LevSeq: Rapid Generation of Sequence-Function Data for Directed Evolution and Machine Learning
🚀Groundbreaking work from the lab of Nobel laureate Frances H. Arnold, a pioneer in the field of directed evolution
@francesarnold 🚀
1. The article introduces LevSeq, a new method combining nanopore sequencing with a dual barcoding strategy, enabling rapid and comprehensive sequence-function data generation, ideal for protein engineering workflows.
2. LevSeq’s integration into directed evolution workflows ensures full-length gene sequencing with minimal cost, optimizing both time and resources while enhancing accuracy in variant detection.
3. The system provides real-time quality control before the screening phase, offering significant reductions in library screening burden by filtering out low-quality sequences early.
4. One of the key innovations is its application in generating machine learning-compatible data, streamlining protein engineering by coupling sequence information with functional data to guide further optimization.
5. LevSeq has been demonstrated on two protein engineering projects, where it accurately detected key mutations and epistatic interactions in enzyme engineering for new-to-nature chemistries.
6. By utilizing long-read nanopore sequencing, LevSeq enhances the ability to explore the protein fitness landscape, providing vital insights that were previously inaccessible with traditional methods.
7. LevSeq is designed with user-friendly, open-source software, enabling researchers to easily analyze mutagenesis libraries and incorporate machine learning for improved directed evolution outcomes.
8. It enables the collection of essential sequence-function data, which serves as a foundation for advanced machine learning models to predict high-performance protein variants, a major leap in data-driven protein design.
9. LevSeq offers a robust tool for protein engineers to explore and optimize vast sequence spaces, with potential applications in industrial, environmental, and pharmaceutical biocatalysis.
💻Code:
github.com/fhalab/LevSeq
📜Paper:
doi.org/10.1101/2024.09.04.6…