Protein secondary structure prediction: A survey of the state of the art

Qian Jiang, Xin Jin, Sj Lee*, Shaowen Yao

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

26 Scopus citations


Protein secondary structure prediction (PSSP) is a fundamental task in protein science and computational biology, and it can be used to understand protein 3-dimensional (3-D) structures, further, to learn their biological functions. In the past decade, a large number of methods have been proposed for PSSP. In order to learn the latest progress of PSSP, this paper provides a survey on the development of this field. It first introduces the background and related knowledge of PSSP, including basic concepts, data sets, input data features and prediction accuracy assessment. Then, it reviews the recent algorithmic developments of PSSP, which mainly focus on the latest decade. Finally, it summarizes the corresponding tendencies and challenges in this field. This survey concludes that although various PSSP methods have been proposed, there still exist several further improvements or potential research directions. We hope that the presented guidelines will help nonspecialists and specialists to learn the critical progress in PSSP in recent years.

Original languageEnglish
Pages (from-to)379-402
Number of pages24
JournalJournal of Molecular Graphics and Modelling
StatePublished - 1 Sep 2017


  • Classification algorithm
  • Feature extraction
  • Machine learning
  • Neural networks
  • Protein secondary structure prediction

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