The directed evolution of biomolecules is an iterative process. Although advancements in language models have expedited protein evolution, effectively evolving RNA remains a challenge. RNA aptamers, selected for their binding properties, provide an ideal system to address this challenge, yet traditional aptamer discovery still relies on labor-intensive, multi-round screening. Here we introduce GRAPE-LM (generator of RNA aptamers powered by activity-guided evolution and language model), a generative artificial intelligence framework designed for the one-round evolution of RNA aptamers. GRAPE-LM integrates a transformer-based conditional autoencoder with nucleic acid language models and is guided by CRISPR−Cas-based aptamer screening data derived from intracellular environments. We validate GRAPE-LM on three disparate targets: the human T cell receptor CD3ε, the receptor-binding domain of the SARS-CoV-2 spike protein and the human oncogenic transcription factor c-Myc (an intracellular disordered protein). GRAPE-LM, informed with only a single round of CRISPR−Cas-based screening, successfully obtains RNA aptamers that outperform those driven from multiple rounds of human selection and optimization.
Zhang et al. (Fri,) studied this question.