ArticleNature communications2022
Prediction of designer-recombinases for DNA editing with generative deep learning.
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 29 papers.
What it found
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Who cites it
29 citing papers in PubMed, 42 citations in OpenAlex.
- Programmable enzymes for targeted gene insertion.Nature reviews. Genetics · 2026Review
- Large serine recombinase-mediated gene insertion for high-throughput screens: advantages, design principles, and applications.Nucleic acids research · 2026Review
- Writing Big in Plant Genomes: Advances, Challenges and Strategies for Targeted Large-Fragment DNA Insertion.Plant, cell & environment · 2026Review
- From Gene Knockouts to Genome Remodeling: Large DNA Fragment Deletion Technologies in Plants.Plants (Basel, Switzerland) · 2026Review
- AI-Driven Enzyme Engineering: Emerging Models and Next-Generation Biotechnological Applications.Molecules (Basel, Switzerland) · 2025Review
- Gene circuit-based sensors.Fundamental research · 2025Review
- Design of diverse, functional mitochondrial targeting sequences across eukaryotic organisms using variational autoencoder.Nature communications · 2025Article
- A data-efficient strategy for building high-performing medical foundation models.Nature biomedical engineering · 2025Article
- Computational scoring and experimental evaluation of enzymes generated by neural networks.Nature biotechnology · 2025Article
- Improving functional protein generation via foundation model-derived latent space likelihood optimization.bioRxiv : the preprint server for biology · 2025Article
- Bayesian estimation of muscle mechanisms and therapeutic targets using variational autoencoders.Biophysical journal · 2025Article
- Thermostable bacterial L-asparaginase for polyacrylamide inhibition and in silico mutational analysis.International microbiology : the official journal of the Spanish Society for Microbiology · 2024Article
- Activation of recombinases at specific DNA loci by zinc-finger domain insertions.Nature biotechnology · 2024Article
- Reaching New Heights in Genetic Code Manipulation with High Throughput Screening.Chemical reviews · 2024Review
- Bayesian Estimation of Muscle Mechanisms and Therapeutic Targets Using Variational Autoencoders.bioRxiv : the preprint server for biology · 2024Article
- Dynamics in Cre-loxP site-specific recombination.Current opinion in structural biology · 2024Review
- Computational tools for plant genomics and breeding.Science China. Life sciences · 2024Review
- Engineering spacer specificity of the Cre/loxP system.Nucleic acids research · 2024Article
- Article
- Bacteriophage lambda site-specific recombination.Molecular microbiology · 2024Review
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Site-specific tyrosine-type recombinases are effective tools for genome engineering, with the first engineered variants having demonstrated therapeutic potential. So far, adaptation to new DNA target site selectivity of designer-recombinases has been achieved mostly through iterative cycles of directed molecular evolution. While effective, directed molecular evolution methods are laborious and time consuming. Here we present RecGen (Recombinase Generator), an algorithm for the intelligent generation of designer-recombinases. We gather the sequence information of over one million Cre-like recombinase sequences evolved for 89 different target sites with which we train Conditional Variational Autoencoders for recombinase generation. Experimental validation demonstrates that the algorithm can predict recombinase sequences with activity on novel target-sites, indicating that RecGen is useful to accelerate the development of future designer-recombinases.
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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.