ArticleNature communications2022
Model-guided engineering of DNA sequences with predictable site-specific recombination rates.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed, 14 citations in OpenAlex.
- Programmable RNA-Guided DNA Recombination: Mechanisms, Engineering, and Applications.Biomedicines · 2026Review
- Synthetic circuits for cell ratio control.Nature · 2026Article
- Site-specific DNA insertion into the human genome with engineered recombinases.Nature biotechnology · 2025Article
- Advances in large-scale DNA engineering with the CRISPR system.Experimental & molecular medicine · 2025Review
- Bacteriophage lambda site-specific recombination.Molecular microbiology · 2024Review
- Next generation synthetic memory via intercepting recombinase function.Nature communications · 2023Article
- Drag-and-drop genome insertion of large sequences without double-strand DNA cleavage using CRISPR-directed integrases.Nature biotechnology · 2023Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Site-specific recombination (SSR) is an important tool in synthetic biology, but its applications are limited by the inability to predictably tune SSR reaction rates. Facile rate manipulation could be achieved by modifying the DNA substrate sequence; however, this approach lacks rational design principles. Here, we develop an integrated experimental and computational method to engineer the DNA attachment sequence attP for predictably modulating the inversion reaction mediated by the recombinase Bxb1. After developing a qPCR method to measure SSR reaction rate, we design, select, and sequence attP libraries to inform a machine-learning model that computes Bxb1 inversion rate as a function of attP sequence. We use this model to predict reaction rates of attP variants in vitro and demonstrate their utility in gene circuit design in Escherichia coli. Our high-throughput, model-guided approach for rationally tuning SSR reaction rates enhances our understanding of recombinase function and expands the synthetic biology toolbox.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.