Bayesian Segmentation of Protein Secondary Structure

We present a novel method for predicting the secondary structure of a protein from its amino acid sequence. Most existing methods predict each position in turn based on a local window of residues, sliding this window along the length of the sequence. In contrast, we develop a probabilistic model of protein sequence/structure relationships in terms of structural segments, and formulate secondary structure prediction as a general Bayesian inference problem. A distinctive feature of our approach is the ability to develop explicit probabilistic models for -helices, -strands, and other classes of secondary structure, incorporating experimentally and empirically observed aspects of protein structure such as helical capping signals, side chain correlations, and segment length distributions. Our model is Markovian in the segments, permitting ef cient exact calculation of the posterior probability distribution over all possible segmentations of the sequence using dynamic programming. The optim...

• Type: Journal
• Year: 2000
• Where: JCB
• Authors: Scott C. Schmidler, Jun S. Liu, Douglas L. Brutlag
• Keywords: JCB 2000, Protein, Secondary Structure, Secondary Structure Prediction