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题名: Improving protein fold recognition by extracting fold-specific features from predicted residue-residue contacts
作者: Zhu, JW ;  Zhang, HC ;  Li, SC ;  Wang, C ;  Kong, LP ;  Sun, SW ;  Zheng, WM ;  Bu, DB
刊名: BIOINFORMATICS
出版日期: 2017
卷号: 33, 期号:23, 页码:3749-3757
学科分类: Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology; Computer Science; Mathematical & Computational Biology; Mathematics
DOI: http://dx.doi.org/10.1093/bioinformatics/btx514
通讯作者: Bu, DB (reprint author), Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China. ;  Zheng, WM (reprint author), Chinese Acad Sci, Inst Theoret Phys, Beijing 100190, Peoples R China.
文章类型: Article
英文摘要: Motivation: Accurate recognition of protein fold types is a key step for template-based prediction of protein structures. The existing approaches to fold recognition mainly exploit the features derived from alignments of query protein against templates. These approaches have been shown to be successful for fold recognition at family level, but usually failed at superfamily/fold levels. To overcome this limitation, one of the key points is to explore more structurally informative features of proteins. Although residue-residue contacts carry abundant structural information, how to thoroughly exploit these information for fold recognition still remains a challenge. Results: In this study, we present an approach (called DeepFR) to improve fold recognition at superfamily/fold levels. The basic idea of our approach is to extract fold-specific features from predicted residue-residue contacts of proteins using deep convolutional neural network (DCNN) technique. Based on these fold-specific features, we calculated similarity between query protein and templates, and then assigned query protein with fold type of the most similar template. DCNN has showed excellent performance in image feature extraction and image recognition; the rational underlying the application of DCNN for fold recognition is that contact likelihood maps are essentially analogy to images, as they both display compositional hierarchy. Experimental results on the LINDAHL dataset suggest that even using the extracted fold-specific features alone, our approach achieved success rate comparable to the state-of-the-art approaches. When further combining these features with traditional alignment-related features, the success rate of our approach increased to 92.3%, 82.5% and 78.8% at family, superfamily and fold levels, respectively, which is about 18% higher than the state-of-the-art approach at fold level, 6% higher at superfamily level and 1% higher at family level. An independent assessment on SCOP_TEST dataset showed consistent performance improvement, indicating robustness of our approach. Furthermore, bi-clustering results of the extracted features are compatible with fold hierarchy of proteins, implying that these features are fold-specific. Together, these results suggest that the features extracted from predicted contacts are orthogonal to alignment-related features, and the combination of them could greatly facilitate fold recognition at superfamily/fold levels and template-based prediction of protein structures.
类目[WOS]: Biochemical Research Methods ;  Biotechnology & Applied Microbiology ;  Computer Science, Interdisciplinary Applications ;  Mathematical & Computational Biology ;  Statistics & Probability
关键词[WOS]: DIRECT-COUPLING ANALYSIS ;  CORRELATED MUTATIONS ;  STRUCTURAL CLASSIFICATION ;  SEQUENCE ;  INFORMATION ;  FRAGMENTS ;  NETWORKS ;  LEVEL ;  SCOP ;  SETS
项目资助者: National Basic Research Program of China [2013CB910104] ;  General Research Fund Grant [9041901 (CityU 118413)] ;  National Natural Science Foundation of China [31671369, 31270834, 61272318, 11175224, 11121403, 31270909, 31770775]
语种: 英语
WOS记录号: WOS:000417004100009
Citation statistics: 
内容类型: 期刊论文
URI标识: http://ir.itp.ac.cn/handle/311006/21924
Appears in Collections:理论物理所2017年知识产出_期刊论文

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Recommended Citation:
Zhu, JW,Zhang, HC,Li, SC,et al. Improving protein fold recognition by extracting fold-specific features from predicted residue-residue contacts[J]. BIOINFORMATICS,2017,33(23):3749-3757.
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