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Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins
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Predicting Conformational Properties of Intrinsically Disordered Proteins from Sequence.

Kiersten M Ruff1

  • 1Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, USA. kiersten.ruff@wustl.edu.

Methods in Molecular Biology (Clifton, N.J.)
|July 23, 2020
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Intrinsically disordered proteins (IDPs) exhibit diverse conformations. Sequence features, including composition and residue patterns, influence these conformational preferences, aiding in classification and prediction.

Keywords:
Amino acid compositionCompactionConformationIDPsPredictionSequence patterning

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Area of Science:

  • Biochemistry
  • Structural Biology
  • Computational Biology

Background:

  • Intrinsically disordered proteins (IDPs) lack stable 3D structures, adopting various conformations like globules or swollen coils.
  • Understanding how sequence dictates IDP conformational preferences is a key challenge in structural biology.

Purpose of the Study:

  • To outline methods for calculating sequence-based features to classify IDP conformational states.
  • To predict relative conformational changes based on sequence composition and residue patterning.

Main Methods:

  • Calculation of global compositional features (e.g., fraction of charged residues).
  • Analysis of specific sequence patterns (e.g., oppositely charged residue patterning, expansion-driving residues).

Main Results:

  • Global compositional features influence IDP conformational biases.
  • Specific sequence patterns further modulate conformational ensembles.
  • Methods allow for classification and relative prediction of IDP conformations.

Conclusions:

  • Sequence composition and residue patterning are critical determinants of IDP conformational preferences.
  • While classification and relative prediction are feasible, accurate quantitative conformational prediction requires sequence-specific simulations.