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基于疏水性分布对选定蛋白质的溶解度和聚集情况的解读

Solubility and Aggregation of Selected Proteins Interpreted on the Basis of Hydrophobicity Distribution.

作者信息

Ptak-Kaczor Magdalena, Banach Mateusz, Stapor Katarzyna, Fabian Piotr, Konieczny Leszek, Roterman Irena

机构信息

Department of Bioinformatics and Telemedicine, Jagiellonian University-Medical College, Medyczna 7, 30-688 Kraków, Poland.

Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, Łojasiewicza 11, 30-348 Kraków, Poland.

出版信息

Int J Mol Sci. 2021 May 8;22(9):5002. doi: 10.3390/ijms22095002.

DOI:10.3390/ijms22095002
PMID:34066830
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8125953/
Abstract

Protein solubility is based on the compatibility of the specific protein surface with the polar aquatic environment. The exposure of polar residues to the protein surface promotes the protein's solubility in the polar environment. The aquatic environment also influences the folding process by favoring the centralization of hydrophobic residues with the simultaneous exposure to polar residues. The degree of compatibility of the residue distribution, with the model of the concentration of hydrophobic residues in the center of the molecule, with the simultaneous exposure of polar residues is determined by the sequence of amino acids in the chain. The fuzzy oil drop model enables the quantification of the degree of compatibility of the hydrophobicity distribution observed in the protein to a form fully consistent with the Gaussian 3D function, which expresses an idealized distribution that meets the preferences of the polar water environment. The varied degrees of compatibility of the distribution observed with the idealized one allow the prediction of preferences to interactions with molecules of different polarity, including water molecules in particular. This paper analyzes a set of proteins with different levels of hydrophobicity distribution in the context of the solubility of a given protein and the possibility of complex formation.

摘要

蛋白质的溶解性基于特定蛋白质表面与极性水环境的兼容性。极性残基暴露于蛋白质表面会促进蛋白质在极性环境中的溶解性。水环境还通过促使疏水残基集中同时暴露极性残基来影响折叠过程。残基分布与分子中心疏水残基浓度模型以及极性残基同时暴露的兼容程度由链中氨基酸序列决定。模糊油滴模型能够将在蛋白质中观察到的疏水性分布的兼容程度量化为与高斯三维函数完全一致的形式,该函数表示一种符合极性水环境偏好的理想化分布。观察到的分布与理想化分布的不同兼容程度使得能够预测与不同极性分子相互作用的偏好,特别是与水分子的相互作用。本文在给定蛋白质的溶解性和形成复合物可能性的背景下,分析了一组具有不同疏水分布水平的蛋白质。

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Metab Eng Commun. 2020 Jun 22;11:e00138. doi: 10.1016/j.mec.2020.e00138. eCollection 2020 Dec.
3
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ACS Omega. 2024 Apr 10;9(16):18412-18428. doi: 10.1021/acsomega.4c00409. eCollection 2024 Apr 23.
4
Development of the 2a, 3a, 6, and artificial Invaplex (Invaplex) vaccines.2a、3a、6 型人工 Invaplex(Invaplex)疫苗的研制。
mSphere. 2023 Aug 24;8(4):e0007323. doi: 10.1128/msphere.00073-23. Epub 2023 Jun 30.
5
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J Mol Evol. 2023 Aug;91(4):382-390. doi: 10.1007/s00239-023-10120-5. Epub 2023 Jun 1.
6
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7
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8
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4
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Amyloid. 2020 Jun;27(2):128-133. doi: 10.1080/13506129.2020.1715363. Epub 2020 Jan 24.
5
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6
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