Unexpected specificity within dynamic transcriptional protein-protein complexes.
Madeleine J HenleyBrian M LinharesBrittany S MorganTomasz CierpickiCarol A FierkeAnna K MappPublished in: Proceedings of the National Academy of Sciences of the United States of America (2020)
A key functional event in eukaryotic gene activation is the formation of dynamic protein-protein interaction networks between transcriptional activators and transcriptional coactivators. Seemingly incongruent with the tight regulation of transcription, many biochemical and biophysical studies suggest that activators use nonspecific hydrophobic and/or electrostatic interactions to bind to coactivators, with few if any specific contacts. Here a mechanistic dissection of a set of representative dynamic activator•coactivator complexes, comprised of the ETV/PEA3 family of activators and the coactivator Med25, reveals a different molecular recognition model. The data demonstrate that small sequence variations within an activator family significantly redistribute the conformational ensemble of the complex while not affecting overall affinity, and distal residues within the activator-not often considered as contributing to binding-play a key role in mediating conformational redistribution. The ETV/PEA3•Med25 ensembles are directed by specific contacts between the disordered activator and the Med25 interface, which is facilitated by structural shifts of the coactivator binding surface. Taken together, these data highlight the critical role coactivator plasticity plays in recognition of disordered activators and indicate that molecular recognition models of disordered proteins must consider the ability of the binding partners to mediate specificity.
Keyphrases
- protein protein
- small molecule
- nuclear factor
- transcription factor
- single molecule
- molecular dynamics simulations
- gene expression
- acute lymphoblastic leukemia
- dna binding
- electronic health record
- molecular dynamics
- toll like receptor
- big data
- binding protein
- heat shock
- convolutional neural network
- genome wide identification
- cross sectional
- genome wide
- data analysis
- structural basis
- immune response
- copy number
- oxidative stress
- hiv infected
- deep learning
- artificial intelligence