Abstract
In this paper we propose several sets of new features for protein fold prediction. The first feature set consisting of 47 features uses only the sequence information. We also define four different sets of features based on hydrophobicity of amino acids. Each such set has 400 features which are motivated by folding energy modeling. To define these features we have considered pair-wise amino acids (AA) interaction potential. The effectiveness of the proposed feature sets is tested using multilayer perceptron and radial basis function networks to solve the 4 class (level 1) and 27 class (level 2) prediction problems as defined in the context of SCOP classification. Our investigation shows that such features have good discriminating powers in predicting protein folds.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
I. Dubchak, I. Muchnik, C. Mayor, I. Dralyuk and S-H Kim, “Recognition of a Protein Fold in the context of the SCOP Classification. PROTEINS: Structure, Function and Genetics, vol. 35, pp. 401–407, 1999.
P. Baldi and S. Brunak, Bioinformatics: the Machine Learning Approach, MIT Press, 1998.
I. Dubchak, I. Muchnik, S. R. Holbrook, and S. H. Kim, “Prediction of protein folding class using global description of amino acid sequence”, Proc. Natl. Acad. Sci., USA, Vol. 92, pp. 8700–8704, 1995.
I. Dubchak and C. H. Q. Ding, “Multi-class protein fold recognition using support vector machines and neural networks,” Bioinformatics, Vol. 17,No. 4, pp. 349–358, 2001.
Antônio F. Pereira de Araújo, “Folding protein models with simple hydrophobic energy function: the fundamenta importanve of monomer inside/outside segregation”, Proc. Natl. Acad. Sci., USA, vol 96,no 22, pp. 12482–12487.
A. G. Murzin, S. E. Brenner, T. Hubbard, and C. Chothia, “SCOP: A structural classification of proteins database for the investigation of sequence and structures. Journal of Molecular Biology, vol. 247, pp. 536–540, 1995.
I-Fang Chung, Chuen-Der Huang, Ya-Hsin Shen and Chin-Teng Lin, “Recognition of Structure Classification of Protein Folding by NN and SVM Hierarchical Learning Architecture, Proceedings of ICONIP 2003.
N.R. Pal and K.K. Chintalapudi, “A connectionist system for feature selection”, Neural, Parallel & Scientific Computations, vol 5.No. 3, 359–381, 1997.
D. Chakraborty and Nikhil R. Pal, “Integrated feature analysis and fuzzy rulebased system identification in a neuro-fuzzy paradigm”, IEEE Trans. on Systems Man Cybernetics B, vol 31,no 3, pp. 391–400, 2001.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Pal, N.R., Chakraborty, D. (2003). Some New Features for Protein Fold Prediction. In: Kaynak, O., Alpaydin, E., Oja, E., Xu, L. (eds) Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003. ICANN ICONIP 2003 2003. Lecture Notes in Computer Science, vol 2714. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44989-2_140
Download citation
DOI: https://doi.org/10.1007/3-540-44989-2_140
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40408-8
Online ISBN: 978-3-540-44989-8
eBook Packages: Springer Book Archive