Evolution of Distinct DNA-binding Specificities Within the Nuclear Receptor Family of Transcription Factors
Overview
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Nuclear receptors are ligand-activated transcription factors that interact with response elements within regulated genes. Most receptors, typified by the estrogen receptor, have three amino acids within the DNA-binding domain that specify recognition of the sequence TGACCT within the response element. However, in the glucocorticoid group of receptors, these residues have evolved to recognize the sequence TGTTCT. Saturation mutagenesis was used to investigate the role played by two of these residues (Gly-439 and Ser-440 of the human glucocorticoid receptor) in receptor specificity. We conclude that these residues, and their equivalents in the estrogen receptor, play roles unique to the respective amino acids. In the glucocorticoid receptor the side chain hydroxyl group is the important component of Ser-440 that contributes to specificity by inhibiting interaction with estrogen response elements. Several substitution mutants at position 439 interact well with estrogen response elements; therefore, the unique specificity feature of Glu-439, which mimics the estrogen receptor, is its inhibition of interaction with noncognate sites. In contrast to position 440, where most substitutions prevent interaction with DNA, replacements of residue 439 have the potential to contribute to the evolution of DNA-binding specificities within the nuclear receptor family. The liver-enriched HNF-4 and Drosophila Tailless transcription factors are known examples of receptors that have diverged at this position.
Alternative evolutionary histories in the sequence space of an ancient protein.
Starr T, Picton L, Thornton J Nature. 2017; 549(7672):409-413.
PMID: 28902834 PMC: 6214350. DOI: 10.1038/nature23902.
Crystal structure of the mineralocorticoid receptor DNA binding domain in complex with DNA.
Hudson W, Youn C, Ortlund E PLoS One. 2014; 9(9):e107000.
PMID: 25188500 PMC: 4154765. DOI: 10.1371/journal.pone.0107000.
Liu W, Reece-Hoyes J, Walhout A, Eisenmann D BMC Dev Biol. 2014; 14:17.
PMID: 24885717 PMC: 4051164. DOI: 10.1186/1471-213X-14-17.
A discriminative approach for unsupervised clustering of DNA sequence motifs.
Stegmaier P, Kel A, Wingender E, Borlak J PLoS Comput Biol. 2013; 9(3):e1002958.
PMID: 23555204 PMC: 3605052. DOI: 10.1371/journal.pcbi.1002958.
Bailey I, Gibson G, Plant K, Graham M, Plant N PLoS One. 2011; 6(2):e16703.
PMID: 21311750 PMC: 3032768. DOI: 10.1371/journal.pone.0016703.