ArticleProceedings of the National Academy of Sciences of the United States of America2015
Dissecting enzyme function with microfluidic-based deep mutational scanning.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 116 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
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Who cites it
116 citing papers in PubMed.
- Predicting Enzyme Turnover Numbers and Enabling Rational Enzyme Evolution.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Functional alignment of protein language models via reinforcement learning.Nature communications · 2026Article
- High-Throughput Screening of Isomeric Reaction Products by Droplet Microfluidics Coupled to Cyclic Ion Mobility-Mass Spectrometry.Analytical chemistry · 2026Article
- Machine learning-driven optimization of specific, compact, and efficient base editors via single-round diversification.Nucleic acids research · 2026Article
- How far can you go? Extrapolating values of catalytic activity from known protein landscapes in natural and directed evolution.Chemical Society reviews · 2026Review
- Advances and opportunities for computational interrogation of plant proteins.The Plant journal : for cell and molecular biology · 2026Review
- Engineering a robust IsPETase for energy-efficient PET depolymerization in natural seawater at ambient temperatures.Advanced biotechnology · 2026Article
- StabLyzeGraph: High-throughput screening of combinatorial mutations using graph neural networks.Protein science : a publication of the Protein Society · 2026Article
- Overcoming extrapolation challenges of deep learning by incorporating physics in protein sequence-function modeling.PLoS computational biology · 2026Article
- Extending the Potential of Digital Droplet PCR by Expressing Amplified Genes and Assaying Their Phenotype.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Overcoming Extrapolation Challenges of Deep Learning by Incorporating Physics in Protein Sequence-Function Modeling.bioRxiv : the preprint server for biology · 2025Article
- PRO-LDM: A Conditional Latent Diffusion Model for Protein Sequence Design and Functional Optimization.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- A Bioelectrochemical Crossbar Architecture Screening Platform (BiCASP) for Extracellular Electron Transfer.bioRxiv : the preprint server for biology · 2025Article
- An augmented transformer model trained on protein family specific variant data leads to improved prediction of variants of uncertain significance.Human genetics · 2025Article
- MMRT: MultiMut Recursive Tree for predicting functional effects of high-order protein variants from low-order variants.Computational and structural biotechnology journal · 2025Article
- DeepPFP: a multi-task-aware architecture for protein function prediction.Briefings in bioinformatics · 2024Article
- Understanding epistatic networks in the B1 β-lactamases through coevolutionary statistical modeling and deep mutational scanning.Nature communications · 2024Article
- popDMS infers mutation effects from deep mutational scanning data.Bioinformatics (Oxford, England) · 2024Article
- Scalable approaches for generating, validating and incorporating data from high-throughput functional assays to improve clinical variant classification.Human genetics · 2024Article
- Carbohydrate-active enzyme (CAZyme) discovery and engineeringRSC chemical biology · 2024Review
56 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Natural enzymes are incredibly proficient catalysts, but engineering them to have new or improved functions is challenging due to the complexity of how an enzyme's sequence relates to its biochemical properties. Here, we present an ultrahigh-throughput method for mapping enzyme sequence-function relationships that combines droplet microfluidic screening with next-generation DNA sequencing. We apply our method to map the activity of millions of glycosidase sequence variants. Microfluidic-based deep mutational scanning provides a comprehensive and unbiased view of the enzyme function landscape. The mapping displays expected patterns of mutational tolerance and a strong correspondence to sequence variation within the enzyme family, but also reveals previously unreported sites that are crucial for glycosidase function. We modified the screening protocol to include a high-temperature incubation step, and the resulting thermotolerance landscape allowed the discovery of mutations that enhance enzyme thermostability. Droplet microfluidics provides a general platform for enzyme screening that, when combined with DNA-sequencing technologies, enables high-throughput mapping of enzyme sequence space.
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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.