Oligonucleotides are short single strands of synthetic DNA or RNA. Although small, these molecules play an important role in molecular and synthetic biology applications. One type of oligonucleotide – aptamers – can selectively bind to specific targets such as proteins, peptides, carbohydrates, viruses, toxins, metal ions and even living cells. Since they are similar to antibodies, they have a variety of uses in the fields of biosensors, therapeutics and diagnostics. However, unlike antibodies, aptamers do not induce an immune reaction in our body and are easy to synthesize and modify. Additionally, the three-dimensional folding structure of an aptamer allows it to bind to a wider range of targets.
Aptamers are generally generated by a in vitro the selection and amplification technology called Systematic Ligand Evolution by Exponential Enrichment, or SELEX. In short, SELEX is based on repeated cycles of binding, separation and amplification of nucleotides. This process results in an enriched pool of nucleotide sequences which is then analyzed for candidate selection. High-throughput SELEX (HT-SELEX) can generate a large number of aptamer candidates, but the sequencing currently applicable in practice allows us to evaluate only a limited number of these candidates (about 106). Therefore, computational processes are essential to optimize the discovery of new aptamers.
Variational autoencoder (VAE, a type of machine learning approach) based compound designs have been reported to be beneficial in the discovery of other small molecules. Now, a team of researchers led by Professor Michiaki Hamada from the Graduate School of Advanced Science and Engineering at Waseda University, Japan, has presented RaptGen, an e-bike that can be used for the generation of aptamers. In their article published in Computational science of nature on June 2, 2022, they describe how RaptGen uses an VAE with a hidden Markov model decoder to create latent spaces in which sequences can form clusters. Using this latent representation, RaptGen was able to generate aptamers that were not even included in the original sequencing data or the HT-SELEX dataset.
When asked how exactly RaptGen could stimulate the discovery of aptamers, Professor Hamada says: “RaptGen first visualizes a latent space with a sequence motif, and then generates several new aptamer sequences via this latent space. 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 allows the design of shortened (or truncated) aptamer sequences.
The team also successfully evaluated RaptGen’s performance using real-world data, subjecting it to data from two independent HT-SELEX datasets. RaptGen could generate aptamer derivatives in an activity-driven manner and provide opportunities to optimize their business. “This is important because it means RaptGen can generate sequences with desired properties, such as inhibiting certain enzymes or protein-protein interactions,” says Professor Hamada. The application of these molecules could open many doors in the future.
Going forward, the team plans to conduct extensive studies to assess whether alternative models can improve the performance of RaptGen and whether RaptGen could advance RNA aptamer generation using RNA sequences. The only disadvantages of using RaptGen are high computational cost and increased training time, both of which can be improved in further studies.
Professor Hamada summarizes by saying, “To our knowledge, RaptGen is the only data-driven method that can design and optimize truncated aptamers directly from HT-SELEX data. We believe that in due course, RaptGen will be recognized as a key tool for effective aptamer discovery.”
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