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Updated: May 10, 2025
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Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web
Published on: July 16, 2017
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Characterization of conformational flexibility in protein structures by applying artificial intelligence to molecular
Kirill Kopylov1, Evgeny Kirilin2, Vladimir Voevodin3
1Lomonosov Moscow State University, Research Computing Center, Leninskie Gory 1-4, 119234 Moscow, Russia; Lomonosov Moscow State University, Belozersky Institute of Physicochemical Biology, Leninskie Gory 1-40, 119992 Moscow, Russia; Lomonosov Moscow State University, Faculty of Bioengineering and Bioinformatics, Leninskie Gory 1-73, 119991 Moscow, Russia.
Journal of Structural Biology
|April 26, 2025
Summary
This study integrates artificial intelligence (AI) and high-performance computing (HPC) to model protein structures. The approach accurately predicts protein conformations and their energy landscapes, advancing structural biology.
Area of Science:
- Structural Biology
- Computational Biology
- Biophysics
Background:
- Artificial intelligence (AI) has advanced the modeling of protein regions with unresolved structures.
- Current AI methods generate numerous structural models, but assessing their energy landscapes requires physics-based approaches.
- Bridging rapid model generation with precise functional conformation determination is crucial.
Purpose of the Study:
- To propose an integrated approach combining molecular modeling, AI, and high-performance computing (HPC).
- To explore potential energy landscapes of flexible protein regions using metadynamics simulations in latent space.
- To validate the approach using known protein folding and conformational plasticity models.
Main Methods:
- Utilized AI-driven modeling tools (e.g., AlphaFold, RosettaFold) for initial approximations of flexible protein regions.
- Employed metadynamics simulations in latent space to explore energy landscapes.
- Integrated AI and HPC for efficient exploration and analysis of molecular conformations.
Main Results:
- Successfully modeled the folding of Trp-cage protein and the conformational plasticity of ubiquitin.
- Identified predominant conformations of mobile regions within the active center of flavin-dependent 2-hydroxybiphenyl-3-monooxygenase (EC 1.14.13.44).
- Estimated the energy associated with identified conformational changes in the enzyme's active center.
Conclusions:
- The integrated AI, molecular modeling, and HPC approach effectively models protein conformational dynamics and energy landscapes.
- This method provides a robust framework for prioritizing AI-generated protein models and understanding functional conformations.
- The approach advances structural biology by enabling precise determination of functionally relevant protein states.
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