Protein Dynamics to Define and Refine Disordered Protein Ensembles

Using NMR diffusion data to validate MD models of disordered proteins: Test case of N-terminal tail of histone H4

MD simulations can provide uniquely detailed models of intrinsically disordered proteins (IDPs). However, these models need careful experimental validation. The coefficient of translational diffusion Dtr, measurable by pulsed field gradient NMR, offers a potentially useful piece of experimental information related to the compactness of the IDP's conformational ensemble. Here, we investigate, both experimentally and via the MD modeling, the translational diffusion of a 25-residue N-terminal fragment from histone H4 (N-H4). We found that the predicted values of Dtr, as obtained from mean-square displacement of the peptide in the MD simulations, are largely determined by the viscosity of the MD water (which has been reinvestigated as a part of our study). Beyond that, our analysis of the diffusion data indicates that MD simulations of N-H4 in the TIP4P-Ew water give rise to an overly compact conformational ensemble for this peptide. In contrast, TIP4P-D and OPC simulations produce the ensembles that are consistent with the experimental Dtr result. These observations are supported by the analyses of the 15N spin relaxation rates. We also tested a number of empirical methods to predict Dtr based on IDP's coordinates extracted from the MD snapshots. In particular, we show that the popular approach involving the program HYDROPRO can produce misleading results. This happens because HYDROPRO is not intended to predict the diffusion properties of highly flexible biopolymers such as IDPs. Likewise, recent empirical schemes that exploit the relationship between the small-angle x-ray scattering-informed conformational ensembles of IDPs and the respective experimental Dtr values also prove to be problematic. In this sense, the first-principle calculations of Dtr from the MD simulations, such as demonstrated in this work, should provide a useful benchmark for future efforts in this area.

Computational modeling multiple conformational states of proteins with residual dipolar coupling data

Solution nuclear magnetic resonance spectroscopy provides unique opportunities to study the structure and dynamics of biomolecules in aqueous environments. While spin relaxation methods are well recognized for their ability to probe timescales of motion, residual dipolar couplings (RDCs) provide access to amplitudes and directions of motion, characteristics that are important to the function of these molecules. Although observed in the 1960s, the acquisition and computational analysis of RDCs has gained significant momentum in recent years, and particularly applications to motion in proteins have become more numerous. This trend may well continue as RDCs can easily leverage structures produced by new computational methods (e.g., AlphaFold) to produce functional descriptions. In this report, we provide examples and a summary of the ways that RDCs have been used to confirm the existence of internal dynamics, characterize the type of dynamics, and recover atomic-scale structural ensembles that define the full range of conformational sampling.

Analysis of the complex between MBD2 and the histone deacetylase core of NuRD reveals key interactions critical for gene silencing

The nucleosome remodeling and deacetylase (NuRD) complex modifies nucleosome positioning and chromatin compaction to regulate gene expression. The methyl-CpG-binding domain proteins 2 and 3 (MBD2 and MBD3) play a critical role in complex formation; however, the molecular details of how they interact with other NuRD components have yet to be fully elucidated. We previously showed that an intrinsically disordered region (IDR) of MBD2 is necessary and sufficient to bind to the histone deacetylase core of NuRD. Building on that work, we have measured the inherent structural propensity of the MBD2-IDR using solvent and site-specific paramagnetic relaxation enhancement measurements. We then used the AlphaFold2 machine learning software to generate a model of the complex between MBD2 and the histone deacetylase core of NuRD. This model is remarkably consistent with our previous studies, including the current paramagnetic relaxation enhancement data. The latter suggests that the free MBD2-IDR samples conformations similar to the bound structure. We tested this model of the complex extensively by mutating key contact residues and measuring binding using an intracellular bioluminescent resonance energy transfer assay. Furthermore, we identified protein contacts that, when mutated, disrupted gene silencing by NuRD in a cell model of fetal hemoglobin regulation. Hence, this work provides insights into the formation of NuRD and highlights critical binding pockets that may be targeted to block gene silencing for therapy. Importantly, we show that AlphaFold2 can generate a credible model of a large complex that involves an IDR that folds upon binding.

Assessment of models for calculating the hydrodynamic radius of intrinsically disordered proteins

Diffusion measurements by pulsed-field gradient NMR and fluorescence correlation spectroscopy can be used to probe the hydrodynamic radius of proteins, which contains information about the overall dimension of a protein in solution. The comparison of this value with structural models of intrinsically disordered proteins is nonetheless impaired by the uncertainty of the accuracy of the methods for computing the hydrodynamic radius from atomic coordinates. To tackle this issue, we here build conformational ensembles of 11 intrinsically disordered proteins that we ensure are in agreement with measurements of compaction by small-angle x-ray scattering. We then use these ensembles to identify the forward model that more closely fits the radii derived from pulsed-field gradient NMR diffusion experiments. Of the models we examined, we find that the Kirkwood-Riseman equation provides the best description of the hydrodynamic radius probed by pulsed-field gradient NMR experiments. While some minor discrepancies remain, our results enable better use of measurements of the hydrodynamic radius in integrative modeling and for force field benchmarking and parameterization.

Ethanol treatment enhances drought stress avoidance in cassava (Manihot esculenta Crantz)

External application of ethanol enhances tolerance to high salinity, drought, and heat stress in various plant species. However, the effects of ethanol application on increased drought tolerance in woody plants, such as the tropical crop "cassava," remain unknown. In the present study, we analyzed the morphological, physiological, and molecular responses of cassava plants subjected to ethanol pretreatment and subsequent drought stress treatment. Ethanol pretreatment induced a slight accumulation of abscisic acid (ABA) and stomatal closure, resulting in a reduced transpiration rate, higher water content in the leaves during drought stress treatment and the starch accumulation in leaves. Transcriptomic analysis revealed that ethanol pretreatment upregulated the expression of ABA signaling-related genes, such as PP2Cs and AITRs, and stress response and protein-folding-related genes, such as heat shock proteins (HSPs). In addition, the upregulation of drought-inducible genes during drought treatment was delayed in ethanol-pretreated plants compared with that in water-pretreated control plants. These results suggest that ethanol pretreatment induces stomatal closure through activation of the ABA signaling pathway, protein folding-related response by activating the HSP/chaperone network and the changes in sugar and starch metabolism, resulting in increased drought avoidance in plants.

IDPConformerGenerator: A Flexible Software Suite for Sampling the Conformational Space of Disordered Protein States

The power of structural information for informing biological mechanisms is clear for stable folded macromolecules, but similar structure–function insight is more difficult to obtain for highly dynamic systems such as intrinsically disordered proteins (IDPs) which must be described as structural ensembles. Here, we present IDPConformerGenerator, a flexible, modular open-source software platform for generating large and diverse ensembles of disordered protein states that builds conformers that obey geometric, steric, and other physical restraints on the input sequence. IDPConformerGenerator samples backbone phi (φ), psi (ψ), and omega (ω) torsion angles of relevant sequence fragments from loops and secondary structure elements extracted from folded protein structures in the RCSB Protein Data Bank and builds side chains from robust Monte Carlo algorithms using expanded rotamer libraries. IDPConformerGenerator has many user-defined options enabling variable fractional sampling of secondary structures, supports Bayesian models for assessing the agreement of IDP ensembles for consistency with experimental data, and introduces a machine learning approach to transform between internal and Cartesian coordinates with reduced error. IDPConformerGenerator will facilitate the characterization of disordered proteins to ultimately provide structural insights into these states that have key biological functions.