Protein threading by learning.

  title={Protein threading by learning.},
  author={I Chang and Marek Cieplak and Ruxandra I. Dima and Amos Maritan and Jayanth R. Banavar},
  journal={Proceedings of the National Academy of Sciences of the United States of America},
  volume={98 25},
By using techniques borrowed from statistical physics and neural networks, we determine the parameters, associated with a scoring function, that are chosen optimally to ensure complete success in threading tests in a training set of proteins. These parameters provide a quantitative measure of the propensities of amino acids to be buried or exposed and to be in a given secondary structure and are a good starting point for solving both the threading and design problems. 

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