Ins and outs of AlphaFold2 transmembrane protein structure predictions
By T. Hegedűs, Markus M. Geisler, G. Lukács, Bianka Farkas. 2022
110 citations recorded by the source corpus.
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.
Subfields: Molecular Biology, Oncology
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In Molecular Biology
Citations in the corpus
- The application of large language models in medicine: A scoping review (2024, unknown): “78,79 In epidemiology, LLMs are instrumental in tracking and forecasting disease spread by analyzing online textual data, offering substantial support for public health decision-making.”
- Estimating conformational heterogeneity of tryptophan synthase with a template‐based Alphafold2 approach (2022, background): “This blend of strategies leads to a larger diversity in the predictions.(41) Even so, the obtained structures are often very similar as they are exploring the lowest in energy conformations of the protein.”