Recognizing and Predicting Thioether Bridges Formed by Lanthionine and beta-Methyllanthionine in Lantibiotics Using a Random Forest Approach with Feature Selection
文献类型：期刊论文
| 作者 | Wang, ShaoPeng3; Cai, Yu-Dong3; Zhang, Yu-Hang1; Huang, Tao1; Zhang, Ning4; Chen, Lei2; , |
| 刊名 | COMBINATORIAL CHEMISTRY & HIGH THROUGHPUT SCREENING
 |
| 出版日期 | 2017 |
| 卷号 | 20期号:7页码:582-593 |
| 关键词 | Post-translational modifications lantibiotics lanthionine beta-methyllanthionine random forest maximal relevance minimal redundancy |
| ISSN号 | 1386-2073 |
| DOI | 10.2174/1386207320666170310115754 |
| 文献子类 | Article |
| 英文摘要 | Background: Lantibiotics, which are usually produced from Gram-positive bacteria, are regarded as one type of special bacteriocins. Lantibiotics have unsaturated amino acid residues formed by lanthionine (Lan) and beta-methyllanthionine (MeLan) residues as a ring structure in the peptide. They are derived from the serine and threonine residues and are essential to preventing the growth of other similar strains. Method: In this pioneering work, we firstly proposed a machine learning method to recognize and predict the Lan and MeLan residues in the protein sequences of lantibiotics. We adopted maximal relevance minimal redundancy (mRMR) and incremental feature selection (IFS) to select optimal features and random forest (RF) to build classifiers determining the Lan and MeLan residues. A 10-fold cross-validation test was performed on the classifiers to evaluate their predicted performances. Results: The Matthew's correlation coefficient (MCC) values for predicting the Lan and MeLan residues were 0.813 and 0.769, respectively. Our constructed RF classifiers were shown to have a reliable ability to recognize Lan and MeLan residues from lantibiotic sequences. Furthermore, three other methods, Dagging, the nearest neighbor algorithm (NNA) and sequential minimal optimization (SMO) were also utilized to build classifiers to predict Lan and MeLan residues for comparison. Analysis was also performed on the optimal features, and the relationships between the optimal features and their biological importance were provided. Conclusion: The selected optimal features and analysis in this work will contribute to a better understanding of the sequence and structural features around the Lan and MeLan residues. It could provide useful information and practical suggestions for experimental and computational methods toward exploring the biological features of such special residues in lantibiotics. |
| 学科主题 | Biochemistry & Molecular Biology ; Chemistry ; Pharmacology & Pharmacy |
| WOS关键词 | SUPPORT VECTOR MACHINES ; GRAM-POSITIVE BACTERIA ; AMINO-ACID-COMPOSITION ; RIBOSOMAL-RNA-BINDING ; LACTOCOCCUS-LACTIS ; STRUCTURAL GENE ; STAPHYLOCOCCUS-EPIDERMIDIS ; ANTIMICROBIAL PEPTIDES ; MUTACIN II ; LIPID-II |
| 语种 | 英语 |
| WOS记录号 | WOS:000413458200002 |
| 出版者 | BENTHAM SCIENCE PUBL LTD |
| 版本 | 出版稿 |
| 源URL | [http://202.127.25.144/handle/331004/668]  |
| 专题 | 中国科学院上海生命科学研究院营养科学研究所 |
| 作者单位 | 1.Chinese Acad Sci, Shanghai Inst Biol Sci, Inst Hlth Sci, Shanghai 200031, Peoples R China; 2.Shanghai Maritime Univ, Coll Informat Engn, Shanghai 201306, Peoples R China, 3.Shanghai Univ, Sch Life Sci, Shanghai 200444, Peoples R China; 4.Tianjin Univ, Tianjin Key Lab Biomed Engn Measurement, Dept Biomed Engn, Tianjin, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Wang, ShaoPeng,Cai, Yu-Dong,Zhang, Yu-Hang,et al. Recognizing and Predicting Thioether Bridges Formed by Lanthionine and beta-Methyllanthionine in Lantibiotics Using a Random Forest Approach with Feature Selection[J]. COMBINATORIAL CHEMISTRY & HIGH THROUGHPUT SCREENING,2017,20(7):582-593. |
| APA | Wang, ShaoPeng.,Cai, Yu-Dong.,Zhang, Yu-Hang.,Huang, Tao.,Zhang, Ning.,...&,.(2017).Recognizing and Predicting Thioether Bridges Formed by Lanthionine and beta-Methyllanthionine in Lantibiotics Using a Random Forest Approach with Feature Selection.COMBINATORIAL CHEMISTRY & HIGH THROUGHPUT SCREENING,20(7),582-593. |
| MLA | Wang, ShaoPeng,et al."Recognizing and Predicting Thioether Bridges Formed by Lanthionine and beta-Methyllanthionine in Lantibiotics Using a Random Forest Approach with Feature Selection".COMBINATORIAL CHEMISTRY & HIGH THROUGHPUT SCREENING 20.7(2017):582-593. |
入库方式： OAI收割
来源：上海营养与健康研究所
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