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Charge-Transfer Knowledge Graph among Amino Acids Derived from High-Throughput Electronic Structure Calculations for Protein Database
[Image: see text] The charge-transfer coupling is an important component in tight-binding methods. Because of the highly complex chemical structure of biomolecules, the anisotropic feature of charge-transfer couplings in realistic proteins cannot be ignored. In this work, we have performed the first...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Chemical Society
2018
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6641752/ https://www.ncbi.nlm.nih.gov/pubmed/31458645 http://dx.doi.org/10.1021/acsomega.8b00336 |
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author | Wang, Hongwei Liu, Fang Dong, Tiange Du, Likai Zhang, Dongju Gao, Jun |
author_facet | Wang, Hongwei Liu, Fang Dong, Tiange Du, Likai Zhang, Dongju Gao, Jun |
author_sort | Wang, Hongwei |
collection | PubMed |
description | [Image: see text] The charge-transfer coupling is an important component in tight-binding methods. Because of the highly complex chemical structure of biomolecules, the anisotropic feature of charge-transfer couplings in realistic proteins cannot be ignored. In this work, we have performed the first large-scale quantitative assessment of charge-transfer preference by calculating the charge-transfer couplings in all 20 × 20 possible amino acid side-chain combinations, which are extracted from available high-quality structures of thousands of protein complexes. The charge-transfer database quantitatively shows distinct features of charge-transfer couplings among millions of amino acid side-chain combinations. The overall distribution of charge-transfer couplings reveals that only one average or representative structure cannot be regarded as the typical charge-transfer preference in realistic proteins. This work provides us an alternative route to comprehensively understand the charge-transfer couplings for the overall distribution of realistic proteins in the foreseen big data scenario. |
format | Online Article Text |
id | pubmed-6641752 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | American Chemical Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-66417522019-08-27 Charge-Transfer Knowledge Graph among Amino Acids Derived from High-Throughput Electronic Structure Calculations for Protein Database Wang, Hongwei Liu, Fang Dong, Tiange Du, Likai Zhang, Dongju Gao, Jun ACS Omega [Image: see text] The charge-transfer coupling is an important component in tight-binding methods. Because of the highly complex chemical structure of biomolecules, the anisotropic feature of charge-transfer couplings in realistic proteins cannot be ignored. In this work, we have performed the first large-scale quantitative assessment of charge-transfer preference by calculating the charge-transfer couplings in all 20 × 20 possible amino acid side-chain combinations, which are extracted from available high-quality structures of thousands of protein complexes. The charge-transfer database quantitatively shows distinct features of charge-transfer couplings among millions of amino acid side-chain combinations. The overall distribution of charge-transfer couplings reveals that only one average or representative structure cannot be regarded as the typical charge-transfer preference in realistic proteins. This work provides us an alternative route to comprehensively understand the charge-transfer couplings for the overall distribution of realistic proteins in the foreseen big data scenario. American Chemical Society 2018-04-11 /pmc/articles/PMC6641752/ /pubmed/31458645 http://dx.doi.org/10.1021/acsomega.8b00336 Text en Copyright © 2018 American Chemical Society This is an open access article published under a Creative Commons Attribution (CC-BY) License (http://pubs.acs.org/page/policy/authorchoice_ccby_termsofuse.html) , which permits unrestricted use, distribution and reproduction in any medium, provided the author and source are cited. |
spellingShingle | Wang, Hongwei Liu, Fang Dong, Tiange Du, Likai Zhang, Dongju Gao, Jun Charge-Transfer Knowledge Graph among Amino Acids Derived from High-Throughput Electronic Structure Calculations for Protein Database |
title | Charge-Transfer Knowledge Graph among Amino Acids
Derived from High-Throughput Electronic Structure Calculations for
Protein Database |
title_full | Charge-Transfer Knowledge Graph among Amino Acids
Derived from High-Throughput Electronic Structure Calculations for
Protein Database |
title_fullStr | Charge-Transfer Knowledge Graph among Amino Acids
Derived from High-Throughput Electronic Structure Calculations for
Protein Database |
title_full_unstemmed | Charge-Transfer Knowledge Graph among Amino Acids
Derived from High-Throughput Electronic Structure Calculations for
Protein Database |
title_short | Charge-Transfer Knowledge Graph among Amino Acids
Derived from High-Throughput Electronic Structure Calculations for
Protein Database |
title_sort | charge-transfer knowledge graph among amino acids
derived from high-throughput electronic structure calculations for
protein database |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6641752/ https://www.ncbi.nlm.nih.gov/pubmed/31458645 http://dx.doi.org/10.1021/acsomega.8b00336 |
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