Quantifying replication fidelity of unnatural base pairs using nanopore sequencing

Expanded genetic alphabets built from unnatural base pairs (UBPs) are part of an emerging frontier in biotechnology. However, their wider adoption is constrained by lower replication fidelity relative to standard DNA bases. To address this challenge, we develop a method to rapidly assess UBP replication fidelity using nanopore sequencing. We train post hoc machine-learning classifiers to detect UBPs in sequencing data, then use these models to extract fidelity measurements from PCR-amplified datasets. This approach provides a general route to assess the replication fidelity of diverse UBPs, enabling optimization across chemistries, enzymes, and reaction conditions. In this work, we apply this strategy to three distinct UBP systems, demonstrating: (i) rapid screening of reaction conditions that yields > 98.6% per-cycle replication fidelity for the B ≡ S base pair, (ii) single-molecule tracking of replication outcomes in an 8-letter UBP system (ATGCBSPZ), and (iii) quantitative decomposition of multiple error pathways for the hydrophobic Ds:Diol-Px system.