Computational modeling of the relationship between amyloid and disease

Abstract

Amyloid is the name given to a special type of linear protein aggregate that exhibits a common set of structural features and dye binding capabilities. The formation of amyloid is associated with over 27 distinct human diseases which are collectively referred to as the amyloidoses. Although there is great diversity amongst the amyloidoses with regard to the polypeptide monomeric precursor, targeted tissues, and the nature and time course of disease development, the common underlying link of a structurally similar amyloid aggregate has prompted the search for a unified theory of disease progression in which amyloid production is the central element. Computational modeling has allowed the formulation and testing of scientific hypotheses for exploring this relationship. However, the majority of computational studies on amyloid aggregation are pitched at the atomistic level of description, in simple ideal solution environments, with simulation time scales of the order of microseconds and system sizes limited to 100 monomers (or fewer). The experimental reality is that disease-related amyloid aggregation processes occur in extremely complex reaction environments (i. e. the human body), over time scales of months to years with monitoring of the reaction achieved using extremely coarse or indirect experimental markers that yield little or no atomistic insight. Clearly, a substantial gap exists between computational and experimental communities with a deficit of 'useful' computational methodology that can be directly related to available markers of disease progression. This review will place its focus on the development of these latter types of computational models and discuss them in relation to disease onset and progression. © 2012 Springer-Verlag (outside the USA).