APIPred: An XGBoost-Based Method for Predicting Aptamer–Protein Interactions

Aptamers are single-stranded DNA
or RNA oligos that can bind to
a variety of targets with high specificity and selectivity and thus
are widely used in the field of biosensing and disease therapies.
Aptamers are generated by SELEX, which is a time-consuming procedure.
In this study, using in silico and computational tools, we attempt
to predict whether an aptamer can interact with a specific protein
target. We present multiple data representations of protein and aptamer
pairs and multiple machine-learning-based models to predict aptamer–protein
interactions with a fair degree of selectivity. One of our models
showed 96.5% accuracy and 97% precision, which are significantly better
than those of the previously reported models. Additionally, we used
molecular docking and SPR binding assays for two aptamers and the
predicted targets as examples to exhibit the robustness of the APIPred
algorithm. This reported model can be used for the high throughput
screening of aptamer–protein pairs for targeting cancer and
rapidly evolving viral epidemics.