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
N1 - Funding Information:
This work was supported by Korea Research Foundation Grant funded by Korea Government (MOEHRD, Basic Research Promotion Fund) (KRF-2005-003-C00158). This work was also supported by the SRC/ERC program of MOST/KOSEF (R11-2005-017-01003-0) and by grant No.R01-2006-000-10515-0 from the Basic Research Program of the Korea Science & Engineering Foundation. This research was also supported in part by NIH Integrated Advanced Information Management Systems (IAIMS) Grant # 2G08LM06230-03A1 from the National Library of Medicine (to W.J.W.).
PY - 2007/10
Y1 - 2007/10
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
UR - http://www.scopus.com/inward/record.url?scp=35148821507&partnerID=8YFLogxK
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 -