Progress in Protein Structural Class Prediction and its Impact to Bioinformatics and Proteomics
Affiliation: Gordon Life Science Institute, 13784 Torrey Del Mar Drive, San Diego, California 92130, USA.
The structural class is an important attribute used to characterize the overall folding type of a protein or its domain. Since the concept of protein structural class was developed about 3 decades ago based on a visual inspection of polypeptide chain topologies in a dataset of only 31 gloular proteins, the number of structure-known proteins has been increased rapidly. For example, as of 12-July-2005, the entries deposited into RCSB PDB Protein Data Bank for proteins, peptides, and viruses whose 3-dimensional structures were determined by X-ray and NMR techniques have been increased to 28,920. To properly cover more and more structure-known proteins, some modification and expansion from the original structural classification scheme have been developed. Meanwhile, many different approaches have been proposed for predicting the structural class of proteins. In this review, the new classification schemes are briefly introduced. The attention is focused on the progress in structural class prediction and its impact in stimulating the development of identifying the other attributes of proteins. It is interesting to point out that the development of the latter has actually in turn greatly enriched the power of the former. Also, some promising approaches for the further development of protein structural class prediction are also addressed.
Keywords: all-a, all-b, a/b, m (multi-domain), s (small protein), r (peptide), pseudo amino acid composition, functional domain composition, fund-pseaa predictor
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