FALCON@home: a high-throughput protein structure prediction server based on remote homologue recognition

doi:http://dx.doi.org/10.1093/bioinformatics/btv581

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	FALCON@home: a high-throughput protein structure prediction server based on remote homologue recognition
	Xu, D; Wang, C; Zhang, HC; Zheng, WM; Wang, B; Ning, K; Sun, SW; Li, SC; Bu, DB; Bu, DB (reprint author), Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing, Peoples R China.
刊名	BIOINFORMATICS
	2016
卷号	32 期号:3 页码:462-464
DOI	http://dx.doi.org/10.1093/bioinformatics/btv581
英文摘要	The protein structure prediction approaches can be categorized into template-based modeling (including homology modeling and threading) and free modeling. However, the existing threading tools perform poorly on remote homologous proteins. Thus, improving fold recognition for remote homologous proteins remains a challenge. Besides, the proteome-wide structure prediction poses another challenge of increasing prediction throughput. In this study, we presented FALCON@home as a protein structure prediction server focusing on remote homologue identification. The design of FALCON@home is based on the observation that a structural template, especially for remote homologous proteins, consists of conserved regions interweaved with highly variable regions. The highly variable regions lead to vague alignments in threading approaches. Thus, FALCON@home first extracts conserved regions from each template and then aligns a query protein with conserved regions only rather than the full-length template directly. This helps avoid the vague alignments rooted in highly variable regions, improving remote homologue identification. We implemented FALCON@home using the Berkeley Open Infrastructure of Network Computing (BOINC) volunteer computing protocol. With computation power donated from over 20 000 volunteer CPUs, FALCON@home shows a throughput as high as processing of over 1000 proteins per day. In the Critical Assessment of protein Structure Prediction (CASP11), the FALCON@homebased prediction was ranked the 12th in the template-based modeling category. As an application, the structures of 880 mouse mitochondria proteins were predicted, which revealed the significant correlation between protein half-lives and protein structural factors.
学科主题	Biochemistry & Molecular Biology ; Biotechnology & Applied Microbiology ; Computer Science ; Mathematical & Computational Biology ; Mathematics
语种	英语
内容类型	期刊论文
源URL	[http://ir.itp.ac.cn/handle/311006/21788]
专题	理论物理研究所_理论物理所1978-2010年知识产出
通讯作者	Bu, DB (reprint author), Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing, Peoples R China.; Li, SC (reprint author), City Univ Hong Kong, Dept Comp Sci, Kowloon, Hong Kong, Peoples R China.
推荐引用方式 GB/T 7714	Xu, D,Wang, C,Zhang, HC,et al. FALCON@home: a high-throughput protein structure prediction server based on remote homologue recognition[J]. BIOINFORMATICS,2016,32(3):462-464.
APA	Xu, D.,Wang, C.,Zhang, HC.,Zheng, WM.,Wang, B.,...&Zhu, JW.(2016).FALCON@home: a high-throughput protein structure prediction server based on remote homologue recognition.BIOINFORMATICS,32(3),462-464.
MLA	Xu, D,et al."FALCON@home: a high-throughput protein structure prediction server based on remote homologue recognition".BIOINFORMATICS 32.3(2016):462-464.