Theory of protein folding

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Abstract

Protein folding should be complex. Proteins organize themselves into specific three-dimensional structures, through a myriad of conformational changes. The classical view of protein folding describes this process as a nearly sequential series of discrete intermediates. In contrast, the energy landscape theory of folding considers folding as the progressive organization of an ensemble of partially folded structures through which the protein passes on its way to the natively folded structure. As a result of evolution, proteins have a rugged funnel-like landscape biased toward the native structure. Connecting theory and simulations of minimalist models with experiments has completely revolutionized our understanding of the underlying mechanisms that control protein folding.

Introduction

Protein folding should be complex. Proteins organize themselves into specific three-dimensional structures, through a myriad of conformational changes. Each conformational change is itself a complex solvent-influenced event. So, in detail, a folding mechanism must involve a complex network of elementary reactions. However, simple empirical patterns of protein folding kinetics, such as linear free energy relationships, have been shown to exist.

This simplicity is owed to the global organization of the landscape of the energies of protein conformations into a funnel (Figure 1). This organization is not characteristic of all polymers with any sequence of amino acids, but is a result of evolution. The discovery of simple kinetic patterns 1., 2., 3., 4., 5. and the existence of a theoretical framework based on the global properties of the energy landscape 6., 7. have, in recent years, allowed a very fruitful collaboration between theory and experiment in the study of folding. This review focuses on recent achievements of this collaboration. We wish to highlight what can be learned from the simplest models and leave others to review the results of highly detailed all-atom simulation.

Section snippets

Basic concepts

The locations of atoms in proteins can be determined, in favorable cases, to an accuracy of less than 3 Å using X-ray crystallography. This specificity of structure arises from the heterogeneity of the protein chain. The differing energies associated with positioning different residues near or far from each other or from solvent enable some structures to be more stable than others. If a sequence is chosen at random, the specificity of structure is still small — a variety of globally different

Perfect funnel landscapes and common features of folding mechanisms

A funneled landscape is responsible for the robust ability of proteins to fold. However, a variety of detailed mechanisms may exist on a funneled energy landscape. For example, secondary structures may form before or after collapse, sidechains may order before or after the mainchain topology, one domain of a protein may fold before another. As no particular evolutionary advantage is apparent for any of these mechanisms, it is reasonable to expect to see examples of them all.

The global landscape

Beyond the perfect funnel: devilish details of folding mechanisms

Fine-level, sequence-dependent variations occur in the kinetics of proteins with the same topology. Laboratory folding studies also reveal a greater level of specificity and granularity than predicted by the simplest landscape-based theories using pairwise additive forces. Landscape theory also allows us to understand these devilish details.

Some deviations occur because frustration should be minimized for evolutionary reasons, but there are many reasons for some residual energetic frustration

Conclusions

The 20th century’s fixation on structure catapulted folding to center stage in molecular biology. The lessons learned about folding may, in the future, increase our understanding of many functional motions and large-scale assembly processes. The appreciation that folding physics plays a role in the allosteric function of proteins is likely to be a recurring theme in the coming years [63].

Acknowledgements

This work has been funded by the National Science Foundation (NSF)-sponsored Center for Theoretical Biological Physics (grants PHY-0216576 and 0225630), with additional support from the NSF to JNO (Grant MCB-0084797) and from the National Institutes of Health to PGW.

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