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

James O'Connell, Zhixiu Li, Jack Hanson, Rhys Heffernan, James Lyons, Kuldip Paliwal, Abdollah Dehzangi, Yuedong Yang, Yaoqi Zhou

Research output: Contribution to journalArticlepeer-review

18 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)629-633
Number of pages5
JournalProteins: Structure, Function and Bioinformatics
Volume86
Issue number6
DOIs
StatePublished - Jun 2018
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Structural Biology
  • Biochemistry
  • Molecular Biology

Keywords

  • bioinformatics
  • deep learning
  • fold recognition
  • neural networks
  • structure prediction

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