Abstract
Transmembrane proteins play crucial roles in a wide variety of biochemical pathways which comprise around 20–30 % of a typical proteome and target for more than half of all available drugs. Knowledge of kinks or bends in helices plays an important role in its functions. Kink prediction from amino acid sequences is of great help in understanding the function of proteins and it is a computationally intensive task. In this paper we have developed Neural Network method based on radial basis function for prediction of kink in the helices with a prediction efficiency of 85 %. A feature vector generated using three physico-chemical properties such as alpha propensity, coil propensity, and EIIP constituted in kinked helices contains most of the necessary information in determining the kink location. The proposed method captures this information more effectively than existing methods.
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Mishra, N., Khamari, A., Meher, J., Raval, M.K. (2015). Effective Detection of Kink in Helices from Amino Acid Sequence in Transmembrane Proteins Using Neural Network. In: Jain, L., Behera, H., Mandal, J., Mohapatra, D. (eds) Computational Intelligence in Data Mining - Volume 1. Smart Innovation, Systems and Technologies, vol 31. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2205-7_40
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DOI: https://doi.org/10.1007/978-81-322-2205-7_40
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