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

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...

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Autores principales:	Teng-Ruei Chen, Sheng-Hung Juan, Yu-Wei Huang, Yen-Cheng Lin, 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/c52978beb14f4a9f8822d4eeb7497831
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spelling	oai:doaj.org-article:c52978beb14f4a9f8822d4eeb74978312021-12-02T20:09:02ZA secondary structure-based position-specific scoring matrix applied to the improvement in protein secondary structure prediction.1932-620310.1371/journal.pone.0255076https://doaj.org/article/c52978beb14f4a9f8822d4eeb74978312021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0255076https://doaj.org/toc/1932-6203Protein 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 <25% sequence identities. In all experiments, the proposed PSSM outperformed the traditional amino acid PSSM. This new PSSM can be easily combined with the amino acid PSSM, and the improvement in accuracy was remarkable. Preliminary tests made by combining the SSE-PSSM and well-known SSP methods showed 2.0% and 5.2% average improvements in three- and eight-state SSP accuracies, respectively. If this PSSM can be integrated into state-of-the-art SSP methods, the overall accuracy of SSP may break the current restriction and eventually bring benefit to all research and applications where secondary structure prediction plays a vital role during development. To facilitate the application and integration of the SSE-PSSM with modern SSP methods, we have established a web server and standalone programs for generating SSE-PSSM available at http://10.life.nctu.edu.tw/SSE-PSSM.Teng-Ruei ChenSheng-Hung JuanYu-Wei HuangYen-Cheng LinWei-Cheng LoPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 7, p e0255076 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Teng-Ruei Chen Sheng-Hung Juan Yu-Wei Huang Yen-Cheng Lin Wei-Cheng Lo A secondary structure-based position-specific scoring matrix applied to the improvement in protein secondary structure prediction.
description	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 <25% sequence identities. In all experiments, the proposed PSSM outperformed the traditional amino acid PSSM. This new PSSM can be easily combined with the amino acid PSSM, and the improvement in accuracy was remarkable. Preliminary tests made by combining the SSE-PSSM and well-known SSP methods showed 2.0% and 5.2% average improvements in three- and eight-state SSP accuracies, respectively. If this PSSM can be integrated into state-of-the-art SSP methods, the overall accuracy of SSP may break the current restriction and eventually bring benefit to all research and applications where secondary structure prediction plays a vital role during development. To facilitate the application and integration of the SSE-PSSM with modern SSP methods, we have established a web server and standalone programs for generating SSE-PSSM available at http://10.life.nctu.edu.tw/SSE-PSSM.
format	article
author	Teng-Ruei Chen Sheng-Hung Juan Yu-Wei Huang Yen-Cheng Lin Wei-Cheng Lo
author_facet	Teng-Ruei Chen Sheng-Hung Juan Yu-Wei Huang Yen-Cheng Lin Wei-Cheng Lo
author_sort	Teng-Ruei Chen
title	A secondary structure-based position-specific scoring matrix applied to the improvement in protein secondary structure prediction.
title_short	A secondary structure-based position-specific scoring matrix applied to the improvement in protein secondary structure prediction.
title_full	A secondary structure-based position-specific scoring matrix applied to the improvement in protein secondary structure prediction.
title_fullStr	A secondary structure-based position-specific scoring matrix applied to the improvement in protein secondary structure prediction.
title_full_unstemmed	A secondary structure-based position-specific scoring matrix applied to the improvement in protein secondary structure prediction.
title_sort	secondary structure-based position-specific scoring matrix applied to the improvement in protein secondary structure prediction.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/c52978beb14f4a9f8822d4eeb7497831
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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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