Using random forest to classify linear B-cell epitopes based on amino acid properties and molecular features.
Identification and characterization of B-cell epitopes in target antigens was one of the key steps in epitopes-driven vaccine design, immunodiagnostic tests, and antibody production. Experimental determination of epitopes was labor-intensive and expensive. Therefore, there was an urgent need of computational methods for reliable identification of B-cell epitopes. In current study, we proposed a novel peptide feature description method which combined peptide amino acid properties with chemical molecular features. Based on these combined features, a random forest (RF) classifier was adopted to classify B-cell epitopes and non-epitopes. RF is an ensemble method that uses recursive partitioning to generate many trees for aggregating the results; and it always produces highly competitive models. The classification accuracy, sensitivity, specificity, Matthews correlation coefficient (MCC), and area under the curve (AUC) values for current method were 78.31%, 80.05%, 72.23%, 0.5836, and 0.8800, respectively. These results showed that an appropriate combination of peptide amino acid features and chemical molecular features with a RF model could enhance the prediction performance of linear B-cell epitopes. Finally, a freely online service was available at http://sysbio.yznu.cn/Research/Epitopesprediction.aspx.
Research Center of Modernization of Traditional Chinese Medicines, Central South University, Changsha 410083, PR China.
Jian-Hua
JH
Huang
Research Center of Modernization of Traditional Chinese Medicines, Central South University, Changsha 410083, PR China.
Ming
M
Wen
Criminal Investigation Detachment, Guilin Public Security Bureau, Guilin 541213, PR China.
Li-Juan
LJ
Tang
School of Chemistry and Chemical Engineering, Yangtze Normal University, Fuling 408100, PR China.
Hua-Lin
HL
Xie
School of Chemistry and Chemical Engineering, Yangtze Normal University, Fuling 408100, PR China.
Liang
L
Fu
Research Center of Modernization of Traditional Chinese Medicines, Central South University, Changsha 410083, PR China. Electronic address: yizeng_liang@263.net.
Yi-Zeng
YZ
Liang
Research Center of Modernization of Traditional Chinese Medicines, Central South University, Changsha 410083, PR China. Electronic address: hongmeilu@csu.edu.cn.
Hong-Mei
HM
Lu
D000465:Algorithms; D000596:Amino Acids / Q000737:chemistry*; D001185:Artificial Intelligence; D055598:Chemical Phenomena; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D018985:Epitopes, B-Lymphocyte / Q000737:chemistry*; D020407:Internet; D012372:ROC Curve; D017385:Sequence Homology
D000596:Amino Acids; D018985:Epitopes, B-Lymphocyte
Amino acid properties; Chemical molecular features; Computational method; Epitopes identification