Moonlighting protein prediction using physico-chemical and evolutional properties via machine learning methods.

Journal: BMC bioinformatics
Published Date:

Abstract

BACKGROUND: Moonlighting proteins (MPs) are a subclass of multifunctional proteins in which more than one independent or usually distinct function occurs in a single polypeptide chain. Identification of unknown cellular processes, understanding novel protein mechanisms, improving the prediction of protein functions, and gaining information about protein evolution are the main reasons to study MPs. They also play an important role in disease pathways and drug-target discovery. Since detecting MPs experimentally is quite a challenge, most of them are detected randomly. Therefore, introducing an appropriate computational approach to predict MPs seems reasonable.

Authors

  • Farshid Shirafkan
    Laboratory of Bioinformatics and Drug Design, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.
  • Sajjad Gharaghani
    Laboratory of Bioinformatics & Drug Design (LBD), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran. Electronic address: s.gharaghani@ut.ac.ir.
  • Karim Rahimian
    Bioinformatics and Computational Omics Lab (BioCOOL), Department of Biophysics, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran.
  • Reza Hasan Sajedi
    Department of Biochemistry, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran.
  • Javad Zahiri
    Department of Neuroscience, University of California San Diego, La Jolla, CA, USA. jzahiri@health.ucsd.edu.