期刊
COMPUTATIONAL BIOLOGY AND CHEMISTRY
卷 31, 期 5-6, 页码 373-377出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.compbiolchem.2007.06.002
关键词
amyloid fibril formation; secondary structure prediction; machine learning; artificial neural network; energy decomposition
资金
- NLM NIH HHS [2G08LM06230-03A1] Funding Source: Medline
- National Research Foundation of Korea [2005-003-C00158] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
The calculation of contact-dependent secondary structure propensity (CSSP) has been reported to sensitively detect non-native beta-strand propensities in the core sequences of amyloidogenic proteins. Here we describe a noble energy-based CSSP method implemented on dual artificial neural networks that rapidly and accurately estimate the potential for the non-native secondary structure formation in local regions of protein sequences. In this method, we attempted to quantify long-range interaction patterns in diverse secondary structures by potential energy calculations and decomposition on a pairwise per-residue basis. The calculated energy parameters and seven-residue sequence information were used as inputs for artificial neural networks (ANN's) to predict sequence potential for secondary structure conversion. The trained single ANN using the >(i, i +/- 4) interaction energy parameter exhibited 74% accuracy in predicting the secondary structure of test sequences in their native energy state, while the dual ANN-based predictor using (i, i +/- 4) and >(i, i +/- 4) interaction energies showed 83% prediction accuracy. The present method provides a simple and accurate tool for predicting sequence potential for secondary structure conversions without using 3D structural information. (C) 2007 Elsevier Ltd. All rights reserved.
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