Using Coarse-Grained Simulations to Characterize the Mechanisms of Protein–Protein Association

Abstract

The formation of functionally versatile protein complexes underlies almost every biological process. The estimation of how fast these complexes can be formed has broad implications for unravelling the mechanism of biomolecular recognition. This kinetic property is traditionally quantified by association rates, which can be measured through various experimental techniques. To complement these time-consuming and labor-intensive approaches, we developed a coarse-grained simulation approach to study the physical processes of protein–protein association. We systematically calibrated our simulation method against a large-scale benchmark set. By combining a physics-based force field with a statistically-derived potential in the simulation, we found that the association rates of more than 80% of protein complexes can be correctly predicted within one order of magnitude relative to their experimental measurements. We further showed that a mixture of force fields derived from complementary sources was able to describe the process of protein–protein association with mechanistic details. For instance, we show that association of a protein complex contains multiple steps in which proteins continuously search their local binding orientations and form non-native-like intermediates through repeated dissociation and re-association. Moreover, with an ensemble of loosely bound encounter complexes observed around their native conformation, we suggest that the transition states of protein–protein association could be highly diverse on the structural level. Our study also supports the idea in which the association of a protein complex is driven by a “funnel-like” energy landscape. In summary, these results shed light on our understanding of how protein–protein recognition is kinetically modulated, and our coarse-grained simulation approach can serve as a useful addition to the existing experimental approaches that measure protein–protein association rates.

A computational study of co-inhibitory immune complex assembly at the interface between T cells and antigen presenting cells

“…These parameters can be estimated more quantitatively through computational methods with higher resolutions, if their experimental measurements are not available. For an example, binding rates and affinities of two interacting proteins can be calculated by simulations with their structural details and using physics-based or knowledge-based scoring functions to describe their interactions [ 56 ]. The calculated binding constants can further be integrated into current model by a multiscale modeling framework [ 57 ], so that the structural and energetic features at the binding interfaces of an immune complex can be explicitly factored in.…”

Protein–Protein Interaction-Gaussian Accelerated Molecular Dynamics (PPI-GaMD): Characterization of Protein Binding Thermodynamics and Kinetics

“…Nevertheless, PPI-GaMD could be further combined with other enhanced sampling methods to facilitate the protein rebinding in future studies, including the replica exchange and weighted ensemble that have been successfully combined with GaMD. On the other hand, coarse-grained modeling, , which significantly reduces the system degrees of freedom, could be applied to extend simulation timescales and investigate PPIs such as binding of the G proteins to GPCRs . These techniques could be incorporated to further improve the PPI-GaMD efficiency and advance studies in enhanced sampling of PPIs.…”

Protein-protein interaction-Gaussian accelerated molecular dynamics (PPI-GaMD): Characterization of protein binding thermodynamics and kinetics

“…Nevertheless, PPI-GaMD could be further combined with other enhanced sampling methods to facilitate the protein rebinding in future studies, including the Replica Exchange 44 and Weighted Ensemble 45 that have been successfully combined with GaMD. On the other hand, coarse-grained modeling, 46 which significantly reduces the system degrees of freedom, could be applied to extend simulation timescales and investigate PPIs such as binding of the G proteins to GPCRs. 47 These techniques could be incorporated to further improve the PPI-GaMD efficiency and advance studies in enhanced sampling of PPIs.…”