An iterative method for improved protein structural motif recognition

Abstract

We present an iterative algorithm that uses randomness and statistical techniques to improve existing methods for recognizing protein structural motifs. Our algorithm is particularly effective in situations where large numbers of sufficiently diverse 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 LearnCoil 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.