alpha-Helix region prediction with stochastic rule learning

We propose a new method, based on the theory of stochastic rule learning, for predicting alpha-helix regions in a given protein sequence. Our method (hereafter referred to as the SR method) produces stochastic rules, each of which assigns, to any region in an amino acid sequence, the probability that it is an alpha-helix region. When learning a stochastic rule from a particular alpha-helix region, our method makes use of positive training examples obtained from a number of regions that are homologous to that region. Each stochastic rule is optimized using the minimum description length (MDL) principle, and such optimized stochastic rules are used to predict alpha-helix regions of any given protein sequence. In our experiments, using 25 proteins selected from the HSSP database as training examples, we applied the SR method to the problem of predicting alpha-helix regions in test examples, which consisted of > 5000 residues with 38% alpha-helix content. Each of these test examples possesses < 25% homology to any proteins in the training and other test examples. Our method achieved 81% average prediction accuracy for the test examples; this compares favorably to Qian and Sejnowski's method, which attains no more than 75% average accuracy, and further which compares to Rost and Sander's method which has proven to be one of the best secondary structure prediction methods.