Intrinsically Disordered Proteins: Where Computation Meets Experiment

Abstract

Proteins are heteropolymers that play important roles in virtually every biological reaction. While many proteins have well-defined three-dimensional structures that are inextricably coupled to their function, intrinsically disordered proteins (IDPs) do not have a well-defined structure, and it is this lack of structure that facilitates their function. As many IDPs are involved in essential cellular processes, various diseases have been linked to their malfunction, thereby making them important drug targets. In this review we discuss methods for studying IDPs and provide examples of how computational methods can improve our understanding of IDPs. We focus on two intensely studied IDPs that have been implicated in very different pathologic pathways. The first, p53, has been linked to over 50% of human cancers, and the second, Amyloid-β (Aβ), forms neurotoxic aggregates in the brains of patients with Alzheimer's disease. We use these representative proteins to illustrate some of the challenges associated with studying IDPs and demonstrate how computational tools can be fruitfully applied to arrive at a more comprehensive understanding of these fascinating heteropolymers. OPEN ACCESSPolymers 2014, 6 2685

p53 Proteoforms and Intrinsic Disorder: An Illustration of the Protein Structure–Function Continuum Concept

“…It was also emphasized that functional conformational changes and the allosteric behavior of globular proteins can rely on the existence of conformational substates, which can be described as the atomic displacements leading to the formation and interconversion of different local configurations of the same overall protein structure [69,70,71,72,73,74,75]. This idea is illustrated by Figure 2, which represents the potential energy landscapes of ordered and disordered proteins [76]. The energy landscape of ordered proteins is characterized by a specific funnel-like shape, where a broad mouth at the top represents a set of unfolded conformations and the narrow end at the bottom shows the lowest energy state that corresponds to the native structure [77,78,79,80,81].…”

“…The energy landscape of ordered proteins is characterized by a specific funnel-like shape, where a broad mouth at the top represents a set of unfolded conformations and the narrow end at the bottom shows the lowest energy state that corresponds to the native structure [77,78,79,80,81]. On the contrary, IDPs are characterized by a relative flat but rough energy landscape with multiple local energy minima separated by small barriers [14,33,76,82]. Finally, careful analysis of the bottom of the funnel-shaped energy landscape revealed that, for many proteins, the surface of the energy minimum is actually not smooth, being rough because of the presence of many smaller minima corresponding to different states sampled by the protein (see Figure 2).…”

“…Finally, careful analysis of the bottom of the funnel-shaped energy landscape revealed that, for many proteins, the surface of the energy minimum is actually not smooth, being rough because of the presence of many smaller minima corresponding to different states sampled by the protein (see Figure 2). This is definitely the case for so-called hybrid proteins containing ordered domains and IDPRs and even for “normal” ordered proteins whose structures have been solved via X-ray crystallography or by nuclear magnetic resonance (NMR) and which are considered to be folded, but still often contain both ordered regions and intrinsically disordered regions lacking a stable tertiary structure [76,83]. …”

Expanding the Range of Protein Function at the Far End of the Order-Structure Continuum

“…By contrast, crystallizing a disordered protein is not possible precisely because the process of crystallization requires the protein in question to adopt similar structures within the crystal environment (20 -22). Structural modeling has therefore played an essential role in the study of very disordered systems (20,23,24). Dynamical simulations, for example, combined with restraints derived from NMR and/or SAXS experiments can be used to model important structural features of these proteins (21,25,26).…”

Flexibility of the “rigid” classics or rugged bottom of the folding funnels of myoglobin, lysozyme, RNase A, chymotrypsin, cytochrome c, and carboxypeptidase A1

“…1 schematically representing the potential energy landscape of an ordered protein. 45 Although such landscape is traditionally considered as a folding funnel with a large set of unfolded conformations constituting a broad mouth at the top of the funnel, and with the lowest energy state corresponding to the native structure being located at the narrow end at the bottom of funnel, [46][47][48][49][50] careful analysis revealed that the surface of the bottom of the funnel for many globular proteins is actually not smooth, being rough or rugged, reflecting the presence of many smaller minima corresponding to different substates sampled by the a protein (see Fig. 1).…”