Transmembrane domain

Physics

Receptor

Biological system

Membrane

Biology

Gene

Biophysics

Membrane protein

Artificial intelligence

Power (physics)

Transmembrane protein

Protein structure prediction

Chemistry

Computer science

Protein structure

Quantum mechanics

Computational biology

Reliability (semiconductor)

Biochemistry

Cellular and Molecular Life Sciences

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.

Ins and outs of AlphaFold2 transmembrane protein structure predictions