In the rapidly evolving field of molecular biotechnology, the directed evolution of biomolecules has been a transformative technique, enabling the enhancement of proteins and nucleic acids for a spectrum of therapeutic and diagnostic applications. Traditionally, this evolutionary approach has depended on multi-round cycles of mutation and selection, which are often laborious, time-consuming, and resource intensive. While advances in artificial intelligence, especially language models, have significantly accelerated protein engineering, the evolution of RNA molecules, particularly RNA aptamers, lags behind due to intrinsic challenges. Addressing this critical bottleneck, a groundbreaking study introduces GRAPE-LM—a generative AI framework heralded as a paradigm shift in RNA aptamer discovery, promising to condense what previously took multiple iterations into a single, robust round of evolution.
RNA aptamers are short, structured nucleic acid sequences that selectively bind to target molecules with high affinity, making them versatile tools for molecular recognition, diagnostics, and therapeutics. However, the traditional methods for aptamer identification, such as SELEX (Systematic Evolution of Ligands by EXponential enrichment), involve repeated rounds of selection and amplification, often spanning weeks or months. This painstaking process limits the throughput and adaptability necessary to keep pace with emerging biomedical challenges. Consequently, the development of computational approaches that can intelligently navigate the vast RNA sequence space, while guided by biologically relevant activity data, has become a research imperative.
GRAPE-LM represents a remarkable synthesis of recent advances in AI with experimental aptamer screening technology. At its core, the framework leverages a transformer-based conditional autoencoder architecture combined with sophisticated nucleic acid language models. Unlike conventional sequence generation methods, these models harness the inherent grammatical rules of nucleic acids learned from massive sequence databases, capturing subtle structural and functional patterns. This intricate training enables the generation of novel RNA sequences that are not only syntactically plausible but functionally promising, significantly enriching the aptamer design landscape.
Crucially, what sets GRAPE-LM apart is its guidance through CRISPR–Cas-based aptamer screening performed within intracellular environments. This in vivo screening strategy serves dual purposes: it provides reliable activity data reflective of physiological contexts and offers a high-throughput platform to rapidly assess binding efficacy and specificity. Feeding this real-world feedback into the language model’s iterative learning loop empowers GRAPE-LM to evolve RNA populations in silico with unprecedented speed and accuracy, safeguarding the biological relevance of all generated candidates.
The practical efficacy of GRAPE-LM was rigorously tested on three distinctly challenging molecular targets, showcasing its versatility across different biological domains. These targets include the human T cell receptor CD3ε, an integral membrane protein pivotal in immune signaling; the receptor-binding domain (RBD) of the SARS-CoV-2 spike protein, a critical determinant of viral infectivity and immune evasion; and c-Myc, a notoriously difficult intracellular oncogenic transcription factor characterized by extensive intrinsic disorder and dynamic conformational plasticity. Aptamers that bind to each of these targets have historically posed significant hurdles due to their structural complexity and biological context.
Remarkably, GRAPE-LM’s single-round evolution successfully yielded RNA aptamers that outperformed those previously developed via traditional, multi-round selection campaigns. For CD3ε, the generated aptamers exhibited enhanced binding affinities and selectivities, hallmark characteristics essential for potential immunotherapeutic applications. In the context of SARS-CoV-2, the aptamers developed against the spike RBD not only demonstrated superior binding but also suggested promising neutralization capabilities, possibly accelerating antiviral agent development. Most notably, the framework’s success with c-Myc validates its potential to tackle disordered and intrinsically flexible intracellular proteins—targets that have long resisted aptamer-based interventions.
Technically, the integration of a conditional autoencoder with the protein and nucleic acid language models allows GRAPE-LM to maintain sequence diversity while honing in on functional motifs identified via CRISPR–Cas selection data. This dual-system synergy avoids common pitfalls such as mode collapse—wherein generated sequences converge too narrowly—and ensures the exploration of sequence space is both broad and targeted. The conditional autoencoder handles sequence reconstruction and latent space representation, facilitating nuanced control over RNA sequence attributes influenced by biological activity metrics.
The reliance on intracellular CRISPR–Cas screening data also marks a significant improvement over conventional in vitro assays, capturing the complexity of cellular milieus including cofactor interactions, molecular crowding effects, and nucleic acid modification states. This strategy ensures that the aptamers evolved by GRAPE-LM are not just theoretical candidates but are tailored for functional performance within living cells, greatly increasing their translational potential.
From a broader scientific and medical perspective, GRAPE-LM ushers in a new era for RNA aptamer technology. Its ability to condense iterative evolutionary cycles into a single computational-experimental round could democratize access to high-affinity aptamers, empowering researchers to rapidly develop bespoke molecular tools tailored to emerging biomedical threats or novel therapeutic targets. This is especially pertinent in the context of infectious diseases, cancer, and autoimmune conditions where time-sensitive intervention design is critical.
Moreover, the flexibility of this framework suggests wide applicability beyond the three targets explored in the study. By simply adjusting the guiding activity dataset, GRAPE-LM could be adapted for aptamer discovery aimed at enzymes, receptors, small molecules, or even complex protein–protein interfaces, paving the way for custom-designed nucleic acid therapeutics and diagnostics unprecedented in specificity and efficacy.
The paradigm set by GRAPE-LM also underscores the growing impact of large-scale language modeling within the life sciences. Traditionally restricted to natural language understanding, the transformer architecture’s transition into nucleic acid sequence modeling reflects an innovative cross-disciplinary leap, capitalizing on the inherent “language-like” properties of biological sequences. This innovation resonates deeply with the ongoing convergence of AI and experimental biology, signaling a future where computational frameworks act as active collaborators in laboratory workflows rather than merely supportive tools.
In practical implementations, the potential reductions in time, cost, and manual labor brought about by GRAPE-LM could transform biotech industry pipelines. The platform’s ability to generate candidate aptamers with higher efficacy from fewer experimental rounds translates directly into accelerated research and development timelines and reduced reliance on extensive wet-lab experimentation, thereby democratically expanding the accessibility of aptamer technologies to labs with limited resources.
As with any pioneering technology, challenges remain, particularly in the thorough characterization of aptamer specificity and off-target effects, as well as the scaling of intracellular screening methods to a broader array of cell types and physiological conditions. Nonetheless, the authors of the study articulate a clear vision for iterative improvements by incorporating larger and more diverse screening datasets, refining model architectures, and extending GRAPE-LM to other classes of noncoding RNAs.
Beyond the immediate benefits, this work sets a precedent for how AI-driven generative models integrated with sophisticated functional screening can redefine biomolecular engineering principles. It opens new frontiers in synthetic biology where in silico evolutionary design driven by real-world activity data could become the norm, enabling not just RNA aptamers but entire functional RNA devices, ribozymes, or regulatory elements to be tailored swiftly and precisely.
In conclusion, GRAPE-LM stands as a testament to the power of interdisciplinary innovation, melding advances in AI, genome editing, and molecular biology to overcome long-standing barriers in RNA aptamer discovery. Its success across structurally and functionally diverse targets heralds new possibilities for rapid, precise, and scalable RNA engineering that could revolutionize how we develop molecular diagnostics and therapeutics in the years to come.
Subject of Research: The development and single-round evolution of RNA aptamers using a generative artificial intelligence framework powered by CRISPR–Cas-based intracellular screening data.
Article Title: Single-round evolution of RNA aptamers with GRAPE-LM.
Article References:
Zhang, J., Zhang, J., Tang, S. et al. Single-round evolution of RNA aptamers with GRAPE-LM. Nat Biotechnol (2026). https://doi.org/10.1038/s41587-026-03007-5
Image Credits: AI Generated
DOI: https://doi.org/10.1038/s41587-026-03007-5
Tags: AI-accelerated biomolecule engineeringcomputational RNA aptamer selectiondirected evolution of nucleic acidsGRAPE-LM generative AI frameworkhigh-affinity RNA aptamer designmachine learning in molecular biotechnologyrapid molecular recognition toolsRNA aptamer discovery techniquesRNA aptamers for diagnosticsSELEX limitations in aptamer identificationsingle-round RNA aptamer evolutiontherapeutic RNA aptamer development
