Structural details of helix-mediated multimerization of the conserved region of TDP-43 C-terminal domain

Domain: physics
Published: 2025-11-26
Editorial outcome: major_revision

Abstract
Pathological inclusions of the C-terminal domain (CTD) of TAR DNA binding protein-43 (TDP-43) are neurodegenerative hallmarks in amyotrophic lateral sclerosis (ALS) and frontotemporal dementia, yet CTD’s aggregation propensity complicates structural characterization of native TDP-43. Here we propose structural models for the physiological multimerization of TDP-43 CTD’s conserved region (CR) essential for TDP-43 RNA processing. Using NMR spectroscopy, we establish that the native state of TDP-43 CR at physiological conditions is α-helical. Hydrophobic residues drive CR helix-helix assembly, phase separation, and TDP-43 nuclear retention, while polar residues down regulate these processes. An integrative approach combining analytical ultracentrifugation, NMR-derived contacts, AlphaFold2-Multimer modeling, and all-atom molecular dynamics simulations together suggest that TDP-43 CR forms dynamic, multimeric helical assemblies stabilized by a methionine-rich core with specific contributions from a tryptophan/leucine pair. These structures show how ALS-associated mutations disrupt TDP-43 function and provide pharmacologically targetable structures to prevent its conversion into pathogenic β-sheet aggregates.

Introduction
TAR DNA-binding protein of 43 kDa (TDP-43) is an RNA-binding protein engaged in diverse cellular functions across both the nucleus and cytoplasm, including RNA transcription, splicing, and transport1,2. Many of TDP-43’s functions are linked to dynamic, intracellular membraneless organelles formed via phase separation3–6. Furthermore, TDP-43 is identified as a major component of ubiquitinated neuronal inclusions in the cytoplasm, a pathological hallmark of amyotrophic lateral sclerosis (ALS), frontotemporal dementia (FTD), and limbic-predominant age-related TDP-43 encephalopathy (LATE), a condition with clinical presentation overlapping with Alzheimer’s disease7–11. TDP-43 consists of a folded N-terminal domain (NTD) responsible for dynamic homo-oligomerization12,13, two RNA recognition motifs (RRMs) that together bind UG-rich RNA sequences14,15, and an intrinsically disordered C-terminal domain (CTD) characterized by a low-complexity (LC) sequence16,17. The aggregation-prone CTD is a hotspot for ~90% of TDP-43’s ALS-associated mutations18,19, and proteolytically cleaved C-terminal fragments are enriched in pathological inclusions7, underscoring its importance in TDP-43 dysfunction. In biochemical assays, the CTD is sufficient for phase separation in the presence of physiological salt concentrations or RNA16. Importantly, TDP-43 CTD condensation is promoted by the hydrophobic conserved region (CR, spanning residues 319–341), working in concert with the adjacent IDRs5,20.

The CR in CTD is uniquely well-conserved (100% identity) throughout vertebrates compared to the flanking IDRs16,21 (Fig. 1A). Previous work from our laboratories16 and others22 show that the CR adopts transient α-helical structure under physiological conditions. Furthermore, the CR mediates intermolecular helix-helix contacts that are essential for liquid-like phase separation of CTD in vitro16, phase separation of TDP-43 reporter constructs in cells21, splicing5, 3’ polyadenylation function3, and TDP-43 RNA-level autoregulation3,6, as well as the cytosolic TDP-43 aggregation under oxidative stress23. Correspondence between the impact of mutations on TDP-43 function in cells and its phase separation in cellular overexpression and in biochemical assays suggest that even if TDP-43 does not form micron-sized membranelles organelles in cells, the same interactions that mediate phase separation drive cellular function3. The disruption of functional interactions by ALS-associated mutations or by engineered helix-disrupting variants within the CR can perturb TDP-43 phase separation5,16 and lead to aggregation via structural conversion of the CR into β-sheet aggregates in disease9,10,23,24. Conversely, helical CR self-assembly can be enhanced by designed CR variants, enabling control over the function and material properties of phase-separated droplets5. Using NMR experiments combined with computer simulations, we previously observed that α-helicity within the CR is enhanced during helix-helix contact, leading to significant structural changes5. Most importantly, the structural details of TDP-43 CR interactions and hence the reasons for the 100% conservation have proved elusive. It remains unclear if the CR forms dimers, higher-order structures, or an ensemble of heterogenous multimeric states. Although definitive evidence has yet to emerge, the presence of helix-mediated higher-order multimers larger than dimers has been suggested based on several lines of evidence including NMR diffusion data as a function of CTD concentration5 and elevated NMR relaxation values extracted from chemical exchange experiments16. However, the molecular structure of these self-assembled species remains unresolved due to the challenges of rapid aggregation of CTD at higher concentrations needed for structure determination and dynamic interconversion of weak affinity states. Determining structural models of TDP-43 CR assembly therefore necessitates an integrative approach that combines artificial intelligence (AI)-based modeling, computer simulations, and constraints from multiple complementary experimental methods.

The emergence of AlphaFold2 (AF2) has revolutionized the field of structural biology, offering accurate predictions of protein 3D structures25. Though best known for highly accurate determination of protein structures solely from their primary sequences, a recent extension of AF2, AlphaFold2-Multimer (AF2-Multimer), has marked a significant step forward in the structure prediction for both homomeric and heteromeric protein complexes26. However, the structural training set and co-evolutionary sequence data for predicting multimerization data is much more limited, especially for transient, weakly-binding, dynamic assemblies that are not extensively found in the Protein Data Bank (PDB). Hence, it is not clear if AF2-Multimer can generate reliable predictions of dynamic multimeric assemblies or discriminate between potential structures. Instead, we propose that the structural outputs from AF2-Multimer can serve as starting configurations for molecular dynamics (MD) simulations, providing insights into the conformation and dynamics of multimeric states. Recent advancements in atomistic force fields27,28, combined with GPU-accelerated MD engines29,30, have significantly improved the accuracy and efficiency of exploring the conformational landscapes and interactions of intrinsically disordered proteins (IDPs) and their assemblies23,31–35. MD simulations can offer feedback on the stability of multimeric assemblies generated by AF2-Multimer, serving as a valuable complement to biophysical experiments and offering atomic-level insights that are not currently achievable through experimental or AI-based tools alone. These approaches provide workable structural hypotheses that can guide experimental validation36. Together, AF2 and MD simulations have become indispensable tools in structural characterization, as demonstrated by their increasing application in recent studies37–42, significantly accelerating progress in structural biology36.

Here, we employed an integrative modeling strategy based on extensive experimental characterization using NMR spectroscopy and sedimentation velocity analytical ultracentrifugation (SV-AUC), coupled with state-of-the-art computational methods such as AF2-Multimer25,26 and all-atom molecular dynamics (AAMD) simulations, to probe the atomic structural details of TDP-43 CR helix-mediated assembly. Importantly, we establish atomic-level structural models of TDP-43 CR multimeric assemblies that provide critical information required to understand the early steps in functional TDP-43 CTD assembly. These structural insights constitute a significant advancement in our comprehension of the molecular pathways underlying TDP-43 phase separation and function, thereby opening avenues for studying and preventing the emergence of its pathogenicity.

Results
The native state of TDP-43 CR at physiological conditions is α-helical

Previously, we provided direct structural evidence for the presence of α-helical structure within the CR of TDP-43 and its stabilization via helix-helix assembly using solution NMR spectroscopy5,16. The experiments were performed at a slightly acidic pH (6.1) and without salt to attain optimal NMR spectra and prevent phase separation. However, recent work using hydrogen-bond disrupting variants suggested (without direct structural evidence) that the CR of TDP-43 adopts a β-sheet conformation at neutral pH and physiological salt and that the α-helical structure previously observed by us and others is an artifact of non-physiological conditions43. To resolve this important issue, we conducted solution NMR spectroscopic measurements at both our previous conditions (slightly acidic pH 6.1 with no salt) and at physiological conditions (neutral pH 7.0 with 150 mM NaCl) and low protein concentration to prevent phase separation.

The largest differences in the spectra come from the loss of several resonances for serine and glycine residues in the IDR regions, (Fig. 1B), due to rapid exchange of the amide hydrogen with water hydrogen, which is elevated for these residue types and is faster at higher pH44. Focusing on the overlay of 1H-15N HSQC spectra for residues within the CR (Fig. 1C), we observed only minimal changes in chemical shifts, suggesting no significant change in the overall structure of this region. To further probe structural changes, we compared the differences between experimental Cα chemical shifts and the random coil shifts, ΔδCα (Fig. 1D)45,46, and found highly similar ΔδCα values in both cases. Positive ΔδCα values within the CR reveal its helical character at neutral pH and physiological salt conditions; in fact, helicity is slightly enhanced at these conditions. Enhancement in helicity could also lead to enhanced helical interaction and cooperate with reduced electrostatic repulsion at higher pH and higher ionic concentration that drive TDP-43 phase separation as well as aggregation as previously observed47,48. Furthermore, our previous computer simulations, performed under physiological salt and pH conditions using a modern state-of-the-art force field for IDPs27, also confirmed the presence of a transient helix spanning residues 321–330 in the monomeric CTD ensemble and its stabilization by intermolecular interactions5,16,20. These observations are also consistent with solid-state NMR experiments on in vitro aged liquid droplets49 and fibrillar aggregates50,51, which showed the presence of helical structures in TDP-43 CR even after transition to fibrils51. Taken together, our direct structural measurements using NMR unequivocally show the presence of native α-helical structure within TDP-43 CR at physiological conditions.

Alanine-scanning mutagenesis within TDP-43 CR reveal key residues in phase separation

TDP-43 CR promotes the formation of phase-separated CTD droplets16. Disease-associated and designed mutations that reduce helical stability or disrupt intermolecular helix-helix interactions decrease CTD phase separation, while variants enhancing helicity promote it5. By simultaneous substitution of all five CR methionines to alanine, we recently found that methionine residues within CR are crucial for intermolecular helix-helix contacts mediating CTD phase separation20. To comprehensively investigate the role of each CR position, we created TDP-43 CTD variants substituting each non-alanine position with alanine to preserve helicity while potentially disrupting CR:CR interactions20. We measured the saturation concentration (csat), the protein concentration threshold for phase separation, using droplet sedimentation assays for all variants. Microscopy was used to qualitatively evaluate phase separation and whether droplets remained dynamic and liquid-like or transitioned to static aggregates.

WT CTD does not undergo phase separation in the absence of salt but forms liquid-like droplets with increasing salt concentrations, having a csat ~15 μM at 150 mM NaCl (Fig. 2A, B, Supplementary Fig. 1). Highlighting the essential role of a single tryptophan in promoting CTD condensation, W334A shows the most profound effect on phase separation, resulting in no droplets at the studied conditions (Fig. 2B, Supplementary Fig. 1). Similarly, csat was increased (decreased phase separation) three-fold by F316A which lies directly adjacent to the helical CR, whereas the effect of F313A in the region preceding the helical CR was less, showing csat twice as high as that of WT (Fig. 2A). Expanding from our previous results for the 5M→A variant within the CR that collectively impaired CTD phase separation, each single methionine substitution within CR (M322A, M323A, M336A, M337A, or M339A) also reduce phase separation with approximately two-fold higher csat than WT (Fig. 2A). Similarly, I318A and L340A result in csat values similar to single methionine to alanine substitutions, suggesting that bulky aliphatic residues contribute similarly to phase separation. Somewhat surprisingly, the substitution of L330A emerges as a major outlier compared to other aliphatic residues, resulting in significant destabilization of the condensate (csat ~60 μM). This effect is much higher than L340A near the C-terminal end of the CR, suggesting a special role of L330 in self-assembly.

Next, we consider the polar residues which are just as conserved as the hydrophobic residues in the CR (Fig. 2A). Unlike substitutions to alanine at hydrophobic residue positions which decrease phase separation, alanine mutations at serine positions enhance phase separation. S333A shows the most enhancement in phase separation, while the adjacent S332A and the more distant S342A show modest enhancement, and S317A shows little change in csat. Glutamine to alanine mutations (Q327A, Q331A, Q343A) display the greatest enhancement of phase separation with csat approximately three-fold lower than WT csat. Microscopy of these variants after phase separation confirms the formation of droplets. For Q327A and S333A, the droplets appear slightly less round, suggesting these variants may form more viscous or viscoelastic assemblies in addition to enhancing phase separation, while N319A robustly forms morphologically irregularly shaped assemblies (Fig. 2B). Similar to how glycine-to-alanine substitutions (G335A, G338A) enhance CTD self-association and phase separation by stabilizing the helicity of the monomeric CR5, the enhancement in phase separation for polar-to-alanine substitutions reinforce a critical role of the CR helix, especially since many of these substitutions are known to discourage phase separation in non-helical contexts52,53. Taken together, in vitro phase separation experiments highlight the importance of the entire CR for phase separation, with phase separation promotion in particular by L330 and W334.

TDP-43 CR helicity and helix-mediated interactions are linked to CTD phase separation

Above we probed the contribution of each CR position to phase separation. Next, we sought to test if and how these effects of alanine substitutions on phase separation are related to helix-mediated interactions probed by NMR. We evaluated the impact of alanine mutations on the helical assembly by monitoring NMR chemical shift perturbations (CSPs) as a function of protein concentration at pH 6.1 and without salt where TDP-43 CTD does not readily phase separate. The relative magnitude of helix-mediated self-assembly for each variant was quantified by the 15N CSP (Δδ15N) for the A328 resonance at 90 μM (where intermolecular interactions are formed) and 20 μM (monomeric reference state), normalized to WT CTD values (CSPnorm). Q327A, S332A, and S333A were excluded due to broadening of CR signals beyond detection in HSQC experiments at the higher concentration (90 μM), unlike Q331A, which remained detectable despite also leading to enhanced self-assembly (Fig. 2A). This difference may result from variations in monomer-multimer exchange kinetics, binding and dissociation rates, and conformational changes within the assemble state. W334A and N319A were also excluded as W334A does not phase separate at these conditions, and N319A results in irregularly shaped aggregates, making the apparent csat unreliable. Remarkably, we observed a strong correlation between CSPnorm and csat for alanine mutants, with a Pearson correlation coefficient (r) of −0.94 (Fig. 2C), i.e., higher CSPnorm values were observed for alanine variants enhancing phase separation and lower values for those reducing phase separation. We note that these CR variants may contribute to disruption in helix-helix interactions either by destabilizing the helicity of the monomeric form (which is significantly populated from 320 to 330) as previously seen for some ALS-associated, engineered, and phosphomimetic variants in the 321–333 region16,54 or by disrupting the helix-helix interface without changing the helicity of the monomer as suggested for other ALS-associated and engineered variants at M337, or a combination of these factors. Previously, we found that simultaneous substitution of all five CR methionines to alanine does not suppress helicity but does disrupt phase separation20. However, the opposing effects on CTD phase separation of alanine variants at two neighboring residues S333 and W334 present an interesting case. NMR spectra of monomeric W334A reveal features suggesting the variant reduces helicity, highlighting W334’s unique role (Supplementary Fig. 2) and suggesting that this variant may suppress phase separation by decreasing helicity and/or weakening inter-helix interactions. Although alanine can moderately enhance local helicity compared to serine5, S333A does not markedly alter the helical signatures across the monomeric CR, implying that its enhancement of phase separation primarily driven by strengthened inter-helical interactions or by local helical stabilization (Supplementary Fig. 2). Although the detailed structural mechanisms for each of the ~20 variants studied here likely have multiple structural contributions, the focus here is that the correlation observed between biochemical and biophysical behaviors suggest that helical assemblies that drive phase separation in physiological salt conditions are structurally related to the helix-mediated transient interactions that stabilize CR contacts in salt-free conditions visible by NMR.

Structural ensemble for TDP-43 CTD dimer

We next sought to probe the contacts formed by CR dimers as the first step in higher-order multimerization. We used AF2-Multimer (see Methods) to predict structures of a TDP-43 subregion containing the CR (residues 310–350) (Supplementary Fig. 3A). This length of protein fragment, from our previous work on TDP-43 CTD5,16, was selected as it is more computationally efficient for MD simulations and better suited for determination of the correct bound conformation using AlphaFold-Multimer55,56. Importantly, AF2-Multimer predicts dimer states with high structural heterogeneity and low model confidence (Supplementary Fig. 3B). We reasoned that molecular simulation starting from predicted structures could help define the structural ensemble and provide insight as to which structures may in fact be stable or populated in solution. To this end, we conducted extensive, unbiased AAMD simulations (18 independent trajectories, aggregate time ~100 μs) starting from the TDP-43 310–350 dimer models obtained from AF2-Multimer (Supplementary Fig. 3A, B). Importantly, all dimer starting structures of TDP-43 310–350 showed lack of stability and dynamic behavior, with dimers able to dissociate and re-associate within a microsecond simulation period (Supplementary Fig. 3C) with highly fluctuating interhelical contacts (Supplementary Fig. 3D).

We then analyzed the structural diversity within the dimer ensemble, finding >80% helicity for residues 320–331 and ~30% for residues 339–343, with a ~20% increase in helicity for residues 320–331 compared to the monomeric ensemble (Supplementary Fig. 3E), aligning with previous simulation results demonstrating helix stabilization upon dimerization5. Secondary structure maps highlighted diverse helical conformations with different helix lengths and positions (Fig. S3F), with both parallel and antiparallel binding modes (Supplementary Fig. 3F). Using the simulated dimer ensemble, we characterized the intermolecular contacts stabilizing the helix-mediated dimerization (Fig. 2D, E), which showed significant differences compared to the initial contacts (Supplementary Fig. 3G), reflecting dynamic structural interconversions.

The total number of per-residue contacts revealed that W334, F313, F316, and L330 form the most contacts (Fig. 2F), correlating with their role in stabilizing CTD condensates, whereas polar residues form fewer contacts, consistent with enhanced phase separation upon polar-to-alanine substitutions. We found a strong correlation (Pearson r=0.7) between the per-residue intermolecular contacts from dimer simulations and experimental phase separation (csat) from alanine mutagenesis across CR (Fig. 2G). These data support the view that residues stabilizing CR helix-helix contacts can contribute to the phase separation of CTD but that the dimer is not likely to populate a single stable structure.

TDP-43 CR helix-mediated assembly affects TDP-43 nuclear retention and growth restriction in cells

We then probed the relevance of the contacts probed by biophysical/biochemical experiments and simulations by testing the impact of a set of TDP-43 CR alanine variants in cells. TDP-43 is a predominantly localized in the nucleus, and loss of its nuclear function is a major contributor to RNA processing defects associated with ALS57. While TDP-43 nuclear import is mostly an active mechanism mediated by a nuclear localization signal and importins α/β (karyopherins α/β)58,59, TDP-43 cytoplasmic exit is a passive and size-dependent process60–62. We previously showed that RNA binding, condensate formation and multivalent assembly through NTD and CTD self-interactions are essential for TDP-43 function and for its nuclear localization, as demonstrated by nuclear-cytoplasmic fractionation and quantitative immunofluorescence6,62. These complexes promote nuclear retention by increasing the size of macromolecular TDP-43 assemblies62. We examined the impact of key alanine variants that alter helical interactions and phase separation on TDP-43 nuclear retention by comparing the nuclear-cytoplasmic distribution of hemagglutinin (HA)-tagged full-length TDP-43 variants stably expressed in isogenic cell lines (HEK293 HA-TDP-43)6,62. Importantly, to minimize undesired and heterogenous overexpression, these cell lines express a single copy of the HA-TDP-43 transgene upon tetracycline induction. We investigated variants that either enhance (Q327A, Q331A, S333A, Q343A), moderately reduce (L340A), or impair (W334A and L330A) CTD condensation. TDP-43 levels in the nuclear and cytoplasmic fractions were quantified via immunoblotting (Fig. 2H, Supplementary Fig. 4A, B). W334A and L330A dramatically reduce TDP-43 nuclear retention by approximately 90% and 80%, respectively, indicating strong nuclear loss upon decreasing phase separation in the context of full-length TDP-43. This aligns with the effects of the deletion of CR62 or the helix-disrupting mutant, A326P, which also significantly diminishes TDP-43 nuclear retention (Fig. 2H). Conversely, variants that enhance helix-mediated self-assembly and phase separation (Q327A, Q331A, S333A, Q343A) promote nuclear retention, with S333A showing a 3-fold increase in nuclear retention compared to WT. Single glutamine-to-alanine substitutions generally increase the mean nuclear retention ratio by about 50%, compared to WT. The results for Q327A and Q331A show a trend toward enhanced nuclear retention, however, they were not statistically significant. Unexpectedly, L340A slightly increased TDP-43 nuclear retention, contrary to our expectations based on its modestly decreased phase separation (small increase of csat). We also performed immunofluorescence microscopy to compare the subcellular localization of HA-tagged TDP-43 variants in intact cells (Supplementary Fig. 4C, D). Consistent with our fractionation assays and previous report62, A326P significantly decreases the nuclear localization. W334A and L330A show a trend toward lower nuclear localization, while S333A shows a moderate increase in TDP-43 nuclear localization compared to WT; however, these differences do not reach statistical significance (Supplementary Fig. 4D). Differences in the extent of nuclear localization loss caused by mutations disrupting phase separation, more pronounced in nuclear-cytoplasmic fractionation than in immunofluorescence, may reflect leakage of HA-TDP-43 during biochemical nuclear isolation. Our previous studies of macromolecular complexes including exogenous HA-TDP-43 in HEK293 HA-TDP-43 cells suggest that endogenous TDP-43 does not significantly contribute to increased nuclear retention. WT HA-TDP-43 expression in these cells decreases endogenous TDP-43 levels by approximately 60% due to autoregulation, which serves as a negative feedback loop controlling protein synthesis6. WT HA-TDP-43 exhibits similar nuclear retention and macromolecular sized distribution as endogenous TDP-4362. In contrast, expression of A326P does not significantly reduce endogenous TDP-43 levels due to defects in autoregulation3. Despite the presence of endogenous TDP-43, A326P shows lower nuclear retention as well as decreased formation of large macromolecular complexes, compared to WT HA-TDP-4362. Overall, these findings indicate that the structures populated upon phase separation in vitro as detected by NMR are important for cellular TDP-43 assembly.

We next explored the impact of helix-mediated assembly of full-length human TDP-43 expressed in yeast on growth restriction using deep mutagenesis data from the Lehner lab, which includes over 1200 single mutations in TDP-43 CTD63. Mutations within the CR showed a stronger influence on yeast growth restriction compared to mutations in the flanking IDRs, underscoring the critical role of the CR in TDP-43 function and toxicity63. Notably, mutations at W334 increased toxicity, except for L and M substitutions, correlating with W334’s essential role in CTD self-assembly and nuclear retention function. A strong correlation (Pearson r=0.79; Spearman r=0.86) was observed between in vitro phase separation propensity and yeast toxicity (Supplementary Fig. 5). Alanine mutations that enhance phase separation reduce toxicity in yeast cells, whereas ones that decrease phase separation do the opposite. This further supports the cellular significance of helical self-assembly, as mutants enhancing cellular toxicity also enhanced accumulation in the cytoplasm at the nuclear periphery63. We propose that helix-mediated TDP-43 self-assembly aids nuclear retention, mitigating improper localization and toxicity.

TDP-43 CTD forms multimers

The importance of the CR in TDP-43 CTD helical assembly has been established, yet the stoichiometry of assembly has not been probed. Here, we used sedimentation velocity analytical ultracentrifugation (SV-AUC), an experimental tool that provides information on macromolecular size in solution, to probe the size of TDP-43 CTD multimers formed in solution without salt, where TDP-43 CTD does not readily phase separate. At the lowest concentrations (17 μM) where the protein remains monomeric, the best-fit model shows a single species with sedimentation coefficient (corrected to standard conditions, water at 20°C), s20,w, of 1.53 S with an estimated mass of 14.0 kDa, consistent with a monomer (Fig. 3A). As the concentration is raised (33 μM and 58 μM), the apparent sedimentation coefficient for the species increases slightly, consistent with weak self-association (Fig. 3A). At the highest concentration studied, 107 μM (a value obtained directly from the AUC data by integration of the absorbance c(s) profile), the best fit model is notably different despite less than a two-fold increase in concentration, showing an additional faster-sedimenting species (Fig. 3A). Although a dimer would be expected to have an s-value approximately 1.58x that of the monomer, the s-value for this species is approximately 2x that of the monomer, suggestive of a weak CTD self-association into multiple higher-order species, consistent with previous NMR data5. To properly account for the contribution of interchanging species to the sedimentation profiles, direct Lamm equation modeling was conducted. “Two state” monomer-dimer or monomer-trimer models and “three state” monomer-dimer-trimer models resulted in poorer fits and/or physically unreasonable sedimentation coefficients (Supplementary Table 1). In contrast, excellent fits were found for a monomer-dimer-tetramer model with sedimentation coefficients of 1.54 S, 2.43 S, and 3.94 S that scale as expected for a CTD monomer, dimer, and tetramer (Fig. 3B). Using the data from the two highest concentrations simultaneously, best fits yielded a monomer-dimer dissociation constant Kd,mon-dim of 2.4 mM (confidence interval corresponding to one standard deviation: 1.1–3.4 mM) and a dimer–tetramer Kd,dim-tet of 2.7 ± 0.6 μM, suggesting that the dimer is only a weakly-binding intermediate, consistent with our simulated dimer ensemble, but that it cooperatively forms a tetramer, with 1000-fold tighter affinity for the dimer-tetramer transition than for monomer-dimer (Fig. 3C). At the 107 μM condition, 6.2 μM is predicted to be in the dimer form and 14.2 μM in the tetramer form. Additionally, a two-state monomer-tetramer model results in similar fit quality and shows comparable effective binding equilibria, as seen in the monomer-dimer-tetramer model (i.e., ceq, the concentration for equal population of monomer and tetramer is in the range of 250–300 μM for both models). Both models are also consistent with previous analyses at slightly different conditions using NMR relaxation dispersion of TDP-43 CTD exchange between monomeric and multimeric structures16 and it remains difficult to conclusively distinguish between models where the dimer is a very weakly populated intermediate (monomer-dimer-tetramer model) or a transiently populated transition state (monomer-tetramer model). We caution that because samples at higher concentrations lead to aggregation, we cannot approach saturation of the multimeric forms. Hence, while the SV-AUC data for WT CTD support the presence of multimers that are likely tetrameric, we cannot conclusively establish the stoichiometry by AUC of WT CTD alone. Therefore, below we employ additional AUC analyses on samples designed to boost self-assembly as well as decrease aggregation, and complementary experimental and computational tools to test this picture further.

NMR shows key contacts for TDP-43 higher-order self-assembly

Given the transient, dynamic nature of CR interactions and their small population, structural characterization of the CR helix-helix interaction is not amenable to x-ray crystallography or electron cryo-microscopy (cryoEM). Hence, we next sought structural information for the assembled CR region of TDP-43 CTD using nuclear Overhauser enhancement (NOE) NMR. However, several features make CR multimerization an extremely challenging target. First, NOE experiments are often conducted at 1 mM but TDP-43 CTD samples at higher concentrations (≥100 μM) are not stable and convert to aggregates, preventing long NMR experiments. Second, the multimer is not fully populated at achievable conditions, reducing NOEs from intermolecular contacts. Third, exchange between the monomeric and multimeric conformations broadens the resonances from the CR16. Fourth, the CR itself is aggregation prone, as short peptides (residues 311–360) containing the CR readily form fibrils64, suggesting the flanking IDR regions contribute to solubility. Hence, we explored mutants of TDP-43 CTD IDRs to further enhance solubility while keeping the CR and adjacent regions (310–350) unaltered to preserve CR multimerization. We recently demonstrated that an aggregation-reducing variant of TDP-43 CTD that replaces phenylalanines in the IDRs with alanine65 (TDP-43 CTD 6F→A) retains phase separation and CR helix-helix interaction at higher protein concentrations20. We conducted filtered, edited NOE-HSQC experiments on samples of 200 μM (1:1 mix of unlabeled and 13C/15N proteins) TDP-43 CTD 6F→A to selectively probe intermolecular contacts66. Experiments were performed at 12°C to enhance multimer population and NOEs by slowing molecular motion as well as to extend sample stability.

Two-dimensional NOE strips for NOEs to 13C-attached hydrogens suggest prevalent interactions involving aliphatic M/L/I/A methyl within the helical region (Fig. 4A), much more than control experiments using 100% labeled protein to account for any intramolecular artifacts from incomplete labeling. NOEs are large for I318 but smaller for A315, consistent with structure forming primarily in the conserved region (Fig. 4B). NOEs to 15N attached hydrogens provide additional intermolecular interactions with residue-by-residue resolution, showing backbone and side chain amide hydrogens (NH and NH2) interact with methyl groups, aromatic and polar groups, and Hα positions (Fig. 4C). Importantly, NOEs to backbone NH hydrogens are observed for several positions from A325 to L340, suggesting interactions across the entire CR (Fig. 4C). Additionally, W334 side chain positions interact with aliphatic methyl, aromatic, backbone amide, and Hα positions (Fig. 4D). Contacts involving the phenyl group of phenylalanine (F313/316) with aliphatic methyl groups are also present (Fig. 4D), suggesting these residues contribute to multimerization.

Collectively, NOE experiments reveal the presence of contacts between many residue types in the TDP-43 CR assembly, prominently involving aliphatic and aromatic residues. However, the repetitive CR sequence (five M, six A, two S, three Q residues), relatively large separation between resolved backbone 1H/15N positions in adjacent helices, and difficulties in obtaining higher resolution/signal-to-noise data due to sample instability make it challenging to derive molecular models directly from these experimental constraints. To complement the structural data from TDP-43 CTD 6F→A, we also extensively characterized the assembly via AUC and contacts via NMR of a series of cross-linked variants of TDP-43 (See Supplementary Notes 1,2, Supplementary Figs. 6–9). Phosphorylation at the very C-terminal part of TDP-43 CTD (S403, S404, S409, S410) is a major feature of TDP-43 inclusions and placing aspartic acid residues substitutions mimicking phosphorylation at these sites can drive aggregation67. In contrast, placing aspartic acid residues at 12 sites across the entire CTD appears to counteract pathological aggregation68, enhance the liquidity and dynamic nature of TDP-43 condensates, while preserving nuclear import or RNA regulatory functions, similar to what we have observed for poly-phosphomimetic FUS69. Similarly, here we made serine to glutamate (S→E) substitutions across the disordered flanking regions of CTD (See Supplementary Table 2), which significantly enhanced TDP-43 solubility while preserving the helical structure in the CR structure as indicated by NMR (Supplementary Fig. 6). We previously showed that adding disulfide cross-links to WT TDP-43 CTD enhances CR interaction5 and we find here that it enhances multimerization as seen by AUC (Supplementary Fig. 7A), but it also enhances aggregation. However, cross-linked S→E variants remain sufficiently soluble for AUC analysis across a range of concentrations to approach saturation of the assembly (Supplementary Figs. 7, 9). These cross-linked variants populate multimeric assemblies requiring the helical region, as seen by AUC, and show enhanced helicity compared to the uncross-linked forms (Supplementary Figs. 7, 9). Using cross-linked forms created from 1:1 mixture of unlabeled and 13C/15N proteins, we again see a similar pattern of methyl NOEs as observed for TDP-43 CTD 6F→A (Supplementary Fig. 10), suggesting that CR assembly is similar in both contexts where the TDP-43 CTD solubility is enhanced without altering the CR.

Structural model of tetrameric helical assemblies of TDP-43 CR

Given the challenges in direct determination of an atomic resolution structure of multimeric TDP-43 CTD using NMR alone, we turned to computer modeling using AF2-Multimer to predict potential multimeric structures (trimer, tetramer, hexamer, octamer). Like for predicted dimers (see above), the model confidence score for all predicted multimeric structures is notably low compared to a control homo-tetrameric coiled-coil sequence (CC-tet)70 (Fig. 5A). This suggests that either AF2-Multimer cannot identify TDP-43 CR multimers or that the dynamic TDP-43 CR multimers5 are unlike stable coiled-coil sequences that are more easily predicted. Hence, we employed AAMD simulations (~4–5 μs in duration, or until a significant structural deviation was observed, defined as distance root-mean-square deviation (dRMS) > 1 nm) to directly assess the structural stability of these multimers and to explore whether AAMD could result in refined interhelical contacts that stabilize the assembled states (Fig. 5B). Interestingly, all trimer (eight), hexamer (five), and octamer (four) models were found to be less stable, exhibiting large structural changes from their initial conformations (Fig. 5B, Supplementary Fig. 11A). Notably, among the tetramer structures, one model (which we named Tet-1) with four-fold symmetry where adjacent helices run antiparallel, exhibited greater stability during the simulation (Fig. 5B). To further test Tet-1’s stability, we performed 50 independent AAMD simulations (each 100 ns) of Tet-1 along with Tet-2, the second most stable tetramer that has a distinct helical arrangement and, unlike Tet-1, quickly loses its symmetry (Fig. 5B). The dRMS distributions from these simulations further confirmed the higher stability for Tet-1 (Fig. 5C). Collectively, AAMD simulations of AF-predicted multimers reveal a stable tetramer structure, suggesting that dynamic nature of CR self-interactions may adopt stable tetramer configurations, providing additional support for the idea that TDP-43 CTD forms tetramers via the CR suggested as the most likely assembly by AUC.

Intermolecular contact analysis reveals that the core of the Tet-1 is stabilized M322, A326, A329, S333, M336 and L340 residues that form the highest total number of contacts (Fig. 5D, E). The stability is further enhanced by interhelical contacts between adjacent helices involving residues M323, A325, L330, S332, W334, M337 and M339 (Fig. 5D, E). Importantly, these residues correspond closely to the positions that give rise to intermolecular backbone HN NOEs. Notably, W334 and L330 form an extended hydrophobic patch, enhancing the overall stability of the complex (Fig. 5E). Taking together, the intermolecular contacts in this model are qualitatively consistent with experimental NOEs, showing interactions primarily from methyl groups and backbone NH atoms of aliphatic residues (M/A/L/I), W334 with methyl groups (M/A/L/I), and polar residues (Q/N/S).

The most stable Tet-1 model also remained stable within the full-length CTD (see Supplementary Note 3, Supplementary Fig. 12). W334, surface-exposed in Tet-1, showed enhanced interactions in the full-length CTD, interacting with hydrophobic segments of the flanking regions (276FGGNPGGF283, 307MGGGMNF313, 383IGWGS387), consistent with its essential role in CTD self-assembly (Fig. 2A, B). Additionally, aromatic (F283, F289, F313, F316, F367, Y374, W385, F397, F401, W412) and aliphatic (M307, M311, I383) residues in the flanking IDRs formed significant intermolecular contacts in agreement with previous findings20. These results reinforce the essential contributions of flanking regions and W334 in CTD self-assembly and further support the structural relevance of the Tet-1 model. Although we were not able to create samples where the assembled state is 100% populated, we did assess whether this structure (Tet-1) appears consistent with NMR-derived measurements we made of the cross-linked forms where the assembly is present. The simulated transverse relaxation rate, 15N R2, of the Tet-1 model demonstrates higher values compared to monomeric and dimeric ensembles (Supplementary Fig. 11B), consistent with the consistent slowed motions observed in experiments where the multimers are partially populated (Supplementary Fig. 6H). The helix fraction computed from atomistic simulation of Tet-1 using DSSP71 also aligns qualitatively with enhanced helicity seen in experiment (Supplementary Fig. 6G), showing stabilization of helical structure within the region spanning G335-Q343 (Supplementary Fig. 11C).

To further assess the structural model, we performed solvent paramagnetic relaxation enhancement (sPRE) measurements (Supplementary Fig. 13). This NMR-based method qualitatively characterizes the relative solvent accessibility of residues within biomolecular structures72. The sPRE pattern for cross-linked dimeric variants of S→E S305C showed some residues are more protected. Cross-linked forms revealed complete signal loss for the side chain NH groups of W334, Q331, Q327, and N319 (Supplementary Fig. 13B), suggesting these groups are highly solvent exposed even upon multimerization. Indeed, our structural model (Tet-1) shows that the side chains of N319, Q327, Q331, and W334 are more solvent-exposed than other residues (Fig. 5E). Given the challenge of incomplete population of the multimeric forms and the inability to directly compute PRE and NOE data due to transient interactions, the qualitative agreement between the model for the TDP-43 CR tetramer and the experimental constraints (surface accessible positions from sPRE experiments and NOEs on intermolecular contacts) provide the strongest evidence currently possible for the essential structural features of TDP-43 CR in its functionally assembled state.

Methods
Expression and purification of recombinant proteins
All TDP-43 CTD variants were produced using codon-optimized sequences from a pJ411 bacterial expression vector16. The expression was carried out in BL21 Star (DE3) Escherichia coli cells obtained from Life Technologies. The proteins were expressed in either LB or M9 minimal media supplemented with 15NH4Cl, following a slightly modified version of previously described, with details as follows5,16. Bacterial cultures were induced at an optical density (OD) of 0.8 with 1 mM IPTG and incubated for 4 h at 37 °C and shaking at 220 rpm. The cells were then collected by centrifugation (7800g, 15 min, 4 °C). The resulting cell pellets (2 liters of culture) were resuspended in a 20 mL buffer (20 mM Tris, 500 mM NaCl, 10 mM imidazole, pH 8.0), lysed using an ultrasonic cell disrupter, and the lysate was cleared by centrifugation at a speed such that inclusion bodies are pelleted but membranes are not completely pelleted (15,000g, 1 h, 4 °C). The insoluble pelleted material, containing the inclusion bodies, was resuspended in a 40 mL solubilizing buffer (8 M urea, 20 mM Tris, 500 mM NaCl, 10 mM imidazole, pH 8.0) and the cell debris was cleared by centrifugation at high speed (74,766g, 1 h, 19 °C).

The supernatant was then filtered using a 0.45 μm syringe filter, and the protein was purified using a 5 mL Histrap HP column with a gradient of 10 to 500 mM imidazole added to the solubilizing buffer. The purified protein fractions were desalted using a HiPrep 26/10 Desalting Column into TEV cleavage buffer (20 mM Tris, 500 mM GdnHCl, pH 8.0) and subjected to TEV cleavage overnight at room temperature. After cleavage, solid urea was added to the solution to reach a concentration of approximately 8 M urea. The resolubilized protein was then applied to the Histrap HP column to remove the histidine tag and histidine-tagged TEV protease. The cleaved protein fractions were concentrated, buffer-exchanged into a storage buffer (20 mM MES, 8 M urea, pH 6.1) at approximately 1.5 mM, aliquoted, flash-frozen, and stored at −80 °C for further use. Samples with disulfide cross-links were created using copper phenanthroline catalysis and purified to ensure removal of residual (non-crosslinked) monomers by size exclusion chromatography, as previously described5.

Microscopy
Following the manufacturer’s instructions, the protein stocks in 8 M urea were diluted 8x (to 1 M urea) with experimental buffer (20 mM MES, pH 6.1) to a final concentration of 150 to 200 μM TDP-43 CTD and then buffer exchanged into experimental buffer with equilibrated 0.5 mL Zeba spin desalting columns from Thermo Scientific following manufacturer instructions. An equal volume of NaCl stock solution prepared in MES buffer was added to obtain final salt concentration of 150 mM to induce phase separation. The samples were gently mixed, and the phase separation was monitored using DIC micrographs obtained with a Nikon Ti2-E Fluorescence Microscope equipped with a 40x objective. To capture the images, 10 μL of each sample was spotted onto a coverslip. The resulting images were subsequently processed using Fiji software.

In vitro phase separation assay for determining saturation concentration
To quantitatively analyze the phase separation of CTD mutants, we performed assays to determine the saturation concentration by measuring the protein concentration in the supernatant after centrifuging samples with increasing salt concentrations, which induced phase separation. During centrifugation, the protein in micrometer-sized droplets sediments represent the phase-separated state. The protein remaining in the supernatant corresponds to the dispersed phase. By measuring the amount of protein remaining in the supernatant, we determined the saturation concentration, csat, which is the concentration above which the protein undergoes phase separation at the given condition. This value decreases with higher salt concentrations. To initiate phase separation, desalted protein samples were diluted to 80 μM using MES buffer. An equal volume of NaCl stock solution prepared in MES buffer was added to achieve final salt concentrations of 0, 37.5, 75, 150, and 300 NaCl. The samples were gently mixed and then centrifuged for 10 min at 18,000g at room temperature. After centrifugation, the protein concentration in the supernatant was measured using a Nanodrop 2000c spectrophotometer. All measurements were performed in triplicate to ensure the accuracy and consistency of the data points.

NMR spectroscopy
NMR spectroscopy was conducted using Bruker Avance III HD 850 MHz or Avance Neo 600 MHz 1H Larmor frequency spectrometers running Topspin 3.4 or 4.2, respectively, equipped with HCN TCI z-gradient cryoprobes at a temperature of 320 K, 298 K, or 285 K, as specified. The NMR samples were prepared in 20 mM MES buffer at pH 6.1, with the addition of 5% 2H2O to serve as a lock solvent. The NMR sample in Fig. 1 for the native condition was prepared in 20 mM HEPES, 150 mM NaCl buffer pH 7 with the addition of 5% 2H2O to serve as a lock solvent. Backbone assignments for the dimers were obtained by transfer of assignment from the monomer (BMRB: 26823, [https://doi.org/10.13018/BMR26823]) and confirmed for the CR through standard triple resonance experiments (HNCACB, HNCA, and CBCACONH) using Bruker pulse sequences (hncacbgp3d, hncagp3d, cbcaconhgp3d, respectively). The acquired NMR data were processed using Bruker Topspin 4.2 software and analyzed using CCPN (2.5.2)83. Detailed information on the parameter choices regarding experimental NMR pulse sequences and parameters can be found in our previous publications5,16. The 15N spin relaxation experiments at 850 MHz were recorded with 128 and 4096 total points, with acquisition times of 39 ms and 200 ms, in the indirect 15N and direct 1H dimensions, respectively using Bruker sequences hsqct1etf3gpsitc3d, hsqct2etf3gpsitc3d, hsqcnoef3gpsitc3d, respectively. The spectral width was set to 19 ppm in the indirect 15N dimension and 12 ppm in the direct 1H dimension, centered at 116.6 ppm and 4.7 ppm. The 15N R2 experiments consisted of six interleaved relaxation delays, with an interscan delay of 2.5 s. The CPMG field was set to 556 Hz, and the total R2 relaxation CPMG loop lengths were 16.5 ms, 264.4 ms, 33.1 ms, 132.2 ms, 66.1 ms, and 198.3 ms. Each 15N R1 experiment consisted of six interleaved 15N R1 relaxation delays: 5 ms, 1000 ms, 100 ms, 800 ms, 500 ms, and 300 ms. The (1H)15N heteronuclear NOE experiments were conducted using interleaved sequences with and without proton saturation and a recycle delay of 5 s. The (1H)15N hetNOE experiments were recorded with 256 and 4096 total points in the indirect 15N and direct 1H dimensions, respectively. Additional information on the experimental relaxation NMR parameters can be found in our previous publication35.

For intermolecular NOE experiments, 3D 13C,15N-filtered/edited NOE–1H–13C-HSQC (noesyhsqcgpwgx13d) experiments were also recorded with a mixing time of 100 ms and with 128, 60 and 3072 total points with spectral widths of 9, 56 and 12 ppm centered at 4.7, 42 and 4.7 ppm for aliphatic regions in the F2 dimension, or spectral widths of 9, 56 and 12 ppm centered at 4.7, 110 and 4.7 ppm for aromatic regions in the F2 dimension. For the PRE experiments, the TEMPOL reagent was purchased from Sigma-Aldrich. The protein stocks were diluted from storage buffer containing 8 M urea and buffer exchange to 20 mM MES at pH 6.1, followed by the addition of 1.0 M TEMPOL stock solutions to achieve final concentrations of 0, 5, and 20 mM TEMPOL in a 30 μM (in dimer units) protein solution. Subsequently, 1H-15N HSQC experiments were conducted on an 850 MHz spectrometer with acquisition parameters set at 4096 direct points and 256 indirect points. The acquired NMR data were processed using NMRPIPE with consistent parameters, and peak intensities were analyzed using CCPN software (2.5.2). Peak intensities were normalized to intensities of samples without TEMPOL to determine the attenuation of resonances induced by the presence of TEMPOL.

Analytical ultracentrifugation
Sedimentation velocity experiments were conducted at 25 °C and 50,000 rpm (corresponding to 201,600 xg at 7.20 cm) on a Beckman Coulter ProteomeLab XL-I or Beckman Optima XL-A analytical ultracentrifuge following standard protocols84. Samples of TDP-43 CTD (wild-type) were prepared by Zeba desalting columns, as described above, flash frozen for shipment, and thawed for measurement. Samples of cross-linked forms of TDP-43 CTD (TDP-43 CTD S273C and TDP-43 CTD S→E S273C, S→E S305C, S→E S317C and S→E A236P S305C were prepared from frozen protein stocks in 20 mM MES pH 6.1 and 8 M urea that were thawed and diluted to 15 mL in a Falcon tube. Buffer was exchanged using a 10 kDa Amicon filter at 3000 xg and 4 °C. The material was washed three times until the urea concentration was below 20 mM and the protein concentration was approximately 100 μM, except for TDP-43 CTD S273C cross-linked (without solubility enhancing S→E substitutions) that was prepared such that the final urea concentration was ~100 mM to avoid issues with sample aggregation. Before the sedimentation experiment, samples were centrifuged at 18,000 xg and 4 °C to clear aggregated material. Proteins were diluted with the final Amicon filtrate and studied at various loading concentrations in 3 mm or 12 mm pathlength cells. Sedimentation velocity scans were collected using absorbance optical systems at 280 nm, and data were analyzed in SEDFIT85 (version 16.1c) in terms of a continuous c(s) distribution of Lamm equations. When necessary, early scans were not used for model fitting to remove contributions from large aggregates. Solution densities ρ and solution viscosities η were measured at 20 °C on an Anton Paar DMA 5000 density meter and Anton Paar AMV n rolling ball viscometer, respectively, and corrected to the experimental temperature. The partial specific volumes of the peptides were calculated in SEDNTERP86 (version 3.0.4) and corrected for isotopic substitution. Experimental sedimentation coefficients, s, were corrected to standard conditions, s20,w. To determine the affinities for the self-association of the S→E cross-linked variants S305C and S317C, the c(s) distributions were integrated to get the weighted-average sedimentation coefficients and sample concentration. The resulting isotherms were modeled in SEDPHAT87 (version 15.2b). The S→E cross-linked variant S273C and helix disrupting cross-linked S→E A236P S305C variant presented as dimers at all concentrations. For WT CTD, sedimentation velocity data at two concentrations were modeled directly using Lamm equations describing a monomer-dimer-tetramer self-association and other alternative models. Similarly, in the case of S→E cross-linked variant S317C, sedimentation velocity data at three concentrations were modeled directly using Lamm equations describing the dimer-tetramer self-association. Here, data were plotted in GUSSI88 (version 2.10). All concentrations are in dimer subunits for cross-linked dimers. For the non-self-associating species, plots describing the dependence of the sedimentation coefficient with concentration were generated in SigmaPlot (https://grafiti.com) (Supplementary Fig. 9).

Cloning, cell culture, and stable cell production
Generation of stable cells lines HEK293 HA-TDP-43 expressing HA-tagged WT, and A326P was previously completed62. Constructs to express TDP-43 mutants Q327A, L330A, Q331A, S333A, W334A, L340A, and Q343A were generated by site-directed mutagenesis3–6 using the primers listed in Table S3. Generation of HEK293 HA-TDP-43 stable cell lines, expressing WT and mutant HA-tagged TDP-43 was carried out according to previously described protocols14,15 as described below.

TDP-43 cellular nuclear retention experiments
In brief, HEK293-Flp-In T-Rex 293 cells (Thermo Fisher Scientific) were stably transfected to express HA-TDP-43 upon induction with tetracycline (1 μg/ml). Cells were grown and maintained in DMEM (Dulbecco’s Modified Eagle’s Medium–High Glucose, Corning) supplemented with 10% FBS (fetal bovine serum) and incubated in a humid atmosphere at 37 °C and 5% CO2. Expression of HA-tagged TDP-43 construct was induced at 30% confluence for 48 h. Cell fractionation, immunoblotting, and quantification of nuclear to cytoplasmic ratio of HA-TDP-43 variants62, probing with TDP-43 antibody (10782-2-AP, Proteintech).

Immunofluorescence analysis
Glass coverslips were pre-coated with poly-D-lysine (0.1 mg/mL) and incubated for 10 min at room temperature. The coverslips were rinsed with Phosphate-buffered saline (PBS) 1x three times and left to dry under the tissue culture hood for at least 45 min. HEK293 HA-TDP-43 wild type and mutant cells were plated on the pre-coated coverslips and incubated at 37 °C in a humid atmosphere incubator with 5% CO2 for 24 h. HA-TDP43 expression was induced by 0.1 μg/mL tetracycline for 48 h. Cells were fixed in 4% paraformaldehyde in PBS 1X for 20 min, and washed with PBS three times, 5 min each. Cells were then treated with 5 μg/mL wheat germ agglutinin Alexa fluor Oregon green 488 conjugate (Invitrogen # W6748) in PBS to stain the cytoplasm region. The coverslips were then washed three times with PBS and permeabilized with 0.2% Triton X-100 for 5 min on ice. Cells were washed with PBS and incubated with the primary antibody anti-HA (dilution 1:200, Cell signaling # 2367S) for 1 h to label HA-TDP-43. Cells were washed three times with PBS and incubated with the secondary antibody Alexa fluor 555 donkey anti-mouse (Invitrogen # A31570) for 1 h protected from light. The coverslips were then incubated for 5 min in DAPI 1 μg/mL in PBS for nuclear staining and washed twice with PBS. The slides were mounted with ProLog Glass Antifade Mountant, according to manufacture instructions (Thermo Fisher # P36980). Imaging was performed with a Keyence BZ-X800 immunofluorescence microscope using a 40X and 60X objective. The intensity of nuclear and cytoplasmic HA-TDP-43 was measured using standard ImageJ protocols.

AlphaFold-multimer predictions
TDP-43 310-350 multimer structures were generated using AF2-Multimer, providing a multi-sequence FASTA file of TDP-43 310-350. The predictions for multimers were executed based on multiple sequence alignment alone, with the exclusion of PDB templates through the use of the parameter --max_template_date=1950-1-1. The full database (--db_preset=full_dbs) was used to create the alignments. Dimer and tetramer models were generated utilizing both AF2-Multimer v2.2.0 and v2.3.0, while trimer, hexamer, and octamer structures were generated using AF2-Multimer v2.3.0. In AF2-Multimer v2.2.0, one seed per model was employed, resulting in a total of five predictions. In AF2-Multimer v2.3.0, five seeds were used per model, yielding a total of 25 predictions. The relaxed structures (--use_gpu_relax=True) without major clashes were employed as the initial configurations for AAMD simulations. It is important to note that the predicted structures contain major clashes, especially in the case of higher-order multimers; thus, the number of structures simulated for each multimeric state differs from one another.

All-atom MD simulations
Initial structure, force-field choice and system setup. The protein was modeled using the Amber03ws force field89 (https://doi.org/10.5281/zenodo.1705993) and solvated with TIP4P/2005 water model91. We selected the AMBER03ws protein force field89 for its proven ability to accurately capture both the global dimensions and local structural features of various intrinsically disordered proteins, including the TDP-43 CTD27. Specifically, AMBER03ws has been shown to reliably reproduce NMR-derived helical propensities in our previous simulations of a subsegment of the TDP-43 CTD (amino acids 310–350) used in this study5,16. Hence, we conducted 45 μs-long unbiased, monomeric simulations of TDP-43 310–350 (five independent replicates, with different starting configurations) using this force field and found good agreement between the simulated and experimental NMR observables, including 3J HNHα scalar coupling constants and chemical shifts, as well as the simulated and NMR-derived helix fractions16 (Supplementary Fig. 15). Based on these results, we selected AMBER03ws to simulate the structural ensembles of the higher-order assemblies of TDP-43 310–350 presented in this study.

The dimer (5 models from AF2-Multimer version 2.2.0 (v2.2.0) and 13 models from AF2-Multimer version 2.3.0 (v2.3.0), 18 models in total; Supplementary Figs. 16, 17), trimer (8 model from AF2-Multimer v2.3.0; Supplementary Figs. 18, 19), tetramer (2 models from AF2-Multimer v2.2.0 and 5 models from AF2-Multimer v2.3.0, 7 models in total; Supplementary Figs. 20, 21), hexamer (5 models from AF2-Multimer v2.3.0; Supplementary Figs. 22, 23), and octamer (4 models from AF2-Multimer v2.3.0; Supplementary Figs. 24, 25) structures of TDP-43 310–350 from AF2-Multimer were employed as the starting configurations for AAMD simulations with explicit solvent and ions. For the cross-linked dimer, TDP-43 310–350 S317C_S–S, serine at position 317 was mutated to cysteine in each chain of the parallel dimer models generated using AF2-Multimer v2.2.0 in Chimera version 1.18. A disulfide bond (S–S) was introduced between the cysteine residues using the pdb2gmx-ss command using GROMACS version 2021.6. Simulation details for all systems are summarized in Table S4.

To mimic the physiological salt concentration (150 mM), Na+ and Cl− ions were added to the protein-water system. The improved salt parameters from Lou and Roux were used for all simulations92. The protein was solvated in a truncated octahedron box, ensuring 1.2 nm separation between protein atoms and box edges.

Simulation protocol. Following solvation, the system was minimized via the steepest descent algorithm using GROMACS 2021.693. After minimization, the Nose-Hoover thermostat94 was used for temperature equilibration at 300 K with a coupling constant of 1 ps for protein, water and ions. After the NVT equilibration, a 100 ns NPT equilibration was run using the Berendsen barostat95 with isotropic coupling and a coupling constant of 5 ps to achieve pressure of 1 bar. Production simulations were conducted in the NPT ensemble (1 bar, 300 K) using the Langevin Middle Integrator96 (friction coefficient = 1 ps-1) and the Monte Carlo Barostat with isotropic coupling in AMBER2229. Hydrogen mass was increased to 1.5 amu to enable a timestep of 4 fs. Short-range nonbonded interactions were calculated based on a cutoff radius of 0.9 nm, while long-range electrostatics were handled with the Particle Mesh Ewald (PME) method97. Hydrogen-related bonds were constrained using the SHAKE algorithm98. Following the production runs, Amber NetCDF trajectory files were converted into GROMACS .xtc compressed trajectories using the CPPTRAJ module99 of AmberTools 2229 for subsequent analysis.

Trajectory analysis. The distance root mean square deviation of atom distances (dRMS) for all heavy atoms across the CR (amino acids 320–341) from reference structures was calculated using the gmx rmsdist command. Minimum distance between the monomeric units in the two-chain simulation trajectories were computed using gmx mindist. For the other analyses described in the text, the first 250 ns of each replica were excluded as equilibration time. In contact analysis, two residues i and j with sequence separation greater than 3 (|i-j|>3) were counted in contact if any two heavy atoms of the residues were within 0.6 nm of each other. This cutoff has been extensively used in our previous studies and has been thoroughly explained in the work of Zheng et al.100. Helix fraction calculations were performed using the gmx do_dssp, relying on the DSSP library71. The NMR relaxation parameters (15N R2) were computed using a previously published method16,34. The most representative structures from AAMD simulations of multimer models were determined using the gmx cluster tool with a GROMOS clustering algorithm. Backbone atoms of CR residues (aa: 320–341) were considered, with an RMSD cutoff of 0.3 nm, based on the last microsecond of the trajectory. Analyses performed with GROMACS were conducted using version 2021.6. All snapshots from atomistic simulations were generated using UCSF Chimera version 1.18101/ChimeraX version 1.8102.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The manuscript by Fawzi et al. investigated the atomic structural details of TDP-43 CR helix-mediated assembly utilizing an integrative modeling strategy including NMR spectroscopy, SV-AUC, AF2-Multimer and all-atom molecular dynamics simulations. They found that the native state of TDP-43 CR is α-helical under physiological conditions, and helix-mediated interactions are linked to phase separation of C-terminal domain. The simulation results showed that TDP-43 CR assembly are stabilized by a methionine-rich core with specific contributions from a tryptophan/leucine pair. Overall, this work is carefully designed and executed. The manuscript is well written and the conclusions are supported by data. Some additional comments are listed below.\n1. In the method section, the simulation setup details including systems, number of simulations, number of atoms are suggested to be summarized. It is also necessary to give the reason for choosing Amber03ws force field for protein structure.\n2. In Figure 5B, it would be easier for readers to understand the plot of structural stability if different size of oligomers are labeled.\n3. The authors performed 5-μs AAMD simulation for each system. However, the duration of some trajectories are less than 5 μs in Figure S8A and S10C.\n4. Previous computational studies (10.1021/acs.jcim.3c00513; 10.1021/acs.jcim.4c00943) have investigated the dimerization of TDP-43 low-complexity domain and reported the conformational transition from helices to β-sheets, which may be helpful for the discussion.", "reviewer_2": "Major comments\nThe authors developed a model for the oligomerization and initial stages of pathogenic aggregation of TAR DNA binding protein-43 (TDP-43) by structure/function analysis of the C-terminal domain (CTD) of TDP-43.\nThe study largely consists of the analysis of wild-type and mutant CTD proteins. The mutant proteins contained serine to glutamic acid mutations (S->E) to preserve solubility. CTD proteins were His-tagged for purification, followed by removal the tags and his-cleaving TEV protease. They were subjected to denaturing conditions in 500 mM GdnHCl during purification and exchanged into 8M urea for storage. It is not clear if, when or how urea was removed before subsequent analysis. What was the solvent used in the experiments? The native oligomeric state of these proteins isolated and stored under these harshly denaturing is open to question and seems to be a major flaw in the study.\nFig. 3A shows SV AUC c(s) distributions as a function of loading concentration of WT CTD. There is a major 1.5 S species and at the highest loading concentration, 107 uM, a minor 2.9 S species. From these results, the authors propose that there is a monomer-dimer equilibrium that possibly proceeds to higher order oligomerization. However, the small 2.9 c(s) peak shown in Fig. 3A does not strongly support a monomer-dimer equilibrium of wild-type CTD, or at least one with a dissociation constant of less than ~100 uM.\nThe authors proceed to state “to better characterize the assembled state, we experimentally trapped a dimeric state by forming a disulfide cross-link (S-S) between engineered cysteine residues at S273C”. This maneuver seems highly suspect – it may represent an artifice to create dimers and subsequent higher order oligomers with no physiologic or pathologic relevance. The rationale for making the mutations S273C, S305C and S317C is not presented. These mutations evidently are not associated disease states.\nSV AUC of the S305C and S317C CTD mutants revealed the formation of higher order oligomers beyond the dimer. Modeling with to dimer-tetramer and dimer-tetramer-octamer equilibria was done by sw isotherm analysis and Lamm equation modeling. Although good fits to the models was obtained, it is not clear if equally good fits to alternative models would be obtained. This does not detract substantially from the results, which clearly reveal some sort of oligomerization or aggregation beyond the dimer. However, as noted above, for proteins that are engineered to be soluble with S -> E mutations, stored in chaotropic buffers and artificially disulfide-linked to form dimers, the relevance of these higher order structures is questionable.\nThe peak areas of c(s) distributions seem inconsistent with the loading concentrations. In Fig. 3A there is a ~6-fold increase in loading concentrations. Are the associated peak areas consistent with this? In Fig. 3B, are the c(s) peak areas consistent with loading concentrations of 11 uM and 21 uM?\nFor nearly monodisperse systems, continuous c(s) distribution analysis often produces an accurate estimate of the molar mass from combined estimates of the diffusion coefficient (expressed in terms of the frictional ratio, f/f0) and sedimentation coefficient. Were the molar mass estimates of the 1.5 S species and the 2.9 S species consistent of monomeric and dimeric CTD? Fig. 3A legend states a molar mass of 31.5 kDa. Where did this number come from?\nMinor comments\nRotor speeds evidently were 50000 rpm, not 5000 as stated in the manuscript.\nThe authors state the SV-AUC interference optical system was used, but only absorbance data are shown.\nReferences are not given for SEDPHAT, Lamm equations modeling or GUSSI.\nALS is not defined in the abstract.\nRegarding the statement “… the dynamic association of TDP-43 CTD …”, it is not clear what TDP-43 CTD is associating with.\nThe authors state in that SV AUC experiments “were conducted at the specified temperature”. Fig. 3 and Fig. S6 state SV AUC was done at 25 C. The temperature is not specified in Fig S7. If all experiments were at 25 C, please revise Methods to state that SV AUC experiments were conducted at 25 C.", "reviewer_3": "The manuscript by Rizuan et al. focuses on the role of the conserved region (aa 310-350) in TDP-43’s low complexity region (LCD), in particular its structural details and role for TDP-43 assembly. For this, they integrate several biophysical and biochemical approaches as well as molecular dynamic simulations to investigate the structural rearrangement and interactions of the conserved region during higher order assembly. The study not only confirms the presence of an alpha-helix under various buffer conditions, but also pinpoints the importance of several residues for promoting self-assembly, in particular L330, W334 and methionines, while most polar residues appear to dampen phase separation. The significance of this study lies in the characterization of the conformational states of the alpha helix of TDP-43, highlighting its role in promoting oligomer formation, which might affect biophysical and functional activity of TDP-43.\nOverall, the authors provide extensive biochemical and biophysical data supporting their findings. The characterization of the role of different residues in TDP-43 LCD can potentially provide important insight into the mechanism of TDP-43 self-assembly. Structural details of TDP-43's CR can so far only be obtained using the LCD, and biochemical experiments have been performed introducing disulfide bonds to promote oligomer formation, which is also a major limitation of the study and should be discussed accordingly by the authors (and to some extent addressed as mentioned below).\nIn summary, we recommend publication of this work in Nature Communications, provided that the following points are being addressed by the authors:\nMajor points:\nI) the condensates presented in Figure 2B are very small and it is nearly impossible to see the described morphological differences described by the authors, such as reduced roundness. Please additionally provide zoomed-in images in this panel. Also, please provide scale bars to allow judgement of the condensate size.\nII) To due the strong focus on structural analysis of the LCD, the study is limited with respect to the full length protein. To support their model for a role of the CR in TDP-43 self-assembly, the authors should support their findings by more cellular data in addition to the ones already provided in the SI. Here they should use key mutants identified in their structural analysis that either disrupt or enhance the alpha-helix (w.g. by comparing WT and A326P with at least L330A and W334A). Are those mutants also in context of the full-length protein in cells less/more prone to undergo higher order assembly/phase separation? This could for example be done by studying their subcellular localization in response to stress or by applying size exclusion chromatography as previously shown by Dos Passos et al (PMID: 38422113) using their stable cell lines. Ideally, this should be performed in absence of endogenous TDP-43 (if possible) to avoid complex formation of mutant with wt TDP-43.Regarding Figure S3, please indicate the difference between upper and lower blot panel in A and B, respectively, as the signal distribution in the different fractions for TDP-43-HA appears to be quite variable. Also, why is endogenous TDP-43 not visible in the upper blot panel? Which blots were used for the quantification? Also, are those mutants in intact cells localized to the nucleus or already cytoplasmically mislocalised (i.e. more than WT as this is an overexpression system)? The provided assay mainly addresses the extractability/ nuclear retention of TDP-43 upon cell lysis – this should also be clear from the experimental description. The authors should therefore only use “nuclear retention” and not refer to “nuclear localization” or “nuclear depletion” (unless specific mutants show a significant cytoplasmic mislocalization in intact cells).\nMinor points:\n1. The authors start their work by showing the conservation of the CR in vertebrates – yet they only show 2 species (zebrafish and homo sapiens). A comparison with additional species would further strengthen this point.\n2. The authors start their result section with referring to “recent work using hydrogen-bond disrupting variants” that suggested a  -sheet confirmation for the CR of TDP-43 – yet they fail to provide any references.\n3. There appears to be a typo in the sentence “Expanding from our previous results for the 5M->A variant within CR, which collectively impaired the CTD phase separation, each single methionine substitution within CR (M322A, M333A, M336A, M337A, M339A) also reduce phase separation with approximately two-fold higher csat than WT (Fig. 2A)” -probably the author refers to M323A.\n4. Sedimentation assay shown in Figure S1: Even though standard deviations of three plicates are mentioned in the figure legend, they appear to be lacking from the figure itself. Did the author compare these results with other methods employed to characterize protein multimers/oligomers (e.g. DLS or are strongly assembly-promoting mutants measurable by SV-AUC?)?\n5. TDP-43 cell fractionation. How many replicates were performed for this experiment? Please indicate this also in the respective figure legend. Also, to allow the reader a better understanding of the reproducibility/variability of this assay, it would be better to present also the individual values for each experiment in Figure 2H. Additionally, the ANOVA statistical test assumes that the data are normal distributed, did the author check this assumption? Alternatively, I would recommend the use of non-parametric test like Kruskal-Wallis.\n6. It is very interesting that a mutation of two closed amino acid very like S333A and W334A leads to two opposite effects on the stability of the alpha helix (Figure 2A, 2B). The author may use this example to emphasize the specific contribution of W on alpha helix stabilization.\n7. In case there is a public-repository for NMR data, the author should upload the raw data to make them accessible for the scientific community (FAIR principles to optimize the reuse of data).\n8. The authors indicate that Q327, S332A and S333A were not measurable by NMR due to broadening of the CR signal and refer to the strong self-assembly they have observed before. Yet, Q331 showed a similar strong self-assembly in Figure 2A, but could be measured by NMR? Do the authors have an explanation for this?\n9. Please include page numbers for revisions.", "reviewer_4": "TDP-43 has been identified as a major component of inclusions found in patients with Amyotrophic Lateral Sclerosis (ALS) and certain forms of dementia. This RNA-binding protein contains an intrinsically disordered region (IDR) approximately 160 amino acids long within its C-terminal domain (residues 267–414). Numerous studies, including analyses of postmortem patient biopsies, have demonstrated that these aggregates primarily consist of fragments from the IDR. Additionally, most disease-associated familial mutations are located within this region. Therefore, understanding the aggregation mechanisms of the IDR is crucial for elucidating the underlying causes of these diseases.\nCurrent knowledge from various biophysical characterizations indicates that the IDR of TDP-43 forms an amyloid-dominant structure, as evidenced by cryo-electron microscopy (cryo-EM) studies on inclusions from postmortem patients. Although not entirely disordered, the IDR contains a transient alpha-helical region (residues 319–341; the CR in this manuscirpt) that has a propensity to self-assemble. This domain facilitates the protein's ability to undergo phase separation, which is related to its role in forming stress granules or RNA granules.\nIn the manuscript by Rizuan et al., the authors aim to understand the detailed structure of the assembly of this alpha-helical region in its dynamic state. While their experiments and simulations are extensive and methodologically sound, the article falls short of achieving its main goal—to provide a detailed understanding of the structural aspects of the C-terminal region of TDP-43. The reliance on molecular dynamics (MD) simulation interpretations is heavy, yet the experimental support is weak. The rationale behind the design of specific mutants and their subsequent interpretations is unclear. Furthermore, some interrelated factors, such as the interplay between alpha-helical propensity and self-association tendency—which could significantly affect the results—have been overlooked.\nThe major concerns of this work are listed below.\n(1) It has been reported that the IDR of TDP-43 exhibits a stronger tendency to aggregate at higher pH levels and increased NaCl concentrations. This is because the net positive charge of the disordered region leads to enhanced self-association as the pH rises, reducing protonation and thereby diminishing electrostatic repulsion (PMID: 30814253). Similarly, higher NaCl concentrations screen electrostatic repulsive forces, further promoting aggregation (PMID: 30489059). Although the alpha-helical propensity remains and the chemical shift perturbations between pH 7 with 150 mM NaCl and pH 6.1 are minimal, the effects on self-association and alpha-helical tendency—which are key for the protein's phase separation or aggregation—are also small, as demonstrated in the authors’ seminal paper (Figures 5 and 6; PMID: 27545621). Given that even small differences in alpha-helical structural propensity under these two conditions can significantly impact the helical component population and self-assembly—as shown in their previous work—such variations should not be dismissed as negligible or overlooked in subsequent studies.\n(2) In the alanine-scanning studies on the CR region, the alpha-helical propensity would differ among these mutations, and this structural tendency modulates self-association, as also noted in the authors’ previous work (PMID: 32132204). However, the dynamic nature of the helical structure in self-association seems to have been overlooked. Although the manuscript discusses helical propensity changes in one paragraph, these are attributed solely to results from \"dimerization\"—bearing in mind that the dimer was the authors' input for AlphaFold-multimer, not an outcome of the simulation. In the chemical shift analysis (δ15N CSP), this parameter is affected by both the equilibrium between different structural populations (random coil versus alpha-helix) and interactions from self-association. Thus, the concluding sentence in this section, \"This support(s) the idea that residues stabilizing CR helix-helix contacts in the dimer state contribute to the phase separation of CTD,\" may not be fully justified.\n(3) In their attempt to verify the helix-mediated assembly and function, the authors used a cell model to measure the nuclear-to-cytoplasmic ratio. They found that mutants enhancing helical assembly also increased nuclear localization. However, the underlying mechanism by which helix-promoted self-assembly enhances nuclear import is not apparent; one might expect that larger assemblies would impede nuclear entry. Although the authors note this discrepancy by stating that it \"highlights the need for further investigation into this mechanism,\" this point should be elucidated. Furthermore, since the most extensively studied characteristics of TDP-43 assembly involve its cellular toxicity or amyloid-fibril formation, the authors should also investigate how these helix-mediated assembly mutants affect these well-characterized phenomena.\n(4) The AUC data indicated the presence of dimers and possibly larger oligomeric species in equilibrium. However, additional complementary methods could be employed to more conclusively determine the size and distribution of these oligomers. Techniques such as dynamic light scattering, nanoparticle tracking analysis, or even cryo-electron microscopy imaging could provide more insights into the oligomerization states. Applying these methods would strengthen the rationale for the subsequent molecular dynamics simulations that use different oligomeric starting models.\n(5) The rationale for mutating 13 serines to glutamates (S to E) in the non-CR regions to enhance solubility is unclear. Many serine phosphorylation sites are associated with this protein's pathological aggregation (S379, S403, S404, Ser409, Ser410), and substituting serine with glutamate is often used to mimic phosphorylation effects, which leads to amyloid-fibril. Introducing an additional cysteine to force dimerization is also questionable. While their previous work investigating alpha-helical propensity using disulfide linkages was reasonable, applying this method here may not be suitable because the aim of this manuscript is to characterize the structure of the dynamic alpha-helical assembly. The antiparallel conformation in the AlphaFold-multimer-generated structures for MD simulations is not considered in these cases. Furthermore, the NMR experiments in Figures 3D–F are expected results because cross-linking at the N-terminus connects two monomers, inducing helix-helix interactions (evident from chemical shifts) and increasing R₂ relaxation rates due to the larger molecule reducing overall tumbling. As mentioned above (point 2), chemical shifts are influenced by both structural contacts and the equilibrium between coil and helix conformations. Here, the measured R₂ is also related to the chemical exchange between coil and helical conformations, adding to the line broadening caused by interactions between monomeric and dimeric states. These factors are ignored and cannot be simply attributed to the formation of higher-order (beyond dimer) structures.\n(6) The NOE data provided information only on the types of amino acid contacts but did not reveal detailed structural insights. While it is understandable that these may be the best results obtainable using NOE to characterize such a heterogeneous interacting system, the NOE experiments themselves are also limited because they were conducted on a construct where six phenylalanine residues in the non-CR region were replaced with alanine to increase solubility. This modification could overlook potential assembly contacts, such as the interaction involving tryptophan 334 in the CR region through pi-pi stacking (PMID: 33909608, PMID: 29511089). While the authors aim to focus on alpha-helix interactions, it has been reported that other parts of the IDR contribute to phase separation—for example, aromatic residues acting as \"stickers\" in the organization and formation of higher-order oligomers. These interactions may be critical in assembly studies and experimental characterizations but are overlooked in the MD modeling work. Furthermore, the \"structure\" of the helical assembly used for MD simulations was derived from AlphaFold-Multimer models. Lacking direct evidence of tetramer formation—and considering that many experimental investigations have used induced dimerization through disulfide linkages—it is difficult to be convinced by the simulated results. Collectively, these data are not convincing that this manuscript significantly advances our current knowledge or offers novel insights into the structural mechanisms of TDP-43 aggregation."}, "author_response": "RESPONSE TO REVIEWER COMMENTS:\nWe thank the reviewers for their helpful comments, which we have addressed extensively through\nadditional experiments, simulations, and analyses, as well as substantial revisions to the manuscript.\nFirst, we provide direct evidence for the presence of tetrameric multimers of WT CTD through\nextensive new AUC analysis, which was a key concern raised by the reviewers.\nSecond, we emphasize the need for an integrative approach to overcome the significant challenges\nin structurally characterizing WT CTD multimers observed by AUC. We also clarify the rationale and\nsignificance of each designed CTD construct and experiment used to support our structural model\nof CTD helical multimers.\nThird, we revised our MD simulation stability metric, replacing inter-dRMS with dRMS, which offers\na more global assessment of structural stability beyond intermolecular contact loss. This analysis\nrevealed that only one tetrameric model (Tet-1) was highly stable across all multimeric structures\nstudied, and remained stable in the new full-length CTD simulations.\nFinally, we incorporated new in-cell experiments and conducted additional analyses of the effect of\na library of TDP-43 variants on cellular growth restriction data already available in the literature.\nThese results demonstrate a direct correspondence between full-length TDP-43 cellular behavior,\nspecifically its nuclear retention and toxicity, and the CTD variants we studied in vitro.\nWe are confident that these new data strengthen our conclusions and provide robust support for our\nNMR and computational findings."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The revisions are well executed, and I have no further suggestions at this stage.", "reviewer_2": "In the revised manuscript, the authors provide more detail of SV AUC of TDP-43 CTD in an attempt to establish the stoichiometry of a putative self-association. They report that Lamm equation modeling SV scans of TDP-43 loaded at 58 uM and 107 uM using SEDPHAT is consistent with a monomer – dimer – tetramer model with weak self-association to the dimer and stronger association of the dimer to the tetramer. The fitted dissociation constants for the monomer – dimer and dimer – tetramer equilibria were 2000 uM and 2.9 uM, respectively. They found that alternative monomer-dimer, monomer-trimer, monomer-tetramer and monomer-dimer-trimer models “resulted in poor fits and/or physically unreasonable sedimentation coefficients”, but that “excellent fits” were found for the monomer-dimer-tetramer model. The analysis represents state of the art use of SV AUC to model self-association equilibria, although they could also have presented F-statistics, which are used in SEDPHAT to show statistical significance of differences in fitting alternative models. The acceptance of the model must be viewed with skepticism. The highest loading concentration of 107 uM is ~20-fold lower than the Kd for the weaker, obligate dimerization step. As a result, as they note, there is very little population of the tetramer. To accept a binding model with confidence, a system should approach saturation. It is surprising that reported errors are relatively small. Overall, the available concentrations limit the investigation of an apparent ultraweak self-association of the protein.", "reviewer_4": "I thank the authors for addressing my previous concerns with their revised manuscript. Especially, the updated analysis of the AUC data demonstrates the monomer-dimer-tetramer model as the best fit, which effectively resolves many of my concerns regarding the computational parts. However, I suggest that the alternative models evaluated (i.e., monomer-dimer, monomer-trimer, monomer-tetramer, and monomer-dimer-trimer) and their respective fitting parameters (such as R_square or reduced chi_square values) be included in the Supporting Information for completeness and clarity.\nAdditionally, based on the relaxation dispersion NMR data previously presented in Figure 7A of the 2016 Structure paper, I did a quick estimation of the apparent dissociation constant (K_d). Using the reported values of k_off = 1100 s⁻¹ and k_on = 100 s⁻¹, and considering the protein concentration ([protein] = 107 µM), the estimated K_d is approximately (1100/100) × 107 = 1177 µM. This estimate aligns well with the magnitude derived from the current AUC fitting and may provide valuable complementary support. If I am correct on this estimation, I recommend that the authors consider including this comparative estimation to strengthen the coherence of their analyses.", "reviewer_5": "By incorporating the revisions and including microscopy analyses highlighting the distinct localization of the mutants (new Fig. S4C), the authors have adequately addressed my comments. Therefore, I recommend the manuscript for publication."}, "author_response": "Response to Reviewer 2:\nWe appreciate the reviewer’s salient comments and the opportunity to revisit our Lamm equation modeling for the SV-AUC data on TDP-43 CTD. During the process of recalculating the confidence interval limits for the interaction affinities, we discovered that some of the best-fitting models were converging to a local minimum in the parameter error space. The problem was corrected, and the affinities and sedimentation coefficients reported for the monomer–dimer–tetramer model have changed slightly. The proper values are now reported in the revised manuscript, noting that the fitted dissociation constants for the monomer–dimer, and dimer–tetramer equilibria are now 2.4 mM and 2.7 μM, respectively (slightly changed but within the previous confidence internal). The confidence interval representing one standard deviation for the monomer – dimer equilibrium are 1.1 – 3.4 mM (it is not technically normally distributed because the dissociation constant cannot be 0 and, when the uncertainty is sizable, the skew in this parameter is more evident) and for the dimer – tetramer equilibrium ± 0.6 μM.\nAs requested, we now report the global reduced chi-square values for the best fits obtained from the various Lamm equation models. The values indicate the goodness of fit and serve as a criterion for identifying the best model. According to the new Supplementary Table S1, the monomer–dimer–tetramer model provides the best fit with a global reduced chi-square value of 0.1657. Additionally, for comparison, we report the estimated values for the critical reduced chi-square value at a 68.3% confidence level (one standard deviation) obtained from SEDPHAT using F-statistics.\nWhile the monomer–dimer–tetramer model returns the best fit based on the globally reduced chi-square, the monomer–tetramer model is now statistically indistinguishable. The monomer–tetramer model yields an affinity K = 3.96 × 1010 M-3 with an equivalent concentration of 2.94 × 10-4 M (= 294 μM), at which the monomer concentration equals the tetramer concentration (because the monomer-tetramer model has an association constant with M-3 units, this concentration where [monomer]=[tetramer] can be useful, similar to how K is often presented).\nThough the monomer-dimer–tetramer model is depicted as a two-step process, the equivalent monomer–tetramer affinity for that model is K = 6.36 × 1010 M-3 with an equivalent [monomer] = [tetramer] concentration of 2.51 × 10-4 M (= 251 μM). In other words, these models are very similar and both agree on the tetramer effective association constant. Additionally, we note that although the reviewer is rightfully concerned that “The highest loading concentration of 107 uM is ~20-fold lower than the Kd for the weaker, obligate dimerization step”, the tetramer is significantly populated due to the much tighter dimer-tetramer affinity. Indeed, the 107 μM condition is less than 3x below the equivalent [monomer] = [tetramer] concentration as represented in both of the models that explain the data. Although it would be ideal to perform the experiments at higher concentrations, this is not tractable as described below.\nResponse to Reviewer #4\nWe thank the reviewer for their comments and now present the global reduced chi-square values for the various Lamm equation models in Supplementary Table S1. As noted in our response to Reviewer #2, a reanalysis of the sedimentation velocity data yields better fits and slightly different sedimentation coefficients and affinities.\nWe also appreciate the reviewer’s suggestion to compare to our previous work more directly. We had not considered trying to extract an apparent K directly from that work. We note the challenges are higher given the complex equilibria and the potential confusion as the actual dissociation constant for a monomer-tetramer or monomer-dimer-tetramer is not easily expressed as a traditional dissociation constant. We have decided to add a description of the effective concentration where the monomer and the tetramer have equal populations in each model and we added the following comment regarding our previous work in the text:\nBoth models are also consistent with previous analyses at slightly different conditions using NMR relaxation dispersion of TDP-43 CTD exchange between monomeric and multimeric structures16."}, {"round": 3, "reviewer_comments": {"reviewer_2": "The authors now present global reduced chi-square values for the fits with various Lamm equation models (Supplementary Table S1). They report a significantly better fit with the monomer–dimer–tetramer model, which has improved the manuscript.\nThe authors state solubility issues limit the highest concentration allowed in SV AUC experiments. While this is unfortunate, it is unsound to assert conclusions that extend beyond the resolution or reliability of the available data."}, "author_response": "We fundamentally agree with the reviewer and appreciate the need for caution. We have carefully updated the manuscript. Our previous manuscript text could be seen as relying too heavily on the AUC of TDP-43 CTD WT to say that the multimers are tetrameric. Now, we have changed the manuscript to highlight that integrative lines of evidence support the model. In this case, we have 1) evidence from AUC for multimers larger than dimer that are most likely tetramer, but cannot say conclusively, and 2) additional evidence pointing to tetramers from AlphaFold2-Multimer combined with molecular simulation showing that the tetramer are the only stable structures and the only structures that explain all the prior and current mutagenesis and NMR data. This model is further supported by additional NMR and AUC on cross-linked forms."}]

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