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uSort-M: Scalable isolation of user-defined sequences from diverse pooled libraries

Olivas, M. B.; Almhjell, P. J.; Shanahan, J. D.; Fordyce, P. M.

2026-01-12 synthetic biology
10.64898/2026.01.12.699065 bioRxiv
Show abstract

High-throughput sequencing and computational protein design have created a growing gap between the discovery of new proteins and their functional characterization. In many instances, functional characterization requires one-to-one measurements--such as when detailed biochemical insights are desired or pooled selections are not possible--necessitating that individual variants be isolated and assayed. A major barrier to closing this gap is the cost to directly synthesize individual genes, which remains prohibitively expensive ($10-100 per sequence) and restricts these studies to small subsets of relevant variants, leaving many sequences without functional annotation. To address this, we developed user-defined Sorted Mutants (uSort-M), which combines pooled DNA synthesis, automated cell sorting of transformed Escherichia coli, and long-read sequencing to rapidly isolate and identify variants from diverse libraries. uSort-M can isolate, sequence, and validate individual variants from pooled libraries produced via diverse existing methods including multiplex assembly, error-prone PCR, or pooled QuickChange mutagenesis. Sorting single bacterial clones into 384-well plates is efficient: eight plates (3,072 wells) can be filled in 1-2 hours, with up to 90% of wells yielding monoclonal cultures. Commercial long-read sequencing enables accessible, fast, and cost-effective identification of individual sequences from isolated clones while tolerating wide variation in fragment length and diversity across the library. Applying this workflow to a 328-member scanning mutagenesis library of a 300-bp gene recovered 96% of desired variants at fivefold lower cost than traditional synthesis. Numerical simulations identify key parameters governing library recovery and enable accurate prediction of the sampling effort required to achieve target coverage. As library size increases, this workflow offers substantial savings over traditional gene synthesis or cloning. Due to its generalizability, efficiency, and reliance on standard instrumentation, uSort-M removes a key barrier to large-scale protein functional characterization.

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