Prediction of aptamer-target interacting pairs with pseudo-amino acid composition.
Aptamers are oligonucleic acid or peptide molecules that bind to specific target molecules. As a novel and powerful class of ligands, aptamers are thought to have excellent potential for applications in the fields of biosensing, diagnostics and therapeutics. In this study, a new method for predicting aptamer-target interacting pairs was proposed by integrating features derived from both aptamers and their targets. Features of nucleotide composition and traditional amino acid composition as well as pseudo amino acid were utilized to represent aptamers and targets, respectively. The predictor was constructed based on Random Forest and the optimal features were selected by using the maximum relevance minimum redundancy (mRMR) method and the incremental feature selection (IFS) method. As a result, 81.34% accuracy and 0.4612 MCC were obtained for the training dataset, and 77.41% accuracy and 0.3717 MCC were achieved for the testing dataset. An optimal feature set of 220 features were selected, which were considered as the ones that contributed significantly to the interacting aptamer-target pair predictions. Analysis of the optimal feature set indicated several important factors in determining aptamer-target interactions. It is anticipated that our prediction method may become a useful tool for identifying aptamer-target pairs and the features selected and analyzed in this study may provide useful insights into the mechanism of interactions between aptamers and targets.
Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, P. R. China.
Bi-Qing
BQ
Li
State Key Laboratory of Medical Genomics, Institute of Health Sciences, Shanghai Jiaotong University School of Medicine and Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, P. R. China.
Yu-Chao
YC
Zhang
Institute of Systems Biology, Shanghai University, Shanghai, P.R. China.
Guo-Hua
GH
Huang
CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, P. R. China.
Wei-Ren
WR
Cui
Department of Biomedical Engineering Tianjin University, Tianjin Key Lab of BME Measurement, Tianjin, P. R. China.
Ning
N
Zhang
Institute of Systems Biology, Shanghai University, Shanghai, P.R. China.
Yu-Dong
YD
Cai
D000465:Algorithms; D000596:Amino Acids / Q000032:analysis; D052157:Aptamers, Nucleotide / Q000737:chemistry*; D052158:Aptamers, Peptide / Q000737:chemistry*; D001185:Artificial Intelligence; D001482:Base Composition; D019295:Computational Biology / Q000379:methods*; D008024:Ligands; D008957:Models, Genetic; D013329:Structure-Activity Relationship
D000596:Amino Acids; D052157:Aptamers, Nucleotide; D052158:Aptamers, Peptide; D008024:Ligands