Back

Determining how MAFA and MAFB transcription factors activity is influenced by structural differences predicted by AlphaFold2

Tong, X.; Cha, J.; Guo, M.; Liu, J.-H.; Reynolds, G.; Loyd, Z.; Walker, E. M.; Soleimanpour, S. A.; Stein, R. A.; Mchaourab, H.; Stein, R.

2023-08-23 molecular biology
10.1101/2023.08.23.554429 bioRxiv
Show abstract

MAFA and MAFB are related basic-leucine-zipper domain containing transcription factors which have important overlapping and distinct regulatory roles in a variety of cellular contexts, including hormone production in pancreatic islet and {beta} cells. Here we first examined how mutating conserved MAF protein-DNA contacts obtained from X-ray crystal structure analysis impacted their DNA-binding and Insulin enhancer-driven activity. While most of these interactions were essential and their disruption severely compromised activity, we identified that regions outside of the contact areas also contributed to activity. AlphaFold 2, an artificial intelligence-based structural prediction program, was next used to determine if there were also differences in the three-dimensional organization of the non-DNA binding/dimerization sequences of MAFA and MAFB. This analysis was conducted on the wildtype (WT) proteins as well as the pathogenic MAFASer64Phe and MAFBSer70Ala trans-activation domain mutants, with differences revealed between MAFAWT and MAFBWT as well as between MAFASer64Phe and MAFAWT, but not between MAFBSer70Ala and MAFBWT. Moreover, dissimilarities between these proteins were also observed in their ability to cooperatively stimulate Insulin enhancer-driven activity in the presence of other islet-enriched transcription factors. Analysis of MAFA and MAFB chimeras disclosed that these properties were greatly influenced by unique C-terminal region structural differences predicted by AlphaFold 2. Importantly, these results have revealed features of these closely related proteins that are functionally significant in islet biology.

Matching journals

The top 10 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.