Accelerating life sciences research
OpenAI and Retro Biosciences achieve 50x increase in expressing stem cell reprogramming markers.
At OpenAI, we believe that AI can meaningfully accelerate life science innovation. To test this belief, we collaborated with the Applied AI team at Retro Bio, a longevity biotech startup, to create and research the impact of GPT‑4b micro, a miniature version of GPT‑4o specialized for protein engineering. We are excited to share that we’ve successfully leveraged GPT‑4b micro to design novel and significantly enhanced variants of the Yamanaka factors, a set of proteins which led to a Nobel Prize for their role in generating induced pluripotent stem cells (iPSCs) and rejuvenating cells. They have also been used to develop therapeutics to combat blindness, reverse diabetes, treat infertility, and address organ shortages. In vitro, these redesigned proteins achieved greater than a 50-fold higher expression of stem cell reprogramming markers than wild-type controls. They also demonstrated enhanced DNA damage repair capabilities, indicating higher rejuvenation potential compared to baseline. This finding, made in early 2025, has now been validated by replication across multiple donors, cell types, and delivery methods, with confirmation of full pluripotency and genomic stability in derived iPSC lines. To ensure the findings are discoverable and replicable to benefit the life sciences industry, we are now sharing insights into the research and development of GPT‑4b micro.
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Increased Marker Expression
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Model Suggestions Outperformed WT
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Reduced Reprogramming Time
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Cells Activating Markers
Executive Impact: Pioneering Next-Gen Life Sciences
Our collaboration with Retro Biosciences demonstrates AI's transformative power in accelerating critical research. GPT-4b micro, a specialized protein engineering model, has achieved unprecedented results in stem cell reprogramming, offering significant implications for therapeutic development and longevity research. This is a leap forward in enterprise-level R&D.
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Custom AI Model for Protein Design
GPT-4b micro, initialized from GPT-4o, was specifically trained on protein sequences, biological text, and tokenized 3D structure data. This enhanced context allows for generating sequences with desired properties, even for intrinsically disordered proteins like Yamanaka factors.
The model demonstrated unprecedented context size (64,000 tokens) and exhibited scaling laws similar to text LLMs, validating its predictive gains and real-world utility in accelerating therapeutic development.
Model Development Lifecycle
Data Collection & Enrichment
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GPT-4o Initialization
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Protein-Specific Training
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Contextual Inference
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In Silico Evaluation
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Wet Lab Validation
64,000
Effective Context Length (Tokens)
| Feature | GPT-4b Micro | Traditional Models |
|---|---|---|
| Training Data | Sequences, biological text, tokenized 3D structures | Typically sequences only |
| Context Length | 64,000 tokens (unprecedented) | Limited (typically <1,000 tokens) |
| Disordered Protein Handling | Excellent (structure-free data) | Challenging |
| Design Space Exploration | Vast, AI-guided | Constrained, mutation-based |
Enhanced Yamanaka Factors for iPSC Generation
GPT-4b micro redesigned SOX2 and KLF4, two key Yamanaka factors, leading to significantly improved stem cell reprogramming efficiency. The novel RetroSOX and RetroKLF variants overcame limitations of traditional mutation-based approaches, which struggle with the vast design space.
The engineered cocktail achieved over 50-fold higher expression of stem cell reprogramming markers, with late markers appearing days sooner. This was validated across multiple donors, cell types (fibroblasts, MSCs), and delivery methods (viral vectors, mRNA), confirming full pluripotency and genomic stability.
50x
Increased Marker Expression (in vitro)
Retro Biosciences Collaboration: Stem Cell Reprogramming
Challenge: Low efficiency and long duration of traditional Yamanaka factor reprogramming (typically <0.1% cells, 3+ weeks). Existing methods explored only a miniscule fraction of the vast protein design space.
Solution: Leveraged GPT-4b micro to design novel RetroSOX and RetroKLF variants, which substantially improved reprogramming efficiency by exploring a significantly larger design space.
Outcome: Over 30% of model suggestions outperformed wild-type SOX2, and 14 RetroKLF variants surpassed existing cocktails (nearly 50% hit rate). Combined, they produced 50x higher marker expression and accelerated iPSC generation within 7-10 days, with confirmed full pluripotency and genomic stability.
| Metric | Wild-type OSKM | RetroSOX/KLF Variants |
|---|---|---|
| Reprogramming Efficiency | <0.1% | Dramatic Rise (50x+) |
| Time to Late Markers | 3+ weeks | Days sooner (7-10 days) |
| Sequence Changes | Baseline/Few | >100 amino acids average |
| Hit Rate in Screens | Typically <10% | 30-50% |
| DNA Damage Repair | Baseline/Limited | Enhanced |
AI-Enhanced Cellular Rejuvenation
Beyond reprogramming, the RetroSOX and RetroKLF variants demonstrated enhanced rejuvenation potential by significantly reducing DNA damage. In doxorubicin-stressed fibroblasts, the engineered cocktail showed visibly less γ-H2AX intensity, a marker for double-strand breaks, compared to standard OSKM.
This suggests a more effective repair mechanism, ameliorating a core hallmark of cellular aging and offering a promising avenue for future cell therapies focused on anti-aging and regenerative medicine.
Reduced
DNA Damage (γ-H2AX Intensity)
Rejuvenation Pathway Assessment
Induce DNA Damage (Doxorubicin)
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Apply Reprogramming Factors
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Quantify DNA Damage (γ-H2AX Immunostaining)
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Assess Rejuvenation Potential
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