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Three-dimensional model for the hormone binding domains of steroid receptors.

ABSTRACT: We have used a motif-based structural search method to identify structural homologs of the hormone binding domains of the nuclear receptors from among a set of known protein structures and have found the closest similarity with members of the subtilisin-like serine proteases. These proteins consist of an open twisted sheet of parallel beta-strands flanked on both sides by alpha-helices. The alignment with the protease scaffold was refined by using multiple sequence prealignment of different sets of nuclear receptors, and alternative model structures were screened by considering their consistency with the results of biochemical experiments defining the ligand binding pocket. In the most favored model, nearly all of the residues thought to be involved in ligand binding map to a pocket of appropriate dimensions where the subtilisin-like proteases have their active site. The three-dimensional model that we propose for the hormone binding domains of the nuclear receptors provides a framework for the design of experiments to further investigate nuclear receptor structure and function.

SUBMITTER: Goldstein RA

PROVIDER: S-EPMC47690 | biostudies-other | 1993 Nov

REPOSITORIES: biostudies-other

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Three-dimensional model for the hormone binding domains of steroid receptors.

Goldstein R A RA Katzenellenbogen J A JA Luthey-Schulten Z A ZA Seielstad D A DA Wolynes P G PG

Proceedings of the National Academy of Sciences of the United States of America 19931101 21

We have used a motif-based structural search method to identify structural homologs of the hormone binding domains of the nuclear receptors from among a set of known protein structures and have found the closest similarity with members of the subtilisin-like serine proteases. These proteins consist of an open twisted sheet of parallel beta-strands flanked on both sides by alpha-helices. The alignment with the protease scaffold was refined by using multiple sequence prealignment of different sets ...[more]

PMID: 8234340

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Project description:Most proteins are regulated by physical interactions with other molecules; some are highly specific, but others interact with many partners. Despite much speculation, we know little about how and why specificity/promiscuity evolves in natural proteins. It is widely assumed that specific proteins evolved from more promiscuous ancient forms and that most proteins' specificity has been tuned to an optimal state by selection. Here we use ancestral protein reconstruction to trace the evolutionary history of ligand recognition in the steroid hormone receptors (SRs), a family of hormone-regulated animal transcription factors. We resurrected the deepest ancestral proteins in the SR family and characterized the structure-activity relationships by which they distinguished among ligands. We found that that the most ancient split in SR evolution involved a discrete switch from an ancient receptor for aromatized estrogens--including xenobiotics--to a derived receptor that recognized non-aromatized progestagens and corticosteroids. The family's history, viewed in relation to the evolution of their ligands, suggests that SRs evolved according to a principle of minimal specificity: at each point in time, receptors evolved ligand recognition criteria that were just specific enough to parse the set of endogenous substances to which they were exposed. By studying the atomic structures of resurrected SR proteins, we found that their promiscuity evolved because the ancestral binding cavity was larger than the primary ligand and contained excess hydrogen bonding capacity, allowing adventitious recognition of larger molecules with additional functional groups. Our findings provide an historical explanation for the sensitivity of modern SRs to natural and synthetic ligands--including endocrine-disrupting drugs and pollutants--and show that knowledge of history can contribute to ligand prediction. They suggest that SR promiscuity may reflect the limited power of selection within real biological systems to discriminate between perfect and "good enough."

Dataset Information

Three-dimensional model for the hormone binding domains of steroid receptors.

Publications

Three-dimensional model for the hormone binding domains of steroid receptors.

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