Outline and computational approaches of protein misfolding.

Protein misfolding is a general causation of classical conformational diseases and many pathogenic changes that are the result of structural conversion. Here I review recent progress in clinical and computational approaches for each stage of the misfolding process, aiming to present readers an outline for swift comprehension of this field.

Simulated pathogenic conformational switch regions matched well with the biochemical findings

Pathogenic conformational conversion is a general causation of many disease, such as transmissible spon- giform encephalopathy (TSE) caused by misfolding of prion, sickle cell anemia, and etc. In such structural changes, misfolding occurs in regions important for the stability of native structure firstly. This destabi- lizes the normal conformation and leads to subsequent errors in folding pathway. Sites involved in the first stage can be deemed switch regions of the protein, and are vital for conformational conversion. Namely it could be a switch of disease at residue level. Here we report an algorithm that can identify such sites computationally with an accuracy of 93%, by calculating the probability of the native structure of a short segment jumping to a mistake one. Knowledge of such switch sites could be used to target clinical therapy, study physiological and pathologic mechanism of protein, and etc.

Switch Region for Pathogenic Structural Change in Conformational Disease and Its Prediction

Many diseases are believed to be related to abnormal protein folding. In the first step of such pathogenic structural changes, misfolding occurs in regions important for the stability of the native structure. This destabilizes the normal protein conformation, while exposing the previously hidden aggregation-prone regions, leading to subsequent errors in the folding pathway. Sites involved in this first stage can be deemed switch regions of the protein, and can represent perfect binding targets for drugs to block the abnormal folding pathway and prevent pathogenic conformational changes. In this study, a prediction algorithm for the switch regions responsible for the start of pathogenic structural changes is introduced. With an accuracy of 94%, this algorithm can successfully find short segments covering sites significant in triggering conformational diseases (CDs) and is the first that can predict switch regions for various CDs. To illustrate its effectiveness in dealing with urgent public health problems, the reason of the increased pathogenicity of H5N1 influenza virus is analyzed; the mechanisms of the pandemic swine-origin 2009 A(H1N1) influenza virus in overcoming species barriers and in infecting large number of potential patients are also suggested. It is shown that the algorithm is a potential tool useful in the study of the pathology of CDs because: (1) it can identify the origin of pathogenic structural conversion with high sensitivity and specificity, and (2) it provides an ideal target for clinical treatment.