We present a learning algorithm that improves existing methods for recognizing protein structural motifs. Our algorithm is an iterative method that exploits randomness and statistical techniques to obtain good performance. Our algorithm is particularly effective in situations where large numbers of examples of the motif are not known. These are precisely the situations that pose significant difficulties for previously known methods.
We have implemented our algorithm and we demonstrate its performance on the coiled coil motif. We test our program Learn-Coil on the domain of 3-stranded coiled coils and subclasses of 2-stranded coiled coils. We show empirically that for these motifs, our method overcomes the problem of limited data.
(Joint work with Bonnie Berger.)
11/28: Fred Hughson, Princeton, Chemistry, On protein structure. 12/5: Doug Deutschman, Cornell, Ecology, Max likelihood models of forest ecology. 12/12: Alex Schaffer, NIH
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