Why/How is Predictor@Home different from Folding@Home, both seem to be addressing the same objectives?
Protein structure prediction starts from a sequence of amino acids and attempts to predict the folded, functioning, form of the protein either a priori, i.e., in the absence of detailed structural knowledge, or by homology with other known, but not identical, proteins. In the case of the a priori folding, it is a blind search based on the sequence alone. Homology modeling first identifies other proteins of known structure with some level of sequence identity to the unknown structure and then constructs a prediction for the unknown protein by homology. Both approaches utilize multi-scale optimization techniques to identify the most favorable structural models and are highly amenable to distributed computing. Predictor@Home is the first project of this type to utilize distributed computing for structure prediction. Predicting the structure of an unknown protein is a critical problem in enabling structure-based drug design to treat new and existing diseases.
Protein folding studies and the characterization of the protein folding process are based on knowledge of the final folded protein structure (in Nature) and aims to understand the process of folding, beginning from an unfolded protein chain. The endpoint of these studies is a comparison between native protein (in nature). Analysis of the folding process too is a critical outcome allowing theories for protein folding to make direct connections to experimental measurements of this process. The Folding@Home project pioneered the use of distributed computing to study the folding process. Understanding the folding process is of significance in understanding the origin of diseases that arise from protein mis-folding, such as Alzheimer's disease and Mad-Cow disease.
Both approaches explore protein structure and folding, but with complementary aims.
