Protein threading by learning

Preprint

10 October 2001

preprint
Published by arXiv in arXiv

https://doi.org/10.48550/arXiv.cond-mat/0110198

Abstract

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.

Keywords

TECHNIQUES BORROWED
STATISTICAL PHYSICS
PROTEIN THREADING
CHOSEN OPTIMALLY
COMPLETE SUCCESS
THREADING TESTS
NEURAL NETWORKS
ENSURE COMPLETE
GOOD STARTING

All Related Versions

Version 1, 2001-10-10, ArXiv
Published version: Proceedings of the National Academy of Sciences, 98 (25), 14350.

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