Binding Free Energy Landscape of Domain-Peptide Interactions

Peptide recognition domains (PRDs) are ubiquitous protein domains which mediate large numbers of protein interactions in the cell. How these PRDs are able to recognize peptide sequences in a rapid and specific manner is incompletely understood. We explore the peptide binding process of PDZ domains, a large PRD family, from an equilibrium perspective using an all-atom Monte Carlo (MC) approach. Our focus is two different PDZ domains representing two major PDZ classes, I and II. For both domains, a binding free energy surface with a strong bias toward the native bound state is found. Moreover, both domains exhibit a binding process in which the peptides are mostly either bound at the PDZ binding pocket or else interact little with the domain surface. Consistent with this, various binding observables show a temperature dependence well described by a simple two-state model. We also find important differences in the details between the two domains. While both domains exhibit well-defined binding free energy barriers, the class I barrier is significantly weaker than the one for class II. To probe this issue further, we apply our method to a PDZ domain with dual specificity for class I and II peptides, and find an analogous difference in their binding free energy barriers. Lastly, we perform a large number of fixed-temperature MC kinetics trajectories under binding conditions. These trajectories reveal significantly slower binding dynamics for the class II domain relative to class I. Our combined results are consistent with a binding mechanism in which the peptide C terminal residue binds in an initial, rate-limiting step. Author Summary: The complex biological processes occurring in living organisms are enabled by numerous networks of interacting proteins. It is therefore of great interest to understand the physical interplay between proteins and, in particular, how this process gives rise to highly specific network connectivities. For a long time, the dominant molecular view of protein-protein interactions was the docking of more or less static folded structures, with specificity obtained from a complementarity in shape and charge distributions. Lately it has been realized that many of the links in protein networks are mediated by interactions between folded domains, on the one hand, and disordered polypeptide segments, on the other. We use an all-atom Monte Carlo based approach which attempts to capture this domain-peptide binding process in full and apply it to representative members of a common domain family. This allows us to examine and compare detailed aspects of the binding free energy landscapes which underlie specificity and affinity. Being able to model domain-peptide binding in a physically sound, yet computationally tractable way is essential for identifying molecular binding mechanisms and opens up possibilities for modifying interaction networks in a controlled way.