Predicting the structures of peptides binding to proteins remains a challenging problem due to the large conformational changes the peptide can undergo. We present computational studies combining NMR chemical shift data with molecular simulations to predict the conformations of several peptides binding to the extra-terminal domain (ET) of the BRD3 protein. We show that the peptides behave as intrinsically disordered in their free form and adopt well-defined structures upon binding. However, different peptide sequences bind along different binding modes -- either helical or hairpins. The chemical shift data provides information about regions in the protein that change conformation during binding, but no structural insight on the bound conformation. Some regions might change conformation due to direct contacts or due to allosteric changes upon binding. We show that our Bayesian inference approach, MELD (Modeling Employing Limited Data) is able to capture the correct bound structures in a series of blind tests with our NMR collaborators. This kind of integrative approach can be a powerful way to solve such structures reducing isotope labeling costs needed for NMR structural characterization.

Studying plasticity in peptides binding the ET domain of BRD3

ACS Spring Meeting 2021

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