Abstract
Aptamers are single-stranded nucleic acid ligands that bind to target molecules with high affinity and specificity. They are typically discovered by searching large libraries for sequences with desirable binding properties. These libraries, however, are practically constrained to a fraction of the theoretical sequence space. Machine learning provides an opportunity to intelligently navigate this space to identify high-performing aptamers. Here, we propose an approach that employs particle display (PD) to partition a library of aptamers by affinity, and uses such data to train machine learning models to predict affinity in silico. Our model predicted high-affinity DNA aptamers from experimental candidates at a rate 11-fold higher than random perturbation and generated novel, high-affinity aptamers at a greater rate than observed by PD alone. Our approach also facilitated the design of truncated aptamers 70% shorter and with higher binding affinity (1.5 nM) than the best experimental candidate. This work demonstrates how combining machine learning and physical approaches can be used to expedite the discovery of better diagnostic and therapeutic agents.
Highlights
Aptamers are single-stranded nucleic acid ligands that bind to target molecules with high affinity and specificity
To produce training data for machine learning models, particle display (PD) was used to measure the relative affinity of every aptamer candidate in the library based on the target concentration and the measured fluorescence (Fig. 1b)[29]
Aptamers were generated from three sets of initial seeds: (1) high-performing aptamer sequences from the initial PD, (2) random sequences that were screened in silico, and (3) as a baseline, completely random sequences
Summary
Aptamers are single-stranded nucleic acid ligands that bind to target molecules with high affinity and specificity. They are typically discovered by searching large libraries for sequences with desirable binding properties. Aptamers are single-stranded nucleic acid ligands that can be developed to bind a wide range of targets with high affinity and specificity. Chemically modified DNA/RNA aptamers are widely appreciated for their non-immunogenic composition and excellent safety profile[1,2,3,4] Their small size and high solubility permit high molar doses and high tissue penetration for maximum bioavailability[5,6]. Modern neural network (NN) approaches have had success generating sequences in other biological domains
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