The characterization of intrinsically disordered proteins is challenging because accurate models of these systems require a description of both their thermally accessible conformers and the associated relative stabilities or weights. These structures and weights are typically chosen such that calculated ensemble averages agree with some set of prespecified experimental measurements; however, the large number of degrees of freedom in these systems typically leads to multiple conformational ensembles that are degenerate with respect to any given set of experimental observables. In this work we demonstrate that estimates of the relative stabilities of conformers within an ensemble are often incorrect when one does not account for the underlying uncertainty in the estimates themselves. Therefore, we present a method for modeling the conformational properties of disordered proteins that estimates the uncertainty in the weights of each conformer. The Bayesian weighting (BW) formalism incorporates information from both experimental data and theoretical predictions to calculate a probability density over all possible ways of weighting the conformers in the ensemble. This probability density is then used to estimate the values of the weights. A unique and powerful feature of the approach is that it provides a built-in error measure that allows one to assess the accuracy of the ensemble. We validate the approach using reference ensembles constructed from the five-residue peptide met-enkephalin and then apply the BW method to construct an ensemble of the K18 isoform of the tau protein. Using this ensemble, we indentify a specific pattern of long-range contacts in K18 that correlates with the known aggregation properties of the sequence.

One of the obstacles for cancer immunotherapy is the inefficiency of CD8+ T-cell recruitment to tumors. STAT3 has been shown to suppress CD8+ T-cell antitumor functions in various cancer models, in part by restricting accumulation of CD8+ T cells. However, the underlying molecular mechanism by which STAT3 in CD8+ T cells inhibits their accumulation in tumors remains to be defined. Here, we show that STAT3 signaling in CD8+ T cells inhibits chemokine CXCL10 production by tumor-associated myeloid cells via reducing IFNγ expression by T cells. We further demonstrate that ablating STAT3 in T cells allows expression of CXCR3, the receptor of CXCL10, on CD8+ T cells, resulting in efficient accumulation of CD8+ T cells at tumor sites. Blocking IFNγ or CXCR3 impairs the accumulation of STAT3-deficient CD8+ T cells in tumor and their antitumor effects. Together, our study reveals a negative regulation by STAT3 signaling in T cells on myeloid cell-T cell crosstalk through IFNγ/CXCR3/CXCL10, which is important for CD8+ T cells homing to tumors. Our results thus provide new insights applicable to cancer immunotherapy and adoptive T-cell strategies.

Tau is a natively unfolded protein that forms intracellular aggregates in the brains of patients with Alzheimer's disease. To decipher the mechanism underlying the formation of tau aggregates, we developed a novel approach for constructing models of natively unfolded proteins. The method, energy-minima mapping and weighting (EMW), samples local energy minima of subsequences within a natively unfolded protein and then constructs ensembles from these energetically favorable conformations that are consistent with a given set of experimental data. A unique feature of the method is that it does not strive to generate a single ensemble that represents the unfolded state. Instead we construct a number of candidate ensembles, each of which agrees with a given set of experimental constraints, and focus our analysis on local structural features that are present in all of the independently generated ensembles. Using EMW we generated ensembles that are consistent with chemical shift measurements obtained on tau constructs. Thirty models were constructed for the second microtubule binding repeat (MTBR2) in wild-type (WT) tau and a ΔK280 mutant, which is found in some forms of frontotemporal dementia. By focusing on structural features that are preserved across all ensembles, we find that the aggregation-initiating sequence, PHF6*, prefers an extended conformation in both the WT and ΔK280 sequences. In addition, we find that residue K280 can adopt a loop/turn conformation in WT MTBR2 and that deletion of this residue, which can adopt nonextended states, leads to an increase in locally extended conformations near the C-terminus of PHF6*. As an increased preference for extended states near the C-terminus of PHF6* may facilitate the propagation of β-structure downstream from PHF6*, these results explain how a deletion at position 280 can promote the formation of tau aggregates.

Probably the most unusual class of proteins in nature is the intrinsically unstructured proteins (IUPs), because they are not structured yet play essential roles in protein-protein signaling. Many IUPs can bind different proteins, and in many cases, adopt different bound conformations. The p21 protein is a small IUP (164 residues) that is ubiquitous in cellular signaling, for example, cell cycle control, apoptosis, transcription, differentiation, and so forth; it binds to approximately 25 targets. How does this small, unstructured protein recognize each of these targets with high affinity? Here, we characterize residual structural elements of the C-terminal segment of p21 encompassing residues 145-164 using a combination of NMR measurements and molecular dynamics simulations. The N-terminal half of the peptide has a significant helical propensity which is recognized by calmodulin while the C-terminal half of the peptide prefers extended conformations that facilitate binding to the proliferating cell nuclear antigen (PCNA). Our results suggest that the final bound conformations of p21 (145-164) pre-exist in the free peptide even without its binding partners. While the conformational flexibility of the p21 peptide is essential for adapting to diverse binding environments, the intrinsic structural preferences of the free peptide enable promiscuous yet high affinity binding to a diverse array of molecular targets.

Implicit solvent models approximate the effects of solvent through a potential of mean force and therefore make solvated simulations computationally efficient. Yet despite their computational efficiency, the inherent approximations made by implicit solvent models can sometimes lead to inaccurate results. To test the accuracy of a number of popular implicit solvent models, we determined whether implicit solvent simulations can reproduce the set of potential energy minima obtained from explicit solvent simulations. For these studies, we focus on a six-residue amino-acid sequence, referred to as the paired helical filament 6 (PHF6), which may play an important role in the formation of intracellular aggregates in patients with Alzheimer's disease. Several implicit solvent models form the basis of this work--two based on the generalized Born formalism, and one based on a Gaussian solvent-exclusion model. All three implicit solvent models generate minima that are in good agreement with minima obtained from simulations with explicit solvent. Moreover, free-energy profiles generated with each implicit solvent model agree with free-energy profiles obtained with explicit solvent. For the Gaussian solvent-exclusion model, we demonstrate that a straightforward ranking of the relative stability of each minimum suggests that the most stable structure is extended, a result in excellent agreement with the free-energy profiles. Overall, our data demonstrate that for some peptides like PHF6, implicit solvent can accurately reproduce the set of local energy minimum arising from quenched dynamics simulations with explicit solvent. More importantly, all solvent models predict that PHF6 forms extended beta-structures in solution, a finding consistent with the notion that PHF6 initiates neurofibrillary tangle formation in patients with Alzheimer's disease.

A number of neurodegenerative disorders, such as Alzheimer's disease and Parkinson's disease, involve the formation of protein aggregates. The primary constituent of these aggregates belongs to a unique class of heteropolymers known as intrinsically disordered proteins (IDPs). While many proteins fold to a unique conformation that is determined by their amino acid sequence, IDPs do not adopt a single well-defined conformation in solution. Instead, they populate a heterogeneous set of conformers under physiological conditions. Despite this intrinsic propensity for disorder, a number of these proteins can form ordered aggregates both in vitro and in vivo. As the formation of these aggregates may play an important role in disease pathogenesis, a detailed structural characterization of these proteins and their mechanism of aggregation is of critical importance. However, new methods are needed to understand the diversity of structures that make up the unfolded ensemble of these systems. In this review, we discuss recent advances in the structural analysis and modeling of IDPs involved in neurodegenerative diseases. While there are challenges in both the experimental characterization and the modeling of such proteins, a comprehensive understanding of the structure of IDPs will likely facilitate the development of effective therapies for a number of neurodegenerative diseases.

The mechanical landscape in biological systems can be complex and dynamic, with contrasting sustained and fluctuating loads regularly superposed within the same tissue. How resident cells discriminate between these scenarios to respond accordingly remains largely unknown. Here, we show that a step increase in compressive stress of physiological magnitude shrinks the lateral intercellular space between bronchial epithelial cells, but does so with strikingly slow exponential kinetics (time constant approximately 110 s). We confirm that epidermal growth factor (EGF)-family ligands are constitutively shed into the intercellular space and demonstrate that a step increase in compressive stress enhances EGF receptor (EGFR) phosphorylation with magnitude and onset kinetics closely matching those predicted by constant-rate ligand shedding in a slowly shrinking intercellular geometry. Despite the modest degree and slow nature of EGFR activation evoked by compressive stress, we find that the majority of transcriptomic responses to sustained mechanical loading require ongoing activity of this autocrine loop, indicating a dominant role for mechanotransduction through autocrine EGFR signaling in this context. A slow deformation response to a step increase in loading, accompanied by synchronous increases in ligand concentration and EGFR activation, provides one means for cells to mount a selective and context-appropriate response to a sustained change in mechanical environment.

Capturing and quantifying dynamic changes in three-dimensional cellular geometries on fast time scales is a challenge because of mechanical limitations of imaging systems as well as of the inherent tradeoffs between temporal resolution and image quality. We have combined a custom high-speed two-photon microscopy approach with a novel image segmentation method, the weighted directional adaptive-threshold (WDAT), to quantify the dimensions of intercellular spaces of cells under compressive stress on timescales previously inaccessible. The adaptation of a high-speed two-photon microscope addressed the need to capture events occurring on short timescales, while the WDAT method was developed to address artifacts of standard intensity-based analysis methods when applied to this system. Our novel approach is demonstrated by the enhanced temporal analysis of the threedimensional cellular and extracellular deformations that accompany compressive loading of airway epithelial cells.