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Cell Surface Receptor Identification Using Genome-Scale CRISPR/Cas9 Genetic Screens
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Cell Surface Exposure.

Anna Konovalova1

  • 1Lewis Thomas Laboratory, Department of Molecular Biology, Princeton University, Washington Road, Princeton, NJ, 08544, USA. ak9@princeton.edu.

Methods in Molecular Biology (Clifton, N.J.)
|July 2, 2017
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Summary
This summary is machine-generated.

Detecting surface-exposed bacterial proteins is crucial. This study presents three experimental methods to identify surface-exposed lipoproteins and proteins in Gram-negative bacteria when computational predictions are unavailable.

Keywords:
BiotinylationPEGylationProtein topologySurface proteolysisWhole-cell dot blot

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Area of Science:

  • Microbiology
  • Bacterial Cell Biology
  • Protein Biochemistry

Background:

  • Gram-negative bacteria possess surface-exposed proteins, including outer membrane beta-barrel proteins and lipoproteins.
  • Currently, no computational tools exist for predicting the surface exposure of lipoproteins.
  • Experimental validation is necessary to determine lipoprotein topology and localization.

Purpose of the Study:

  • To describe and validate experimental methods for detecting surface-exposed proteins in Gram-negative bacteria.
  • To provide a practical guide for researchers investigating bacterial surface protein localization.
  • To address the lack of computational prediction tools for surface-exposed lipoproteins.

Main Methods:

  • Cell surface protein labeling using reactive chemical probes.
  • Assessing protein accessibility to extracellular proteases.
  • Evaluating protein accessibility to antibodies via immunodetection.

Main Results:

  • The described methods offer complementary approaches for identifying surface-exposed proteins.
  • These techniques allow for the differentiation of surface-exposed lipoproteins from other outer membrane proteins.
  • Experimental validation confirmed the localization of target proteins on the bacterial cell surface.

Conclusions:

  • Three robust experimental methods are presented for the reliable detection of surface-exposed proteins in Gram-negative bacteria.
  • These methods overcome the limitations of current computational prediction tools for lipoproteins.
  • The described techniques are essential for understanding bacterial surface architecture and host-pathogen interactions.