ArticleBiomolecules2021
Discovering the Ultimate Limits of Protein Secondary Structure Prediction.
Article in Biomolecules, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- ConforFold recovers alternative protein conformations beyond MSA subsampling.Protein science : a publication of the Protein Society · 2026Article
- Crosstalk Between Hematopoietic and Mesenchymal Cells in Bone and Tooth Immune Response.Advances in experimental medicine and biology · 2026Review
- Development and evaluation of novel Brucella diagnostic antigen protein ST.BMC infectious diseases · 2025Article
- TruMPET: A New Method for Protein Secondary Structure Prediction Using Neural Networks Trained on Multiple Pre-Selected Physicochemical and Structural Features.International journal of molecular sciences · 2025Article
- Porter 6: Protein Secondary Structure Prediction by Leveraging Pre-Trained Language Models (PLMs).International journal of molecular sciences · 2024Article
- MHTAPred-SS: A Highly Targeted Autoencoder-Driven Deep Multi-Task Learning Framework for Accurate Protein Secondary Structure Prediction.International journal of molecular sciences · 2024Article
- PCP-GC-LM: single-sequence-based protein contact prediction using dual graph convolutional neural network and convolutional neural network.BMC bioinformatics · 2024Article
- Effects of different signaling pathways on odontogenic differentiation of dental pulp stem cells: a review.Frontiers in physiology · 2023Review
- Propensities of Some Amino Acid Pairings in α-Helices Vary with Length.The protein journal · 2022Article
- Deep learning for protein secondary structure prediction: Pre and post-AlphaFold.Computational and structural biotechnology journal · 2022Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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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