NetSurfP-2.0: improved prediction of protein structural features by integrated deep learning

Research output: Contribution to journalJournal articleResearchpeer-review


  • Michael Schantz Klausen
  • Martin Closter Jespersen
  • Henrik Nielsen
  • Kamilla Kjærgaard Jensen
  • Vanessa Isabell Jurtz
  • Casper Kaae Sønderby
  • Morten Otto Alexander Sommer
  • Winther, Ole
  • Morten Nielsen
  • Petersen, Bent
  • Paolo Marcatili

The ability to predict local structural features of a protein from the primary sequence is of paramount importance for unraveling its function in absence of experimental structural information. Two main factors affect the utility of potential prediction tools: their accuracy must enable extraction of reliable structural information on the proteins of interest, and their runtime must be low to keep pace with sequencing data being generated at a constantly increasing speed. Here, we present NetSurfP-2.0, a novel tool that can predict the most important local structural features with unprecedented accuracy and runtime. NetSurfP-2.0 is sequence-based and uses an architecture composed of convolutional and long short-term memory neural networks trained on solved protein structures. Using a single integrated model, NetSurfP-2.0 predicts solvent accessibility, secondary structure, structural disorder, and backbone dihedral angles for each residue of the input sequences. We assessed the accuracy of NetSurfP-2.0 on several independent test datasets and found it to consistently produce state-of-the-art predictions for each of its output features. We observe a correlation of 80% between predictions and experimental data for solvent accessibility, and a precision of 85% on secondary structure 3-class predictions. In addition to improved accuracy, the processing time has been optimized to allow predicting more than 1000 proteins in less than 2 hours, and complete proteomes in less than 1 day.

Original languageEnglish
JournalProteins: Structure, Function and Bioinformatics
Issue number6
Pages (from-to)520-527
Publication statusPublished - 2019

    Research areas

  • deep learning, disorder, local structure prediction, secondary structure, solvent accessibility

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