A simple strategy to enhance the speed of protein secondary structure prediction without sacrificing accuracy
The secondary structure prediction of proteins is a classic topic of computational structural biology with a variety of applications. During the past decade, the accuracy of prediction achieved by state-of-the-art algorithms has been >80%; meanwhile, the time cost of prediction increased rapidly...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7326220/ https://www.ncbi.nlm.nih.gov/pubmed/32603341 http://dx.doi.org/10.1371/journal.pone.0235153 |
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author | Juan, Sheng-Hung Chen, Teng-Ruei Lo, Wei-Cheng |
author_facet | Juan, Sheng-Hung Chen, Teng-Ruei Lo, Wei-Cheng |
author_sort | Juan, Sheng-Hung |
collection | PubMed |
description | The secondary structure prediction of proteins is a classic topic of computational structural biology with a variety of applications. During the past decade, the accuracy of prediction achieved by state-of-the-art algorithms has been >80%; meanwhile, the time cost of prediction increased rapidly because of the exponential growth of fundamental protein sequence data. Based on literature studies and preliminary observations on the relationships between the size/homology of the fundamental protein dataset and the speed/accuracy of predictions, we raised two hypotheses that might be helpful to determine the main influence factors of the efficiency of secondary structure prediction. Experimental results of size and homology reductions of the fundamental protein dataset supported those hypotheses. They revealed that shrinking the size of the dataset could substantially cut down the time cost of prediction with a slight decrease of accuracy, which could be increased on the contrary by homology reduction of the dataset. Moreover, the Shannon information entropy could be applied to explain how accuracy was influenced by the size and homology of the dataset. Based on these findings, we proposed that a proper combination of size and homology reductions of the protein dataset could speed up the secondary structure prediction while preserving the high accuracy of state-of-the-art algorithms. Testing the proposed strategy with the fundamental protein dataset of the year 2018 provided by the Universal Protein Resource, the speed of prediction was enhanced over 20 folds while all accuracy measures remained equivalently high. These findings are supposed helpful for improving the efficiency of researches and applications depending on the secondary structure prediction of proteins. To make future implementations of the proposed strategy easy, we have established a database of size and homology reduced protein datasets at http://10.life.nctu.edu.tw/UniRefNR. |
format | Online Article Text |
id | pubmed-7326220 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-73262202020-07-10 A simple strategy to enhance the speed of protein secondary structure prediction without sacrificing accuracy Juan, Sheng-Hung Chen, Teng-Ruei Lo, Wei-Cheng PLoS One Research Article The secondary structure prediction of proteins is a classic topic of computational structural biology with a variety of applications. During the past decade, the accuracy of prediction achieved by state-of-the-art algorithms has been >80%; meanwhile, the time cost of prediction increased rapidly because of the exponential growth of fundamental protein sequence data. Based on literature studies and preliminary observations on the relationships between the size/homology of the fundamental protein dataset and the speed/accuracy of predictions, we raised two hypotheses that might be helpful to determine the main influence factors of the efficiency of secondary structure prediction. Experimental results of size and homology reductions of the fundamental protein dataset supported those hypotheses. They revealed that shrinking the size of the dataset could substantially cut down the time cost of prediction with a slight decrease of accuracy, which could be increased on the contrary by homology reduction of the dataset. Moreover, the Shannon information entropy could be applied to explain how accuracy was influenced by the size and homology of the dataset. Based on these findings, we proposed that a proper combination of size and homology reductions of the protein dataset could speed up the secondary structure prediction while preserving the high accuracy of state-of-the-art algorithms. Testing the proposed strategy with the fundamental protein dataset of the year 2018 provided by the Universal Protein Resource, the speed of prediction was enhanced over 20 folds while all accuracy measures remained equivalently high. These findings are supposed helpful for improving the efficiency of researches and applications depending on the secondary structure prediction of proteins. To make future implementations of the proposed strategy easy, we have established a database of size and homology reduced protein datasets at http://10.life.nctu.edu.tw/UniRefNR. Public Library of Science 2020-06-30 /pmc/articles/PMC7326220/ /pubmed/32603341 http://dx.doi.org/10.1371/journal.pone.0235153 Text en © 2020 Juan et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Juan, Sheng-Hung Chen, Teng-Ruei Lo, Wei-Cheng A simple strategy to enhance the speed of protein secondary structure prediction without sacrificing accuracy |
title | A simple strategy to enhance the speed of protein secondary structure prediction without sacrificing accuracy |
title_full | A simple strategy to enhance the speed of protein secondary structure prediction without sacrificing accuracy |
title_fullStr | A simple strategy to enhance the speed of protein secondary structure prediction without sacrificing accuracy |
title_full_unstemmed | A simple strategy to enhance the speed of protein secondary structure prediction without sacrificing accuracy |
title_short | A simple strategy to enhance the speed of protein secondary structure prediction without sacrificing accuracy |
title_sort | simple strategy to enhance the speed of protein secondary structure prediction without sacrificing accuracy |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7326220/ https://www.ncbi.nlm.nih.gov/pubmed/32603341 http://dx.doi.org/10.1371/journal.pone.0235153 |
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