NetTurnP--neural network prediction of beta-turns by use of evolutionary information and predicted protein sequence features.

NetTurnP--neural network prediction of beta-turns by use of evolutionary information and predicted protein sequence features.
复制标题

DOI:
10.1371/journal.pone.0015079
复制
发表时间:
2010-11-30
期刊:
影响因子:
3.7
通讯作者:
Petersen TN
Petersen TN
中科院分区:
综合性期刊3区
文献类型:
--
作者:
Petersen B;Lundegaard C;Petersen TN

文献摘要

参考文献

被引文献

相似文献

β-turns are the most common type of non-repetitive structures, and constitute on average 25% of the amino acids in proteins. The formation of β-turns plays an important role in protein folding, protein stability and molecular recognition processes. In this work we present the neural network method NetTurnP, for prediction of two-class β-turns and prediction of the individual β-turn types, by use of evolutionary information and predicted protein sequence features. It has been evaluated against a commonly used dataset BT426, and achieves a Matthews correlation coefficient of 0.50, which is the highest reported performance on a two-class prediction of β-turn and not-β-turn. Furthermore NetTurnP shows improved performance on some of the specific β-turn types. In the present work, neural network methods have been trained to predict β-turn or not and individual β-turn types from the primary amino acid sequence. The individual β-turn types I, I', II, II', VIII, VIa1, VIa2, VIba and IV have been predicted based on classifications by PROMOTIF, and the two-class prediction of β-turn or not is a superset comprised of all β-turn types. The performance is evaluated using a golden set of non-homologous sequences known as BT426. Our two-class prediction method achieves a performance of: MCC  = 0.50, Qtotal = 82.1%, sensitivity  = 75.6%, PPV  = 68.8% and AUC  = 0.864. We have compared our performance to eleven other prediction methods that obtain Matthews correlation coefficients in the range of 0.17 – 0.47. For the type specific β-turn predictions, only type I and II can be predicted with reasonable Matthews correlation coefficients, where we obtain performance values of 0.36 and 0.31, respectively. The NetTurnP method has been implemented as a webserver, which is freely available at http://www.cbs.dtu.dk/services/NetTurnP/. NetTurnP is the only available webserver that allows submission of multiple sequences.
DOI: 10.1093/bioinformatics/bth322
发表时间: 2004-11-01
期刊: BIOINFORMATICS
影响因子: 5.8
作者:
Kaur, H;Raghava, GPS
通讯作者: Raghava, GPS
DOI: 10.1093/bioinformatics/btg368
发表时间: 2004-01-01
期刊: BIOINFORMATICS
影响因子: 5.8
作者:
Kim, S
通讯作者: Kim, S
DOI: 10.1021/jm9811007
发表时间: 1999-03-25
影响因子: 7.3
作者:
Ettmayer, P;France, D;Bair, KW
通讯作者: Bair, KW
DOI: 10.1110/ps.0228903
发表时间: 2003-03-01
期刊: PROTEIN SCIENCE
影响因子: 8
作者:
Kaur, H;Raghava, GPS
通讯作者: Raghava, GPS
DOI: 10.1016/s0264-410x(99)00329-1
发表时间: 1999-09-23
期刊: VACCINE
影响因子: 5.5
作者:
Alix, AJP
通讯作者: Alix, AJP