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Exploring Caspase Mutations and Post-Translational Modification by Molecular Modeling Approaches
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Consistent refinement of submitted models at CASP using a knowledge-based potential.

Gaurav Chopra1, Nir Kalisman, Michael Levitt

  • 1Department of Structural Biology, Stanford University, Stanford, CA 94305, USA. gaurav.chopra@stanford.edu

Proteins
|July 1, 2010
PubMed
Summary
This summary is machine-generated.

Protein structure refinement is crucial for predicting biological function. A new, efficient protocol uses energy minimization to improve protein models, enhancing structural accuracy and stereochemistry for better function prediction.

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Achieving Efficient Fragment Screening at XChem Facility at Diamond Light Source
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08:35

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Published on: May 29, 2021

Area of Science:

  • Computational biology
  • Structural bioinformatics
  • Protein modeling

Background:

  • Protein structure prediction accuracy is critical for understanding biological function.
  • Existing methods, particularly in comparative modeling, often yield results less accurate than the template structures.
  • Refinement of predicted protein structures remains a significant challenge.

Purpose of the Study:

  • To develop and evaluate a computationally efficient protein structure refinement protocol.
  • To improve the accuracy and stereochemistry of protein models generated by various prediction methods.
  • To assess the protocol's performance across different categories and homology levels in protein structure prediction challenges.

Main Methods:

  • Utilized a knowledge-based potential of mean force derived from interaction statistics of 167 atom types.
  • Employed direct energy minimization for structure refinement.
  • Applied the protocol to submitted predictions from the Critical Assessment of Techniques for Protein Structure Prediction (CASP) 7 and 8.

Main Results:

  • Achieved an average structural improvement of 1% in Global Distance Test score (GDT_TS) for models with low to medium homology (GDT_TS 50-80%).
  • Demonstrated significant improvements in model stereochemistry.
  • Observed substantial gains (>10% increase in GDT_TS) even for top-tier predictions and consistently improved best models in CASP refinement categories.

Conclusions:

  • The developed protocol offers a computationally inexpensive, powerful, and automatic solution for protein structure refinement.
  • The protocol effectively enhances both the structural accuracy and stereochemical quality of protein models.
  • Improved protein models facilitate more reliable prediction of biological functions sensitive to structural details.