Use of adaptive networks to define highly predictable protein secondary-structure classes

We present an adaptive, neural network method that determinesnew classes of protein secondary structure that are significantly more predictable from local amino-acid sequence than conventional classifications. Accurate prediction of the conventional secondary-structure classes, alpha-helix, beta-strand, and coil, from primary sequence has long been an important problem in computational molecular biology, with many ramifications, including multiple-sequence alignment, prediction of functionally important regions of proteins, and prediction of tertiary structure from primary sequence. The algorithm presented here uses adaptive networks to simultaneously examine both sequence and structure data, as available from, for example, the Brookhaven Protein Database, and to determine new secondary-structure classes that can be predicted from sequence with high accuracy. These new classes have both similarities to, and differences from, conventional secondary-structure classes. They represent a new, nontrivial classification of protein secondary structure that is predictable from primary sequence.

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