qProtein: Exploring Physical Features of Protein Thermostability Based on Structural Proteomics

Thermostability, which is essential for the functional performance of enzymes, is largely determined by intramolecular physical interactions. Although many tools have been developed, existing computational methods have struggled to find the universal principles of protein thermostability. Recent advancements in structural proteomics have been driven by the introduction of deep neural networks such as AlphaFold2 and ESMFold. These innovations have enabled the characterization of protein structures with unprecedented speed and accuracy. Here, we introduce qProtein, a Python-implemented workflow designed for the quantitative analysis of physical interactions on the scale of structural proteomics. This platform accepts protein sequences as input and produces four structural features, including hydrophobic clusters, hydrogen bonds, electrostatic interactions, and disulfide bonds. To demonstrate the use of qProtein, we investigate the structural features related to protein thermostability in six glycoside hydrolase (GH) families, comprising a total of 3,811 protein structures. Our results indicate that in five enzyme families (GH11, GH12, GH5_2, GH10, and GH48), the thermophilic enzymes have a larger average area of hydrophobic clusters compared to the nonthermophilic enzymes within each family. Furthermore, our analysis of the local-structure regions reveals that the hydrophobic clusters are predominantly distributed in the distal regions of the GH11 enzymes. In addition, the average hydrophobic cluster area of the thermophilic enzymes is significantly higher than that of the nonthermophilic enzymes in the distal regions of the GH11 enzymes. Therefore, qProtein is a well-suited platform for analyzing the structural features of thermal stability at the level of structural proteomics. We provide the source code for qProtein at https://github.com/bj600800/qProtein, and the web server is available at http://qProtein.sdu.edu.cn:8888.

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Publication Details

Journal
Journal of Chemical Information and Modeling
Published
2024-10-07
DOI
https://doi.org/10.1021/acs.jcim.4c01303
Citations
4
Primary Topic
Protein Structure and Dynamics
Type
article
Field-Weighted Citation Impact
0.94

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article

qProtein: Exploring Physical Features of Protein Thermostability Based on Structural Proteomics

Xiuyun Wu, Zhixin Dou, Yanwei Zheng, Jian Yang et al.
4 citations
Journal of Chemical Information and Modeling
Protein Structure and Dynamics
0.94
article

qProtein: Exploring Physical Features of Protein Thermostability Based on Structural Proteomics

Xiuyun Wu, Zhixin Dou, Yanwei Zheng, Jian Yang, Lin Wan, Chao Han, Bin Gong, Lushan Wang, Jiaxin He
article en
4 citations

Abstract

Thermostability, which is essential for the functional performance of enzymes, is largely determined by intramolecular physical interactions. Although many tools have been developed, existing computational methods have struggled to find the universal principles of protein thermostability. Recent advancements in structural proteomics have been driven by the introduction of deep neural networks such as AlphaFold2 and ESMFold. These innovations have enabled the characterization of protein structures with unprecedented speed and accuracy. Here, we introduce qProtein, a Python-implemented workflow designed for the quantitative analysis of physical interactions on the scale of structural proteomics. This platform accepts protein sequences as input and produces four structural features, including hydrophobic clusters, hydrogen bonds, electrostatic interactions, and disulfide bonds. To demonstrate the use of qProtein, we investigate the structural features related to protein thermostability in six glycoside hydrolase (GH) families, comprising a total of 3,811 protein structures. Our results indicate that in five enzyme families (GH11, GH12, GH5_2, GH10, and GH48), the thermophilic enzymes have a larger average area of hydrophobic clusters compared to the nonthermophilic enzymes within each family. Furthermore, our analysis of the local-structure regions reveals that the hydrophobic clusters are predominantly distributed in the distal regions of the GH11 enzymes. In addition, the average hydrophobic cluster area of the thermophilic enzymes is significantly higher than that of the nonthermophilic enzymes in the distal regions of the GH11 enzymes. Therefore, qProtein is a well-suited platform for analyzing the structural features of thermal stability at the level of structural proteomics. We provide the source code for qProtein at https://github.com/bj600800/qProtein, and the web server is available at http://qProtein.sdu.edu.cn:8888.

Journal of Chemical Information and ModelingVol. 64(20)
Shandong University (CN), Qingdao Binhai University (CN), State Key Laboratory of Microbial Technology, Shandong Agricultural University (CN)
National Natural Science Foundation of China, National Key Research and Development Program of China
Openalex Percentile: Top 25%
Protein Structure and Dynamics
0.94
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