Abstract
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.
Highlights
Aptamers, first identified by three laboratories independently in 1990 [1,2,3], are synthetic single-stranded nucleic acids or peptides
Additional File S4): one was called MaxRel feature table that ranked the features according to their relevance to the class of samples; the other was called maximum relevance minimum redundancy (mRMR) feature table that ranked the features according to the maximum relevance and minimum redundancy criteria
Such a list of ranked features was to be used in the following Incremental Feature Selection (IFS) procedure for the optimal feature set selection
Summary
First identified by three laboratories independently in 1990 [1,2,3], are synthetic single-stranded nucleic acids or peptides. These artificial molecules folding into specific spatial conformations can bind to certain targets with extremely high specificity. They mimic properties of antibodies, but possess several advantages compared with antibodies. After selected, aptamers can be amplified through polymerase chain reactions to obtain large quantities of high-purity molecules. Aptamers with simple chemical structures can be amended by adding some functional groups making the molecules more stable than antibodies in harsh conditions. As a novel and powerful class of ligands, are thought to have excellent potential for applications in the fields of diagnostics, therapeutics and biosensing [4,5]
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