“Human gene expression is regulated by over 2,000 proteins called transcription factors (TFs) and chromatin regulators (CRs). These proteins contain: (1) DNA-binding domains that bind the genome site-specifically, and (2) effector domains that can activate or repress transcription depending on which transcriptional cofactor proteins they bind in the cell. However, we are missing effector domain annotations for more than half of these proteins – we just don’t know which proteins contain them and where they are. Furthermore, even though interactions between effector domains and cofactors are essential for converting CR & TF binding into a downstream transcriptional signal, we are currently missing quantitative measurements describing these interactions, likely due to their weak binding affinities and apparent lack of specificity.
During my PhD, I have been addressing these questions by first, using a high-throughput method, HT-Recruit (Tycko J., DelRosso N., …, Cell 2020), to systematically measure the transcriptional activities of >100,000 candidate transcriptional effector domains in human cells. With these measurements I annotated thousands of effector domains and discovered a new class of over fifty domains with both activating and repressing functions (DelRosso N., et al. Nature in press).
Additionally, I developed a microfluidic platform that allows recombinant expression and purification of thousands of effector domains in parallel followed by direct measurement of co-factor binding affinities (STAMMPPING, for Simultaneous Trapping of Affinity Measurements via a Microfluidic Protein-Protein INteraction Generator, extended from Aditham A., Markin C., Mokhtari D., DelRosso N., …, Cell Systems 2021). To date, we have measured binding affinities >1,000 activation domain, co-activator interactions.
Our systematic annotation and characterization of effector domains provide a rich resource for understanding the function of human transcription factors and chromatin regulators, engineering compact tools for controlling gene expression, and refining predictive models of effector domain function.”