Predicting RNA distance-based contact maps by integrated deep learning on physics-inferred secondary structure and evolutionary-derived mutational coupling.

Journal: Bioinformatics (Oxford, England)
Published Date: Aug 10, 2022

Abstract

MOTIVATION: Recently, AlphaFold2 achieved high experimental accuracy for the majority of proteins in Critical Assessment of Structure Prediction (CASP 14). This raises the hope that one day, we may achieve the same feat for RNA structure prediction for those structured RNAs, which is as fundamentally and practically important similar to protein structure prediction. One major factor in the recent advancement of protein structure prediction is the highly accurate prediction of distance-based contact maps of proteins.

Authors

  • Jaswinder Singh
    Signal Processing Laboratory , Griffith University , Brisbane , QLD 4122 , Australia.
  • Kuldip Paliwal
    Signal Processing Laboratory, School of Engineering, Griffith University, Brisbane, Australia.
  • Thomas Litfin
    School of Information and Communication Technology, Griffith University, Gold Coast 4222, Australia.
  • Jaspreet Singh
    Signal Processing Laboratory, Griffith University, Brisbane, Queensland 4122, Australia.
  • Yaoqi Zhou
    Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen, Guangdong, 518106, China. Electronic address: zhouyq@szbl.ac.cn.

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