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Improving protein fold recognition by extracting fold-specific features from predicted residue-residue contacts

文献类型：期刊论文

作者Zhu, Jianwei2,3; Zhang, Haicang2; Li, Shuai Cheng1; Wang, Chao2; Kong, Lupeng2,3; Sun, Shiwei2; Zheng, Wei-Mou4; Bu, Dongbo2
刊名BIOINFORMATICS
出版日期2017-12-01
卷号33期号:23页码:3749-3757
ISSN号1367-4803
DOI10.1093/bioinformatics/btx514
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.
资助项目National Basic Research Program of China[2013CB910104] ; General Research Fund Grant[9041901 (CityU 118413)] ; National Natural Science Foundation of China[31671369] ; National Natural Science Foundation of China[31270834] ; National Natural Science Foundation of China[61272318] ; National Natural Science Foundation of China[11175224] ; National Natural Science Foundation of China[11121403] ; National Natural Science Foundation of China[31270909] ; National Natural Science Foundation of China[31770775]
WOS研究方向Biochemistry & Molecular Biology ; Biotechnology & Applied Microbiology ; Computer Science ; Mathematical & Computational Biology ; Mathematics
语种英语
WOS记录号WOS:000417004100009
出版者OXFORD UNIV PRESS
源URL[http://119.78.100.204/handle/2XEOYT63/5490]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Zheng, Wei-Mou; Bu, Dongbo
作者单位1.City Univ Hong Kong, Dept Comp Sci, Kowloon, Hong Kong, Peoples R China
2.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China
3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
4.Chinese Acad Sci, Inst Theoret Phys, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Zhu, Jianwei,Zhang, Haicang,Li, Shuai Cheng,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, Jianwei.,Zhang, Haicang.,Li, Shuai Cheng.,Wang, Chao.,Kong, Lupeng.,...&Bu, Dongbo.(2017).Improving protein fold recognition by extracting fold-specific features from predicted residue-residue contacts.BIOINFORMATICS,33(23),3749-3757.
MLA Zhu, Jianwei,et al."Improving protein fold recognition by extracting fold-specific features from predicted residue-residue contacts".BIOINFORMATICS 33.23(2017):3749-3757.

入库方式： OAI收割

来源：计算技术研究所

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