Energy Landscapes of Protein Self-Assembly: Lessons from Native Topology-Based Models

Many cellular functions rely on interactions among proteins and between proteins and nucleic acids. Our understanding of the principles that govern protein folding has been advanced in the recent years using the energy landscape theory and thanks to tight collaborations between experimentalists and theoreticians. It is likely that our current understanding of protein folding can be applied to understand more complex cellular self-organization processes. The limited success of binding predictions may suggest that the physical and chemical principles of protein binding have to be revisited to correctly capture the essence of protein recognition. In this review, we discuss the power of reduced models to study the physics of protein assembly. Since energetic frustration is sufficiently small, native topology-based models, which correspond to perfectly unfrustrated energy landscapes, have shown that binding mechanisms are robust and governed primarily by the protein’s native topology. These models impressively capture many of the binding characteristics found in experiments and highlights the fundamental role of flexibility in binding. The essential role of solvent molecules and electrostatic interactions in binding is also discussed. Despite the success of the minimally frustrated models to describe the dynamics and mechanisms of binding, the actual degree of frustration has to be explored to quantify the capacity of a protein to bind specifically to other proteins. We have found that introducing mutations can significantly reduce specificity by introducing an additional binding mode. Deciphering and quantifying the key ingredients for biological self-assembly is invaluable to reading out genomic sequences and understanding cellular interaction networks.