red) to very infrequent (blue). (C) Energy landscape with the interface energy between the peptide and protein in terms of MedusaScore. (D) The lowest energy conformation (magenta) of the peptide from the largest cluster and its experimental pose (black). See also Figure S1.

Figure 2. Analysis of flexible side-chain simulation of Keap1-peptide complex

Two binding sites exist for this peptide, as exhibited by two low-energy clusters in the energy landscape. The purple ribbon is the lowest energy peptide pose from the most populated cluster, whereas the black ribbon is the experimentally-determined pose. The global minimum energy corresponds to the red conformation; however, that state is less populated than the purple conformation. For the results of all complexes, see also Figure S2 and Figure S3.

Figure 3. Proposed model for the structural and dynamic determinants of peptide recognition

The dotted line represents binding without electrostatic interactions. The dashed line represents binding with electrostatic interactions. In the presence of electrostatics, the number of decoy states decreases, whereas the native-like funnel becomes more populated. The solid line represents the binding with both electrostatic interactions and conformational flexibility. Here, the native-like funnel experiences more sampling and a decrease in its energy.

Figure 4. MedusaDock-refined experimental and predicted conformations

Using MedusaDock, we improve the prediction accuracy of (A) 1DDV, (B) 1PRM, (C) 1X2R, (D) 2FNX, (E) 2PQ2 complexes. The selected conformations from the simulations with flexible side-chain constraints in the presence of electrostatics are employed as initial conformations for docking optimization. Shown are the native binding pose (black) and the predicted binding pose before (magenta) and after (blue) docking refinement.