Biomolecules (Nov 2021)

Discovering the Ultimate Limits of Protein Secondary Structure Prediction

  • Chia-Tzu Ho,
  • Yu-Wei Huang,
  • Teng-Ruei Chen,
  • Chia-Hua Lo,
  • Wei-Cheng Lo

DOI
https://doi.org/10.3390/biom11111627
Journal volume & issue
Vol. 11, no. 11
p. 1627

Abstract

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Secondary structure prediction (SSP) of proteins is an important structural biology technique with many applications. There have been ~300 algorithms published in the past seven decades with fierce competition in accuracy. In the first 60 years, the accuracy of three-state SSP rose from ~56% to 81%; after that, it has long stayed at 81–86%. In the 1990s, the theoretical limit of three-state SSP accuracy had been estimated to be 88%. Thus, SSP is now generally considered not challenging or too challenging to improve. However, we found that the limit of three-state SSP might be underestimated. Besides, there is still much room for improving segment-based and eight-state SSPs, but the limits of these emerging topics have not been determined. This work performs large-scale sequence and structural analyses to estimate SSP accuracy limits and assess state-of-the-art SSP methods. The limit of three-state SSP is re-estimated to be ~92%, 4–5% higher than previously expected, indicating that SSP is still challenging. The estimated limit of eight-state SSP is 84–87%. Several proposals for improving future SSP algorithms are made based on our results. We hope that these findings will help move forward the development of SSP and all its applications.

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