Statistical Inference of Protein "LEGO Bricks"

Proteins are biomolecules of life. They fold into a great variety of three-dimensional (3D) shapes. Underlying these folding patterns are many recurrent structural fragments or building blocks (analogous to 'LEGO® bricks'). This paper reports an innovative statistical inference approach to discover a comprehensive dictionary of protein structural building blocks from a large corpus of experimentally determined protein structures. Our approach is built on the Bayesian and information theoretic criterion of minimum message length. To the best of our knowledge, this work is the first systematic and rigorous treatment of a very important data mining problem that arises in the cross-disciplinary area of structural bioinformatics. The quality of the dictionary we find is demonstrated by its explanatory power - any protein within the corpus of known 3D structures can be dissected into successive regions assigned to fragments from this dictionary. This induces a novel one-dimensional representation of three-dimensional protein folding patterns, suitable for application of the rich repertoire of character-string processing algorithms, for rapid identification of folding patterns of newly determined structures. This paper presents the details of the methodology used to infer the dictionary of building blocks, and is supported by illustrative examples to demonstrate its effectiveness and utility.