A neural network learning approach for improving the prediction of residue depth based on sequence-derived features
文献类型：期刊论文
| 作者 | Yan, Renxiang1,2; Wang, Xiaofeng3; Xu, Weiming1; Cai, Weiwen1; Lin, Juan1,2; Li, Jian4; Song, Jiangning4,5,6 |
| 刊名 | RSC ADVANCES
 |
| 出版日期 | 2016 |
| 卷号 | 6期号:72页码:67729-67738 |
| 英文摘要 | Residue depth is a solvent exposure measure that quantitatively describes the depth of a residue from the protein surface. It is an important parameter in protein structural biology. Residue depth can be used in protein ab initio folding, protein function annotation, and protein evolution simulation. Accordingly, accurate prediction of residue depth is an essential step towards the characterization of the protein function and development of novel protein structure prediction methods with optimized sensitivity and specificity. In this work, we propose an effective method termed as NNdepth for improved residue depth prediction. It uses sequence-derived features, including four types of sequence profiles, solvent accessibility, secondary structure and sequence length. Two sequence-to-depth neural networks were first constructed by incorporating various sources of information. Subsequently, a simple depth-to-depth equation was used to combine the two NN models and was shown to achieve an improved performance. We have designed and performed several experiments to systematically examine the performance of NNdepth. Our results demonstrate that NNdepth provides a more competitive performance when compared with our previous method evaluated using the Student t-test with a p-value < 0.001. Furthermore, we performed an in-depth analysis of the effect and importance of various features used by the models and also presented a case study to illustrate the utility and predictive power of NNdepth. To facilitate the wider research community, the NNdepth web server has been implemented and seamlessly incorporated as one of the components of our previously developed outer membrane prediction systems (available at http://genomics.fzu.edu.cn/OMP). In addition, a stand-alone software program is also publicly accessible and downloadable at the website. We envision that NNdepth should be a powerful tool for high-throughput structural genomics and protein functional annotations. |
| WOS标题词 | Science & Technology ; Physical Sciences |
| 类目[WOS] | Chemistry, Multidisciplinary |
| 研究领域[WOS] | Chemistry |
| 关键词[WOS] | PROTEIN SECONDARY STRUCTURE ; FOLD RECOGNITION ; AMINO-ACID ; SOLVENT ACCESSIBILITY ; STRUCTURAL FEATURES ; ACCURATE PREDICTION ; WEB SERVER ; PSI-BLAST ; DATABASE ; EXPOSURE |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000380362700033 |
| 源URL | [http://124.16.173.210/handle/834782/2916]  |
| 专题 | 天津工业生物技术研究所_结构生物信息学和整合系统生物学实验室 宋江宁_期刊论文 |
| 作者单位 | 1.Fuzhou Univ, Sch Biol Sci & Engn, Fuzhou 350108, Peoples R China 2.Fujian Key Lab Marine Enzyme Engn, Fuzhou 350002, Peoples R China 3.Shanxi Normal Univ, Coll Math & Comp Sci, Linfen 041004, Peoples R China 4.Monash Univ, Biomed Discovery Inst, Infect & Immun Program, Melbourne, Vic 3800, Australia 5.Monash Univ, Fac Informat Technol, Monash Ctr Data Sci, Melbourne, Vic 3800, Australia 6.Chinese Acad Sci, Tianjin Inst Ind Biotechnol, Natl Engn Lab Ind Enzymes, Key Lab Syst Microbial Biotechnol, Tianjin 300308, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yan, Renxiang,Wang, Xiaofeng,Xu, Weiming,et al. A neural network learning approach for improving the prediction of residue depth based on sequence-derived features[J]. RSC ADVANCES,2016,6(72):67729-67738. |
| APA | Yan, Renxiang.,Wang, Xiaofeng.,Xu, Weiming.,Cai, Weiwen.,Lin, Juan.,...&Song, Jiangning.(2016).A neural network learning approach for improving the prediction of residue depth based on sequence-derived features.RSC ADVANCES,6(72),67729-67738. |
| MLA | Yan, Renxiang,et al."A neural network learning approach for improving the prediction of residue depth based on sequence-derived features".RSC ADVANCES 6.72(2016):67729-67738. |
入库方式： OAI收割
来源：天津工业生物技术研究所
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