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Green Fluorescent Protein-based Expression Screening of Membrane Proteins in Escherichia coli
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A statistical model for improved membrane protein expression using sequence-derived features.

Shyam M Saladi1, Nauman Javed1, Axel Müller1

  • 1Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California 91125.

The Journal of Biological Chemistry
|January 31, 2018
PubMed
Summary
This summary is machine-generated.

Predicting integral membrane protein (IMP) expression is now possible using sequence data. The new IMProve tool increases the success rate of expressing these challenging proteins in Escherichia coli.

Keywords:
computational biologymachine-learningmembrane biogenesismembrane biophysicsmembrane proteinpredictionprotein expressionstructural biology

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Area of Science:

  • Biochemistry
  • Structural Biology
  • Molecular Biology

Background:

  • Integral membrane proteins (IMPs) are crucial but difficult to express heterologously.
  • Current methods for IMP expression are unpredictable and inefficient for characterization.
  • Sequence variations significantly impact IMP expression, but generalizable strategies are lacking.

Purpose of the Study:

  • To develop a predictive model for selecting IMPs with higher expression likelihood in Escherichia coli.
  • To create a data-driven tool that utilizes only sequence information for IMP expression prediction.

Main Methods:

  • Developed a statistical predictor, IMProve, trained on experimental IMP expression data.
  • Utilized sequence-derived features to generate an IMProve score correlating with expression success.
  • Validated the IMProve model against diverse independent datasets with varying experimental outcomes.

Main Results:

  • The IMProve model accurately predicts IMP expression levels based solely on sequence information.
  • Using IMProve can more than double the number of successfully expressed IMP targets.
  • Higher IMProve scores indicate a greater probability of successful IMP expression.

Conclusions:

  • IMP expression levels can be reliably predicted from protein sequence data.
  • IMProve offers a significant advancement for identifying suitable IMP targets for structural and biophysical studies.
  • This predictive approach enhances the efficiency and success rate of IMP characterization.