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

Abstract

Many cellular functions rely on interactions among proteins and betweenproteins and nucleic acids. Our understanding of the principles thatgovern protein folding has been advanced in the recent years using theenergy landscape theory and thanks to tight collaborations betweenexperimentalists and theoreticians. It is likely that our currentunderstanding of protein folding can be applied to understand morecomplex cellular self-organization processes. The limited success ofbinding predictions may suggest that the physical and chemicalprinciples of protein binding have to be revisited to correctly capturethe essence of protein recognition. In this review, we discuss the powerof reduced models to study the physics of protein assembly. Sinceenergetic frustration is sufficiently small, native topology-basedmodels, which correspond to perfectly unfrustrated energy landscapes,have shown that binding mechanisms are robust and governed primarily bythe protein's native topology. These models impressively capture many ofthe binding characteristics found in experiments and highlights thefundamental role of flexibility in binding. The essential role ofsolvent molecules and electrostatic interactions in binding is alsodiscussed. Despite the success of the minimally frustrated models todescribe the dynamics and mechanisms of binding, the actual degree offrustration has to be explored to quantify the capacity of a protein tobind specifically to other proteins. We have found that introducingmutations can significantly reduce specificity by introducing anadditional binding mode. Deciphering and quantifying the key ingredientsfor biological self-assembly is invaluable to reading out genomicsequences and understanding cellular interaction networks.