Nat. Biotechnol. | 面向先导编辑结果预测的机制机器学习
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TL;DR — OptiPrime is a mechanism-informed machine-learning model that accurately predicts pegRNA efficiency across prime-editing systems. It accelerated therapeutic design and achieved substantial Kif1a correction in mouse cortex.
- Trained on 297,962 measurements spanning 40 experimental settings, it outperformed DeepPrime-FT and PRIDICT2.0 on independent tests.
- Interpretable pseudo-rates model Cas9 activity, reverse transcription, flap formation, mismatch repair, and other DNA-repair processes.
- Its mechanistic modules generalized to PE3 and twinPE without direct training on those systems.
- Following four weeks of optimization, AAV9 delivery produced over 40% average correction in mouse cortex and over 70% correction among transduced brain cells.
Note: The digital-twin source describes an unrelated biotechnology feature rather than OptiPrime, so its details were not merged.
Sources (2)
Nat. Biotechnol. | 面向先导编辑结果预测的机制机器学习
TL;DR - OptiPrime embeds prime-editing mechanisms into a machine-learning model to predict pegRNA efficiency more accurately and transfer across editing systems. It accelerated therapeutic design and enabled over 40% average Kif1a correction in mouse cortex.
- Trained on 297,962 measurements across 40 experimental settings, OptiPrime outperformed DeepPrime-FT and PRIDICT2.0 on independent tests.
- The model represents Cas9 activity, reverse transcription, flap formation, mismatch repair, and other DNA-repair steps as interpretable pseudo-rates.
- Mechanistic modules generalized to PE3 and twinPE despite not being directly trained on those systems.
- In vivo AAV9 delivery achieved over 70% correction in transduced mouse brain cells after a four-week optimization process.
Nat. Biotechnol.｜生物技术领域的数字孪生困境
TL;DR - A Nature Biotechnology feature surveys digital twins in biotechnology, highlighting their potential in drug discovery and clinical trials alongside unresolved definition and validation problems. Narrow, purpose-built models currently appear more practical than complete virtual replicas of patients.
- Three approaches dominate: mechanism-based causal models, data-driven clinical-trial simulations, and organ-chip systems coupled with pharmacological modeling.
- Applications include identifying drug targets, stratifying patients, predicting control-group outcomes, and reducing clinical-trial enrollment requirements.
- Biological uncertainty, incomplete mechanistic knowledge, black-box predictions, and inconsistent definitions complicate validation and trust.
- Progress depends on use-specific evaluation standards, uncertainty reporting, and transparency about each model’s limitations.