Recognizing and predicting thioether bridges formed by lanthionine and β-methyllanthionine in lantibiotics using a random forest approach with feature selection.

Recognizing and predicting thioether bridges formed by lanthionine and β-methyllanthionine in lantibiotics using a random forest approach with feature selection. Comb Chem High Throughput Screen. 2017 Mar 10;: Authors: Wang S, Zhang YH, Zhang N, Chen L, Huang T, Cai YD Abstract 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. 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. As a result, 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 othe...
Source: Combinatorial Chemistry and High Throughput Screening - Category: Chemistry Authors: Tags: Comb Chem High Throughput Screen Source Type: research