Ins and outs of AlphaFold2 transmembrane protein structure predictions

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 employe...

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Bibliographic Details
Published in:Cellular and molecular life sciences : CMLS 2022-01, Vol.79 (1), p.73-73, Article 73
Main Authors: Hegedűs, Tamás, Geisler, Markus, Lukács, Gergely László, Farkas, Bianka
Format: Article
Language:English
Subjects:
Algorithms
Biochemistry
Biomedical and Life Sciences
Biomedicine
Cell Biology
Computational Biology - methods
Computer Simulation
Dimers
Drug development
Dynamic stability
Genome
Genomes
Humans
Learning algorithms
Life Sciences
Lipids - chemistry
Machine Learning
Membrane Proteins - chemistry
Molecular dynamics
Molecular Dynamics Simulation
Molecular structure
Neural networks
Original
Original Article
Protein Conformation
Protein Domains
Protein Folding
Protein structure
Protein Structure, Secondary
Proteins
Proteome
Proteomics
Reliability analysis
Reproducibility of Results
Software
Structural models
Therapeutic targets
Transmembrane proteins
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Summary: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.
ISSN:1420-682X
1420-9071
DOI:10.1007/s00018-021-04112-1