Abstract
In this paper we present a new Neural-Network-based predictor trained and tested on a set of well annotated proteins to tackle the problem of predicting the signal peptide in protein sequences. The method trained on a set of experimentally derived signal peptides from Eukaryotes and Prokaryotes, identifies the presence of the sorting signal and predicts their cleavage sites. The accuracy in cross-validation is comparable with previously presented programs reaching the 97%, 96% and 95% for Gram negative, Gram positive and Eukaryotes, respectively.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
von Heijne, G.: A new method for predicting signal sequence cleavage sites. Nucleic Acids Research 14, 4683–4690 (1986)
Vert, J.P.: Support vector machine prediction of signal peptide cleavage site using a new class of kernels for strings. Pac. Symp. Biocomput., 649–660 (2002)
Nielsen, H., Krogh, A.: Prediction of signal peptides and signal anchors by a hidden Markov model. In: Proceedings of the Sixth International Conference on Intelligent Systems for Molecular Biology, pp. 122–130. AAAI Press, Menlo Park (1998)
Nielsen, H., Brunak, S., von Heijne, G.: Machine learning approaches for the prediction of signal peptides and other protein sorting signals. Protein Eng. 12, 3–9 (1999)
Menne, K.M.L., Hermjakob, H., Apweiler, R.: A comparison of signal sequence prediction methods using a test set of signal peptides. Bioinformatics 16, 741–742 (2000)
Baldi, P., Brunak, S.: Bioinformatics: the Machine Learning Approach
Altschul, S.F., Madden, T.L., Schaffer, A.A., Zhang, J., Zhang, Z., Miller, W., Lipman, D.J.: Gapped BLAST and PSI-BLAST: A new generation of protein database search programs. Nucleic Acid. Res. 25, 3389–3402 (1997)
Kyte, J., Doolittle, R.F.: A simple method for displaying the hydropathic character of a protein. J. Mol. Biol. 157(1982), 105–132 (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
Fariselli, P., Finocchiaro, G., Casadio, R. (2003). Prediction of Signal Peptide in Proteins with Neural Networks. In: Apolloni, B., Marinaro, M., Tagliaferri, R. (eds) Neural Nets. WIRN 2003. Lecture Notes in Computer Science, vol 2859. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45216-4_27
Download citation
DOI: https://doi.org/10.1007/978-3-540-45216-4_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-20227-1
Online ISBN: 978-3-540-45216-4
eBook Packages: Springer Book Archive