Conformational Space Research Articles

Roodmus: a toolkit for benchmarking heterogeneous electron cryo-microscopy reconstructions.

Conformational heterogeneity of biological macromolecules is a challenge in single-particle averaging (SPA). Current standard practice is to employ classification and filtering methods that may allow a discrete number of conformational states to be reconstructed. However, the conformation space accessible to these molecules is continuous and, therefore, explored incompletely by a small number of discrete classes. Recently developed heterogeneous reconstruction algorithms (HRAs) to analyse continuous heterogeneity rely on machine-learning methods that employ low-dimensional latent space representations. The non-linear nature of many of these methods poses a challenge to their validation and interpretation and to identifying functionally relevant conformational trajectories. These methods would benefit from in-depth benchmarking using high-quality synthetic data and concomitant ground truth information. We present a framework for the simulation and subsequent analysis with respect to the ground truth of cryo-EM micrographs containing particles whose conformational heterogeneity is sourced from molecular dynamics simulations. These synthetic data can be processed as if they were experimental data, allowing aspects of standard SPA workflows as well as heterogeneous reconstruction methods to be compared with known ground truth using available utilities. The simulation and analysis of several such datasets are demonstrated and an initial investigation into HRAs is presented.

Ramachandran-like Conformational Space for DNA.

DNA's ability to exist in a wide variety of structural forms, subforms, and secondary motifs is fundamental to numerous biological processes and has driven the development of biotechnological applications. Major determinants of DNA flexibility are the multiple torsional degrees of freedom around the phosphodiester backbone. This high complexity can be rationalized by using two pseudotorsional angles linking atoms P and C4', from which Ramachandran-like plots can be built. In this contribution, we explore the distribution of η (eta: C4'i-1-Pi-C4'i-Pi+1) and θ (theta: Pi-C4'i-Pi+1-C4'i+1) angles in known experimental structures retrieved from the Protein Data Bank (PDB), subdividing the conformational space into different datasets. After the removal of the canonical/helical conformations typical of the B-form, we find the existence of a conformational map with clearly permitted and forbidden regions. Some of these regions are populated with specific DNA forms, like Z- or A-DNA, or by specific secondary motifs, like G-quadruplexes and junctions. We evaluated the sequence dependency and energy relationship among the high-density regions identified in the η-θ space. Furthermore, we analyzed the effect produced by proteins and cations when bound to DNA, finding that specific proteins produce some nonhelical conformations, while other regions appear to be stabilized by divalent cations.

Hairygami: Analysis of DNA Nanostructures' Conformational Change Driven by Functionalizable Overhangs.

DNA origami is a widely used method to construct nanostructures by self-assembling designed DNA strands. These structures are often used as "pegboards" for templated assembly of proteins, gold nanoparticles, aptamers, and other molecules, with applications ranging from therapeutics and diagnostics to plasmonics and photonics. Imaging these structures using atomic force microscopy (AFM) or transmission electron microscope (TEM) does not capture their full conformation ensemble as they only show their shape flattened on a surface. However, certain conformations of the nanostructure can position guest molecules into distances unaccounted for in their intended design, thus leading to spurious interactions between guest molecules that are designed to be separated. Here, we use molecular dynamics simulations to capture a conformational ensemble of two-dimensional (2D) DNA origami tiles and show that introducing single-stranded overhangs, which are typically used for functionalization of the origami with guest molecules, induces a curvature of the tile structure in the bulk. We show that the shape deformation is of entropic origin, with implications for the design of robust DNA origami breadboards as well as a potential approach to modulate structure shape by introducing overhangs. We then verify experimentally that the DNA overhangs introduce curvature into the DNA origami tiles under divalent as well as monovalent salt buffer conditions. We further experimentally verify that DNA origami functionalized with attached proteins also experiences such induced curvature. We provide the developed simulation code implementing the enhanced sampling to characterize the conformational space of DNA origami as open source software.

Potent and Protease Resistant Azapeptide Agonists of the GLP‐1 and GIP Receptors

AbstractThe gut‐derived peptide hormones glucagon‐like peptide‐1 (GLP‐1) and glucose‐dependent insulinotropic polypeptide (GIP) play important physiological roles including glucose homeostasis and appetite suppression. Stabilized agonists of the GLP‐1 receptor (GLP‐1R) and dual agonists of GLP‐1R and GIP receptor (GIPR) for the management of type 2 diabetes and obesity have generated widespread enthusiasm and have become blockbuster drugs. These therapeutics are refractory to the action of dipeptidyl peptidase‐4 (DPP4), that catalyzes rapid removal of the two N‐terminal residues of the native peptides, in turn severely diminishing their activity profiles. Here we report that a single atom change from carbon to nitrogen in the backbone of the entire peptide makes them refractory to DPP4 action while still retaining full potency and efficacy at their respective receptors. This was accomplished by use of aza‐amino acids, that are bioisosteric replacements for α‐amino acids that perturb the structural backbone and local side chain conformations. Molecular dynamics simulations reveal that aza‐amino acid can populate the same conformational space that GLP‐1 adopts when bound to the GLP‐1R. The insertion of an aza‐amino acid at the second position from the N‐terminus in semaglutide and in a dual agonist of GLP‐1R and GIPR further demonstrates its capability as a viable alternative to current DPP4 resistance strategies while offering additional structural variation that may influence downstream signaling.

Visualizing nucleation, condensation and propagation of β-tubulin folding in chaperonin TRiC.

The folding nucleus (FN) initiates protein folding and enables an efficient folding pathway. Here we directly visualize the tubulin FN consisting of a nonnative, partially assembled Rossmann fold, in the closed chamber of human chaperonin TRiC. Chaperonin TRiC interacts with non-natively folded secondary structural elements, stabilizing the nucleus for transition into its first native domain. Through progressive folding, the unfolded sequence goes through drastic spatial arrangement in the TRiC chamber to sample the conformational space, mediated by the highly dynamic CCT tails. The observed presence of individual nonnative secondary structures first in the nonnative FN and then around the incrementally folded native domains supports the hypothesis that tubulin folding in TRiC is a hierarchical process of nucleation, condensation and propagation in cooperation with TRiC subunits.

Understanding and Quantifying Molecular Flexibility: Torsion Angular Bin Strings.

Molecular flexibility is a commonly used, but not easily quantified term. It is at the core of understanding composition and size of a conformational ensemble and contributes to many molecular properties. For many computational workflows, it is necessary to reduce a conformational ensemble to meaningful representatives, however defining them and guaranteeing the ensemble's completeness is difficult. We introduce the concepts of torsion angular bin strings (TABS) as a discrete vector representation of a conformer's dihedral angles and the number of possible TABS (nTABS) as an estimation for the ensemble size of a molecule, respectively. Here, we show that nTABS corresponds to an upper limit for the size of the conformational space of small molecules and compare the classification of conformer ensembles by TABS with classifications by RMSD. Overcoming known drawbacks like the molecular size dependency and threshold picking of the RMSD measure, TABS is shown to meaningfully discretize the conformational space and hence allows e.g. for fast checks of the coverage of the conformational space. The current proof-of-concept implementation is based on the ETKDGv3 conformer generator as implemented in the RDKit and known torsion preferences extracted from small-molecule crystallographic data.

Networks of ion-pairs are responsible for the large differences in the thermal stability of two structurally similar aminopeptidases.

Aminopeptidases are an important class of enzymes for protein metabolism. Leucyl aminopeptidase (PepL) preferably removes leucine from the N-terminus of small peptides. PepL of Lacticaseibacillus casei was observed to be thermally unstable, while a structurally similar aminopeptidase T (AmpT) of Thermus thermophilus is highly stable. To understand the molecular interaction responsible for the large difference in their stability, molecular dynamics simulations were carried out to study the thermal stability of PepL and AmpT at 300 K to 450 K temperature range over 100 ns. PepL sampled a larger conformational space with a rugged free-energy landscape, while AmpT navigated a smoother energy landscape to reach the global minimum. The RMSD, RMSF, radius of gyration and principal component analysis suggested large movements in PepL than in AmpT with an increase in temperature. Analysis of residue-interaction network revealed AmpT possessing a greater number of low, medium and high energy contacts in comparison to PepL. AmpT showed a higher abundance of ion-pair clusters and ionic residues per cluster compared to PepL. Moreover, AmpT retained a greater number of high-energy contacts at elevated temperatures. These findings showed that the inherently lower stability of PepL originates from a comparatively smaller number of contacts and can be pivotal in engineering PepL for higher stability.

Eliminating the Deadwood: A Machine Learning Model for CCS Knowledge-Based Conformational Focusing for Lipids.

Accurate elucidation of gas-phase chemical structures using collision cross section (CCS) values obtained from ion-mobility mass spectrometry benefits from a synergism between experimental and in silico results. We have shown in recent work that for a molecule of modest size with a proscribed conformational space we can successfully capture a conformation(s) that can match experimental CCS values. However, for flexible systems such as fatty acids that have many rotatable bonds and multiple intramolecular London dispersion interactions, it becomes necessary to sample a much greater conformational space. Sampling more conformers, however, accrues significant computational cost downstream in optimization steps involving quantum mechanics. To reduce this computational expense for lipids, we have developed a novel machine learning (ML) model to facilitate conformer filtering according to the estimated gas-phase CCS values. Herein we report that the implementation of our CCS knowledge-based approach for conformational sampling resulted in improved structure prediction agreement with experiment by achieving favorable average CCS prediction errors of ∼2% for lipid systems in both the validation set and the test set. Moreover, most of the gas-phase candidate conformations obtained by using CCS focusing achieved lower energy-minimum geometries than the candidate conformations without focusing. Altogether, the implementation of this ML model into our modeling workflow has proven to be beneficial for both the quality of the results and the turnaround time. Finally, while our approach is limited to lipids, it can be readily extended to other molecules of interest.

Chemically Transferable Electronic Coarse Graining for Polythiophenes.

Recent advances in machine-learning-based electronic coarse graining (ECG) methods have demonstrated the potential to enable electronic predictions in soft materials at mesoscopic length scales. However, previous ECG models have yet to confront the issue of chemical transferability. In this study, we develop chemically transferable ECG models for polythiophenes using graph neural networks. Our models are trained on a data set that samples over the conformational space of random polythiophene sequences generated with 15 different monomer chemistries and three different degrees of polymerization. We systematically explore the impact of coarse-grained representation on ECG accuracy, highlighting the significance of preserving the C-β coordinates in thiophene. We also find that integrating unique polymer sequences into training enhances the model performance more efficiently than augmenting conformational sampling for sequences already in the training data set. Moreover, our ECG models, developed initially for one property and one level of quantum chemical theory, can be efficiently transferred to related properties and higher levels of theory with minimal additional data. The chemically transferable ECG model introduced in this work will serve as a foundation model for new classes of chemically transferable ECG predictions across chemical space.

Reshaping the aggregation landscape of benzothiadiazole employing steroidal scaffolds and conformationally free alkyne tethers

In this study, we target the introduction of conformational freedom to BTD, aiming to unlock new solid-state arrangements that were previously inaccessible, while striving to maintain its high fluorescence quantum yield. Our approach is grounded in a detailed exploration of the conformational space of designed molecules, employing molecular mechanics to predict and evaluate the impact of structural modifications on solubility and solid-state behavior. By strategically flanking the BTD core with steroidal fragments through alkyne bridges, we not only introduce bulky elements that disrupt the native crystal-packing landscape of pristine BTD, but also ensure significant rotational freedom around the carbon-carbon triple bonds. This design strategy allows the molecules to explore a wide range of conformations, potentially facilitating the formation of novel solid-state supramolecular arrangements.The synthesis of these compounds revealed that they exhibit adequate processability in solution and form solids that prove crystalline by powder X-ray diffraction, but do not readily form macroscopic crystals as BTD normally does. The steroid fragments play a crucial role in determining the supramolecular organization of these compounds. Depending on the specific functional groups present on the steroid skeleton, primary interactions such as hydrogen bonding and secondary interactions related to facial amphiphilicity dictate the overall arrangement of molecules in the solid state.Remarkably, the modifications successfully preserve the intrinsic high fluorescence quantum yield of the BTD core. This achievement underscores the potential of incorporating conformational flexibility into molecular design to access new material properties while retaining desirable photophysical characteristics.

The role of structure in regulatory RNA elements.

Regulatory RNA elements fulfill functions such as translational regulation, control of transcript levels, and regulation of viral genome replication. Trans-acting factors (i.e. RNA-binding proteins) bind the so-called cis elements and confer functionality to the complex. The specificity during protein-RNA complex (RNP) formation often exploits the structural plasticity of RNA. Functional integrity of cis-trans pairs depends on the availability of properly folded RNA elements, and RNA conformational transitions can cause diseases. Knowledge of RNA structure and the conformational space is needed for understanding complex formation and deducing functional effects. However, structure determination of RNAs under in vivo conditions remains challenging. This review provides an overview of structured eukaryotic and viral RNA cis elements and discusses the effect of RNA structural equilibria on RNP formation. We showcase implications of RNA structural changes for diseases, outline strategies for RNA structure-based drug targeting, and summarize the methodological toolbox for deciphering RNA structures.

Protocol for Designing De Novo Noncanonical Peptide Binders in OSPREY.

D-peptides, the mirror image of canonical L-peptides, offer numerous biological advantages that make them effective therapeutics. This article details how to use DexDesign, the newest OSPREY-based algorithm, for designing these D-peptides de novo. OSPREY physics-based models precisely mimic energy-equivariant reflection operations, enabling the generation of D-peptide scaffolds from L-peptide templates. Due to the scarcity of D-peptide:L-protein structural data, DexDesign calls a geometric hashing algorithm, Method of Accelerated Search for Tertiary Ensemble Representatives, as a subroutine to produce a synthetic structural dataset. DexDesign enables mixed-chirality designs with a new user interface and also reduces the conformation and sequence search space using three new design techniques: Minimum Flexible Set, Inverse Alanine Scanning, and K*-based Mutational Scanning.

A Method for Treating Significant Conformational Changes in Alchemical Free Energy Simulations of Protein-Ligand Binding.

Relative binding free energy (RBFE) simulation is a rigorous approach to the calculation of quantitatively accurate binding free energy values for protein-ligand binding in which a reference binder is gradually converted to a target binder through alchemical transformation during the simulation. The success of such simulations relies on being able to accurately sample the correct conformational phase space for each alchemical state; however, this becomes a challenge when a significant conformation change occurs between the reference and target binder-receptor complexes. Increasing the simulation time and using enhanced sampling methods can be helpful, but effects can be limited, especially when the free energy barrier between conformations is high or when the correct target complex conformation is difficult to find and maintain. Current RBFE protocols seed the reference complex structure into every alchemical window of the simulation. In our study, we describe an improved protocol in which the reference structure is seeded into the first half of the alchemical windows, and the target structure is seeded into the second half of the alchemical windows. By applying information about the relevant correct end point conformations to different simulation windows from the beginning, the need for large barrier crossings or simulation prediction of the correct structures during an alchemical simulation is in many cases obviated. In the diverse cases we examine below, the simulations yielded free energy predictions that are satisfactory as compared to experiment and superior to running the simulations utilizing the conventional protocol. The method is straightforward to implement for publicly available FEP workflows.

Assessing AF2's ability to predict structural ensembles of proteins.

Recent breakthroughs in protein structure prediction have enhanced the precision and speed at which protein configurations can be determined. Additionally, molecular dynamics (MD) simulations serve as a crucial tool for capturing the conformational space of proteins, providing valuable insights into their structural fluctuations. However, the scope of MD simulations is often limited by the accessible timescales and the computational resources available, posing challenges to comprehensively exploring protein behaviors. Recently emerging approaches have focused on expanding the capability of AlphaFold2 (AF2) to predict conformational substates of protein. Here, we benchmark the performance of various workflows that have adapted AF2 for ensemble prediction and compare the obtained structures with ensembles obtained from MD simulations and NMR. We provide an overview of the levels of performance and accessible timescales that can currently be achieved with machine learning (ML) based ensemble generation. Significant minima of the free energy surfaces remain undetected.

Real-space heterogeneous reconstruction, refinement, and disentanglement of CryoEM conformational states with HetSIREN.

Single-particle analysis by Cryo-electron microscopy (CryoEM) provides direct access to the conformation of each macromolecule. However, the image's signal-to-noise ratio is low, and some form of classification is usually performed at the image processing level to allow structural modeling. Classical classification methods imply the existence of a discrete number of structural conformations. However, new heterogeneity algorithms introduce a novel reconstruction paradigm, where every state is represented by a lower number of particles, potentially just one, allowing the estimation of conformational landscapes representing the different structural states a biomolecule explores. In this work, we present a novel deep learning-based method called HetSIREN. HetSIREN can fully reconstruct or refine a CryoEM volume in real space based on the structural information summarized in a conformational latent space. The unique characteristics that set HetSIREN apart start with the definition of the approach as a real space-based only method, a fact that allows spatially focused analysis, but also the introduction of a novel network architecture specifically designed to make use of meta-sinusoidal activations, with proven high analytics capacities. Continuing with innovations, HetSIREN can also refine the pose parameters of the images at the same time that it conditions the network with prior information/constraints on the maps, such as Total Variation and denoising, ultimately yielding cleaner volumes with high-quality structural features. Finally, but very importantly, HetSIREN addresses one of the most confusing issues in heterogeneity analysis, as it is the fact that real structural heterogeneity estimation is entangled with pose estimation (and to a lesser extent with CTF estimation), in this way, HetSIREN introduces a novel encoding architecture able to decouple pose and CTF information from the conformational landscape, resulting in more accurate and interpretable conformational latent spaces. We present results on computer-simulated data, public data from EMPIAR, and data from experimental systems currently being studied in our laboratories. An important finding is the sensitivity of the structure and dynamics of the SARS-CoV-2 Spike protein on the storage temperature.

Implicit Solvent with Explicit Ions Generalized Born Model in Molecular Dynamics: Application to DNA.

The ion atmosphere surrounding highly charged biomolecules, such as nucleic acids, is crucial for their dynamics, structure, and interactions. Here, we develop an approach for the explicit treatment of ions within an implicit solvent framework suitable for atomistic simulations of biomolecules. The proposed implicit solvent/explicit ions model, GBION, is based on a modified generalized Born (GB) model; it includes separate, modified GB terms for solute-ion and ion-ion interactions. The model is implemented in the AMBER package (version 24), and its performance is thoroughly investigated in atomistic molecular dynamics (MD) simulations of double-stranded DNA on a microsecond time scale. The aggregate characteristics of monovalent (Na+ and K+) and trivalent (Cobalt Hexammine, CoHex3+) counterion distributions around double-stranded DNA predicted by the model are in reasonable agreement with the experiment (where available), all-atom explicit water MD simulations, and the expectation from the Manning condensation theory. The radial distributions of monovalent cations around DNA are reasonably close to the ones obtained using the explicit water model: expressed in units of energy, the maximum deviations of local ion concentrations from the explicit solvent reference are within 1 kBT, comparable to the corresponding deviations expected between different established explicit water models. The proposed GBION model is able to simulate DNA fragments in a large volume of solvent with explicit ions with little additional computational overhead compared with the fully implicit GB treatment of ions. Ions simulated using the developed model explore conformational space at least 2 orders of magnitude faster than in the explicit solvent. These advantages allowed us to observe and explore an unexpected "stacking" mode of DNA condensation in the presence of trivalent counterions (CoHex3+) that was revealed by recent experiments.

Ab-initio Study of Structural, Spectroscopic and Electronic Properties of High Energy Explosive Molecules: DFT/TD-DFT Calculations

This research explores the ground state geometry and molecular properties of FOX-7 and nitroguanidine molecules, with a focus on their spectroscopic and electronic characteristics. Initially, the conformational space of each molecule was systematically scanned using molecular mechanic calculations and the most probable conformer structure was obtained for each molecule. Subsequently, geometry optimizations of molecules were conducted by using ab initio density functional theory (DFT) with Becke’s three-parameter hybrid-exchange functional, which combines the Lee–Yang–Parr correlation functional (B3LYP) method, and the standard 6-311++G(d,p) basis set. The theoretically determined geometrical parameters from optimized structure and experimental values available in the literature were compared, providing validation for the structural properties of both molecules. Furthermore, the stability and reactivity properties of both molecules are estimated in terms of HOMO-LUMO energies. Overall, this study contributes to a comprehensive understanding of the ground state geometry, molecular structure, and spectroscopic behavior of FOX-7 and nitroguanidine, paving the way for potential applications in various fields of science and technology.

Toward Reliable Conformational Energies of Amino Acids and Dipeptides─The DipCONFS Benchmark and DipCONL Datasets.

Simulating peptides and proteins is becoming increasingly important, leading to a growing need for efficient computational methods. These are typically semiempirical quantum mechanical (SQM) methods, force fields (FFs), or machine-learned interatomic potentials (MLIPs), all of which require a large amount of accurate data for robust training and evaluation. To assess potential reference methods and complement the available data, we introduce two sets, DipCONFL and DipCONFS, which cover large parts of the conformational space of 17 amino acids and their 289 possible dipeptides in aqueous solution. The conformers were selected from the exhaustive PeptideCS dataset by Andris et al. [ J. Phys. Chem. B 2022, 126, 5949-5958]. The structures, originally generated with GFN2-xTB, were reoptimized using the accurate r2SCAN-3c density functional theory (DFT) composite method including the implicit CPCM water solvation model. The DipCONFS benchmark set contains 918 conformers and is one of the largest sets with highly accurate coupled cluster conformational energies so far. It is employed to evaluate various DFT and wave function theory (WFT) methods, especially regarding whether they are accurate enough to be used as reliable reference methods for larger datasets intended for training and testing more approximated SQM, FF, and MLIP methods. The results reveal that the originally provided BP86-D3(BJ)/DGauss-DZVP conformational energies are not sufficiently accurate. Among the DFT methods tested as an alternative reference level, the revDSD-PBEP86-D4 double hybrid performs best with a mean absolute error (MAD) of 0.2 kcal mol-1 compared with the PNO-LCCSD(T)-F12b reference. The very efficient r2SCAN-3c composite method also shows excellent results, with an MAD of 0.3 kcal mol-1, similar to the best-tested hybrid ωB97M-D4. With these findings, we compiled the large DipCONFL set, which includes over 29,000 realistic conformers in solution with reasonably accurate r2SCAN-3c reference conformational energies, gradients, and further properties potentially relevant for training MLIP methods. This set, also in comparison to DipCONFS, is used to assess the performance of various SQM, FF, and MLIP methods robustly and can complement training sets for those.

Vibrational Features of Oxyamines: A Comparative Study of N,N-Diethylhydroxylamine and N,N-Diethylacetyloxyamine.

The ability of oxyamines to undergo homolytic cleavage of the O-C bond, leading to the formation of stable radicals, is widely used in polymerization processes and to prevent oxidative stress in materials. We present a mid and near-infrared spectroscopy study on two model compounds, N,N-diethylhydroxyloxyamine (C4H11NO) and its acetyl derivative N,N-diethylacetyloxyamine (C6H13NO2) in the liquid phase. The analysis of the spectra is based on a complete exploration of the conformational space, coupled to harmonic and anharmonic calculations performed using the generalized second-order vibrational perturbation theory (GVPT2) formalism at the B3LYP-D3(BJ)/Def2-TZVP level of calculation and potential energy distribution analysis. In the most stable species out of 25, the three amine chains present an all-anti arrangement, with the carbonyl oxygen atom pointing towards the nitrogen lone pair. The simulated spectra are in overall good agreement with the experimental ones, and suitable for the assignment of the main observed bands. Furthermore, similarities and divergences between the two molecules are discussed.

RNA binding tunes the conformational plasticity and intradomain stability of TDP-43 tandem RNA recognition motifs

TAR DNA binding protein 43 (TDP-43) is a nuclear RNA/DNA-binding protein with pivotal roles in RNA-related processes such as splicing, transcription, transport, and stability. The high binding affinity and specificity of TDP-43 toward its cognate RNA sequences (GU-rich) is mediated by highly conserved residues in its tandem RNA recognition motif (RRM) domains (aa: 104–263). Importantly, the loss of RNA binding to the tandem RRMs caused by physiological stressors and chemical modifications promotes cytoplasmic mislocalization and pathological aggregation of TDP-43. Despite the substantial implications of RNA binding in TDP-43 function and pathology, its precise effects on the intradomain stability, and conformational dynamics of the tandem RRMs is not properly understood. Here, we employed all-atom molecular dynamics (MD) simulations to assess the effect of RNA binding on the conformational landscape and intradomain stability of TDP-43 tandem RRMs. RNA limits the overall conformational space of the tandem RRMs and promotes intradomain stability through a combination of specific base stacking interactions and transient electrostatic interactions. In contrast, tandem RRMs exhibit a high intrinsic conformational plasticity in the absence of RNA, which, surprisingly, is accompanied by a tendency of RRM1 to adopt partially unfolded conformations. Overall, our simulations reveal how RNA binding dynamically tunes the structural and conformational landscape of TDP-43 tandem RRMs, contributing to physiological function and mitigating pathological aggregation.