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Learning the sequence code of protein expression in human immune cells.

Benoît P Nicolet1,2,3, Anouk P Jurgens1,2,3, Kaspar Bresser1,2,3

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Scientists developed SONAR, a machine learning tool, to predict protein abundance in human immune cells using sequence features. SONAR reveals how sequence code influences protein levels, aiding in therapeutic applications.

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Area of Science:

  • Molecular Biology
  • Immunology
  • Bioinformatics

Background:

  • Accurate protein expression is crucial for human immune cell function.
  • Protein abundance is regulated by complex transcriptional, posttranscriptional, and posttranslational mechanisms.

Purpose of the Study:

  • To present SONAR, a machine learning pipeline that deciphers the endogenous sequence code regulating protein abundance in human cells.
  • To identify sequence features (SFs) that predict protein levels independently of regulatory elements like promoters and enhancers.

Main Methods:

  • Developed SONAR, a machine learning pipeline utilizing thousands of sequence features (SFs).
  • Applied SONAR to predict protein abundance in human cells.
  • Analyzed cell type-specific and activation-dependent SF usage.

Main Results:

  • SONAR predicts up to 63% of protein abundance using sequence features alone.
  • Uncovered cell type-specific and activation-dependent patterns in SF usage.
  • Identified potentially biologically active SFs for manipulating protein expression.

Conclusions:

  • SONAR provides fundamental insights into protein expression regulation.
  • Demonstrated SONAR's utility in designing enhancer sequences to enhance T cell receptor expression and function.
  • Offers innovative strategies for therapeutic and biotechnology applications by controlling protein expression amplitude, timing, and cell type specificity.