SPIN2: Predicting sequence profiles from protein structures using deep neural networks.

Journal: Proteins
Published Date: Mar 25, 2018

Abstract

Designing protein sequences that can fold into a given structure is a well-known inverse protein-folding problem. One important characteristic to attain for a protein design program is the ability to recover wild-type sequences given their native backbone structures. The highest average sequence identity accuracy achieved by current protein-design programs in this problem is around 30%, achieved by our previous system, SPIN. SPIN is a program that predicts sequences compatible with a provided structure using a neural network with fragment-based local and energy-based nonlocal profiles. Our new model, SPIN2, uses a deep neural network and additional structural features to improve on SPIN. SPIN2 achieves over 34% in sequence recovery in 10-fold cross-validation and independent tests, a 4% improvement over the previous version. The sequence profiles generated from SPIN2 are expected to be useful for improving existing fold recognition and protein design techniques. SPIN2 is available at http://sparks-lab.org.

Authors

  • James O'Connell
    Signal Processing Laboratory, Griffith University, Nathan, Australia.
  • Zhixiu Li
    Institute for Glycomics, Griffith University, Gold Coast, Australia.
  • Jack Hanson
    Signal Processing Laboratory, Griffith University, Brisbane 4111, Australia.
  • Rhys Heffernan
    Signal Processing Laboratory, School of Engineering, Griffith University, Brisbane, Australia.
  • James Lyons
    Signal Processing Laboratory, School of Engineering, Griffith University, Brisbane, Australia.
  • Kuldip Paliwal
    Signal Processing Laboratory, School of Engineering, Griffith University, Brisbane, Australia.
  • Abdollah Dehzangi
    1] Signal Processing Laboratory, School of Engineering, Griffith University, Brisbane, Australia [2] Institute for Integrated and Intelligent Systems, Griffith University, Brisbane, Australia.
  • Yuedong Yang
    Institute for Glycomics and School of Information and Communication Technique, Griffith University, Parklands Dr. Southport, QLD 4222, 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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