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Will it gel? Successful computational prediction of peptide gelators using physicochemical properties and molecular fingerprints
The self-assembly of low molecular weight gelators to form gels has enormous potential for cell culturing, optoelectronics, sensing, and for the preparation of structured materials. There is an enormous “chemical space” of gelators. Even within one class, functionalised dipeptides, there are many st...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Royal Society of Chemistry
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6016447/ https://www.ncbi.nlm.nih.gov/pubmed/30155120 http://dx.doi.org/10.1039/c6sc00722h |
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author | Gupta, Jyoti K. Adams, Dave J. Berry, Neil G. |
author_facet | Gupta, Jyoti K. Adams, Dave J. Berry, Neil G. |
author_sort | Gupta, Jyoti K. |
collection | PubMed |
description | The self-assembly of low molecular weight gelators to form gels has enormous potential for cell culturing, optoelectronics, sensing, and for the preparation of structured materials. There is an enormous “chemical space” of gelators. Even within one class, functionalised dipeptides, there are many structures based on both natural and unnatural amino acids that can be proposed and there is a need for methods that can successfully predict the gelation propensity of such molecules. We have successfully developed computational models, based on experimental data, which are robust and are able to identify in silico dipeptide structures that can form gels. A virtual computational screen of 2025 dipeptide candidates identified 9 dipeptides that were synthesised and tested. Every one of the 9 dipeptides synthesised and tested were correctly predicted for their gelation properties. This approach and set of tools enables the “dipeptide space” to be searched effectively and efficiently in order to deliver novel gelator molecules. |
format | Online Article Text |
id | pubmed-6016447 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Royal Society of Chemistry |
record_format | MEDLINE/PubMed |
spelling | pubmed-60164472018-08-28 Will it gel? Successful computational prediction of peptide gelators using physicochemical properties and molecular fingerprints Gupta, Jyoti K. Adams, Dave J. Berry, Neil G. Chem Sci Chemistry The self-assembly of low molecular weight gelators to form gels has enormous potential for cell culturing, optoelectronics, sensing, and for the preparation of structured materials. There is an enormous “chemical space” of gelators. Even within one class, functionalised dipeptides, there are many structures based on both natural and unnatural amino acids that can be proposed and there is a need for methods that can successfully predict the gelation propensity of such molecules. We have successfully developed computational models, based on experimental data, which are robust and are able to identify in silico dipeptide structures that can form gels. A virtual computational screen of 2025 dipeptide candidates identified 9 dipeptides that were synthesised and tested. Every one of the 9 dipeptides synthesised and tested were correctly predicted for their gelation properties. This approach and set of tools enables the “dipeptide space” to be searched effectively and efficiently in order to deliver novel gelator molecules. Royal Society of Chemistry 2016-07-01 2016-04-13 /pmc/articles/PMC6016447/ /pubmed/30155120 http://dx.doi.org/10.1039/c6sc00722h Text en This journal is © The Royal Society of Chemistry 2016 http://creativecommons.org/licenses/by/3.0/ This article is freely available. This article is licensed under a Creative Commons Attribution 3.0 Unported Licence (CC BY 3.0) |
spellingShingle | Chemistry Gupta, Jyoti K. Adams, Dave J. Berry, Neil G. Will it gel? Successful computational prediction of peptide gelators using physicochemical properties and molecular fingerprints |
title | Will it gel? Successful computational prediction of peptide gelators using physicochemical properties and molecular fingerprints
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title_full | Will it gel? Successful computational prediction of peptide gelators using physicochemical properties and molecular fingerprints
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title_fullStr | Will it gel? Successful computational prediction of peptide gelators using physicochemical properties and molecular fingerprints
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title_full_unstemmed | Will it gel? Successful computational prediction of peptide gelators using physicochemical properties and molecular fingerprints
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title_short | Will it gel? Successful computational prediction of peptide gelators using physicochemical properties and molecular fingerprints
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title_sort | will it gel? successful computational prediction of peptide gelators using physicochemical properties and molecular fingerprints |
topic | Chemistry |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6016447/ https://www.ncbi.nlm.nih.gov/pubmed/30155120 http://dx.doi.org/10.1039/c6sc00722h |
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