CSSP2: An improved method for predicting contact-dependent secondary structure propensity
- Authors
- Yoon, Sukjoon; Welsh, William J.; Jung, Heeyoung; Do Yoo, Young
- Issue Date
- Oct-2007
- Publisher
- ELSEVIER SCI LTD
- Keywords
- amyloid fibril formation; secondary structure prediction; machine learning; artificial neural network; energy decomposition
- Citation
- COMPUTATIONAL BIOLOGY AND CHEMISTRY, v.31, no.5-6, pp 373 - 377
- Pages
- 5
- Journal Title
- COMPUTATIONAL BIOLOGY AND CHEMISTRY
- Volume
- 31
- Number
- 5-6
- Start Page
- 373
- End Page
- 377
- URI
- https://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/14631
- DOI
- 10.1016/j.compbiolchem.2007.06.002
- ISSN
- 1476-9271
1476-928X
- Abstract
- 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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