Comment on the Optimal Parameters to Derive Intrinsically Disordered Protein Conformational Ensembles from Small-Angle X-ray Scattering Data Using the Ensemble Optimization Method

A molten globule ensemble primes Arf1-GDP for the nucleotide switch

The adenosine di-phosphate (ADP) ribosylation factor (Arf) small guanosine tri-phosphate (GTP)ases function as molecular switches to activate signaling cascades that control membrane organization in eukaryotic cells. In Arf1, the GDP/GTP switch does not occur spontaneously but requires guanine nucleotide exchange factors (GEFs) and membranes. Exchange involves massive conformational changes, including disruption of the core β-sheet. The mechanisms by which this energetically costly switch occurs remain to be elucidated. To probe the switch mechanism, we coupled pressure perturbation with nuclear magnetic resonance (NMR), Fourier Transform infra-red spectroscopy (FTIR), small-angle X-ray scattering (SAXS), fluorescence, and computation. Pressure induced the formation of a classical molten globule (MG) ensemble. Pressure also favored the GDP to GTP transition, providing strong support for the notion that the MG ensemble plays a functional role in the nucleotide switch. We propose that the MG ensemble allows for switching without the requirement for complete unfolding and may be recognized by GEFs. An MG-based switching mechanism could constitute a pervasive feature in Arfs and Arf-like GTPases, and more generally, the evolutionarily related (Ras-like small GTPases) Rags and Gα GTPases.

Are Protein Conformational Ensembles in Agreement with Experimental Data? A Geometrical Interpretation of the Problem

The conformational variability of biological macromolecules can play an important role in their biological function. Therefore, understanding conformational variability is expected to be key for predicting the behavior of a particular molecule in the context of organism-wide studies. Several experimental methods have been developed and deployed for accessing this information, and computational methods are continuously updated for the profitable integration of different experimental sources. The outcome of this endeavor is conformational ensembles, which may vary significantly in properties and composition when different ensemble reconstruction methods are used, and this raises the issue of comparing the predicted ensembles against experimental data. In this article, we discuss a geometrical formulation to provide a framework for understanding the agreement of an ensemble prediction to the experimental observations.

The role of intrinsic disorder in binding of plant microtubule-associated proteins to the cytoskeleton

Microtubules (MTs) represent one of the main components of the eukaryotic cytoskeleton and support numerous critical cellular functions. MTs are in principle tube-like structures that can grow and shrink in a highly dynamic manner; a process largely controlled by microtubule-associated proteins (MAPs). Plant MAPs are a phylogenetically diverse group of proteins that nonetheless share many common biophysical characteristics and often contain large stretches of intrinsic protein disorder. These intrinsically disordered regions are determinants of many MAP-MT interactions, in which structural flexibility enables low-affinity protein-protein interactions that enable a fine-tuned regulation of MT cytoskeleton dynamics. Notably, intrinsic disorder is one of the major obstacles in functional and structural studies of MAPs and represents the principal present-day challenge to decipher how MAPs interact with MTs. Here, we review plant MAPs from an intrinsic protein disorder perspective, by providing a complete and up-to-date summary of all currently known members, and address the current and future challenges in functional and structural characterization of MAPs.

The Prenucleation Equilibrium of the Parathyroid Hormone Determines the Critical Aggregation Concentration and Amyloid Fibril Nucleation

Nucleation and growth of amyloid fibrils were found to only occur in supersaturated solutions above a critical concentration (ccrit ). The biophysical meaning of ccrit remained mostly obscure, since typical low values of ccrit in the sub-μM range hamper investigations of potential oligomeric states and their structure. Here, we investigate the parathyroid hormone PTH84 as an example of a functional amyloid fibril forming peptide with a comparably high ccrit of 67±21 μM. We describe a complex concentration dependent prenucleation ensemble of oligomers of different sizes and secondary structure compositions and highlight the occurrence of a trimer and tetramer at ccrit as possible precursors for primary fibril nucleation. Furthermore, the soluble state found in equilibrium with fibrils adopts to the prenucleation state present at ccrit . Our study sheds light onto early events of amyloid formation directly related to the critical concentration and underlines oligomer formation as a key feature of fibril nucleation. Our results contribute to a deeper understanding of the determinants of supersaturated peptide solutions. In the current study we present a biophysical approach to investigate ccrit of amyloid fibril formation of PTH84 in terms of secondary structure, cluster size and residue resolved intermolecular interactions during oligomer formation. Throughout the investigated range of concentrations (1 μM to 500 μM) we found different states of oligomerization with varying ability to contribute to primary fibril nucleation and with a concentration dependent equilibrium. In this context, we identified the previously described ccrit of PTH84 to mark a minimum concentration for the formation of homo-trimers/tetramers. These investigations allowed us to characterize molecular interactions of various oligomeric states that are further converted into elongation competent fibril nuclei during the lag phase of a functional amyloid forming peptide.

The intrinsically disordered protein glue of the myelin major dense line: Linking AlphaFold2 predictions to experimental data

Numerous human proteins are classified as intrinsically disordered proteins (IDPs). Due to their physicochemical properties, high-resolution structural information about IDPs is generally lacking. On the other hand, IDPs are known to adopt local ordered structures upon interactions with e.g. other proteins or lipid membrane surfaces. While recent developments in protein structure prediction have been revolutionary, their impact on IDP research at high resolution remains limited. We took a specific example of two myelin-specific IDPs, the myelin basic protein (MBP) and the cytoplasmic domain of myelin protein zero (P0ct). Both of these IDPs are crucial for normal nervous system development and function, and while they are disordered in solution, upon membrane binding, they partially fold into helices, being embedded into the lipid membrane. We carried out AlphaFold2 predictions of both proteins and analysed the models in light of experimental data related to protein structure and molecular interactions. We observe that the predicted models have helical segments that closely correspond to the membrane-binding sites on both proteins. We furthermore analyse the fits of the models to synchrotron-based X-ray scattering and circular dichroism data from the same IDPs. The models are likely to represent the membrane-bound state of both MBP and P0ct, rather than the conformation in solution. Artificial intelligence-based models of IDPs appear to provide information on the ligand-bound state of these proteins, instead of the conformers dominating free in solution. We further discuss the implications of the predictions for mammalian nervous system myelination and their relevance to understanding disease aspects of these IDPs.

Pressure pushes tRNALys3 into excited conformational states

Conformational dynamics play essential roles in RNA function. However, detailed structural characterization of excited states of RNA remains challenging. Here, we apply high hydrostatic pressure (HP) to populate excited conformational states of tRNALys3, and structurally characterize them using a combination of HP 2D-NMR, HP-SAXS (HP-small-angle X-ray scattering), and computational modeling. HP-NMR revealed that pressure disrupts the interactions of the imino protons of the uridine and guanosine U-A and G-C base pairs of tRNALys3. HP-SAXS profiles showed a change in shape, but no change in overall extension of the transfer RNA (tRNA) at HP. Configurations extracted from computational ensemble modeling of HP-SAXS profiles were consistent with the NMR results, exhibiting significant disruptions to the acceptor stem, the anticodon stem, and the D-stem regions at HP. We propose that initiation of reverse transcription of HIV RNA could make use of one or more of these excited states.

Conformational Disorder Analysis of the Conditionally Disordered Protein CP12 from Arabidopsis thaliana in Its Different Redox States

CP12 is a redox-dependent conditionally disordered protein universally distributed in oxygenic photosynthetic organisms. It is primarily known as a light-dependent redox switch regulating the reductive step of the metabolic phase of photosynthesis. In the present study, a small angle X-ray scattering (SAXS) analysis of recombinant Arabidopsis CP12 (AtCP12) in a reduced and oxidized form confirmed the highly disordered nature of this regulatory protein. However, it clearly pointed out a decrease in the average size and a lower level of conformational disorder upon oxidation. We compared the experimental data with the theoretical profiles of pools of conformers generated with different assumptions and show that the reduced form is fully disordered, whereas the oxidized form is better described by conformers comprising both the circular motif around the C-terminal disulfide bond detected in previous structural analysis and the N-terminal disulfide bond. Despite the fact that disulfide bridges are usually thought to confer rigidity to protein structures, in the oxidized AtCP12, their presence coexists with a disordered nature. Our results rule out the existence of significant amounts of structured and compact conformations of free AtCP12 in a solution, even in its oxidized form, thereby highlighting the importance of recruiting partner proteins to complete its structured final folding.

Structural plasticity of NFU1 upon interaction with binding partners: insights into the mitochondrial [4Fe-4S] cluster pathway

In humans, the biosynthesis and trafficking of mitochondrial [4Fe-4S]²⁺ clusters is a highly coordinated process that requires a complex protein machinery. In a mitochondrial pathway among various proposed to biosynthesize nascent [4Fe-4S]²⁺ clusters, two [2Fe-2S]²⁺ clusters are converted into a [4Fe-4S]²⁺ cluster on a ISCA1-ISCA2 complex. Along this pathway, this cluster is then mobilized from this complex to mitochondrial apo recipient proteins with the assistance of accessory proteins. NFU1 is the accessory protein that first receives the [4Fe-4S]²⁺ cluster from ISCA1-ISCA2 complex. A structural view of the protein-protein recognition events occurring along the [4Fe-4S]²⁺ cluster trafficking as well as how the globular N-terminal and C-terminal domains of NFU1 act in such process is, however, still elusive. Here, we applied small-angle X-ray scattering coupled with on-line size-exclusion chromatography and paramagnetic NMR to disclose structural snapshots of ISCA1-, ISCA2- and NFU1-containing apo complexes as well as the coordination of [4Fe-4S]²⁺ cluster bound to the ISCA1-NFU1 complex, which is the terminal stable species of the [4Fe-4S]²⁺ cluster transfer pathway involving ISCA1-, ISCA2- and NFU1 proteins. The structural modelling of ISCA1-ISCA2, ISCA1-ISCA2-NFU1 and ISCA1-NFU1 apo complexes, here reported, reveals that the structural plasticity of NFU1 domains is crucial to drive protein partner recognition and modulate [4Fe-4S]²⁺ cluster transfer from the cluster-assembly site in ISCA1-ISCA2 complex to the cluster-binding site in ISCA1-NFU1 complex. These structures allowed us to provide a first rational for the molecular function of the N-domain of NFU1, which can act as a modulator in the [4Fe-4S]²⁺ cluster transfer.

Learning to evolve structural ensembles of unfolded and disordered proteins using experimental solution data

The structural characterization of proteins with a disorder requires a computational approach backed by experiments to model their diverse and dynamic structural ensembles. The selection of conformational ensembles consistent with solution experiments of disordered proteins highly depends on the initial pool of conformers, with currently available tools limited by conformational sampling. We have developed a Generative Recurrent Neural Network (GRNN) that uses supervised learning to bias the probability distributions of torsions to take advantage of experimental data types such as nuclear magnetic resonance J-couplings, nuclear Overhauser effects, and paramagnetic resonance enhancements. We show that updating the generative model parameters according to the reward feedback on the basis of the agreement between experimental data and probabilistic selection of torsions from learned distributions provides an alternative to existing approaches that simply reweight conformers of a static structural pool for disordered proteins. Instead, the biased GRNN, DynamICE, learns to physically change the conformations of the underlying pool of the disordered protein to those that better agree with experiments.

WASCO: A Wasserstein-based statistical tool to compare conformational ensembles of intrinsically disordered proteins

The structural investigation of intrinsically disordered proteins (IDPs) requires ensemble models describing the diversity of the conformational states of the molecule. Due to their probabilistic nature, there is a need for new paradigms that understand and treat IDPs from a purely statistical point of view, considering their conformational ensembles as well-defined probability distributions. In this work, we define a conformational ensemble as an ordered set of probability distributions and provide a suitable metric to detect differences between two given ensembles at the residue level, both locally and globally. The underlying geometry of the conformational space is properly integrated, one ensemble being characterized by a set of probability distributions supported on the three-dimensional Euclidean space (for global-scale comparisons) and on the two-dimensional flat torus (for local-scale comparisons). The inherent uncertainty of the data is also taken into account to provide finer estimations of the differences between ensembles. Additionally, an overall distance between ensembles is defined from the differences at the residue level. We illustrate the interest of the approach with several examples of applications for the comparison of conformational ensembles: (i) produced from molecular dynamics (MD) simulations using different force fields, and (ii) before and after refinement with experimental data. We also show the usefulness of the method to assess the convergence of MD simulations, and discuss other potential applications such as in machine-learning-based approaches. The numerical tool has been implemented in Python through easy-to-use Jupyter Notebooks available at https://gitlab.laas.fr/moma/WASCO.

Reweighting methods for elucidation of conformation ensembles of proteins

Proteins are inherently dynamic macromolecules that exist in equilibrium among multiple conformational states, and motions of protein backbone and side chains are fundamental to biological function. The ability to characterize the conformational landscape is particularly important for intrinsically disordered proteins, multidomain proteins, and weakly bound complexes, where single-structure representations are inadequate. As the focus of structural biology shifts from relatively rigid macromolecules toward larger and more complex systems and molecular assemblies, there is a need for structural approaches that can paint a more realistic picture of such conformationally heterogeneous systems. Here, we review reweighting methods for elucidation of structural ensembles based on experimental data, with the focus on applications to multidomain proteins.

Performance of the new biological small- and wide-angle X-ray scattering beamline 13A at the Taiwan Photon Source

A new endstation for biological small- and wide-angle X-ray scattering is detailed, which provides development opportunities for studying correlated local and global structures of biomolecules in solution.

Recent developments in the instrumentation and data analysis of synchrotron small-angle X-ray scattering (SAXS) on biomolecules in solution have made biological SAXS (BioSAXS) a mature and popular tool in structural biology. This article reports on an advanced endstation developed at beamline 13A of the 3.0 GeV Taiwan Photon Source for biological small- and wide-angle X-ray scattering (SAXS–WAXS or SWAXS). The endstation features an in-vacuum SWAXS detection system comprising two mobile area detectors (Eiger X 9M/1M) and an online size-exclusion chromatography system incorporating several optical probes including a UV–Vis absorption spectrometer and refractometer. The instrumentation and automation allow simultaneous SAXS–WAXS data collection and data reduction for high-throughput biomolecular conformation and composition determinations. The performance of the endstation is illustrated with the SWAXS data collected for several model proteins in solution, covering a scattering vector magnitude q across three orders of magnitude. The crystal-model fittings to the data in the q range ∼0.005–2.0 Å⁻¹ indicate high similarity of the solution structures of the proteins to their crystalline forms, except for some subtle hydration-dependent local details. These results open up new horizons of SWAXS in studying correlated local and global structures of biomolecules in solution.