ITP OpenIR  > SCI期刊论文
Zhu, JW; Zhang, HC; Li, SC; Wang, C; Kong, LP; Sun, SW; Zheng, WM; Bu, DB; 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.
Improving protein fold recognition by extracting fold-specific features from predicted residue-residue contacts
Source PublicationBIOINFORMATICS
Language英语
AbstractMotivation: 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.
2017
Volume33Issue:23Pages:3749-3757
Subject AreaBiochemistry & Molecular Biology ; Biotechnology & Applied Microbiology ; Computer Science ; Mathematical & Computational Biology ; Mathematics
DOIhttp://dx.doi.org/10.1093/bioinformatics/btx514
Funding OrganizationNational Basic Research Program of China [2013CB910104] ; National Basic Research Program of China [2013CB910104] ; National Basic Research Program of China [2013CB910104] ; National Basic Research Program of China [2013CB910104] ; General Research Fund Grant [9041901 (CityU 118413)] ; General Research Fund Grant [9041901 (CityU 118413)] ; General Research Fund Grant [9041901 (CityU 118413)] ; General Research Fund Grant [9041901 (CityU 118413)] ; National Natural Science Foundation of China [31671369, 31270834, 61272318, 11175224, 11121403, 31270909, 31770775] ; National Natural Science Foundation of China [31671369, 31270834, 61272318, 11175224, 11121403, 31270909, 31770775] ; National Natural Science Foundation of China [31671369, 31270834, 61272318, 11175224, 11121403, 31270909, 31770775] ; National Natural Science Foundation of China [31671369, 31270834, 61272318, 11175224, 11121403, 31270909, 31770775]
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Cited Times:4[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.itp.ac.cn/handle/311006/21924
CollectionSCI期刊论文
Corresponding AuthorBu, 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.
Recommended Citation
GB/T 7714
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.
APA Zhu, JW.,Zhang, HC.,Li, SC.,Wang, C.,Kong, LP.,...&Zheng, WM .(2017).Improving protein fold recognition by extracting fold-specific features from predicted residue-residue contacts.BIOINFORMATICS,33(23),3749-3757.
MLA Zhu, JW,et al."Improving protein fold recognition by extracting fold-specific features from predicted residue-residue contacts".BIOINFORMATICS 33.23(2017):3749-3757.
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