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Specific and intrinsic sequence patterns extracted by deep learning from intra-protein binding and non-binding peptide fragments
The key finding in the DNA double helix model is the specific pairing or binding between nucleotides A-T and C-G, and the pairing rules are the molecule basis of genetic code. Unfortunately, no such rules have been discovered for proteins. Here we show that intrinsic sequence patterns between intra-...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5668431/ https://www.ncbi.nlm.nih.gov/pubmed/29097708 http://dx.doi.org/10.1038/s41598-017-14877-w |
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author | Wang, Yuhong Huang, Junzhou Li, Wei Wang, Sheng Ding, Chuanfan |
author_facet | Wang, Yuhong Huang, Junzhou Li, Wei Wang, Sheng Ding, Chuanfan |
author_sort | Wang, Yuhong |
collection | PubMed |
description | The key finding in the DNA double helix model is the specific pairing or binding between nucleotides A-T and C-G, and the pairing rules are the molecule basis of genetic code. Unfortunately, no such rules have been discovered for proteins. Here we show that intrinsic sequence patterns between intra-protein binding peptide fragments exist, they can be extracted using a deep learning algorithm, and they bear an interesting semblance to the DNA double helix model. The intra-protein binding peptide fragments have specific and intrinsic sequence patterns, distinct from non-binding peptide fragments, and multi-millions of binding and non-binding peptide fragments from currently available protein X-ray structures are classified with an accuracy of up to 93%. The specific binding between short peptide fragments may provide an important driving force for protein folding and protein-protein interaction, two open and fundamental problems in molecular biology, and it may have significant potential in design, discovery, and development of peptide, protein, and antibody drugs. |
format | Online Article Text |
id | pubmed-5668431 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-56684312017-11-15 Specific and intrinsic sequence patterns extracted by deep learning from intra-protein binding and non-binding peptide fragments Wang, Yuhong Huang, Junzhou Li, Wei Wang, Sheng Ding, Chuanfan Sci Rep Article The key finding in the DNA double helix model is the specific pairing or binding between nucleotides A-T and C-G, and the pairing rules are the molecule basis of genetic code. Unfortunately, no such rules have been discovered for proteins. Here we show that intrinsic sequence patterns between intra-protein binding peptide fragments exist, they can be extracted using a deep learning algorithm, and they bear an interesting semblance to the DNA double helix model. The intra-protein binding peptide fragments have specific and intrinsic sequence patterns, distinct from non-binding peptide fragments, and multi-millions of binding and non-binding peptide fragments from currently available protein X-ray structures are classified with an accuracy of up to 93%. The specific binding between short peptide fragments may provide an important driving force for protein folding and protein-protein interaction, two open and fundamental problems in molecular biology, and it may have significant potential in design, discovery, and development of peptide, protein, and antibody drugs. Nature Publishing Group UK 2017-11-02 /pmc/articles/PMC5668431/ /pubmed/29097708 http://dx.doi.org/10.1038/s41598-017-14877-w Text en © The Author(s) 2017 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Wang, Yuhong Huang, Junzhou Li, Wei Wang, Sheng Ding, Chuanfan Specific and intrinsic sequence patterns extracted by deep learning from intra-protein binding and non-binding peptide fragments |
title | Specific and intrinsic sequence patterns extracted by deep learning from intra-protein binding and non-binding peptide fragments |
title_full | Specific and intrinsic sequence patterns extracted by deep learning from intra-protein binding and non-binding peptide fragments |
title_fullStr | Specific and intrinsic sequence patterns extracted by deep learning from intra-protein binding and non-binding peptide fragments |
title_full_unstemmed | Specific and intrinsic sequence patterns extracted by deep learning from intra-protein binding and non-binding peptide fragments |
title_short | Specific and intrinsic sequence patterns extracted by deep learning from intra-protein binding and non-binding peptide fragments |
title_sort | specific and intrinsic sequence patterns extracted by deep learning from intra-protein binding and non-binding peptide fragments |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5668431/ https://www.ncbi.nlm.nih.gov/pubmed/29097708 http://dx.doi.org/10.1038/s41598-017-14877-w |
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