Less is more: towards an optimal universal description of protein folds.

MOTIVATION: Identification and characterization of protein structure regularities can reveal the mechanisms governing protein structure, function and evolution. Here we focus on an intermediate level of regularity. We have developed automated methods to systematically construct a dictionary of supersecondary structures that can be used as 'protein parts' to describe fold-sized structures.
RESULTS: The dictionary was constructed by aligning representative structures of all known folds, clustering similar substructures and selecting the most descriptive substructures in a minimum description length fashion. We show that the dictionary is compact and descriptive, capable of describing a substantial fraction of all known protein folds. We performed simulations using independent sets of training and testing folds. Dictionaries generated using the training set had high coverage over the folds in the testing set, suggesting that dictionary entries reflect general features of protein structures and should be capable of describing novel protein folds.