Binding Free Energy Landscape of Domain-Peptide Interactions

Abstract

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.

Computational Design of Selective Peptides to Discriminate between Similar PDZ Domains in an Oncogenic Pathway

“…Kortemme and co-workers demonstrated that structure-based modeling can predict peptide sequence features recognized by individual PDZ domains in good agreement with high-throughput studies [20]. Stanefa and Wallin studied PDZ/peptide binding free energy landscapes using implicit-solvent Monte Carlo simulations, showing qualitative agreement with experiments [21]. Other studies have demonstrated reasonable ability to describe the space of peptide binders using structure-based calculations [22-24] or sequence-based models trained on high-throughput experimental data [11, 25].…”

Exploring Protein-Peptide Binding Specificity through Computational Peptide Screening

“…[36]. It is an implicit-solvent model combining an all-atom representation of the protein chain with an effective energy function taking into account the major contributions of protein interactions, hydrogen bonding, electrostatic attraction, and the hydrophobic effect [40].…”

“…It is an implicit-solvent model combining an all-atom representation of the protein chain with an effective energy function taking into account the major contributions of protein interactions, hydrogen bonding, electrostatic attraction, and the hydrophobic effect [40]. The model was developed and tested based on the folding of small peptides and proteins and thereafter adapted particularly for protein-peptide binding [35], [36]. The potential energy function can be decomposed into five terms,representing excluded-volume interactions, local backbone interactions, hydrogen bonding, sidechain-sidechain interactions, and a backbone desolvation effect, respectively [36].…”

“…The model was developed and tested based on the folding of small peptides and proteins and thereafter adapted particularly for protein-peptide binding [35], [36]. The potential energy function can be decomposed into five terms,representing excluded-volume interactions, local backbone interactions, hydrogen bonding, sidechain-sidechain interactions, and a backbone desolvation effect, respectively [36]. Because of the effective nature of the energy function, assigning a physical unit to the energy is not straightforward.…”

“…In these simulations, two different types of MC updates are included. Updates of the first type are conventional conformational updates () and include pivot moves, -angle rotamer turns, and rigid body rotation and translations, as described in previous work [35], [36]. The second type of updates produces changes to the amino acid sequence of the peptide ().…”