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Local Interactions That Contribute Minimal Frustration Determine Foldability.

Taisong Zou1, Brian W Woodrum2, Nicholas Halloran2

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Designing foldable proteins using evolutionary information requires minimizing folding frustration. Identifying key local contacts improves prediction accuracy and enables redesign of stable protein sequences.

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Area of Science:

  • Protein engineering
  • Computational biology
  • Biophysics

Background:

  • Evolutionary information (conservation and coevolution) in protein sequences is thought to determine protein fold.
  • Previous work lacked computational quantification of evolutionary information's effect on protein folding dynamics.

Purpose of the Study:

  • To computationally and experimentally investigate the role of early folding steps in sequences designed using coevolution and conservation.
  • To identify critical local contacts governing protein folding and develop accurate foldability prediction models.
  • To redesign non-foldable protein sequences into stable, functional structures.

Main Methods:

  • Simulated native and designed WW domain sequences to analyze early local contact formation.
  • Employed a maximum likelihood approach to identify critical local contacts for folding.
  • Developed a classification model based on early-stage contact probabilities to predict foldability.
  • Redesigned unstable WW domain sequences using the classification model and validated experimentally.

Main Results:

  • Unfoldable designed sequences exhibited incorrect N-terminal β-hairpin formation due to non-native local contacts.
  • Identified five critical local contacts essential for WW domain folding.
  • Achieved 81% accuracy in predicting WW sequence foldability using a classification model.
  • Redesigned sequences demonstrated successful folding and native-like binding affinity to polyproline peptides.

Conclusions:

  • Evolutionary-designed protein sequences must balance folding stability with a minimally frustrated folding landscape.
  • A small set of critical local contacts can guide the design of foldable protein sequences.
  • Computational models integrating evolutionary information and folding dynamics can accurately predict and improve protein foldability.