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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| Veröffentlicht in: | PloS one 2021-07, Vol.16 (7), p.e0255076-e0255076 |
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| creator | Chen, Teng-Ruei Juan, Sheng-Hung Huang, Yu-Wei Lin, Yen-Cheng Lo, Wei-Cheng |
| 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 |
| doi_str_mv | 10.1371/journal.pone.0255076 |
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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</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0255076</identifier><identifier>PMID: 34320027</identifier><language>eng</language><publisher>San Francisco: Public Library of Science</publisher><subject>Accuracy ; Algorithms ; Amino acid sequence ; Amino acids ; Analysis ; Binding sites ; Bioengineering ; Bioinformatics ; Biology ; Biology and Life Sciences ; Computer and Information Sciences ; Data mining ; Datasets ; Learning algorithms ; Machine learning ; Methods ; Neural networks ; Physical Sciences ; Predictions ; Protein structure ; Proteins ; Research and Analysis Methods ; Secondary structure ; Structure</subject><ispartof>PloS one, 2021-07, Vol.16 (7), p.e0255076-e0255076</ispartof><rights>COPYRIGHT 2021 Public Library of Science</rights><rights>2021 Chen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2021 Chen et al 2021 Chen et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c669t-5a6025dd12a7283b5e925859fed7f21abe3675a0a174e016e1417485e37212dd3</citedby><cites>FETCH-LOGICAL-c669t-5a6025dd12a7283b5e925859fed7f21abe3675a0a174e016e1417485e37212dd3</cites><orcidid>0000-0001-6772-5303 ; 0000-0001-8865-5113</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8318245/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8318245/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,2102,2928,23866,27924,27925,53791,53793,79600,79601</link.rule.ids></links><search><contributor>de Brevern, Alexandre G.</contributor><creatorcontrib>Chen, Teng-Ruei</creatorcontrib><creatorcontrib>Juan, Sheng-Hung</creatorcontrib><creatorcontrib>Huang, Yu-Wei</creatorcontrib><creatorcontrib>Lin, Yen-Cheng</creatorcontrib><creatorcontrib>Lo, Wei-Cheng</creatorcontrib><title>A secondary structure-based position-specific scoring matrix applied to the improvement in protein secondary structure prediction</title><title>PloS one</title><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</description><subject>Accuracy</subject><subject>Algorithms</subject><subject>Amino acid sequence</subject><subject>Amino acids</subject><subject>Analysis</subject><subject>Binding sites</subject><subject>Bioengineering</subject><subject>Bioinformatics</subject><subject>Biology</subject><subject>Biology and Life Sciences</subject><subject>Computer and Information Sciences</subject><subject>Data mining</subject><subject>Datasets</subject><subject>Learning algorithms</subject><subject>Machine learning</subject><subject>Methods</subject><subject>Neural networks</subject><subject>Physical Sciences</subject><subject>Predictions</subject><subject>Protein structure</subject><subject>Proteins</subject><subject>Research and Analysis Methods</subject><subject>Secondary 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secondary structure-based position-specific scoring matrix applied to the improvement in protein secondary structure prediction</title><author>Chen, Teng-Ruei ; Juan, Sheng-Hung ; Huang, Yu-Wei ; Lin, Yen-Cheng ; Lo, Wei-Cheng</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c669t-5a6025dd12a7283b5e925859fed7f21abe3675a0a174e016e1417485e37212dd3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Accuracy</topic><topic>Algorithms</topic><topic>Amino acid sequence</topic><topic>Amino acids</topic><topic>Analysis</topic><topic>Binding sites</topic><topic>Bioengineering</topic><topic>Bioinformatics</topic><topic>Biology</topic><topic>Biology and Life Sciences</topic><topic>Computer and Information Sciences</topic><topic>Data mining</topic><topic>Datasets</topic><topic>Learning algorithms</topic><topic>Machine learning</topic><topic>Methods</topic><topic>Neural 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Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Chen, Teng-Ruei</au><au>Juan, Sheng-Hung</au><au>Huang, Yu-Wei</au><au>Lin, Yen-Cheng</au><au>Lo, Wei-Cheng</au><au>de Brevern, Alexandre G.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A secondary structure-based position-specific scoring matrix applied to the improvement in protein secondary structure prediction</atitle><jtitle>PloS one</jtitle><date>2021-07-28</date><risdate>2021</risdate><volume>16</volume><issue>7</issue><spage>e0255076</spage><epage>e0255076</epage><pages>e0255076-e0255076</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>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</abstract><cop>San Francisco</cop><pub>Public Library of Science</pub><pmid>34320027</pmid><doi>10.1371/journal.pone.0255076</doi><tpages>e0255076</tpages><orcidid>https://orcid.org/0000-0001-6772-5303</orcidid><orcidid>https://orcid.org/0000-0001-8865-5113</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Accuracy Algorithms Amino acid sequence Amino acids Analysis Binding sites Bioengineering Bioinformatics Biology Biology and Life Sciences Computer and Information Sciences Data mining Datasets Learning algorithms Machine learning Methods Neural networks Physical Sciences Predictions Protein structure Proteins Research and Analysis Methods Secondary structure Structure |
| title | A secondary structure-based position-specific scoring matrix applied to the improvement in protein secondary structure prediction |
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