Ins and outs of AlphaFold2 transmembrane protein structure predictions
Open accessAbstract
Transmembrane (TM) proteins are major drug targets, but their structure determination, a prerequisite for rational drug design, remains challenging. Recently, the DeepMind's AlphaFold2 machine learning method greatly expanded the structural coverage of sequences with high accuracy. Since the employed algorithm did not take specific properties of TM proteins into account, the reliability of the generated TM structures should be assessed. Therefore, we quantitatively investigated the quality of structures at genome scales, at the level of ABC protein superfamily folds and for specific membrane proteins (e.g. dimer modeling and stability in molecular dynamics simulations). We tested template-free structure prediction with a challenging TM CASP14 target and several TM protein structures published after AlphaFold2 training. Our results suggest that AlphaFold2 performs well in the case of TM proteins and its neural network is not overfitted. We conclude that cautious applications of AlphaFold2 structural models will advance TM protein-associated studies at an unexpected level.
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AF2 performs poorly on targets embedded in membrane thickness outside 15-35 Å and on targets with novel features uncommon in the PDB.
“AF2 performs poorly for targets embedded in membrane thickness outside the 15–35 Å range as well as targets with novel features not commonly present in the PDB”
From The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins (2024) · cites this paper as [38]
Hegedűs et al benchmarked membrane proteins absent from AF2's original training set and concluded AF2 predicts transmembrane proteins as well as soluble proteins on average.
“Hegedűs et al benchmarked several membrane proteins not included in the original AF2 training set and concluded that on average AF2 predicts transmembrane proteins as well as soluble proteins38”
From The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins (2024) · cites this paper as [38]
AF2 models with transmembrane lengths matching non-physiological membrane thickness can have very high pLDDT, so pLDDT alone cannot select native membrane protein conformations.
“AF2 models with trans-membrane region lengths corresponding to non-physiological membrane thickness values can exhibit very high pLDDT scores (high model confidence), suggesting pLDDT scores alone are not sufficient to select native membrane protein conformations”
From The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins (2024) · cites this paper as [38]
Benchmarking AF2 on membrane proteins is difficult because current AlphaFold versions do not directly account for the membrane environment.
“Benchmarking AF2 against membrane proteins represents a challenge since the membrane environment, which includes lipids and other proteins, is not directly considered by current versions of AlphaFold38”
From The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins (2024) · cites this paper as [38]
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- Authors
- Hegedűs, T., Geisler, M., Lukács, G. L., & Farkas, B.
- Affiliations
- Semmelweis University (HU)
- University of Fribourg (CH)
- McGill University (CA)
- Corresponding author
- Hegedűs, Tamás
- Venue
- Cellular and Molecular Life Sciences
- Published
- 2022-01
- Volume
- 79
- Issue
- 1
- Pages
- 73-73
- ISSN
- 1420-682X (ISSN-L)
- 1420-9071
- PMC
- PMC8761152
- OpenAlex
- W4205172550
- Cited by
- 127
- References
- 69
- Citations by year
- 2026: 15 · 2025: 19 · 2024: 23 · 2023: 41 · 2022: 28 · 2012: 1
- FWCI
- 9.06
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Cellular and Molecular Life Sciences (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Nemzeti Kutatási Fejlesztési és Innovációs Hivatal (K127961)
- Cystic Fibrosis Foundation (HEGEDU20I0)
- Cystic Fibrosis Canada (LUKACS20G0)
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (310030_197563)
- Canadian Institutes of Health Research
- Canada Foundation for Innovation
- Canada Research Chairs
- Semmelweis University
- 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
- AlphaFold2, transmembrane proteins, protein structure prediction, prediction accuracy assessment, ABC transporters, membrane protein dimerization, CASP14, overfitting, structural genomics
- MeSH terms
- Machine Learning, Protein Domains, Algorithms, Computer Simulation, Humans, Lipids, Membrane Proteins, Protein Conformation, Software, Reproducibility of Results, Genome, Protein Structure, Secondary, Protein Folding, Computational Biology, Proteome, Proteomics, Molecular Dynamics Simulation
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