Seq-SetNet: directly exploiting multiple sequence alignment for protein secondary structure prediction

doi:10.1093/bioinformatics/btab777

CORC > 理论物理研究所 > 中国科学院理论物理研究所 > 理论物理所科研产出 > 理论物理所1978-2010年知识产出

	Seq-SetNet: directly exploiting multiple sequence alignment for protein secondary structure prediction
	Ju, Fusong ; Zhu, Jianwei 2; Zhang, Qi ; Wei, Guozheng ; Sun, Shiwei 3; Zheng, Wei-Mou ; Bu, Dongbo 1,3
刊名	BIOINFORMATICS
	2022
卷号	38 期号:4 页码:990-996
关键词	RECOGNITION FRAGMENTS FOLD
ISSN号	1367-4803
DOI	10.1093/bioinformatics/btab777
英文摘要	Motivation: Accurate prediction of protein structure relies heavily on exploiting multiple sequence alignment (MSA) for residue mutations and correlations as this information specifies protein tertiary structure. The widely used prediction approaches usually transform MSA into inter-mediate models, say position-specific scoring matrix or profile hidden Markov model. These inter-mediate models, however, cannot fully represent residue mutations and correlations carried by MSA; hence, an effective way to directly exploit MSAs is highly desirable. Results: Here, we report a novel sequence set network (called Seq-SetNet) to directly and effectively exploit MSA for protein structure prediction. Seq-SetNet uses an `encoding and aggregation' strategy that consists of two key elements: (i) an encoding module that takes a component homologue in MSA as input, and encodes residue mutations and correlations into context-specific features for each residue; and (ii) an aggregation module to aggregate the features extracted from all component homologues, which are further transformed into structural properties for residues of the query protein. As Seq-SetNet encodes each homologue protein individually, it could consider both insertions and deletions, as well as long-distance correlations among residues, thus representing more information than the inter-mediate models. Moreover, the encoding module automatically learns effective features and thus avoids manual feature engineering. Using symmetric aggregation functions, Seq-SetNet processes the homologue proteins as a sequence set, making its prediction results invariable to the order of these proteins. On popular benchmark sets, we demonstrated the successful application of Seq-SetNet to predict secondary structure and torsion angles of residues with improved accuracy and efficiency.
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/311006/27854]
专题	理论物理研究所_理论物理所1978-2010年知识产出
作者单位	1.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Microsoft Res Asia, Beijing 100080, Peoples R China 4.Zhongke Big Data Acad, Zhengzhou 450046, Henan, Peoples R China 5.Chinese Acad Sci, Inst Theoret Phys, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Ju, Fusong,Zhu, Jianwei,Zhang, Qi,et al. Seq-SetNet: directly exploiting multiple sequence alignment for protein secondary structure prediction[J]. BIOINFORMATICS,2022,38(4):990-996.
APA	Ju, Fusong.,Zhu, Jianwei.,Zhang, Qi.,Wei, Guozheng.,Sun, Shiwei.,...&Bu, Dongbo.(2022).Seq-SetNet: directly exploiting multiple sequence alignment for protein secondary structure prediction.BIOINFORMATICS,38(4),990-996.
MLA	Ju, Fusong,et al."Seq-SetNet: directly exploiting multiple sequence alignment for protein secondary structure prediction".BIOINFORMATICS 38.4(2022):990-996.