Abstract
Post-translational modification is the attachment of biochemical functional groups after translation from mRNA. Among the different post translational modifications, phosphorylation happens to be one of the most important types which is responsible for important cellular operations. In this research work, we have used multilayer perceptron (MLP) to predict protein residues which are phosphorylated. As features, we have used position-specific scoring matrices (PSSM) generated by PSI-BLAST algorithm for each protein sequence after three runs against 90 % redundancy reduced Uniprot database. For an independent set of 141 proteins, our system was able to provide the best AUC score for 36 proteins, highest for any other predictor. Our system achieved an AUC score of 0.7239 for all the protein sequences combined, which is comparable to the state-of-the art predictors.
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Acknowledgments
Authors are indebted to CMATER, department of computer science and Engineering, Jadavpur University for providing the necessary support for carrying out this experiment.
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Banerjee, S., Ghosh, D., Basu, S., Nasipuri, M. (2016). JUPred_MLP: Prediction of Phosphorylation Sites Using a Consensus of MLP Classifiers. In: Das, S., Pal, T., Kar, S., Satapathy, S., Mandal, J. (eds) Proceedings of the 4th International Conference on Frontiers in Intelligent Computing: Theory and Applications (FICTA) 2015. Advances in Intelligent Systems and Computing, vol 404. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2695-6_4
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DOI: https://doi.org/10.1007/978-81-322-2695-6_4
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