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A systematic structural comparison of all solved small proteins deposited in PDB. The effect of disulfide bonds in protein fold
Defensins are small proteins, usually ranging from 3 to 6 kDa, amphipathic, disulfide-rich, and with a small or even absent hydrophobic core. Since a hydrophobic core is generally found in globular proteins that fold in an aqueous solvent, the peculiar fold of defensins can challenge tertiary protei...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Research Network of Computational and Structural Biotechnology
2021
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8712280/ https://www.ncbi.nlm.nih.gov/pubmed/35024090 http://dx.doi.org/10.1016/j.csbj.2021.11.015 |
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author | Moreira, Mariana H. Almeida, Fabio C.L. Domitrovic, Tatiana Palhano, Fernando L. |
author_facet | Moreira, Mariana H. Almeida, Fabio C.L. Domitrovic, Tatiana Palhano, Fernando L. |
author_sort | Moreira, Mariana H. |
collection | PubMed |
description | Defensins are small proteins, usually ranging from 3 to 6 kDa, amphipathic, disulfide-rich, and with a small or even absent hydrophobic core. Since a hydrophobic core is generally found in globular proteins that fold in an aqueous solvent, the peculiar fold of defensins can challenge tertiary protein structure predictors. We performed a Protein Data Bank survey of small proteins (3–6 kDa) to understand the similarities of defensins with other small disulfide-rich proteins. We found no differences when we compared defensins with non-defensins regarding the proportion of apolar, polar and charged residues and their exposure to the solvent. Then we divided all small proteins (3–6 kDa) in the Protein Data Bank into two groups, one group with at least one disulfide bond (bonded, defensins included) and another group without any disulfide bond (unbonded). The group of bonded proteins contained apolar residues more exposed to the solvent than the unbonded group. The ab initio algorithm for tertiary protein structure prediction Robetta was more accurate at predicting unbonded than bonded proteins. On the other hand, the trRosetta algorithm, which uses artificial intelligence, improved the prediction of most bonded proteins, while for the unbonded group no improvement was obtained. Our work highlights one more layer of complexity for the prediction of protein tertiary structure: The ability of small disulfide-rich proteins to fold even with a poorly hydrophobic core. |
format | Online Article Text |
id | pubmed-8712280 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Research Network of Computational and Structural Biotechnology |
record_format | MEDLINE/PubMed |
spelling | pubmed-87122802022-01-11 A systematic structural comparison of all solved small proteins deposited in PDB. The effect of disulfide bonds in protein fold Moreira, Mariana H. Almeida, Fabio C.L. Domitrovic, Tatiana Palhano, Fernando L. Comput Struct Biotechnol J Research Article Defensins are small proteins, usually ranging from 3 to 6 kDa, amphipathic, disulfide-rich, and with a small or even absent hydrophobic core. Since a hydrophobic core is generally found in globular proteins that fold in an aqueous solvent, the peculiar fold of defensins can challenge tertiary protein structure predictors. We performed a Protein Data Bank survey of small proteins (3–6 kDa) to understand the similarities of defensins with other small disulfide-rich proteins. We found no differences when we compared defensins with non-defensins regarding the proportion of apolar, polar and charged residues and their exposure to the solvent. Then we divided all small proteins (3–6 kDa) in the Protein Data Bank into two groups, one group with at least one disulfide bond (bonded, defensins included) and another group without any disulfide bond (unbonded). The group of bonded proteins contained apolar residues more exposed to the solvent than the unbonded group. The ab initio algorithm for tertiary protein structure prediction Robetta was more accurate at predicting unbonded than bonded proteins. On the other hand, the trRosetta algorithm, which uses artificial intelligence, improved the prediction of most bonded proteins, while for the unbonded group no improvement was obtained. Our work highlights one more layer of complexity for the prediction of protein tertiary structure: The ability of small disulfide-rich proteins to fold even with a poorly hydrophobic core. Research Network of Computational and Structural Biotechnology 2021-11-17 /pmc/articles/PMC8712280/ /pubmed/35024090 http://dx.doi.org/10.1016/j.csbj.2021.11.015 Text en © 2021 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology. https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Research Article Moreira, Mariana H. Almeida, Fabio C.L. Domitrovic, Tatiana Palhano, Fernando L. A systematic structural comparison of all solved small proteins deposited in PDB. The effect of disulfide bonds in protein fold |
title | A systematic structural comparison of all solved small proteins deposited in PDB. The effect of disulfide bonds in protein fold |
title_full | A systematic structural comparison of all solved small proteins deposited in PDB. The effect of disulfide bonds in protein fold |
title_fullStr | A systematic structural comparison of all solved small proteins deposited in PDB. The effect of disulfide bonds in protein fold |
title_full_unstemmed | A systematic structural comparison of all solved small proteins deposited in PDB. The effect of disulfide bonds in protein fold |
title_short | A systematic structural comparison of all solved small proteins deposited in PDB. The effect of disulfide bonds in protein fold |
title_sort | systematic structural comparison of all solved small proteins deposited in pdb. the effect of disulfide bonds in protein fold |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8712280/ https://www.ncbi.nlm.nih.gov/pubmed/35024090 http://dx.doi.org/10.1016/j.csbj.2021.11.015 |
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