Benchmarking AlphaFold for protein complex modeling reveals accuracy determinants.

ABSTRACT: High-resolution experimental structural determination of protein-protein interactions has led to valuable mechanistic insights, yet due to the massive number of interactions and experimental limitations there is a need for computational methods that can accurately model their structures. Here we explore the use of the recently developed deep learning method, AlphaFold, to predict structures of protein complexes from sequence. With a benchmark of 152 diverse heterodimeric protein complexes, multiple implementations and parameters of AlphaFold were tested for accuracy. Remarkably, many cases (43%) had near-native models (medium or high critical assessment of predicted interactions accuracy) generated as top-ranked predictions by AlphaFold, greatly surpassing the performance of unbound protei

SUBMITTER: Yin R 

PROVIDER: S-EPMC9278006 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature