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Related Experiment Video

Updated: May 19, 2026

A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Robustness of atomistic Gō models in predicting native-like folding intermediates.

S G Estácio1, C S Fernandes, H Krobath

  • 1Centro de Física da Matéria Condensada and Departamento de Física, Universidade de Lisboa, Av. Prof. Gama Pinto 2, 1649-003 Lisboa, Portugal.

The Journal of Chemical Physics
|September 4, 2012
PubMed
Summary

Gō models simulate protein folding using native structures. This study shows atomistic details significantly influence folding pathways and intermediate states predicted by these popular models.

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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
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Last Updated: May 19, 2026

A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
10:58

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

Area of Science:

  • Computational Biology
  • Biophysics
  • Protein Dynamics

Background:

  • Gō models are widely used for protein folding simulations due to their native-centric construction.
  • Understanding how atomistic details of native structures affect Gō model predictions is crucial for their accurate application.
  • The robustness of Gō models in predicting folding intermediates requires thorough investigation.

Purpose of the Study:

  • To assess the impact of native structure atomistic details on Gō model-predicted protein folding behavior.
  • To investigate the reliability of Gō models in predicting the existence and nature of folding intermediate states.
  • To compare the folding pathway of a specific mutant (N47G Spc-SH3) using its native structure against in silico generated alternatives.

Main Methods:

  • Discrete molecular dynamics simulations employing a Gō potential with a full atomistic protein representation.
  • Equilibrium folding simulations to capture protein dynamics.
  • Structural clustering and principal component analysis to analyze folding pathways and identify conformational states.

Main Results:

  • Gō model predictions for protein folding pathways are sensitive to the specific atomistic details of the input native structure.
  • The presence and characteristics of predicted intermediate states can vary significantly based on subtle differences in the native structure.
  • Simulations revealed distinct folding behaviors when comparing the N47G Spc-SH3 mutant's native structure with in silico generated alternatives.

Conclusions:

  • Atomistic details encoded in native structures play a critical role in dictating protein folding pathways and intermediate states predicted by Gō models.
  • The native-centric approach of Gō models necessitates careful consideration of structural input to ensure reliable predictions.
  • Further research is needed to refine Gō models to better account for structural variations and improve their predictive power in protein folding studies.