Effective Detection of Kink in Helices from Amino Acid Sequence in Transmembrane Proteins Using Neural Network

  • Nivedita Mishra
  • Adikanda Khamari
  • Jayakishan Meher
  • Mukesh Kumar Raval
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 31)


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.


Transmembrane proteins Kink prediction Radial basis function neural network Physico-chemical properties Amino acid sequence 



The authors wish to thank management members and the principal of the college for all kinds of supports to complete this work.


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Copyright information

© Springer India 2015

Authors and Affiliations

  • Nivedita Mishra
    • 1
  • Adikanda Khamari
    • 2
  • Jayakishan Meher
    • 3
  • Mukesh Kumar Raval
    • 4
  1. 1.Department of ChemistryRajendra CollegeBalangirIndia
  2. 2.Department of PhysicsRajendra CollegeBalangirIndia
  3. 3.Department of Computer Science and EngineeringVikash College of Engineering for WomenBargarhIndia
  4. 4.Department of ChemistryGangadhar Meher CollegeSambalpurIndia

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