Articles
NN-PRED: A novel consensus secondary structure prediction program using neural networks
Published 2013-03-09
Keywords
- predicción de la estructura secundaria,
- estrategia consenso,
- redes neuronales
How to Cite
Bedoya Leiva, O. F., & Satizábal Tascón, E. A. (2013). NN-PRED: A novel consensus secondary structure prediction program using neural networks. Revista UIS Ingenierías, 12(1), 51–59. Retrieved from https://revistas.uis.edu.co/index.php/revistauisingenierias/article/view/3711
Abstract
In this paper, a new program for protein secondary structure prediction is proposed. The program, which is called NN-Pred, allows multiple sequences to be submitted and it returns predictions from fve secondary structure prediction algorithms. In addition, NN-Pred calculates a consensus prediction, which is based on a neural network strategy that is used in this paper to improve the prediction accuracy. NN-Pred was obtained by using a methodology called consensus strategy, which tries to make a better prediction by integrating some of the most remarkable existing techniques. The NN-Pred program provides a three-state (alpha-helix, beta-sheet, and other) prediction of secondary structure. According to the test sets, the prediction accuracy of NN-Pred is at least 70%, surpassing most of the existing methods. The experiments showed that neural networks can be used as a consensus strategy to producing accurate models for protein secondary prediction.
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References
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B. Matthews, “Comparison of the predicted and observed secondary structure of T4 phage lysozyme,” Biochimica Biophysica, vol. 405, 1975, pp. 442-451.
W. Kabsch and C. Sander, “Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features,” Biopolymers, vol. 22, 1983, pp. 2577-637.
V. Lim, “Structural principles of the globular organisation of protein chains. A stereochemical theory of globular protein secondary structure,” Journal of Molecular Biology, vol. 88, no. 1, 1974, pp. 857-872.
J. Garnier, D. Osguthorpe and B. Robson, “Analysis of the accuracy and implications of simple methods for predicting the secondary structure of globular proteins,” Journal of Molecular Biology, vol, 120, no 1, 1978, pp. 97-120.
B. Rost and C. Sander, “Improved prediction of protein secondary structure by use of sequence profiles and neuronal networks,” Proceedings of the National Academy of Science, vol. 90, no. 1, 1990, pp. 7558-7562.
B. Rost, “PHD: predicting one-dimensional protein structure by profile based neural networks,” Methods Enzimol, vol. 266, no. 1, 1996, pp. 525-539.
C. Cole, J. Barber and G. Barton, “The JPred 3 secondary structure prediction server,” Nucleic Acids Res, vol. 36, no. 1, 2008, pp. 197-201.
M. Osman, M. Abdullah and R. AbdulRashid, “RNA secondary structure prediction using dynamic programming algorithm - A review and proposed work,” Information Technology, vol. 2, 2010, pp. 551-556.
D. Mojie, Z. Yanhong, H. Huiyan, “A Protein Secondary Structure Prediction Tool Using Two-Level Strategy to Improve the Prediction Accuracy of Secondary Structures and Structure Boundaries,” Information Engineering and Computer Science, vol. 1, 2009, pp.1-4.
H. Tsang and K. Wiese, “SARNA-Predict: Accuracy Improvement of RNA Secondary Structure Prediction Using Permutation-Based Simulated Annealing,” Computational Biology and Bioinformatics, vol. 7, no. 4, 2010, pp. 727-740.
B. Tang, X. Wang and X. Wang, “Protein Secondary Structure Prediction Using Large Margin Methods,” Computer and Information Science, vol. 1, 2009, pp. 142-146.
D. Kneller, F. Cohen and R. Langridge, “Improvements in protein secondary structure prediction by an enhanced neural network,” Journal of Molecular Biology, vol. 214, no. 1, 1990, pp. 171-182.
D. Jones, “Protein secondary structure prediction based on position-specific scoring matrices,” Journal of Molecular Biology, vol. 292, 1999, pp. 195-202.
C. Geourjon and G. Deleage, “SOPM: a self-optimized method for protein secondary structure prediction,” Protein Eng, vol. 7, no. 2, 1994, pp. 157-164.
C. Geourjon and G. Deleage, “SOPMA: significant improvements in protein secondary structure prediction by consensus prediction from multiple alignments,” Comput. Appl. Biosci, vol. 1, no. 6, 1995, pp. 681-684.
D. Frishman and P. Argos, “75% accuracy in protein secondary structure prediction,” Proteins, vol. 27, 1997, pp. 329-335.
R. King and M. Sternberg, “Identification and application of the concepts important for accurate and reliable protein secondary structure prediction,” Protein Science, vol. 5, 1996, pp. 2298-2310.
A. Thomas and Y. Zheng, “Improved Prediction of HIV-1 Protease Genotypic Resistance Testing Assays using a Consensus Technique,” Neural Networks, vol. 1, 2006, pp. 2308-2314.
Y. Zhao and W. Zhengzhi, ”Consensus RNA Secondary Structure Prediction Based on SVMs,“ Bioinformatics and Biomedical Engineering, vol. 1, 2008, pp. 101-104.
C. Mazo and O. Bedoya, “PESPAD: una nueva herramienta para la predicción de la estructura secundaria de la proteína basada en árboles de decisión,” Ingeniería y Competitividad, vol. 12, no. 2, 2010, pp. 9-22.
Q. Wang, Y. Shang and D. Xu, “Protein structure selection based on consensus,” Evolutionary Computation, vol. 1, 2010, pp.1-7.
M. Siek, “Nonlinear multi-model ensemble prediction using dynamic Neural Network with incremental learning,” The 2011 International Joint Conference on Neural Networks (IJCNN), vol. 1, 2011, pp. 2873-2880.
Liyu Lin, Shuanqiang Yang, and Ruijuan Zuo, “Protein secondary structure prediction based on multi-SVM ensemble,” International Conference on Intelligent Control and Information Processing(ICICIP), vol. 1, 2010, pp. 356-358.
Bidargaddi, N., Chetty M., and Kamruzzaman, J, “An Architecture Combining Bayesian segmentation and Neural Network Ensembles for Protein Secondary Structure Prediction,” Proceedings of the 2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), vol. 1, 2005, pp. 1-8.
J. De Haan and J. Leunissen, “Protein Secondary Structure Prediction: Comparison of Ten Common Prediction Algorithms Using a Neural Network,” Nato Science Series Sub Series I Life and behavioural sciences, vol. 368, 2005, pp. 149-161.
J. Allen, M. Pertea and S. Salzberg, “Computational Gene Prediction Using Multiple Sources of Evidence,” Genome Res, vol. 14, 2004, pp. 142-148.
A. Lukashin and M. Bordovsky, “GeneMark.hmm: New solutions for gene finding,” Nucleic Acids Res, vol. 26, 1998, pp. 1107-1115.
M. Pertea and S. Salzberg, “Computational gene finding in plants,” Plant Mol. Biol, vol. 48, 2002, pp. 39-48.
C. Burge and S. Karlin, “Prediction of complete gene structures in human genomic DNA,” J. Mol. Biol, vol. 268, 1997, pp. 78-84.
J. Garnier, J. Gibrat and B. Robson, “GOR method for predicting protein secondary structure from amino acid sequence,” Methods in Enzymology, vol. 266, 1996, pp. 540-553.
J. Levin, “Exploring the limits of nearest neighbour secondary structure prediction,” Protein Engineering, vol. 7, 1997, pp. 771-776.
C. Combet, C. Blanchet, C. Geourjon and G. Deléage, “NPS@: Network Protein Sequence Analysis,“ TIBS, vol. 25, no. 3, 2000, pp. 147-150.
B. Matthews, “Comparison of the predicted and observed secondary structure of T4 phage lysozyme,” Biochimica Biophysica, vol. 405, 1975, pp. 442-451.
W. Kabsch and C. Sander, “Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features,” Biopolymers, vol. 22, 1983, pp. 2577-637.