Detailed Information

Cited 0 time in webofscience Cited 13 time in scopus
Metadata Downloads

CSSP2: An improved method for predicting contact-dependent secondary structure propensity

Authors
Yoon, SukjoonWelsh, William J.Jung, HeeyoungDo 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
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
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.
Files in This Item
There are no files associated with this item.
Appears in
Collections
이과대학 > 생명시스템학부 > 1. Journal Articles

qrcode

Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.

Related Researcher

Researcher Yoon, Suk Joon photo

Yoon, Suk Joon
이과대학 (생명시스템학부)
Read more

Altmetrics

Total Views & Downloads

BROWSE