Novel Computational Model Enables Efficient Aptamer Discovery

Researchers in Japan have developed a computational model for aptamer generation that has wide applications across molecular and synthetic biology.

Aptamers are short, single strands of synthetic DNA or RNA that can selectively bind to specific targets such as proteins, peptides, carbohydrates, viruses, toxins, metal ions and even live cells. As they are similar to antibodies, they have a variety of uses in the fields of biosensors, therapeutics, and diagnostics. However, compared to antibodies, aptamers do not induce an immune reaction in our bodies, and are easy to synthesize and modify. Moreover, an aptamer’s three-dimensional folding structure allows it to bind to a wider range of targets.

Currently, however, tools to generate aptamers suffer from key limitations. Aptamers are usually generated by an in vitro selection and amplification technology called systematic evolution of ligands by exponential enrichment (SELEX). Based on repeated cycles of binding, separation, and amplification of nucleotides, SELEX results in an enriched pool of nucleotide sequences that is then analyzed for candidate selection. High-throughput SELEX (HT-SELEX) can generate a vast number of aptamer candidates, but current practically-applicable sequencing only allows a limited number of these candidates—approximately 10⁶—to be evaluated. Therefore, computational processes are essential to optimize the discovery of new aptamers.

In a paper published recently in Nature Computational Science, a team from Waseda University describes RaptGen, a variational autoencoder (VAE) with a profile-hidden Markov Model decoder that creates latent spaces in which sequences can form clusters. By using this latent representation, RaptGen was able to generate aptamers that were not included even in the original sequencing data or HT-SELEX dataset.

“RaptGen first visualizes a latent space with a sequence motif, then generates multiple new aptamer sequences via this latent space,” says Professor Michiaki Hamada of the Graduate School of Advanced Science and Engineering. “For example, it searches for optimized aptamer sequences in the latent space by considering additional information after analyzing the activity of a subset of sequences. Additionally, RaptGen enables the design of shortened (or truncated) aptamer sequences.”

Hamada and colleagues successfully evaluated RaptGen’s performance by subjecting it to data from two independent HT-SELEX datasets. RaptGen could generate aptamer derivatives in an activity-guided manner and provide opportunities to optimize their activities. “This is important as it means that RaptGen can generate sequences having desired properties, such as the inhibition of certain enzymes or protein-protein interactions,” Hamada explains.

The team plans to conduct further studies evaluating if alternative models can improve the performance of RaptGen, and whether RaptGen could advance RNA aptamer generation by using RNA sequences. The only drawbacks in using RaptGen are the high computational cost and increased training time, both of which can be improved in further studies.

 “To the best of our knowledge, RaptGen is the only data-driven method that can design and optimize truncated aptamers directly from HT-SELEX data,” Hamada adds.  “We believe that in due time, RaptGen will be recognized as a key tool for efficient aptamer discovery.”