Journal Article

Ins and outs of AlphaFold2 transmembrane protein structure predictions

Geisler, Markus, Farkas, Bianka, Lukács, Gergely László, Hegedűs, Tamás

2022

Overview

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.

• Publisher: Springer International Publishing,Springer Nature B.V
• Subject: ABC transporters / Algorithms / Biochemistry / Biomedical and Life Sciences / Biomedicine / Cell Biology / Computational Biology - methods / Computer Simulation / Dimers / drug design / Drug development / drugs / 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 / prediction / 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