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Transcription factors (TFs) play important roles in many biochemical processes. Many human genetic disorders have been associated with mutations in the genes encoding these transcription factors, and so those mutations became targets for medications and drug design. In parallel, since many transcription factors act either as tumor suppressors or oncogenes, their mutations are mostly associated with cancer. In this perspective, we studied the  GATA3  transcription factor when bound to  DNA  in a crystal structure and assessed the effect of different mutations encountered in patients with different diseases and phenotypes. We generated all missense mutants of  GATA3  protein and DNA within the adjacent and the opposite  GATA3:DNA  complex models. We mutated every amino acid and studied the new binding of the complex after each mutation. Similarly, we did for every  DNA  base. We applied Poisson-Boltzmann electrostatic calculations feeding into free energy calculations. After analyzing our data, we identified amino acids and DNA bases keys for binding. Furthermore, we validated those findings against experimental genetic data. Our results are the first to propose in silico modeling for  GATA:DNA  bound complexes that could be used to score effects of missense mutations in other classes of transcription factors involved in common and genetic diseases.

Mutational signatures in GATA3 transcription factor and its DNA binding domain that stimulate breast cancer and HDR syndrome