Prediction of disulfide bond engineering sites using a machine learning method

doi:10.1038/s41598-020-67230-z

	Prediction of disulfide bond engineering sites using a machine learning method
	Gao, Xiang 1,2; Dong, Xiaoqun 1,2; Li, Xuanxuan 1,3; Liu, Zhijie4 ; Liu, Haiguang 1
	2020-06-25
发表期刊	SCIENTIFIC REPORTS (IF:3.8[JCR-2023],4.3[5-Year])
ISSN	2045-2322
卷号	10 期号:1
发表状态	已发表
DOI	10.1038/s41598-020-67230-z
摘要	Disulfide bonds are covalently bonded sulfur atoms from cysteine pairs in protein structures. Due to the importance of disulfide bonds in protein folding and structural stability, artificial disulfide bonds are often engineered by cysteine mutation to enhance protein structural stability. To facilitate the experimental design, we implemented a method based on neural networks to predict amino acid pairs for cysteine mutations to form engineered disulfide bonds. The designed neural network was trained with high-resolution structures curated from the Protein Data Bank. The testing results reveal that the proposed method recognizes 99% of natural disulfide bonds. In the test with engineered disulfide bonds, the algorithm achieves similar accuracy levels with other state-of-the-art algorithms in published dataset and better performance for two comprehensively studied proteins with 70% accuracy, demonstrating potential applications in protein engineering. The neural network framework allows exploiting the full features in distance space, and therefore improves accuracy of the disulfide bond engineering site prediction. The source code and a web server are available at http://liulab.csrc.ac.cn/ssbondpre.
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收录类别	SCI ; SCIE
语种	英语
资助项目	National Natural Science Foundation of China[31971136][U1530402][U1430237]
WOS研究方向	Science & Technology - Other Topics
WOS类目	Multidisciplinary Sciences
WOS记录号	WOS:000546571800007
出版者	NATURE PUBLISHING GROUP
WOS关键词	PROTEIN ; STABILITY
原始文献类型	Article
引用统计	正在获取...
文献类型	期刊论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/122445
专题	iHuman研究所_PI研究组_刘志杰组
通讯作者	Liu, Haiguang
作者单位	1.Beijing Computat Sci Res Ctr, Complex Syst Div, 8 E Xibeiwang Rd, Beijing 100193, Peoples R China 2.Univ Sci & Technol China, Sch Software Engn, Suzhou 215123, Jiangsu, Peoples R China 3.Tsinghua Univ, Dept Engn Phys, Beijing 100084, Peoples R China 4.ShanghaiTech Univ, iHuman Inst, 393 Middle Huaxia Rd, Shanghai 201210, Peoples R China
推荐引用方式 GB/T 7714	Gao, Xiang,Dong, Xiaoqun,Li, Xuanxuan,et al. Prediction of disulfide bond engineering sites using a machine learning method[J]. SCIENTIFIC REPORTS,2020,10(1).
APA	Gao, Xiang,Dong, Xiaoqun,Li, Xuanxuan,Liu, Zhijie,&Liu, Haiguang.(2020).Prediction of disulfide bond engineering sites using a machine learning method.SCIENTIFIC REPORTS,10(1).
MLA	Gao, Xiang,et al."Prediction of disulfide bond engineering sites using a machine learning method".SCIENTIFIC REPORTS 10.1(2020).