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Rosetta Machine Learning Models Accurately Classify Positional Effects of Thioamides on Proteolysis.

Sam Giannakoulias1, Sumant R Shringari1, Chunxiao Liu1

  • 1Department of Chemistry, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States.

The Journal of Physical Chemistry. B
|September 2, 2020
PubMed
Summary

Thioamide modifications stabilize peptides against proteolysis. A new Rosetta score function predicts these effects, identifying steric hindrance as key to enhanced peptide stability and resistance to degradation.

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Area of Science:

  • Computational chemistry
  • Biochemistry
  • Structural biology

Background:

  • Peptide backbone thioamide substitutions enhance stability against proteolysis.
  • Rational design of these modifications requires understanding positional effects.

Purpose of the Study:

  • Develop a novel Rosetta custom score function.
  • Classify thioamide positional effects on proteolysis for serine and cysteine proteases.

Main Methods:

  • Peptide docking into proteases using Rosetta's FlexPepDock.
  • Thioamide parametrization via custom atom types and ab initio simulations.
  • Machine learning classification using Rosetta score function features.

Main Results:

  • Developed an ensemble, majority voting model for robust prediction.
  • Model accurately predicts previously unpublished thioamide proteolysis data.
  • Simulations implicate steric effects on peptide binding as primary drivers of proteolytic resistance.

Conclusions:

  • The developed Rosetta score function effectively predicts thioamide effects on proteolysis.
  • Steric effects are crucial for understanding thioamide-mediated proteolytic resistance.
  • This work enables rational design of stabilized therapeutic and imaging peptides.