Pattern recognition of enrichment levels of SELEX-based candidate aptamers for human C-reactive protein

Selecting aptamers for human C-reactive protein (CRP) would be of critical importance in predicting the risk for cardiovascular disease. The enrichment level of DNA aptamers is an important parameter for selecting candidate aptamers for further affinity and specificity determination. This paper is the first report on pattern recognition used for CRP aptamer enrichment levels in the systematic evolution of ligands by exponential enrichment (SELEX) process, by applying structure-activity relationship models. After generating 10 rounds of graphene oxide (GO)-SELEX and 1670 molecular descriptors, eight molecular descriptors were selected and five latent variables were then obtained with principal component analysis (PCA), to develop a support vector classification (SVC) model. The SVC model (C=8.1728 and γ=0.2333) optimized by the particle swarm optimization (PSO) algorithm possesses an accuracy of 88.15% for the training set. Prediction results of enrichment levels for the sequences with the frequencies of 6 and 5 are reasonable and acceptable, with accuracies of 70.59% and 76.37%, respectively.