Template-Directed Protein Misfolding in Silico and in the Cell

Abstract

An energy landscape theory and corresponding algorithm has been constructed to predict the mechanism of protein misfolding, in environments mimicking those in the cell (see accompanying graphic). Energetic and entropic terms, as well as solvation free energy, must be accurately accounted for in this physicochemical rather than bioinformatic approach. The algorithm is capable of predicting regions of protein that are thermodynamically prone to misfolding, how mutations or altered cellular environment can affect the stability of these regions, and how rationally designed small molecules or antibody therapies can block misfolding pathways. Antibodies prepared against free peptides that mimic these regions may be experimentally tested for selective affinity in vitro, and these epitopes can be validated in animal models of ALS. Mechanical stability as probed in silico using adaptively-biased force simulations support the energy landscape predictions, and through stability and metal affinity measurements, further show a remarkable ability to predict the lifetime of ALS patients once neurodegenerative symptoms have been diagnosed.View Large Image | View Hi-Res Image | Download PowerPoint Slide