Computational probing protein-protein interactions targeting small molecules

doi:10.1093/bioinformatics/btv528

CSpace > 应用数学研究所

	Computational probing protein-protein interactions targeting small molecules
	Wang, Yong-Cui 1; Chen, Shi-Long 1; Deng, Nai-Yang 2; Wang, Yong3
	2016-01-15
发表期刊	BIOINFORMATICS
ISSN	1367-4803
卷号	32 期号:2 页码:226-234
摘要	Motivation: With the booming of interactome studies, a lot of interactions can be measured in a high throughput way and large scale datasets are available. It is becoming apparent that many different types of interactions can be potential drug targets. Compared with inhibition of a single protein, inhibition of protein-protein interaction (PPI) is promising to improve the specificity with fewer adverse side-effects. Also it greatly broadens the drug target search space, which makes the drug target discovery difficult. Computational methods are highly desired to efficiently provide candidates for further experiments and hold the promise to greatly accelerate the discovery of novel drug targets. Results: Here, we propose a machine learning method to predict PPI targets in a genomic-wide scale. Specifically, we develop a computational method, named as PrePPItar, to Predict PPIs as drug targets by uncovering the potential associations between drugs and PPIs. First, we survey the databases and manually construct a gold-standard positive dataset for drug and PPI interactions. This effort leads to a dataset with 227 associations among 63 PPIs and 113 FDA-approved drugs and allows us to build models to learn the association rules from the data. Second, we characterize drugs by profiling in chemical structure, drug ATC-code annotation, and side-effect space and represent PPI similarity by a symmetrical S-kernel based on protein amino acid sequence. Then the drugs and PPIs are correlated by Kronecker product kernel. Finally, a support vector machine (SVM), is trained to predict novel associations between drugs and PPIs. We validate our PrePPItar method on the well-established gold-standard dataset by cross-validation. We find that all chemical structure, drug ATC-code, and side-effect information are predictive for PPI target. Moreover, we can increase the PPI target prediction coverage by integrating multiple data sources. Follow-up database search and pathway analysis indicate that our new predictions are worthy of future experimental validation. Conclusion: In conclusion, PrePPItar can serve as a useful tool for PPI target discovery and provides a general heterogeneous data integrative framework.
DOI	10.1093/bioinformatics/btv528
语种	英语
资助项目	National Natural Science Foundation of China[11201470] ; National Natural Science Foundation of China[31270270] ; National Natural Science Foundation of China[61171007] ; National Natural Science Foundation of China[11422108] ; National Natural Science Foundation of China[11131009] ; National Natural Science Foundation of China[11371365] ; Strategic Priority Research Program of the Chinese Academy of Sciences (CAS)[XDB13040700]
WOS研究方向	Biochemistry & Molecular Biology ; Biotechnology & Applied Microbiology ; Computer Science ; Mathematical & Computational Biology ; Mathematics
WOS类目	Biochemical Research Methods ; Biotechnology & Applied Microbiology ; Computer Science, Interdisciplinary Applications ; Mathematical & Computational Biology ; Statistics & Probability
WOS记录号	WOS:000368360100010
出版者	OXFORD UNIV PRESS
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/21740
专题	应用数学研究所
通讯作者	Wang, Yong-Cui
作者单位	1.Chinese Acad Sci, Northwest Inst Plateau Biol, Key Lab Adaptat & Evolut Plateau Biota, Xining 810001, Peoples R China 2.China Agr Univ, Coll Sci, Beijing 100083, Peoples R China 3.Chinese Acad Sci, Acad Math & Syst Sci, Natl Ctr Math & Interdisciplinary Sci, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Wang, Yong-Cui,Chen, Shi-Long,Deng, Nai-Yang,et al. Computational probing protein-protein interactions targeting small molecules[J]. BIOINFORMATICS,2016,32(2):226-234.
APA	Wang, Yong-Cui,Chen, Shi-Long,Deng, Nai-Yang,&Wang, Yong.(2016).Computational probing protein-protein interactions targeting small molecules.BIOINFORMATICS,32(2),226-234.
MLA	Wang, Yong-Cui,et al."Computational probing protein-protein interactions targeting small molecules".BIOINFORMATICS 32.2(2016):226-234.