Assessment of machine-learning predictions for the Mediator complex subunit MED25 ACID domain interactions with transactivation domains.

Abstract

The human Mediator complex subunit MED25 binds transactivation domains (TADs) present in various cellular and viral proteins using two binding interfaces, named H1 and H2, which are found on opposite sides of its ACID domain. Here, we use and compare deep learning methods to characterize human MED25-TAD interfaces and assess the predicted models to published experimental data. For the H1 interface, AlphaFold produces predictions with high-reliability scores that agree well with experimental data, while the H2 interface predictions appear inconsistent, preventing reliable binding modes. Despite these limitations, we experimentally assess the validity of MED25 interface predictions with the viral transcriptional activators Lana-1 and IE62. AlphaFold predictions also suggest the existence of a unique hydrophobic pocket for the Arabidopsis MED25 ACID domain.