RNAGEN: A Generative Adversarial Network-Based Model to Generate Synthetic RNA Sequences to Target Proteins.

RNA-protein binding plays an important role in regulating protein activity by affecting localization and stability. While proteins are usually targeted via small molecules or other proteins, easy-to-design and synthesize small RNAs are a rather unexplored and promising venue. The problem is the lack of methods to generate RNA molecules that have the potential to bind to certain proteins. Here, we propose a method based on generative adversarial networks that learn to generate short RNA sequences with natural RNA-like properties such as GC content and free energy. Using an optimization technique, we fine-tune these sequences to have them bind to a target protein. We use RNA-protein binding prediction models from the literature to guide the model. We show that even if there is no available guide model trained specifically for the target protein, we can use models trained for similar proteins, such as proteins from the same family, to successfully generate a binding RNA molecule to the target protein. Using this approach, we generated piRNAs that are tailored to bind to SOX2 protein using models trained for its relative (SOX15, SOX14, and SOX7) and experimentally validated in vitro that the top-2 molecules we generated specifically bind to SOX2. We demonstrate that our generative model matched with the gradient-based optimization method is capable of generating piRNA sequences with high expected binding scores to the target protein. State-of-the-art RNA-Protein binding prediction models validate our results.