Can Protein Structure Prediction Methods Capture Alternative Conformations of Membrane Transporters?
Open accessPublished version of a preprint
Abstract
Understanding the conformational dynamics of proteins, such as the inward-facing (IF) and outward-facing (OF) transition observed in transporters, is vital for elucidating their functional mechanisms. Despite significant advances in protein structure prediction (PSP) over the past three decades, most efforts have been focused on single-state prediction, leaving multistate or alternative conformation prediction (ACP) relatively unexplored. This discrepancy has led to the development of highly accurate PSP methods such as AlphaFold, yet their capabilities for ACP remain limited. To investigate the performance of current PSP methods in ACP, we curated a data set, named IOMemP, consisting of 32 experimentally determined high-resolution IF and OF structures of 16 membrane proteins with substantial conformational changes. We benchmarked 12 representative PSP methods, along with two recent multistate methods based on AlphaFold, against this data set. Our findings reveal a remarkably consistent preference for specific states across various PSP methods. We elucidated how coevolution information in MSAs influences state preference. Moreover, we showed that AlphaFold, when excluding coevolution information, estimated similar energies between the experimental IF and OF conformations, indicating that the energy model learned by AlphaFold is not biased toward any particular state. Our IOMemP data set and benchmark results are anticipated to advance the development of robust ACP methods.
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- Authors
- Xie, T., & Huang, J.
- Affiliations
- Westlake University (CN)
- Corresponding author
- Huang, Jing
- Venue
- Journal of Chemical Information and Modeling
- Publisher
- American Chemical Society (ACS)
- Published
- 2024-04-02
- Volume
- 64
- Issue
- 8
- Pages
- 3524-3536
- ISSN
- 1549-9596 (ISSN-L)
- 1549-960X
- OpenAlex
- W4393559298
- Cited by
- 23
- References
- 11
- Citations by year
- 2026: 8 · 2025: 10 · 2024: 5
- FWCI
- 3.13
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Journal of Chemical Information and Modeling (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nd)
- PubMed (publishedVersion)
- Funders
- Key Research and Development Program of Zhejiang Province (2023C03109, 2024SSYS0036)
- National Natural Science Foundation of China (21803057, 32171247)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Physical Sciences › Materials Science › Materials Chemistry › Enzyme Structure and Function
- Keywords
- protein structure prediction, membrane transporters, conformational dynamics, AlphaFold, multiple sequence alignment, benchmarking, energy model
- MeSH terms
- Membrane Proteins, Models, Molecular, Protein Conformation, Membrane Transport Proteins, Databases, Protein, Molecular Dynamics Simulation
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