Support Vector Machine Classification of Streptavidin-Binding Aptamers

Abstract

BackgroundSynthesizing and characterizing aptamers with high affinity and specificity have been extensively carried out for analytical and biomedical applications. Few publications can be found that describe structure–activity relationships (SARs) of candidate aptamer sequences.MethodologyThis paper reports pattern recognition with support vector machine (SVM) classification techniques for the identification of streptavidin-binding aptamers as “low” or “high” affinity aptamers. The SVM parameters C and γ were optimized using genetic algorithms. Four descriptors, the topological descriptor PW4 (path/walk 4 - Randic shape index), the connectivity index X3A (average connectivity index chi-3), the topological charge index JGI2 (mean topological charge index of order 2), and the free energy E of the secondary structure, were used to describe the structures of candidate aptamer sequences from SELEX selection (Schütze et al. (2011) PLoS ONE (12):e29604).ConclusionsThe predicted fractions of winning streptavidin-binding aptamers for ten rounds of SELEX conform to the aptamer evolutionary principles of SELEX-based screening. The feasibility of applying pattern recognition based on SVM and genetic algorithms for streptavidin-binding aptamers has been demonstrated.

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“…The model possesses a classification accuracy of 97.98% for the training set. Furthermore, the prediction fractions of winning aptamers from the 1st round to the 10th round of SELEX consist of the enrichment characteristics of aptamer based on SELEX selection . In addition, Yu et al developed another classification model for the binding affinity of aptamers of hepatocarcinoma cell line SMMC-7721 .…”

“…Furthermore, the prediction fractions of winning aptamers from the 1st round to the 10th round of SELEX consist of the enrichment characteristics of aptamer based on SELEX selection. 10 In addition, Yu et al developed another classification model for the binding affinity of aptamers of hepatocarcinoma cell line SMMC-7721. 11 The classification model has good statistical qualities with specificity and sensitivity greater than 80%.…”