Recognition of candidate aptamer sequences for human hepatocellular carcinoma in SELEX screening using structure–activity relationships

Abstract

Selecting and synthesizing aptamers for human hepatic carcinoma (HCC) cells with high affinity and specificity would be of critical importance for diagnosing liver cancer early. This paper is the first report on pattern recognition used for SELEX-based aptamer screening by applying a support vector classification (SVC) technique for a two-class problem. The candidate aptamer sequences that show different degrees of affinity and specificity for SMMC-7721 liver carcinoma cells were selected through whole cell-SELEX. After calculating 1670 molecular descriptors, 13 descriptors were selected, which were compressed to 6 latent variables used as the inputs for classification models. The predicted fractions of winner aptamers from the SELEX selection of the 3rd, 5th, 7th, 9th, 11th, and 13th rounds are 0.033, 0.427, 0.678, 0.828, 0.912 and 0.983, respectively, which conform to the aptamer evolutionary principles of SELEX based screening. By the pattern recognition analysis based on a structure–activity relationship (SAR) model, 6 DNA candidate aptamer sequences belonging to the class of sequences with high affinity and specificity have experimental dissociation constants Kd in the nanomolar range. The feasibility of applying pattern recognition for the design and selection of aptamers has been demonstrated.