Motif Discovery in Protein Structure Databases

This chapter overviews the topic of protein motif discovery. It presents current approaches to knowledge discovery, focusing on their applications to the protein domain. In general, a motif is considered an abstraction over a set of recurring patterns in a dataset. Although we are primarily concerned with protein structure motifs, the chapter also considers sequence motifs and combinations of sequence/structure motifs. The research described is motivated by our need to organize and understand the rapidly growing protein databases. Discovered motifs are also useful in automating the process of structure determination from crystallographic databases. The eld of knowledge discovery is concerned with the theory and processes involved in the representation and extraction of patterns or motifs from large databases. Discovered patterns can be used to group data into meaningful classes, to summarize data or to reveal deviant entries. Motifs stored in a database can be brought to bear on di cult instances of structure prediction or determination from X-ray crystallography or NMR experiments. The need for automated discovery techniques is central to the understanding and analysis of the rapidly expanding repositories of protein sequence and structure data. This chapter deals with the discovery of protein structure motifs. A motif is an abstraction over a set of recurring patterns observed in a dataset; it captures the essential features shared by a set of similar or related objects. In many domains, such as computer vision and speech recognition, there exist special regularities that permit such motif abstraction. In the protein science domain, the regularities derive from evolutionary and biophysical constraints on amino acid sequences and structures. The identi cation of a known pattern in a new protein sequence or structure permits the immediate retrieval and application of knowledge obtained from the analysis of other proteins. The discovery and manipulation of motifs | in DNA, RNA, and protein sequences and structures | is thus an important component of computational molecular biology and genome informatics. In particular, identifying protein structure classi cations at varying levels of abstraction allows us to organize and increase our understanding of the rapidly growing protein structure datasets. Discovered motifs are also useful for improving the e ciency and e ectiveness of X-ray crystallographic study of proteins, for drug design, for understanding protein evolution, and ultimately for predicting the structure of proteins from sequence data. Motifs may be designed by hand, based on expert knowledge. For example, the ChouFasman protein secondary structure prediction program (1978), which dominated the eld for many years, depended on the recognition of pre-de ned, user-encoded sequence motifs for -helices and -sheets. Several hundred sequence motifs have been catalogued in Prosite (Bairoch 1991); the identi cation of one of these motifs in a novel protein often allows for From Glasgow, J.I. , Steeg, E. and Fortier, S. , \Motif Discovery in Protein Structure Database," in \Pattern Discovery in Molecular Biology: Tools, Techniques and Applications," by Jason Wang, Bruce Shapiro and Dennis Sha sha (Eds.). Reprinted by permission of Oxford University Press, Inc.