Agnostic classification of Markovian sequences
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
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We describe a novel method for detecting the domain structure of a protein from sequence information alone. The method is based on analyzing multiple sequence alignments that are derived from a database search. Multiple measures are defined to quantify the domain information content of each position along the sequence, and are combined into a single predictor using a neural network. The output is further smoothed and post-processed using a probabilistic model to predict the most likely transition or boundary positions between domains. The method was assessed using the domain definitions in SCOP for proteins of known structures and was compared to several other existing methods. Our method improves significantly over the best method available, the semi-manual PFam domain database, while being fully automatic. Our method can also be used to verify domain partitions based on structural data. Few examples of predicted domain definitions and alternative partitions, as suggested by our method, are also discussed.