De novo design of protein-binding aptamers through deep reinforcement learning assembly of nucleic acid fragments
Guo, G.; Guo, L.; Qian, J.; He, X.; Qian, X.; Wang, L.; Huang, Q.
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Nucleic acid aptamers targeting proteins are becoming increasingly important in biopharmaceuticals and molecular diagnostics. Traditionally, aptamers are discovered through labor-intensive screening of nucleic acid libraries using the SELEX method. However, de novo design approaches without experimental screening remain a significant challenge. Here, we employed deep reinforcement learning to develop an artificial intelligence (AI) agent capable of de novo aptamer design, termed AiDTA (AI-driven Docking-Then-Assembling). First, nucleic acid fragments were docked to the target protein to identify target-binding fragments. Then, AiDTA automatically assembled these fragments into aptamers using the Monte Carlo tree search algorithm and a policy-value neural network to guide the agent in generating aptamers with secondary structures similar to the original constituent fragments. Experimental validation demonstrated that the AiDTA-designed DNA aptamers targeting disease-related proteins exhibited high binding affinities in the nanomolar range, achieving the de novo design of protein-binding aptamers for the first time. Our study establishes a new approach to obtaining protein-binding aptamers for potential applications in biopharmaceuticals and diagnostics.
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