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Updated: Jun 20, 2026

A Protocol for Computer-Based Protein Structure and Function Prediction

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Structure prediction for CASP8 with all-atom refinement using Rosetta.

Srivatsan Raman¹, Robert Vernon, James Thompson

¹Department of Biochemistry, University of Washington, Seattle, Washington 98195, USA.

|August 25, 2009

Summary

This summary is machine-generated.

The Rosetta structure prediction methodology improved protein model accuracy. Refinement techniques enhanced predictions for many targets, demonstrating Rosetta

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A Protocol for Computer-Based Protein Structure and Function Prediction

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Published on: November 3, 2011

Exploring Caspase Mutations and Post-Translational Modification by Molecular Modeling Approaches

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Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

Published on: January 26, 2024

Area of Science:

Computational Biology
Structural Biology
Bioinformatics

Background:

Protein structure prediction is crucial for understanding biological function.
Accurate protein models are essential for drug discovery and disease research.
The Critical Assessment of Techniques for Protein Structure Prediction (CASP) benchmarks prediction methodologies.

Purpose of the Study:

To evaluate the Rosetta structure prediction methodology in the Eighth CASP.
To assess the effectiveness of Rosetta's template-based and de novo modeling approaches.
To demonstrate the power of Rosetta's all-atom refinement in improving protein model accuracy.

Main Methods:

Utilized Rosetta for structure prediction across various targets in CASP8.
Employed aggressive sampling and all-atom refinement for model generation.
Combined multiple alignment methodologies for template-based modeling.
Applied Rosetta's de novo methodology for targets with distant sequence homology.

Main Results:

For template-based predictions, Rosetta's best models surpassed template alignments in 24 of 64 cases and improved starting models in 43 cases.
For de novo predictions, Rosetta models outperformed template-based models in several cases with distant homology.
Rosetta's refinement improved starting models in 7 out of 12 refinement challenges.

Conclusions:

Rosetta structure refinement significantly enhances protein model accuracy.
The methodology shows robust performance in both template-based and de novo prediction scenarios.
Rosetta's capabilities are validated by its performance in the CASP assessment.