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A secondary structure-based position-specific scoring matrix applied to the improvement in protein secondary structure prediction

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  • Teng-Ruei Chen
  • Sheng-Hung Juan
  • Yu-Wei Huang
  • Yen-Cheng Lin
  • Wei-Cheng Lo

Abstract

Protein secondary structure prediction (SSP) has a variety of applications; however, there has been relatively limited improvement in accuracy for years. With a vision of moving forward all related fields, we aimed to make a fundamental advance in SSP. There have been many admirable efforts made to improve the machine learning algorithm for SSP. This work thus took a step back by manipulating the input features. A secondary structure element-based position-specific scoring matrix (SSE-PSSM) is proposed, based on which a new set of machine learning features can be established. The feasibility of this new PSSM was evaluated by rigid independent tests with training and testing datasets sharing

Suggested Citation

  • Teng-Ruei Chen & Sheng-Hung Juan & Yu-Wei Huang & Yen-Cheng Lin & Wei-Cheng Lo, 2021. "A secondary structure-based position-specific scoring matrix applied to the improvement in protein secondary structure prediction," PLOS ONE, Public Library of Science, vol. 16(7), pages 1-28, July.
    • Handle: RePEc:plo:pone00:0255076
    DOI: 10.1371/journal.pone.0255076
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