Long-Range Modulation of Chain Motions within the Intrinsically Disordered Transactivation Domain of Tumor Suppressor p53

ABSTRACT: The tumor suppressor p53 is a hub protein with a multitude of binding partners, many of which target its intrinsically disordered N-terminal domain, p53-TAD. Partners, such as the N-terminal domain of MDM2, induce formation of local structure and leave the remainder of the domain apparently disordered. We investigated segmental chain motions in p53-TAD using fluorescence quenching of an extrinsic label by tryptophan in combination with fluorescence correlation spectroscopy (PET-FCS). We studied the loop closure kinetics of four consecutive segments within p53-TAD and their response to protein binding and phosphorylation. The kinetics was multiexponential, showing that the conformational ensemble of the domain deviates from random coil, in agreement with previous findings from NMR spectroscopy. Phosphorylations or binding of MDM2 changed the pattern of intrachain kinetics. Unexpectedly, we found that upon binding and phosphorylation chain motions were altered not only within the targeted segments but also in remote regions. Long-range interactions can be induced in an intrinsically disordered domain by partner proteins that induce apparently only local structure or by post-translational modification

Intrinsically Disordered Proteins Display No Preference for Chaperone Binding In Vivo

Intrinsically disordered/unstructured proteins (IDPs) are extremely sensitive to proteolysis in vitro, but show no enhanced degradation rates in vivo. Their existence and functioning may be explained if IDPs are preferentially associated with chaperones in the cell, which may offer protection against degradation by proteases. To test this inference, we took pairwise interaction data from high-throughput interaction studies and analyzed to see if predicted disorder correlates with the tendency of chaperone binding by proteins. Our major finding is that disorder predicted by the IUPred algorithm actually shows negative correlation with chaperone binding in E. coli, S. cerevisiae, and metazoa species. Since predicted disorder positively correlates with the tendency of partner binding in the interactome, the difference between the disorder of chaperone-binding and non-binding proteins is even more pronounced if normalized to their overall tendency to be involved in pairwise protein–protein interactions. We argue that chaperone binding is primarily required for folding of globular proteins, as reflected in an increased preference for chaperones of proteins in which at least one Pfam domain exists. In terms of the functional consequences of chaperone binding of mostly disordered proteins, we suggest that its primary reason is not the assistance of folding, but promotion of assembly with partners. In support of this conclusion, we show that IDPs that bind chaperones also tend to bind other proteins

Disorder Predictors Also Predict Backbone Dynamics for a Family of Disordered Proteins

Several algorithms have been developed that use amino acid sequences to predict whether or not a protein or a region of a protein is disordered. These algorithms make accurate predictions for disordered regions that are 30 amino acids or longer, but it is unclear whether the predictions can be directly related to the backbone dynamics of individual amino acid residues. The nuclear Overhauser effect between the amide nitrogen and hydrogen (NHNOE) provides an unambiguous measure of backbone dynamics at single residue resolution and is an excellent tool for characterizing the dynamic behavior of disordered proteins. In this report, we show that the NHNOE values for several members of a family of disordered proteins are highly correlated with the output from three popular algorithms used to predict disordered regions from amino acid sequence. This is the first test between an experimental measure of residue specific backbone dynamics and disorder predictions. The results suggest that some disorder predictors can accurately estimate the backbone dynamics of individual amino acids in a long disordered region

Malleable Machines in Transcription Regulation: The Mediator Complex

The Mediator complex provides an interface between gene-specific regulatory proteins and the general transcription machinery including RNA polymerase II (RNAP II). The complex has a modular architecture (Head, Middle, and Tail) and cryoelectron microscopy analysis suggested that it undergoes dramatic conformational changes upon interactions with activators and RNAP II. These rearrangements have been proposed to play a role in the assembly of the preinitiation complex and also to contribute to the regulatory mechanism of Mediator. In analogy to many regulatory and transcriptional proteins, we reasoned that Mediator might also utilize intrinsically disordered regions (IDRs) to facilitate structural transitions and transmit transcriptional signals. Indeed, a high prevalence of IDRs was found in various subunits of Mediator from both Saccharomyces cerevisiae and Homo sapiens, especially in the Tail and the Middle modules. The level of disorder increases from yeast to man, although in both organisms it significantly exceeds that of multiprotein complexes of a similar size. IDRs can contribute to Mediator's function in three different ways: they can individually serve as target sites for multiple partners having distinctive structures; they can act as malleable linkers connecting globular domains that impart modular functionality on the complex; and they can also facilitate assembly and disassembly of complexes in response to regulatory signals. Short segments of IDRs, termed molecular recognition features (MoRFs) distinguished by a high protein–protein interaction propensity, were identified in 16 and 19 subunits of the yeast and human Mediator, respectively. In Saccharomyces cerevisiae, the functional roles of 11 MoRFs have been experimentally verified, and those in the Med8/Med18/Med20 and Med7/Med21 complexes were structurally confirmed. Although the Saccharomyces cerevisiae and Homo sapiens Mediator sequences are only weakly conserved, the arrangements of the disordered regions and their embedded interaction sites are quite similar in the two organisms. All of these data suggest an integral role for intrinsic disorder in Mediator's function

A Network-Based Approach to Prioritize Results from Genome-Wide Association Studies

Genome-wide association studies (GWAS) are a valuable approach to understanding the genetic basis of complex traits. One of the challenges of GWAS is the translation of genetic association results into biological hypotheses suitable for further investigation in the laboratory. To address this challenge, we introduce Network Interface Miner for Multigenic Interactions (NIMMI), a network-based method that combines GWAS data with human protein-protein interaction data (PPI). NIMMI builds biological networks weighted by connectivity, which is estimated by use of a modification of the Google PageRank algorithm. These weights are then combined with genetic association p-values derived from GWAS, producing what we call ‘trait prioritized sub-networks.’ As a proof of principle, NIMMI was tested on three GWAS datasets previously analyzed for height, a classical polygenic trait. Despite differences in sample size and ancestry, NIMMI captured 95% of the known height associated genes within the top 20% of ranked sub-networks, far better than what could be achieved by a single-locus approach. The top 2% of NIMMI height-prioritized sub-networks were significantly enriched for genes involved in transcription, signal transduction, transport, and gene expression, as well as nucleic acid, phosphate, protein, and zinc metabolism. All of these sub-networks were ranked near the top across all three height GWAS datasets we tested. We also tested NIMMI on a categorical phenotype, Crohn’s disease. NIMMI prioritized sub-networks involved in B- and T-cell receptor, chemokine, interleukin, and other pathways consistent with the known autoimmune nature of Crohn’s disease. NIMMI is a simple, user-friendly, open-source software tool that efficiently combines genetic association data with biological networks, translating GWAS findings into biological hypotheses