Artificial neural networks for prediction of mycobacterial promoter sequences

被引:30
作者
Kalate, RN
Tambe, SS
Kulkarni, BD
机构
[1] Keio Univ, Dept Biosci & Informat, Kohoku Ku, Yokohama, Kanagawa 2238522, Japan
[2] Natl Chem Lab, Div Chem Engn, Pune 411008, Maharashtra, India
关键词
mycobacterial promoters; error-back-propagation algorithm; caliper randomization approach;
D O I
10.1016/j.compbiolchem.2003.09.004
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
A multilayered feed-forward ANN architecture trained using the error-back-propagation (EBP) algorithm has been developed for predicting whether a given nucleotide sequence is a mycobacterial promoter sequence. Owing to the high prediction capability (congruent to97%) of the developed network model, it has been further used in conjunction with the caliper randomization (CR) approach for determining the structurally/functionally important regions in the promoter sequences. The results obtained thereby indicate that: (i) upstream region of -35 box, (ii) -35 region, (iii) spacer region and, (iv) -10 box, are important for mycobacterial promoters. The CR approach also suggests that the -38 to -29 region plays a significant role in determining whether a given sequence is a mycobacterial promoter. In essence, the present study establishes ANNs as a tool for predicting mycobacterial promoter sequences and determining structurally/functionally important sub-regions therein. (C) 2003 Elsevier Ltd. All rights reserved.
引用
收藏
页码:555 / 564
页数:10
相关论文
共 34 条
[1]   Identification of Mycobacterium paratuberculosis gene expression signals [J].
Bannantine, JP ;
Barletta, RG ;
Thoen, CO ;
Andrews, RE .
MICROBIOLOGY-UK, 1997, 143 :921-928
[2]   A study of the mycobacterial transcriptional apparatus: Identification of novel features in promoter elements [J].
Bashyam, MD ;
Kaushal, D ;
Dasgupta, SK ;
Tyagi, AK .
JOURNAL OF BACTERIOLOGY, 1996, 178 (16) :4847-4853
[3]   IDENTIFICATION OF RIBOSOME BINDING-SITES IN ESCHERICHIA-COLI USING NEURAL-NETWORK MODELS [J].
BISANT, D ;
MAIZEL, J .
NUCLEIC ACIDS RESEARCH, 1995, 23 (09) :1632-1639
[4]   NEURAL NETWORK OPTIMIZATION FOR ESCHERICHIA-COLI PROMOTER PREDICTION [J].
DEMELER, B ;
ZHOU, GW .
NUCLEIC ACIDS RESEARCH, 1991, 19 (07) :1593-1599
[5]  
Freeman J., 1992, NEURAL NETWORKS ALGO
[6]   NEURAL NETWORKS IN CHEMISTRY [J].
GASTEIGER, J ;
ZUPAN, J .
ANGEWANDTE CHEMIE-INTERNATIONAL EDITION IN ENGLISH, 1993, 32 (04) :503-527
[7]   RATE OF RIBONUCLEIC-ACID CHAIN GROWTH IN MYCOBACTERIUM-TUBERCULOSIS H37RV [J].
HARSHEY, RM ;
RAMAKRISHNAN, T .
JOURNAL OF BACTERIOLOGY, 1977, 129 (02) :616-622
[8]   Analysis of DNA curvature distribution in mycobacterial promoters using theoretical models [J].
Kalate, RN ;
Kulkarni, BD ;
Nagaraja, V .
BIOPHYSICAL CHEMISTRY, 2002, 99 (01) :77-97
[9]   ANALYSIS OF THE MYCOBACTERIUM-TUBERCULOSIS 85A ANTIGEN PROMOTER REGION [J].
KREMER, L ;
BAULARD, A ;
ESTAQUIER, J ;
CONTENT, J ;
CAPRON, A ;
LOCHT, C .
JOURNAL OF BACTERIOLOGY, 1995, 177 (03) :642-653
[10]   NEURAL NETWORK MODELS FOR PROMOTER RECOGNITION [J].
LUKASHIN, AV ;
ANSHELEVICH, VV ;
AMIRIKYAN, BR ;
GRAGEROV, AI ;
FRANKKAMENETSKII, MD .
JOURNAL OF BIOMOLECULAR STRUCTURE & DYNAMICS, 1989, 6 (06) :1123-1133