An ultra-high-throughput method for measuring biomolecular activities
Abstract Large datasets of biomolecular activities are crucial for protein engineering, yet their scarcity due to limited experimental throughput hampers progress. We introduce Direct High-throughput Activity Recording and Measurement Assay (DHARMA), an innovative method enabling ultra-high-throughput measurement of biomolecular activities. DHARMA employs molecular recording techniques to link activity directly to editing rates of DNA segments contiguous with the coding sequence of biomolecule of interest. Leveraging a Bayesian inference-based denoising model, we mapped the fitness landscape of TEV protease across 160,000 variants. Using these datasets, we benchmarked popular protein models and showed the impact of data size on model performance. We also developed circuit self-optimization strategies and demonstrated DHARMA’s capability to measure a wide range of biomolecular activities. DHARMA represents a leap forward, offering the machine learning community unparalleled datasets for accurate protein fitness prediction and enhancing our understanding of sequence-to-function relationships.Abstract Figure <jats:fig id="ufig1" position="float" fig-type="figure" orientation="portrait"><jats:graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="483646v4_ufig1" position="float" orientation="portrait" /></jats:fig>.
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Preprint| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
bioRxiv.org - (2024) vom: 27. März Zur Gesamtaufnahme - year:2024 |
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| Sprache: |
Englisch |
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| Beteiligte Personen: |
Tu, Boqiang [VerfasserIn] |
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| Links: |
Volltext [kostenfrei] |
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| doi: |
10.1101/2022.03.09.483646 |
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XBI035462310 |
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| 520 | |a Abstract Large datasets of biomolecular activities are crucial for protein engineering, yet their scarcity due to limited experimental throughput hampers progress. We introduce Direct High-throughput Activity Recording and Measurement Assay (DHARMA), an innovative method enabling ultra-high-throughput measurement of biomolecular activities. DHARMA employs molecular recording techniques to link activity directly to editing rates of DNA segments contiguous with the coding sequence of biomolecule of interest. Leveraging a Bayesian inference-based denoising model, we mapped the fitness landscape of TEV protease across 160,000 variants. Using these datasets, we benchmarked popular protein models and showed the impact of data size on model performance. We also developed circuit self-optimization strategies and demonstrated DHARMA’s capability to measure a wide range of biomolecular activities. DHARMA represents a leap forward, offering the machine learning community unparalleled datasets for accurate protein fitness prediction and enhancing our understanding of sequence-to-function relationships.Abstract Figure <jats:fig id="ufig1" position="float" fig-type="figure" orientation="portrait"><jats:graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="483646v4_ufig1" position="float" orientation="portrait" /></jats:fig> | ||
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