Biology subjects

Wrabl, J. O.

Publications and source records attributed to Wrabl, J. O..

3 recordsLinked to original sources

bioRxiv2021

A Predictive Energy Landscape Model of MetamorphicProtein Conformational Specificity

"Metamorphic" proteins challenge state-of-the-art structure prediction methods reliant on amino acid similarity. Unfortunately, this obviates a more effective thermodynamic approach necessary to properly evaluate the impact of amino acid changes on the stability of two different folds. A vital capability of such a thermodynamic approach would be the quantification of the free energy differences between 1) the energy landscape minima of each native fold, and 2) each fold and the denatured state. Here we develop an energetic framework for conformational specificity, based on an ensemble description of protein thermodynamics. This energetic framework was able to successfully recapitulate the structures of high-identity enginerered sequences experimentally shown to adopt either Streptococcus protein GA or GB folds, demonstrating that this approach indeed reflected the energetic determinants of fold. Residue-level decomposition of the conformational specificity suggested several testable hypotheses, notably among them that fold-switching could be affected by local de-stabilization of the populated fold at positions sensitive to equilibrium perturbation. Since this ensemble-based compatibility framework is applicable to any structure and any sequence, it may be practically useful for the future targeted design, or large-scale proteomic detection, of novel metamorphic proteins. Impact StatementMetamorphic proteins are single amino acid sequences capable of adopting more than one structure at equilibrium. Detection and design of these molecules hold great promise for biological understanding and materials engineering, but to do so requires a thermodynamic framework capable of estimating the free energy differences between the two structures and the denatured state. We present such a framework, show it to be effective for the well-studied metamorphic protein GA/GB system, and suggest testable hypotheses for engineering novel fold-switch proteins.

biochemistry↗

bioRxiv2020

Tumor susceptibility gene-101 regulates glucocorticoid receptor through disorder-mediated allostery

Tumor Susceptibility Gene-101 (TSG101) is involved in endosomal maturation and has been implicated in the transcriptional regulation of several steroid hormone receptors (SHRs), although a detailed characterization of such regulation has yet to be conducted. Here we directly measure binding of TSG101 to one SHR, glucocorticoid receptor (GR). Using biophysical and cellular assays, we show that the coiled-coil domain of TSG101; 1) binds and folds the disordered N-terminal domain (NTD) of GR, 2) upon binding, improves DNA-binding of GR in vitro, and 3) enhances the transcriptional activity of GR in vivo. Our findings suggest that TSG101 is a bona fide transcriptional co-regulator of GR.

biophysics↗

bioRxiv2019

Hidden dynamic signatures drive substrate selectivity in the disordered phosphoproteome

Phosphorylation sites are hyper-abundant in the disordered proteins of eukaryotes, suggesting that conformational dynamics (or heterogeneity) may play a major role in determining to what extent a kinase interacts with a particular substrate. In biophysical terms, substrate selectivity may be determined not just by the structural and chemical complementarity between the kinase and its protein substrates, but also by the free energy difference between the conformational ensembles that are recognized by the kinase and those that are not. To test this hypothesis, we developed an informatics framework based on statistical thermodynamics, which allows us to probe for dynamic contributions to phosphorylation, as evaluated by the ability to predict Ser/Thr/ Tyr phosphorylation sites in the disordered proteome. Essential to this framework is a decomposition of substrate sequence information into two types: vertical information encoding conserved kinase specificity motifs and horizontal (distributed) information encoding substrate conformational dynamics that are embedded, but often not apparent, within position specific conservation patterns. We find not only that conformational dynamics play a major role, but that they are the dominant contribution to substrate selectivity. In fact, the main substrate classifier distinguishing selectivity is the magnitude of change in compaction of the disordered chain upon phosphorylation. Thus, in addition to providing fundamental insights into the underlying mechanistic consequences of phosphorylation across the entire proteome, our approach provides a novel statistical thermodynamic strategy for partitioning any sequence-based search into contributions from direct chemical and structural complementarity and those from changes in conformational dynamics. Using this framework, we developed a high-performance open-source phosphorylation site predictor, PHOSforUS, which is freely available at https://github.com/bxlab/PHOSforUS.

biophysics↗