Ins and outs of AlphaFold2 transmembrane protein structure predictions.

ABSTRACT: 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 publis

SUBMITTER: Hegedus T 

PROVIDER: S-EPMC8761152 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature