Abstract
The calculation of contact-dependent secondary structure propensity (CSSP) has been reported to sensitively detect non-native β-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 (ANNs) 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.
| Original language | English |
|---|---|
| Pages (from-to) | 373-377 |
| Number of pages | 5 |
| Journal | Computational Biology and Chemistry |
| Volume | 31 |
| Issue number | 5-6 |
| DOIs | |
| Publication status | Published - 2007 Oct 1 |
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Keywords
- Amyloid fibril formation
- Artificial neural network
- Energy decomposition
- Machine learning
- Secondary structure prediction
ASJC Scopus subject areas
- Biochemistry
- Structural Biology
- Analytical Chemistry
- Physical and Theoretical Chemistry
Cite this
CSSP2 : An improved method for predicting contact-dependent secondary structure propensity. / Yoon, Sukjoon; Welsh, William J.; Jung, Heeyoung; Yoo, Young Do.
In: Computational Biology and Chemistry, Vol. 31, No. 5-6, 01.10.2007, p. 373-377.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - CSSP2
T2 - An improved method for predicting contact-dependent secondary structure propensity
AU - Yoon, Sukjoon
AU - Welsh, William J.
AU - Jung, Heeyoung
AU - Yoo, Young Do
PY - 2007/10/1
Y1 - 2007/10/1
N2 - The calculation of contact-dependent secondary structure propensity (CSSP) has been reported to sensitively detect non-native β-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 (ANNs) 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.
AB - The calculation of contact-dependent secondary structure propensity (CSSP) has been reported to sensitively detect non-native β-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 (ANNs) 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.
KW - Amyloid fibril formation
KW - Artificial neural network
KW - Energy decomposition
KW - Machine learning
KW - Secondary structure prediction
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U2 - 10.1016/j.compbiolchem.2007.06.002
DO - 10.1016/j.compbiolchem.2007.06.002
M3 - Article
C2 - 17644485
AN - SCOPUS:35148821507
VL - 31
SP - 373
EP - 377
JO - Computational Biology and Chemistry
JF - Computational Biology and Chemistry
SN - 1476-9271
IS - 5-6
ER -