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

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

Published on: July 25, 2013

Physics-based protein structure refinement in the era of artificial intelligence.

Lim Heo¹, Giacomo Janson¹, Michael Feig¹

¹Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan, USA.

|June 22, 2021

Summary

Physics-based refinement using molecular dynamics (MD) simulations can improve protein structure prediction models. However, current methods struggle with advanced AI models like AlphaFold2, highlighting challenges for future refinement strategies.

Keywords:

CASP Markov state models conformational sampling machine learning molecular dynamics simulation protein structure prediction structure refinement

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Area of Science:

Computational biology
Structural biology
Biophysics

Background:

Protein structure refinement is crucial for accurate protein structure prediction.
Molecular dynamics (MD) simulations offer a physics-based approach for refinement.
Recent advancements in AI, particularly deep learning, have revolutionized initial model generation.

Purpose of the Study:

To evaluate a new MD-based refinement protocol using enhanced sampling strategies.
To analyze the effectiveness of physics-based refinement on AI-generated models, including those from AlphaFold2.
To identify challenges and opportunities for MD-based refinement in the era of AI.

Main Methods:

Utilized MD simulations at an elevated temperature (360 K) with optimized biasing restraints.
Employed multiple starting models and enhanced sampling techniques.
Applied Markov state modeling for detailed analysis of refinement pathways.

Main Results:

The new protocol generally improved model quality for most initial models, including deep learning-based ones.
The protocol was less effective on AlphaFold2 models, sometimes reducing their initial high quality.
Identified challenges such as inter-domain contacts, oligomeric interactions, and kinetic barriers.

Conclusions:

MD-based refinement holds potential for improving AI-driven protein structure predictions.
Practical challenges hinder the full realization of this potential.
Future physics-based refinement strategies must address AI model specificities and complex biological interactions.