ISSEC: inferring contacts among protein secondary structure elements using deep object detection

doi:10.1186/s12859-020-03793-y

	ISSEC: inferring contacts among protein secondary structure elements using deep object detection
	Zhang, Qi 1,2; Zhu, Jianwei 1,2; Ju, Fusong 1,2; Kong, Lupeng 1,2; Sun, Shiwei 1,2; Zheng, Wei-Mou 3; Bu, Dongbo 1,2
	2020-11-05
发表期刊	BMC BIOINFORMATICS
ISSN	1471-2105
卷号	21 期号:1 页码:13
摘要	Background The formation of contacts among protein secondary structure elements (SSEs) is an important step in protein folding as it determines topology of protein tertiary structure; hence, inferring inter-SSE contacts is crucial to protein structure prediction. One of the existing strategies infers inter-SSE contacts directly from the predicted possibilities of inter-residue contacts without any preprocessing, and thus suffers from the excessive noises existing in the predicted inter-residue contacts. Another strategy defines SSEs based on protein secondary structure prediction first, and then judges whether each candidate SSE pair could form contact or not. However, it is difficult to accurately determine boundary of SSEs due to the errors in secondary structure prediction. The incorrectly-deduced SSEs definitely hinder subsequent prediction of the contacts among them. Results We here report an accurate approach to infer the inter-SSE contacts (thus called as ISSEC) using the deep object detection technique. The design of ISSEC is based on the observation that, in the inter-residue contact map, the contacting SSEs usually form rectangle regions with characteristic patterns. Therefore, ISSEC infers inter-SSE contacts through detecting such rectangle regions. Unlike the existing approach directly using the predicted probabilities of inter-residue contact, ISSEC applies the deep convolution technique to extract high-level features from the inter-residue contacts. More importantly, ISSEC does not rely on the pre-defined SSEs. Instead, ISSEC enumerates multiple candidate rectangle regions in the predicted inter-residue contact map, and for each region, ISSEC calculates a confidence score to measure whether it has characteristic patterns or not. ISSEC employs greedy strategy to select non-overlapping regions with high confidence score, and finally infers inter-SSE contacts according to these regions. Conclusions Comprehensive experimental results suggested that ISSEC outperformed the state-of-the-art approaches in predicting inter-SSE contacts. We further demonstrated the successful applications of ISSEC to improve prediction of both inter-residue contacts and tertiary structure as well.
关键词	Protein structure Secondary structure elements Inter-SSE contacts
DOI	10.1186/s12859-020-03793-y
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2018YFC0910405] ; National Natural Science Foundation of China[31671369] ; National Natural Science Foundation of China[31770775]
WOS研究方向	Biochemistry & Molecular Biology ; Biotechnology & Applied Microbiology ; Mathematical & Computational Biology
WOS类目	Biochemical Research Methods ; Biotechnology & Applied Microbiology ; Mathematical & Computational Biology
WOS记录号	WOS:000586943200001
出版者	BMC
引用统计	被引频次：3[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/16017
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Bu, Dongbo
作者单位	1.Chinese Acad Sci, Big Data Acad, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Comp Sci, Beijing, Peoples R China 3.Chinese Acad Sci, Inst Theoret Phys, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Zhang, Qi,Zhu, Jianwei,Ju, Fusong,et al. ISSEC: inferring contacts among protein secondary structure elements using deep object detection[J]. BMC BIOINFORMATICS,2020,21(1):13.
APA	Zhang, Qi.,Zhu, Jianwei.,Ju, Fusong.,Kong, Lupeng.,Sun, Shiwei.,...&Bu, Dongbo.(2020).ISSEC: inferring contacts among protein secondary structure elements using deep object detection.BMC BIOINFORMATICS,21(1),13.
MLA	Zhang, Qi,et al."ISSEC: inferring contacts among protein secondary structure elements using deep object detection".BMC BIOINFORMATICS 21.1(2020):13.