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A Protocol for Computer-Based Protein Structure and Function Prediction
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Protein ensemble modeling and analysis with MMMx.

Gunnar Jeschke1

  • 1Department of Chemistry and Applied Biosciences, ETH Zürich, Zürich, Switzerland.

Protein Science : a Publication of the Protein Society
|February 15, 2024
PubMed
Summary
This summary is machine-generated.

The MMMx toolbox aids in analyzing protein structures, especially disordered regions. It helps characterize protein flexibility and folding patterns from computational predictions.

Keywords:
ensemble comparisonensemble modelingintrinsic disorderweak order

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Area of Science:

  • Structural biology
  • Computational biophysics

Background:

  • Eukaryotic proteins frequently feature intrinsically disordered regions (IDRs) and multiple folded domains with dynamic spatial arrangements.
  • Understanding the structural ensemble of these proteins is crucial for deciphering their function.

Purpose of the Study:

  • To introduce the MMMx ensemble modeling and analysis toolbox for characterizing the distributed structure of proteins with disordered domains.
  • To provide methods for analyzing protein chain flexibility and conformational ensembles.

Main Methods:

  • Utilizing AlphaFold2 predictions or existing folded domain structures as input.
  • Analyzing weak disorder against random coil models and Ramachandran angle distributions.
  • Calculating mean square end-to-end distance to identify chain compaction or expansion.
  • Visualizing disordered chain shapes using principal axes of inertia.
  • Comparing conformational ensembles using a similarity parameter.

Main Results:

  • The MMMx toolbox facilitates the analysis of protein structural ensembles, including disordered regions.
  • Methods are provided to quantify chain flexibility, localized structural changes, and overall shape.
  • A parameter for comparing different ensemble sizes and conformer weightings is established.

Conclusions:

  • The MMMx toolbox offers a comprehensive approach to studying the structural heterogeneity of proteins.
  • It supports experimental design by providing quantitative insights into protein dynamics and conformational landscapes.