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| Title: | Evolution teaches neural networks to predict protein structure |
| Author: | Burkhard Rost |
| Quote: | In: John W Clark, Thomas Lindenau, Manfred L Ristig (eds.) 'Scientific Applications of Neural Nets' Springer: Heidelberg, 1999 (ISBN 3-540-65737-1), 207-223 |
In the wake of the genome data flow, we need - more urgently than
ever - accurate tools to predict protein structure. The problem
of predicting protein structure from sequence remains fundamentally
unsolved despite more than three decades of intensive research
effort. However, the wealth of evolutionary information deposited
in current databases enabled a significant improvement for methods
predicting protein structure in 1D: secondary structure, transmembrane
helices, and solvent accessibility. In particular, the combination
of evolutionary information with neural networks proved extremely
successful. The new generation of prediction methods proved to
be accurate and reliable enough to be useful in genome analysis,
and in experimental structure determination. Moreover, the new
generation of theoretical methods is increasingly influencing
experiments in molecular biology.
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