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Related Concept Videos

Maxam-Gilbert Sequencing01:05

Maxam-Gilbert Sequencing

In the same year as the discovery of the Sanger sequencing method, another group of scientists, Allan Maxam and Walter Gilbert, demonstrated their chemical-cleavage method for DNA sequencing. The Maxam-Gilbert method relies on using different chemicals that can cleave the DNA sequence at specific sites, the separation of resulting DNA fragments of variable size using electrophoresis, and deciphering the DNA sequence from the resulting gel bands.
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Leaky Scanning

During most eukaryotic translation processes, the small 40S ribosome subunit scans an mRNA from its 5' end until it encounters the first start AUG codon. The large 60S ribosomal subunit then joins the smaller one to initiate protein synthesis. The location of the translation initiation is largely determined by the nucleotides near the start codon as there may be multiple translation initiation sites present on the mRNA.  Marilyn Kozak discovered that the sequence RCCAUGG (where R stands for...

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Updated: Jun 16, 2026

Lensless Fluorescent Microscopy on a Chip
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Lensless Fluorescent Microscopy on a Chip

Published on: August 17, 2011

Decoding cryptic defluorinases through a latent generative sequence landscape.

Ke Ji1, Sydney S Barnes1, Cheyenne Ziegler2

  • 1Department of Chemistry and Biochemistry, The University of Texas at Dallas Richardson TX 75080 USA sheel.dodani@utdallas.edu.

Chemical Science
|June 15, 2026
PubMed
Summary
This summary is machine-generated.

Machine learning identified thousands of novel defluorinases, enzymes that break down carbon-fluorine bonds. Experimental validation confirmed five new defluorinases, enhancing our understanding of this rare natural chemistry.

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Published on: April 14, 2015

Area of Science:

  • Biochemistry and enzymology
  • Bioinformatics and computational biology
  • Environmental microbiology

Background:

  • The carbon-fluorine bond's strength makes fluorinated natural products rare, despite the existence of natural defluorinase enzymes.
  • Fluoroacetate dehalogenase is a well-characterized defluorinase, but the full scope of defluorination chemistry in nature is underexplored.
  • Understanding and discovering defluorinases is crucial for both fundamental science and potential biotechnological applications.

Purpose of the Study:

  • To develop and apply a machine learning framework, latent generative landscapes (LGLs), to explore the functional sequence space of the alpha/beta-hydrolase superfamily.
  • To identify novel defluorinase enzymes within the biosphere.
  • To experimentally validate and characterize newly discovered defluorinases.

Main Methods:

  • Training and application of a machine learning-based framework (latent generative landscapes - LGLs) to predict enzyme function.
  • Mapping the functional sequence space of the alpha/beta-hydrolase superfamily to identify potential defluorinases.
  • Experimental validation including enzyme assays, thermal stability measurements (Tm), and enantioselectivity determination.

Main Results:

  • Identification of 3014 putative defluorinases previously unannotated or misannotated.
  • Experimental validation and reclassification of five novel defluorinases.
  • The newly discovered defluorinases exhibit high thermal stability (Tm > 70 °C) and diverse catalytic efficiencies with conserved enantioselectivity.
  • One novel enzyme (A0A4Z0BVY8) demonstrated 2.7-fold higher activity than the current state-of-the-art enzyme (Q6NAM1).

Conclusions:

  • Latent generative landscapes (LGLs) modeling is a powerful strategy for uncovering cryptic carbon-fluorine bond chemistry in nature.
  • This approach significantly expands the known repertoire of defluorinating biocatalysts.
  • The findings pave the way for future discovery and engineering of novel defluorination enzymes for biotechnological purposes.