Discovering the Ultimate Limits of Protein Secondary Structure Prediction

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%;...

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Autores principales:	Chia-Tzu Ho, Yu-Wei Huang, Teng-Ruei Chen, Chia-Hua Lo, Wei-Cheng Lo
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	protein secondary structure prediction protein sequence protein structure protein sequence-based predictions structural biology Microbiology QR1-502
Acceso en línea:	https://doaj.org/article/3b0c56d6d9d54bb8ada528187c03b362
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spelling	oai:doaj.org-article:3b0c56d6d9d54bb8ada528187c03b3622021-11-25T16:52:57ZDiscovering the Ultimate Limits of Protein Secondary Structure Prediction10.3390/biom111116272218-273Xhttps://doaj.org/article/3b0c56d6d9d54bb8ada528187c03b3622021-11-01T00:00:00Zhttps://www.mdpi.com/2218-273X/11/11/1627https://doaj.org/toc/2218-273XSecondary 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.Chia-Tzu HoYu-Wei HuangTeng-Ruei ChenChia-Hua LoWei-Cheng LoMDPI AGarticleprotein secondary structure predictionprotein sequenceprotein structureprotein sequence-based predictionsstructural biologyMicrobiologyQR1-502ENBiomolecules, Vol 11, Iss 1627, p 1627 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	protein secondary structure prediction protein sequence protein structure protein sequence-based predictions structural biology Microbiology QR1-502
spellingShingle	protein secondary structure prediction protein sequence protein structure protein sequence-based predictions structural biology Microbiology QR1-502 Chia-Tzu Ho Yu-Wei Huang Teng-Ruei Chen Chia-Hua Lo Wei-Cheng Lo Discovering the Ultimate Limits of Protein Secondary Structure Prediction
description	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.
format	article
author	Chia-Tzu Ho Yu-Wei Huang Teng-Ruei Chen Chia-Hua Lo Wei-Cheng Lo
author_facet	Chia-Tzu Ho Yu-Wei Huang Teng-Ruei Chen Chia-Hua Lo Wei-Cheng Lo
author_sort	Chia-Tzu Ho
title	Discovering the Ultimate Limits of Protein Secondary Structure Prediction
title_short	Discovering the Ultimate Limits of Protein Secondary Structure Prediction
title_full	Discovering the Ultimate Limits of Protein Secondary Structure Prediction
title_fullStr	Discovering the Ultimate Limits of Protein Secondary Structure Prediction
title_full_unstemmed	Discovering the Ultimate Limits of Protein Secondary Structure Prediction
title_sort	discovering the ultimate limits of protein secondary structure prediction
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/3b0c56d6d9d54bb8ada528187c03b362
work_keys_str_mv	AT chiatzuho discoveringtheultimatelimitsofproteinsecondarystructureprediction AT yuweihuang discoveringtheultimatelimitsofproteinsecondarystructureprediction AT tengrueichen discoveringtheultimatelimitsofproteinsecondarystructureprediction AT chiahualo discoveringtheultimatelimitsofproteinsecondarystructureprediction AT weichenglo discoveringtheultimatelimitsofproteinsecondarystructureprediction
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Discovering the Ultimate Limits of Protein Secondary Structure Prediction

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