Identification of Protein Motifs Using Conserved Amino Acid Properties and Partitioning Techniques

Proceedings of the Twentieth International Conference on Machine Learning, 1995

Volume

Proceedings of the Twentieth International Conference on Machine Learning, 1995

Contents

Analyzing a set of protein sequences involves a fundamental relationship between the coherency of the set and the specificity of the motif that describes it. Motifs may be obscured by training sets that contain incoherent sequences, in part due to protein subclasses, contamination, or errors. We develop an algorithm for motif identification that systematically explores possible patterns of coherency within a set of protein sequences. Our algorithm constructs alternative partitions of the training set data, where one subset of each partition is presumed to contain coherent data and is used for forming a motif. The motif is represented by multiple overlapping amino acid groups based on evolutionary, biochemical, or physical properties. We demonstrate our method on a training set of reverse transcriptases that contains subclasses, sequence errors, misalignments, and contaminating sequences. Despite these complications, our program identifies a novel motif for the subclass of retroviral and retrovirus-related reverse transcriptases. This motif has a much higher specificity than previously reported motifs and suggests the importance of conserved hydrophilic and hydrophobic residues in the structure of reverse transcriptases.