Proteins rely on three-dimensional structure for function, yet many proteins are marginally stable and prone to misfolding. In this issue of Structure, Brock et al. (2015) present a novel computational modeling method to gain insights into protein stability and misfolding. To function correctly, most proteins must adopt a proper three-dimensional structure that allows them to operate with high precision and fidelity. However, protein synthesis is not free of errors, and nascent proteins can initially misfold into defective structures. Even when a nascent protein folds properly, post-synthesis exposure to chemical or physical stresses can damage the functional structure and cause misfolding. The stochastic generation of misfolded proteins is a fundamental problem that all cells continuously face. If left unattended, misfolded proteins can form toxic aggregates that can ultimately lead to cell death. This dire consequence is underscored by the more than 50 human maladies causally linked with aggregation, which include devastating neurodegenerative disorders such as Alzheimer's, Parkinson's, Huntington's, and ALS Studying what features misfolded proteins present to PQC degradation systems in their abnormal state is a very difficult task. Misfolded proteins are highly recalcitrant to the biochemical and biophysical techniques typically used to probe the structural features of normally folded proteins. Just ask any protein biochemist-the bane of their existence is the aggregation that can occur during the purification of proteins for structural analyses. Yet, aggregation is the ''normal'' behavior of misfolded proteins that is important to understand for PQC biologists. An additional complicating feature is that when a pool of protein misfolds, different misfolded conformations will likely exist within the pool. This presents a considerable problem for biochemists using structural analyses that rely on a single uniform conformation within a protein pool. Given the difficulty of working with misfolded proteins by traditional means, it is useful to think outside the typical experimental toolkit used for probing the structure of normal proteins. Computational methods to predict protein structure have a long history; the Ramachandran plot is still used for theoretical models and validations of structure In this issue of Structure, The authors tested their method with the von Hippel-Lindau protein (VHL), which has been used as a model substrate for PQC because it is prone to misfolding when it does not interact with its partner proteins Structure 23, July 7
