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A high-throughput predictive method for sequence-similar fold switchers.

Allen K Kim1,2, Loren L Looger3, Lauren L Porter1,2

  • 1National Library of Medicine, National Institutes of Health, Bethesda, Maryland, USA.

Biopolymers
|January 19, 2021
PubMed
Summary

Identifying sequence-similar fold switchers is crucial as they can cause disease. Inconsistencies in secondary structure predictions, particularly from JPred4, can reliably detect these proteins from amino acid sequences alone.

Keywords:
bioinformaticsmetamorphic proteinsprotein fold switchingprotein secondary structure predictionprotein structure prediction

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Area of Science:

  • Protein structure and function
  • Bioinformatics and computational biology
  • Genomics

Background:

  • Most proteins with similar sequences adopt similar structures and functions.
  • Exceptions exist, including sequence-similar fold switchers that alter secondary structures (alpha-helix to beta-sheet transitions).
  • Identifying these fold switchers is important due to their disease association and functional diversity.

Purpose of the Study:

  • To develop predictive methods for identifying sequence-similar fold switchers using only amino acid sequences.
  • To leverage homology-based secondary structure predictions for this identification.

Main Methods:

  • Predicted secondary structures of known sequence-similar fold switchers using PSIPRED, JPred4, and SPIDER3.
  • Analyzed discrepancies in alpha-helix/beta-strand predictions between different software.
  • Utilized these prediction discrepancies as a classification feature.

Main Results:

  • JPred4's alpha-helix/beta-strand prediction discrepancies significantly differentiated fold switcher conformations (P < 1.8*10^-19).
  • The discrepancies achieved a Matthews Correlation Coefficient of 0.82 in discriminating fold switchers from proteins with stable folds.
  • JPred4 demonstrated robustness due to its curated database and Hidden Markov Model-based sequence profiles.

Conclusions:

  • Inconsistencies in JPred4 secondary structure predictions can identify sequence-similar fold switchers from sequence data alone.
  • This approach offers a method to detect fold switchers within large genomic datasets by analyzing prediction discrepancies.