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.