An Integrative Computational Framework Based on a Two-Step Random Forest Algorithm Improves Prediction of Zinc-Binding Sites in Proteins
文献类型：期刊论文
| 作者 | Zheng, Cheng; Wang, Mingjun; Takemoto, Kazuhiro; Akutsu, Tatsuya; Zhang, Ziding; Song, Jiangning |
| 刊名 | PLOS ONE
 |
| 出版日期 | 2012-11-14 |
| 卷号 | 7期号:11页码:e49716 |
| 英文摘要 | Zinc-binding proteins are the most abundant metalloproteins in the Protein Data Bank where the zinc ions usually have catalytic, regulatory or structural roles critical for the function of the protein. Accurate prediction of zinc-binding sites is not only useful for the inference of protein function but also important for the prediction of 3D structure. Here, we present a new integrative framework that combines multiple sequence and structural properties and graph-theoretic network features, followed by an efficient feature selection to improve prediction of zinc-binding sites. We investigate what information can be retrieved from the sequence, structure and network levels that is relevant to zinc-binding site prediction. We perform a two-step feature selection using random forest to remove redundant features and quantify the relative importance of the retrieved features. Benchmarking on a high-quality structural dataset containing 1,103 protein chains and 484 zinc-binding residues, our method achieved >80% recall at a precision of 75% for the zinc-binding residues Cys, His, Glu and Asp on 5-fold cross-validation tests, which is a 10%-28% higher recall at the 75% equal precision compared to SitePredict and zincfinder at residue level using the same dataset. The independent test also indicates that our method has achieved recall of 0.790 and 0.759 at residue and protein levels, respectively, which is a performance better than the other two methods. Moreover, AUC (the Area Under the Curve) and AURPC (the Area Under the Recall-Precision Curve) by our method are also respectively better than those of the other two methods. Our method can not only be applied to large-scale identification of zinc-binding sites when structural information of the target is available, but also give valuable insights into important features arising from different levels that collectively characterize the zinc-binding sites. The scripts and datasets are available at http://protein.cau.edu.cn/zincidentifier/. |
| WOS标题词 | Science & Technology |
| 类目[WOS] | Multidisciplinary Sciences |
| 研究领域[WOS] | Science & Technology - Other Topics |
| 关键词[WOS] | SEQUENCE-BASED PREDICTION ; SECONDARY STRUCTURE ; AMINO-ACID ; DISULFIDE CONNECTIVITY ; FEATURE-SELECTION ; NEURAL-NETWORKS ; RESIDUE DEPTH ; ACCURATE ; RECOGNITION ; INFORMATION |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000311151900184 |
| 公开日期 | 2013-01-16 |
| 源URL | [http://124.16.173.210/handle/312001/302]  |
| 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 | Chinese Acad Sci, Tianjin Inst Ind Biotechnol, Natl Engn Lab Ind Enzymes, Tianjin, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zheng, Cheng,Wang, Mingjun,Takemoto, Kazuhiro,et al. An Integrative Computational Framework Based on a Two-Step Random Forest Algorithm Improves Prediction of Zinc-Binding Sites in Proteins[J]. PLOS ONE,2012,7(11):e49716. |
| APA | Zheng, Cheng,Wang, Mingjun,Takemoto, Kazuhiro,Akutsu, Tatsuya,Zhang, Ziding,&Song, Jiangning.(2012).An Integrative Computational Framework Based on a Two-Step Random Forest Algorithm Improves Prediction of Zinc-Binding Sites in Proteins.PLOS ONE,7(11),e49716. |
| MLA | Zheng, Cheng,et al."An Integrative Computational Framework Based on a Two-Step Random Forest Algorithm Improves Prediction of Zinc-Binding Sites in Proteins".PLOS ONE 7.11(2012):e49716. |
入库方式： OAI收割
来源：天津工业生物技术研究所
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