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Revealing Free Energy Landscape from MD Data via Conditional Angle Partition Tree
Open accessPreprint — published as 10.1109/tcbb.2022.3172352 · Paper page
Abstract
Deciphering the free energy landscape of biomolecular structure space is crucial for understanding many complex molecular processes, such as protein-protein interaction, RNA folding, and protein folding. A major source of current dynamic structure data is Molecular Dynamics (MD) simulations. Several methods have been proposed to investigate the free energy landscape from MD data, but all of them rely on the assumption that kinetic similarity is associated with global geometric similarity, which may lead to unsatisfactory results. In this paper, we proposed a new method called Conditional Angle Partition Tree to reveal the hierarchical free energy landscape by correlating local geometric similarity with kinetic similarity. Its application on the benchmark alanine dipeptide MD data showed a much better performance than existing methods in exploring and understanding the free energy landscape. We also applied it to the MD data of Villin HP35. Our results are more reasonable on various aspects than those from other methods and very informative on the hierarchical structure of its energy landscape.
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Metadata
- Authors
- Jiang, H., Li, H., Wong, W. H., & Fan, X.
- Affiliations
- Zhejiang University (CN)
- Stanford University (US)
- Chinese University of Hong Kong (HK)
- Corresponding author
- Fan, Xiaodan
- Venue
- IEEE Transactions on Computational Biology and Bioinformatics
- Publisher
- openRxiv
- Published
- 2021-09-27
- Volume
- 20
- Issue
- 2
- Pages
- 1384-1394
- ISSN
- 1545-5963 (ISSN-L)
- OpenAlex
- W4225416386
- Cited by
- 4
- References
- 1
- Citations by year
- 2026: 1 · 2025: 3
- FWCI
- 0.24
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- cc-by-nc-nd
- Locations
- IEEE Transactions on Computational Biology and Bioinformatics (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Funders
- National Science Foundation (1811920)
- National Natural Science Foundation of China (61972355, 11901517, 14173817)
- National Science Foundation of Sri Lanka (DMS-1811920)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Physical Sciences › Chemistry › Physical and Theoretical Chemistry › Advanced Physical and Chemical Molecular Interactions
- Keywords
- free energy landscape, molecular dynamics data, geometric similarity, hierarchy, alanine dipeptide, protein folding, RNA folding, protein-protein interaction
- MeSH terms
- Dipeptides, Kinetics, Thermodynamics, Trees, Protein Folding, Molecular Dynamics Simulation
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