The influence of dataset homology and a rigorous evaluation strategy on protein secondary structure prediction.

The secondary structure prediction (SSP) of proteins has long been an essential structural biology technique with various applications. Despite its vital role in many research and industrial fields, in recent years, as the accuracy of state-of-the-art secondary structure predictors approaches the th...

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Autores principales:	Teng-Ruei Chen, Chia-Hua Lo, Sheng-Hung Juan, Wei-Cheng Lo
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/e18894c3c3cf46d8b893b6449e00f157
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spelling	oai:doaj.org-article:e18894c3c3cf46d8b893b6449e00f1572021-12-02T20:07:02ZThe influence of dataset homology and a rigorous evaluation strategy on protein secondary structure prediction.1932-620310.1371/journal.pone.0254555https://doaj.org/article/e18894c3c3cf46d8b893b6449e00f1572021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0254555https://doaj.org/toc/1932-6203The secondary structure prediction (SSP) of proteins has long been an essential structural biology technique with various applications. Despite its vital role in many research and industrial fields, in recent years, as the accuracy of state-of-the-art secondary structure predictors approaches the theoretical upper limit, SSP has been considered no longer challenging or too challenging to make advances. With the belief that the substantial improvement of SSP will move forward many fields depending on it, we conducted this study, which focused on three issues that have not been noticed or thoroughly examined yet but may have affected the reliability of the evaluation of previous SSP algorithms. These issues are all about the sequence homology between or within the developmental and evaluation datasets. We thus designed many different homology layouts of datasets to train and evaluate SSP prediction models. Multiple repeats were performed in each experiment by random sampling. The conclusions obtained with small experimental datasets were verified with large-scale datasets using state-of-the-art SSP algorithms. Very different from the long-established assumption, we discover that the sequence homology between query datasets for training, testing, and independent tests exerts little influence on SSP accuracy. Besides, the sequence homology redundancy between or within most datasets would make the accuracy of an SSP algorithm overestimated, while the redundancy within the reference dataset for extracting predictive features would make the accuracy underestimated. Since the overestimating effects are more significant than the underestimating effect, the accuracy of some SSP methods might have been overestimated. Based on the discoveries, we propose a rigorous procedure for developing SSP algorithms and making reliable evaluations, hoping to bring substantial improvements to future SSP methods and benefit all research and application fields relying on accurate prediction of protein secondary structures.Teng-Ruei ChenChia-Hua LoSheng-Hung JuanWei-Cheng LoPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 7, p e0254555 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Teng-Ruei Chen Chia-Hua Lo Sheng-Hung Juan Wei-Cheng Lo The influence of dataset homology and a rigorous evaluation strategy on protein secondary structure prediction.
description	The secondary structure prediction (SSP) of proteins has long been an essential structural biology technique with various applications. Despite its vital role in many research and industrial fields, in recent years, as the accuracy of state-of-the-art secondary structure predictors approaches the theoretical upper limit, SSP has been considered no longer challenging or too challenging to make advances. With the belief that the substantial improvement of SSP will move forward many fields depending on it, we conducted this study, which focused on three issues that have not been noticed or thoroughly examined yet but may have affected the reliability of the evaluation of previous SSP algorithms. These issues are all about the sequence homology between or within the developmental and evaluation datasets. We thus designed many different homology layouts of datasets to train and evaluate SSP prediction models. Multiple repeats were performed in each experiment by random sampling. The conclusions obtained with small experimental datasets were verified with large-scale datasets using state-of-the-art SSP algorithms. Very different from the long-established assumption, we discover that the sequence homology between query datasets for training, testing, and independent tests exerts little influence on SSP accuracy. Besides, the sequence homology redundancy between or within most datasets would make the accuracy of an SSP algorithm overestimated, while the redundancy within the reference dataset for extracting predictive features would make the accuracy underestimated. Since the overestimating effects are more significant than the underestimating effect, the accuracy of some SSP methods might have been overestimated. Based on the discoveries, we propose a rigorous procedure for developing SSP algorithms and making reliable evaluations, hoping to bring substantial improvements to future SSP methods and benefit all research and application fields relying on accurate prediction of protein secondary structures.
format	article
author	Teng-Ruei Chen Chia-Hua Lo Sheng-Hung Juan Wei-Cheng Lo
author_facet	Teng-Ruei Chen Chia-Hua Lo Sheng-Hung Juan Wei-Cheng Lo
author_sort	Teng-Ruei Chen
title	The influence of dataset homology and a rigorous evaluation strategy on protein secondary structure prediction.
title_short	The influence of dataset homology and a rigorous evaluation strategy on protein secondary structure prediction.
title_full	The influence of dataset homology and a rigorous evaluation strategy on protein secondary structure prediction.
title_fullStr	The influence of dataset homology and a rigorous evaluation strategy on protein secondary structure prediction.
title_full_unstemmed	The influence of dataset homology and a rigorous evaluation strategy on protein secondary structure prediction.
title_sort	influence of dataset homology and a rigorous evaluation strategy on protein secondary structure prediction.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/e18894c3c3cf46d8b893b6449e00f157
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The influence of dataset homology and a rigorous evaluation strategy on protein secondary structure prediction.

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