TFIDF-Random Forest: Prediction of Aptamer-Protein Interacting Pairs.

Abstract

Aptamers are short, single-stranded oligonucleotides or peptides generated from in vitro selection to selectively bind with various molecules. Due to their molecular recognition capability for proteins, aptamers are becoming promising reagents in new drug development. Aptamers can fold into specific spatial configuration that bind to certain targets with extremely high specificity. The ability of aptamers to reversibly bind proteins has generated increasing interest in using them to facilitate controlled release of therapeutic biomolecules. In-vitro selection experiments to produce the aptamer-protein binding pairs is very complex and MD/MM in-silico experiments can be computationally expensive. In this study, we introduce a natural language processing approach for data-driven computational selection. We compared our method to the sequential model with the embedding layer, applied in the literature. We transformed the DNA/RNA and protein sequences into text format using a sliding window approach. This methodology showed that efficiency was notably higher than those observed from the literature. This indicates that our preliminary model has marked improvement over previous models which brings us closer to a data-driven computational selection method.