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We developed user-defined Sorted Mutants (uSort-M) to rapidly isolate and identify protein variants from pooled libraries. This cost-effective method accelerates functional characterization, bridging the gap between protein discovery and understanding.

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Area of Science:

  • Biochemistry and Molecular Biology
  • Genomics and Proteomics
  • Synthetic Biology

Background:

  • High-throughput sequencing and computational protein design accelerate protein discovery but outpace functional characterization.
  • Current methods for individual variant analysis are limited by the high cost of gene synthesis, restricting studies to small variant subsets.
  • A significant gap exists between identifying novel protein sequences and understanding their functions due to analysis bottlenecks.

Purpose of the Study:

  • To develop a cost-effective and efficient workflow for isolating and identifying individual protein variants from large, pooled libraries.
  • To overcome the limitations imposed by expensive gene synthesis in large-scale functional protein characterization.
  • To enable rapid functional annotation of diverse protein variants discovered through high-throughput methods.

Main Methods:

  • Developed user-defined Sorted Mutants (uSort-M), integrating pooled DNA synthesis, automated cell sorting of *Escherichia coli*, and long-read sequencing.
  • uSort-M accommodates pooled libraries generated by various methods, including multiplex assembly, error-prone PCR, and pooled site-directed mutagenesis.
  • Automated sorting of single bacterial clones into 384-well plates achieved high efficiency (up to 90% monoclonal cultures) within 1-2 hours for eight plates.

Main Results:

  • Successfully isolated and identified individual variants from pooled libraries using uSort-M.
  • Application to a 328-member scanning mutagenesis library recovered 96% of desired variants at fivefold lower cost than traditional synthesis.
  • Long-read sequencing provided accessible, fast, and cost-effective identification of sequences, tolerating library diversity and fragment length variations.
  • Numerical simulations demonstrated the workflow's scalability and predicted sampling effort for target coverage.

Conclusions:

  • uSort-M significantly reduces the cost and time required for isolating and sequencing individual protein variants from pooled libraries.
  • The workflow's generalizability, efficiency, and use of standard instrumentation remove a key barrier to large-scale protein functional characterization.
  • This method facilitates bridging the gap between protein discovery and functional annotation, accelerating biological research.