Recognizing and Predicting Thioether Bridges Formed by Lanthionine and β-Methyllanthionine in Lantibiotics Using a Random Forest Approach with Feature Selection

Title:Recognizing and Predicting Thioether Bridges Formed by Lanthionine and β-Methyllanthionine in Lantibiotics Using a Random Forest Approach with Feature Selection

Volume: 20 Issue: 7

Author(s): ShaoPeng Wang, Yu-Hang Zhang, Ning Zhang, Lei Chen*, Tao Huang*Yu-Dong Cai*

Affiliation:

• College of Information Engineering, Shanghai Maritime University, Shanghai 201306,China

• Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031,China

• School of Life Sciences, Shanghai University, Shanghai 200444,China

Keywords: Post-translational modifications, lantibiotics, lanthionine, β-methyllanthionine, random forest, maximal relevance minimal redundancy.

Abstract: 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 β-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.

Cite this article as:

Wang ShaoPeng , Zhang Yu-Hang , Zhang Ning , Chen Lei *, Huang Tao *, Cai Yu-Dong*, Recognizing and Predicting Thioether Bridges Formed by Lanthionine and β-Methyllanthionine in Lantibiotics Using a Random Forest Approach with Feature Selection, Combinatorial Chemistry & High Throughput Screening 2017; 20 (7) . https://dx.doi.org/10.2174/1386207320666170310115754

DOI https://dx.doi.org/10.2174/1386207320666170310115754	Print ISSN 1386-2073
Publisher Name Bentham Science Publisher	Online ISSN 1875-5402