{"paper_id":"a9a242ce-e882-4511-adbf-33ad86fefd9b","body_text":"1 \n \nProbing the energy landscape of α-Synuclein amyloid fibril \nformation by systematic K-to-Q mutagenesis. \nAntonin Kunka[a], Azad Farzadfard[a], Jacob Aunstrup Larsen[a], Federica Saraceno[a], \nRasmus Krogh Norrild[a], Celia Fricke[a], Hossein Mohammad-Beigi[a], Ahmed Sadek[b], \nJonas Folke[c], Susana Aznar[c], Alexander K. Buell#[a] \n[a] Department of Biotechnology and Biomedicine \n Technical University of Denmark \n Søltofts Plads, Building 227, 2800 Kgs. Lyngby, Denmark \n[b] Department of Biotechnology and Bioengineering \n Ecole polytechnique fédérale de Lausanne EPFL \n[c] Centre for Neuroscience and Stereology, Department of Neurology, Copenhagen University Hospital, \nBispebjerg and Frederiksberg Hospital, Nielsine Nielsens Vej 6B, Entrance 11B, 2. Floor, 2400 \nCopenhagen, Denmark. \n \n# E-mail: alebu@dtu.dk  \n \nAbstract \nThe aggregation of natively disordered α-Synuclein (αSyn) into amyloid fibrils is a hallmark of \nParkinson’s and other neurodegenerative diseases. Understanding αSyn’s pathological role \nremains a major challenge due to its complex, context -dependent energy landscape \ncharacterized by conformational plasticity and fibril polymorphism . Here, we present a  \nsystematic mutational analysis as a quantitative probe of the αSyn energy landscape, focusing \non electrostatic contributions to key aggregation pathways. We engineered αSyn variants with \none to eight lysine-to-glutamine substitutions and analyzed their aggregation under controlled \nconditions to delineate their effects on nucleation, elongation, seed amplification, fibril stability, \nand fibril polymorphism. We find that αSyn aggregation from a homogenous solution can be \nmodelled well using global properties, including protein concentration, charge, and ionic \nstrength. Microscopic pathways and the resulting fibril polymorphs are instead modulated by \nsequence-specific effects. We identify mutations of residues found in fibril cores as \nperturbations that significantly modify the αSyn free energy landscape, creating pathways and \nenergy minima not accessible to the WT under the same experimental conditions. In contrast, \nmutations outside of the fibril core affect the magnitude of the relevant energy barriers whilst \noverall maintaining a WT-like free energy landscape. Our work outlines a scalable, quantitative \nframework that increases the informational output of the mutational studies of α Syn using \nconventional assays. The approach can be extended by incorporating additional mutational \nand functional data to deepen our understanding of αSyn’s energy landscape and its role in \nhealth and disease. \n  \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted November 3, 2025. ; https://doi.org/10.1101/2025.11.01.685997doi: bioRxiv preprint \n\n2 \n \nIntroduction \nα-Synuclein ( αSyn) is a small 14. 5 kDa protein found in neuronal cells , as well as  the \nperipheral nervous system, and red blood cells. (1–5) It plays diverse roles in synaptic vesicle \ntrafficking and recycling, dopamine regulation, calcium signaling, mitochondrial function, and \nlipid metabolism. (6–12) Pathologically, αSyn is the principal component of intracellular \ninclusions that characterize synucleinopathies, including Parkinson’s disease (PD), Lewy body \ndementia (LBD), and multiple system atrophy (MSA). (13) Although it has been demonstrated \nthat αSyn aggregates are toxic and propagate between cells in a prion-like manner, it remains \nunclear whether disease arises primarily from gain-of-function toxicity or loss of normal protein \nfunction. (14–18) \nOne major obstacle to resolving α Syn’s role in disease and developing effective therapies is \nthe complexity of the protein itself. Even outside of its biological background, characterization \nof αSyn presents a multifaceted challenge due to its extreme conformational plasticity and \ncontext-dependent behaviour. Its ability to transition from its disordered monomeric state to \nvarious oligomeric or fibrillar forms, each potentially associated with distinct cellular outcomes, \ncomplicates both mechanistic understanding of the diseases and their therapeutic targeting. \n(16,18–22) This structural flexibility is also  modulated by mutations, post -translational \nmodifications (PTMs), differential expression, and cellular localization, further complicating the \ndistinction between physiological and pathological states. (23–29)  \nThe complex energy landscape of αSyn is often probed through a combination of advanced \nbiophysical methods  (e.g., NMR), which provide invaluable resolution but are often not \nscalable and/or are technically demanding. (30–34) A compelling approach is to instead exploit \nmutational analysis which is well established for studying energetics of folding, binding, and \nstability of globular proteins. (35–38) Studies involving familiar mutants  (39,40), N- and C - \nterminal truncations  (41–43), PTMs and their mimetics  (44–48), and large-scale variant \nanalyses (49,50) have collectively highlighted regions critical for αSyn aggregation, suggesting \nthat mutational scanning can indirectly map features of the underlying energy landscape, \nanalogous to what is possible for folded proteins. Despite the wealth of insightful results from \nthese and other studies (51–58), our ability to predict how individual substitutions alter specific \nassembly pathways of αSyn (e.g., nucleation, elongation, seed amplification)  within specific \nbiological contexts remains limited. \nMutational studies of α Syn generally investigate three classes of protein variants: (i) \nphysiologically or pathologically relevant variants (e.g., disease-associated familial mutants), \n(ii) targeted sequence perturbations designed to test specific hypotheses or probe \nmechanisms (e.g., Φ -value analysis) (59) , and (iii) random variants used for unbiased \nsearches that typically require large libraries to be informative. The first class provides direct \ninsight into pathogenic mechanisms by revealing how disease mutations reshape the energy \nlandscape and assembly behaviour . The second enables detailed physico- chemical and \nmechanistic interpretation through systematic comparison with the WT protein. However, such \nanalyses are only valid if the introduced mutations do not substantially distort the energy \nlandscape or introduce alternative folding or assembly pathways. Unlike folded proteins, \nintrinsically disordered proteins are characterized by  shallow, frustrated energy landscapes, \nmaking them particularly sensitive to even subtle sequence perturbations that can markedly \nalter their conformational or assembly behaviour. Consequently, it is essential to characterize \nthe accessible structural states of each variant and interpret mechanistic differences with \ncaution when mutagenesis substantially remodels the underlying energy landscape. \nHere, we investigate to what degree systematic mutational analysis coupled with scalable bulk \nassays yield quantitative insights into how electrostatic interactions  shape the α Syn energy \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted November 3, 2025. ; https://doi.org/10.1101/2025.11.01.685997doi: bioRxiv preprint \n\n3 \n \nlandscape. By constraining the aggregation conditions to favour a limited set of microscopic \npathways, we aimed to assess how these sequence changes reshape the energy landscape \nof αSyn under in vitro  conditions reflecting specific biological contexts . We observed that \nmutational effects fall broadly into two categories: those that affect the energy barriers on a \nWT-like landscape, and those that re- shape the energy landscape and create minima not \naccessible by WT under the same solution conditions . Our work  outlines a scalable, \nquantitative framework that increases the informational output of the mutational studies of \nαSyn using conventional assays. The presented approach can be extended by incorporating \nadditional mutations and assays, with the aim of providing deeper insights into the energy \nlandscape of αSyn and its link to physiological and pathological functions. \nResults \nDesign, overview and naming convention of αSyn mutants used in this study \nαSyn contains 15 lysines distributed across the sequence, grouped in clusters containing 1 to \n3 lysine residues within imperfect repeats (Figure 2a). We substituted lysines with glutamines \nas an amino-acid with similar size to lysine with physiochemical properties mimicking lysine \nacetylation as a physiologically relevant posttranslational modification. (48,60–62) We \ngenerated 62 variants including all 15 single-point K-to-Q mutants, six mutant cluster variants \n(two 3-point mutants: KQ1 and KQ7; four 2- point mutants: KQ2, KQ3, KQ4, and KQ5), and \ntheir combinations ( Figure 2a , Supplementary Table 1).  We primarily focused on t he KQ \ncluster variants (i.e., numbered according to their position in the sequence from N to C \nterminus, with KQ6 designating the single point mutation K80Q for completeness) as reporters \nof the sequence dependency of the lysine mutation. The rest of the variants were used to \nsupport and extend the observations made with the KQ cluster  variants, e.g., discern \ncontributions of individual mutations within each KQ cluster, or study epistatic effects between \nthe clusters in aggregation assays (Figure 1b, Supplementary Figure 1).  \n \nFigure 1. Overview of the α Syn mutants and aggregation pathways involved in this study. (a) Schematic \noverview of the αSyn sequence with highlighted positions of lysine residues mutated to glutamines in this study. \nThe αSyn variants containing 2-3 mutations of adjacent lysines are referred to as KQ1-7 cluster mutants based on \ntheir position in the sequence from N to C terminus (e.g., KQ1 = K6Q + K10Q + K12Q, KQ2 = K21Q + K23Q, etc., \nSupplementary Table 1). b) Schematic view of the distinct aggregation pathways and aggregate properties \nof αSyn probed in this study.  \n  \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted November 3, 2025. ; https://doi.org/10.1101/2025.11.01.685997doi: bioRxiv preprint \n\n4 \n \nDissecting global and sequence- specific effects of KQ mutations in α-Synuclein \naggregation \nTo gain a global understanding of how positive charge removal influences αSyn aggregation \n(Figure 2a), we analyzed the aggregation kinetics across a range of solution conditions, \nincluding varying monomer concentration, pH, and salt using a Thioflavin-T (ThT) fluorescence \nassay (Supplementary Figures 2-4, Example data for WT Figure 2b). Each measurement was \nfitted with a logistic function (Equation M1, materials and methods) to obtain aggregation half-\ntimes, yielding a comprehensive dataset of 268 measurements (Supplementary file 1).(63) We \nparameterized all datapoints by variables describing both extrinsic factors (buffer  type, salt \ntype, ionic strength, pH) and intrinsic sequence features (number of mutations, variant, charge, \nEquation M2) and used them to model the aggregation half-time by linear regression \n(Figure 2c). \nWe tested model s based on three key assumptions . (i) Under a simple nucleation–\npolymerization mechanism, the log- transformed aggregation half -time ( ln(th)) scale linearly \nwith the logarithm of the initial monomer concentration ( ln(c)) and with the square of the net \ncharge, consistent with interactions between two charged monomers. (ii) The majority of the \nobserved variance in aggregation kinetics can be accounted for by global physicochemical \ndescriptors. (iii) Any residual variance can be explained by a combination of variant -specific \neffects on aggregation, other sources not accounted for by global descriptors, and \nexperimental noise. \nTo this end, we modeled ln(th) as a function of initial monomer concentration, nominal charge, \nionic strength, and variant -specific intercepts (Equation 1, Figure 2c). The global terms  \naccounting for electrostatics and protein concentration alone (Equation 2) explained ~30% of \nthe total variance, which increased to 56% when the number of mutations  was included \n(Equation 3, Supplementary Figure 5a). Using variant-specific intercepts instead of number of \nmutations improved the fit ( adjusted R² = 0. 71, Equation 1, Figure 2d), consistent with \nassumptions (ii) and (iii): global scaling parameters capture most of the variance, but \nsequence-dependent effects still contribute significantly beyond noise.  S tatistical tests \nsupported linear over quadratic charge scaling, but since the difference was only marginal, we \nrefrain from drawing further mechanistic conclusions (Supplementary Tables 2 and 3). In the \nfinal model (Equation 1), we therefore implemented a linear scaling of charge (Q) and square \nroot of ionic strength (√I) as parameters that modulate electrostatic interactions (Figure 2c).  \nEquation 1  ln(𝑡𝑡ℎ) =  𝛽𝛽0 + 𝛽𝛽𝑐𝑐ln(𝑐𝑐) + 𝛽𝛽𝑄𝑄𝑄𝑄+ 𝛽𝛽𝐼𝐼√𝐼𝐼+ ∑ 𝛽𝛽𝑣𝑣𝑣𝑣𝑣𝑣𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑡𝑡+  𝜀𝜀 \nE\nquation 2  ln(𝑡𝑡ℎ) =  𝛽𝛽0 + 𝛽𝛽𝑐𝑐ln(𝑐𝑐) + 𝛽𝛽𝑄𝑄𝑄𝑄+ 𝛽𝛽𝐼𝐼√𝐼𝐼+ 𝜀𝜀 \nEquation 3  ln(𝑡𝑡ℎ) =  𝛽𝛽0 + 𝛽𝛽𝑐𝑐ln(𝑐𝑐) + 𝛽𝛽𝑄𝑄𝑄𝑄+ 𝛽𝛽𝐼𝐼√𝐼𝐼+ 𝛽𝛽𝑚𝑚𝑚𝑚𝑚𝑚(#𝑚𝑚𝑚𝑚𝑡𝑡) +  𝜀𝜀 \nW\nhere β0 is the model intercept (baseline), ln(c) is logarithm of initial monomer concentration, \nQ is theoretical net charge, I is ionic strength, #mut is the number of mutations, Variant is a \nbinary encoding for each variant, βc, βQ, and βI are coefficients for the global parameters, βvar \nare the variant-specific coefficients (Figure 2f), and ε is the residual error between fitted and \nexperimental data representing unexplained variance (Supplementary Figure 5e). \nThe model compr ised the global physical factors known to influence α Syn aggregation and \ndescribed it well. Specifically, the model captured the increasing aggregation rate with \ndecreasing net charge (βQ = -0.4) and a strong aggregation-enhancing effect of ionic strength \nvia charge screening ( β√I = –1.7) (Figure 2e, Supplementary Table 2). The global monomer \nscaling coefficient ( βc = - 0.5) is consistent with weakly monomer -dependent secondary \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted November 3, 2025. ; https://doi.org/10.1101/2025.11.01.685997doi: bioRxiv preprint \n\n5 \n \nprocesses (e.g., fragmentation, or saturated secondary nucleation) as dominant aggregation \nmechanisms under these conditions, as previously described for WT αSyn (43,64–68). \nWe modelled the s equence-dependent effects through variant -specific coefficients that shift \nthe global baseline by fixed intercept (Figure 2f). This  approach assumes that all variants \nshare the same scaling with respect to global parameters as WT—a simplification that avoids \noverfitting but may not strictly hold. We observed that variants containing mutations in multiple \n(three) lysine clusters systematically accelerated aggregation beyond what could be explained \nby global effects or by the additive contributions of single- cluster mutations. Arguably, this \neffect can be caused by (i) a discrepancy between the theoretically calculated and the actual \nnet charge (69), (ii) distinct aggregation mechanism s of triple KQ cluster variants, such as \naltered nucleation pathways or enhanced secondary processes; or (iii) non-linear contributions \nand epistatic effects of the individual cluster mutations. We cannot distinguish between these \nscenarios, since these variants have been tested in only one type of assay. We confirmed the \nrobustness of the model  coefficients by re-fitting it to a dataset excluding the triple-cluster \nvariants to avoid potential bias (Supplementary Table 3). \nThe effects of the single- cluster mutations were generally mild, with KQ 6 significantly \naccelerating aggregation (Figure 2f). Although not statistically significant, the coefficients of  \nthe remaining KQ clusters were highly consistent across different tested models, whether fitted \nto the full dataset or with the triple-cluster variants excluded (Supplementary Tables 2 and 3). \nOn average, variants with mutations at the N -terminus (KQ1–KQ3) had inhibitory effects \n(slower aggregation), those with mutations of lysines found near or within the fibril cores (KQ4 \nand KQ6) had enhancing effects (faster aggregation), while the remaining variants (KQ5 and \nKQ7) exhibited behavior close to the wild- type (Figure 2f, Supplementary Table 2). Variants \nKQ4 and KQ6 displayed the largest residual errors  (Supplementary Figures 5e) , mostly in \ndatasets where concentration and type of salts were varied (Supplementary Figures 2d, 4e). \nThis indicates their altered sensitivity to solution conditions  and potential deviation from the \nWT-like global aggregation behavior captured by the model.  \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted November 3, 2025. ; https://doi.org/10.1101/2025.11.01.685997doi: bioRxiv preprint \n\n6 \n \n \nFigure 2: Effect of KQ mutations on de novo  aggregation of α -Synuclein. (a) Dominant microscopic \naggregation pathways  probed in the unseeded ThT assays. (b) Example of ThT aggregation kinetics of  \nαSynWT as a function of (left) increasing salt concentration, (middle) pH, and (right) initial monomer concentration. \nRaw data for all mutant variants are shown in Supplementary Figures 2-4. (c) Weighted-least squares linear \nregression model of aggregation half-times. The selected parameters that were varied across the assays were \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted November 3, 2025. ; https://doi.org/10.1101/2025.11.01.685997doi: bioRxiv preprint \n\n7 \n \nused to model log-transformed aggregation half-times ln(th) as a combination of global and variant specific effects. \nThe global terms involved charge (Q), ionic strength (√I), and initial monomer concentration (ln (c)) multiplied by \nglobal slopes shared across the variants ( β), and global intercept ( β0) representing the global baseline. The  \nsequence dependent term consisted of variant-specific coefficients (βvar) and binary variant encodings. ε  denotes \nresidual variance between fit and experimental data. (d) Model  evaluation. Comparison of observed log-\ntransformed half-times and their prediction by the model. The proportion of explained variance (goodness -of-fit, \nadjusted R2) is shown. The red line corresponds to 1:1 line, with residuals showing difference between observed \nand predicted values. (e) Isolated global effects from the model . The model prediction (red line ) is overlayed \nwith the observed data (black circles) normalized for all but one model parameter (e.g., for top graph depicting \ninitial monomer scaling: norm_ln(th) = ln(th) − (βQ*Q + βI*√I + βvar*Variant)). (f) Variant-specific intercepts for single \n(top) and triple (bottom) cluster variants. Positive values indicate that the variants are on average slower compared \nto the global model predictions, whereas variants with negative intercepts are faster.  Error bars indicate ± 95% \nconfidence intervals. Asterisks denote levels of statistical significance: * p < 0.05; ** p < 0.01; *** p < 0.001. \nMonomer compaction is a poor predictor of half-time scaling.  \nAlthough simple, the model captured both the global and sequence -dependent effects \nreasonably well given the noise level of the raw data (noise ceiling for triplicate means based \non Fisher r ≈ 0.85; Supplementary Figure 2-4, Figure 2). Consistently, we find that the effects \nof the charge modulations  persist even at 200 mM salt, where long- range electrostatic \ninteractions are screened (Debye length < 1 nm), suggesting that modified local interactions \nare responsible for the altered aggregation propensity . To investigate the effect of mutations \non the conformational space of αSyn monomers, we complemented our experimental analysis \nwith coarse-grained molecular dynamics simulations of WT and KQ cluster variants using the \nCALVADOS2 force field (Supplementary Figure 6 ).(70) Across individual variants, we  \nobserved negative correlations between aggregation half -times and single- chain \ncompactness, measured by the radius of gyration (Rg) (Supplementary Figure 6d). However, \nneither Rg nor other ensemble- averaged descriptors captured the global variation in \nexperimental aggregation kinetics of all variants  (R2=0.27, Supplementary Figure 6d). In  \nparticular, the half-time scaling at physiological salt concentration remained unexplained, as \nall variants sampled predominantly extended conformations yet exhibited widely differing \naggregation kinetics (half -times of ~30 –130 h, Supplementary Figure 6 d). Together, these \nfindings suggest that while single- chain compaction is correlated with  aggregation kinetics, \nthe sequence dependence of the KQ mutants arises from altered localized interactions rather \nthan monomer conformational properties. This finding refl ects the fact that the  free energy \nlandscape of an isolated monomer is distinct from that of a monomer in contact with another \nmonomer or a fibril end, because of the additional electrostatic and other interactions provided \nby the intermolecular contacts.  \nVariants with KQ mutations near the fibril core form stable fibrils that are not efficiently \nelongated by wild type αSyn. \nIn order to gain deeper mechanistic understanding in the role of changes in global and local \nelectrostatic interactions, we studied the effects of KQ mutations on αSyn fibril polymorphism, \nthermodynamic stability and seeding properties. We prepared fibrils from all KQ cluster \nvariants under the conditions at which WT structures of polymorphs 2a and 2b have been \nsolved previously using cryo-EM microscopy (Table 4). (71) \nFirst, we us ed urea depolymerization and microfluidic transient incomplete separation to \nmeasure the thermodynamic stability of the fibrils (Figure 3a and b, Supplementary Figure 7) \n(72). We used Bayesian inference with Markov Chain Monte Carlo (MCMC) sampling to fit the \ndata and to verify  that no parameter correlation between Gibbs free energies ( ΔG) and \ndenaturant m -values obscured our conclusions  (Supplementary Figure 7 ). (73,74) Fibrils \nformed by variants carrying mutations outside or at the periphery of the resolved fibril cores \n(KQ1, KQ2, KQ3 and KQ7) (75) exhibited stability comparable to or lower than that of the WT. \nIn contrast, variants with mutations of  residues resolved in α Syn fibril core structures (KQ4, \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted November 3, 2025. ; https://doi.org/10.1101/2025.11.01.685997doi: bioRxiv preprint \n\n8 \n \nKQ5, and KQ6) formed fibrils with higher stability than the WT  under the tested solution \nconditions (Figure 3b, Supplementary Table 4). \n \nFigure 3. Analysis of fibrils formed by KQ cluster variants. a) Urea depolymerization of fibrils formed by KQ \ncluster variants. The monomer concentration in equilibrium with fibrils at increasing concentrations of urea was \ndetermined from the area under the Gaussian peak (y-axis) using transient incomplete separation in laminar flow. \n(72) The data were fitted to the isodesmic depolymerization model using Markov-chain sampling of solutions as \ndescribed in our previous study. (74) Hundred randomly selected solutions from a total of 2000 sampled solutions \nare shown as curves with the best solution highlighted in bold. b) Stability of WT and KQ fibrils  obtained from \nfitting the urea depolymerization experiments. The mean and median values from  three independent \nmeasurements are depicted by open squares and line, respectively. The SE and SD (n = 3) are visualized as box \nand whiskers, respectively (Supplementary Table 4). The individual depolymerization curves and correlation plots \nbetween ΔG and the m-values are shown in Supplementary Figure 7. c) Elongation kinetics of KQ variant fibrils \nmonitored by ThT fluorescence and quartz crystal microbalance ( QCM). (Top and middle) QCM experiments of \nKQ fibril growth. Immobilized variant  fibrils were first allowed to elongate by variant  monomers (colored boxes), \nfollowed by a washing step and elongation by WT monomers (grey boxes). The elongation rates of the variants  \nand WT were quantified from the slopes (highlighted in red) of the changes of third harmonic overtone frequency \n(black lines) during the first and second injections, respectively.  (Bottom) Comparison of aggregation kinetics of \n80 μM WT (black) or KQ3 (green), KQ4 (yellow), or KQ6 (orange) monomers in the presence of 2.5 μM respective \nvariant seeds monitored by ThT fluorescence assay. Aggregation of all variants together with analysis of monomer \nconversions to fibrils are shown in Supplementary Figure 8. d) Relative growth rate ( ΔΔGǂ) of mutant variants \nand WT monomers on fibrils of KQ cluster variants . Negative ΔΔGǂ values indicate that KQ seeds are elongated \nfaster by their respective monomers compared to the WT monomer. The black and red symbols represent values \nderived from ThT and QCM experiments, respectively. The mean, median, SE, and SD of  three independent \nmeasurements are depicted by open squares , line, box and whiskers, respectively.  The open symbol in KQ6 \ncorresponds to dataset where elongation of WT was not observed.  e) Correlation between stability (ΔG) and \nrelative growth rate (ΔΔGǂ) of KQ fibrils. Datapoints and error bars correspond to the mean ± SE  from b and d. \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted November 3, 2025. ; https://doi.org/10.1101/2025.11.01.685997doi: bioRxiv preprint \n\n9 \n \nf) Representative AFM images of elongated fibrils. The scale bar corresponds to 1 μm. Results from fibril height \nanalysis together with all values related to this figure are provided in Supplementary Table 4.  \nWe next quantified the changes to the free energy barriers of elongation (ΔΔGǂ)  from relative \ngrowth rates fibrils mutant and WT monomers on the fibrils formed by KQ cluster variants \n(Equation M3, Figure 3c and d) .(59) This strategy avoids complications that could stem from \nvariations in the number of growing fibril ends  between experiments (see method section for \ndetails). We observed little difference between elongation of KQ1, 2, 3, and 7 fibrils by WT and \nthe respective mutant  monomers. In contrast,  variants KQ4 , KQ5  and KQ6 which carry \nmutations near the known fibril cores showed distinct behaviour. WT monomer was inefficient \nin elongating their seeds, especially those formed by KQ4 and KQ6, even though elongation \nby their respective KQ monomers was fast.  \nTo understand the structural bases for such differences, we characterized the morphology of \nthe elongated fibrils in terms of their height and apparent helical pitch length  using atomic \nforce microscopy (AFM) (Figure 3f, Supplementary Figure 9). The WT fibrils were formed by \ntwo distinct populations characterized by shorter (ca. 160 nm, pink box in Figure 4f ) and \nslightly longer (ca. 220 nm, green in Figure 4f) apparent pitch lengths with heights ranging \nfrom 6 to 8 nm. The KQ7 variant formed WT-like fibrils, whereas KQ5 fibrils had regular surface \npatterns with short helical pitch (~100 nm). The rest of the variant fibrils appeared mostly flat \nor exhibited irregular patterns or short frequencies along their main axis (apparent pitch < 100 \nnm). The lack of twist in KQ4 and KQ6 fibrils was further confirmed by TEM, which revealed a \ndominant population of flat fibrils often forming stacks or clusters on the grid (Supplementary \nFigure 10). Fibrils formed by KQ1, KQ2, KQ3, and KQ5 variants exhibited lower heights (3–6 \nnm) compared to WT fibrils, suggesting either their tighter packing or that they are formed by \na single protofilament (Supplementary Table 4). Interestingly, the denaturant m-values scaling \nwas consistent with the expected dependence of exposed surface area in a rolled “Clarkson’s \nscroll” geometry ( see Materials and Methods for details, Supplementary Figure 11). This \nsuggests that the depolymerization m-values correlate with solvent exposure analogously to \nprotein folding. (76) \nAltogether, we observed that the effects of the K-to-Q mutations on fibril stability are governed \nmore strongly by their position within the sequence than their total number. AFM data of the \nfibrils formed by the different KQ cluster  variants support the hypothesis that the fibril twists \nare modulated by interactions between N- and C-termini within the fibrils . (77) Our findings \nthat near-core lysines (K43, K45, K58, K60, K80 and K96) modulate the fibril structure and \nstability is consistent with high-resolution structural characterization of αSyn WT fibrils, where \nthese residues are found to stabilize protofilament interfaces by forming salt bridges or binding \npolyanionic molecules. (71,77–85). Hence, their perturbation by K-to-Q mutations could lead \nto formation of different fibril polymorphs. The  correlation between variant fibril stability and \ntheir ability to be efficiently elongated by WT monomer ( Figure 3e) suggests that mutations \nnear the fibril core alter the conformational landscape of α Syn more dramatically, creating \nstabilizing interactions and structural features that are not easily amenable to WT elongation \nunder identical solution conditions.  Although the absence of a well -defined helical pitch \nprecludes high- resolution structural analysis of these potentially distinct polymorphs , the ir \nincompatibility with  WT monomer elongation likely arises from the energetic penalty \nassociated with incorporating lysine residues into fibril cores where glutamines are present. \n  \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted November 3, 2025. ; https://doi.org/10.1101/2025.11.01.685997doi: bioRxiv preprint \n\n10 \n \nSeeded growth of WT αSyn fibrils is governed strongly by sequence-specific mutational \neffects.  \nWe established that the effect of KQ mutations on the αSyn free energy landscape is sequence \nspecific, yet it remained unclear whether the different ial seeding competence is governed \nmore by the intrinsic structural properties of the fibrils, or the monomers. We performed seeded \naggregation assays to assess how the mutations influence fibril elongation across different \nstructural polymorphs (Figure 4a and b). We used three types of seeds  including full-length \nWT fibrils assembled under two distinct solution conditions that yield different sets of \npolymorphs (WT–Fm and WT –Ri) (71,74,86–88), and fibrils formed by a C -terminally \ntruncated variant lacking six negative charges per monomer unit from the fuzzy coat (αSyn1-\n125, Table 4). We analyzed the elongation of WT–Fm seeds with 41 variants carrying one to \nsix mutations to discern the contribution of the monomer (Figure 4c, Supplementary Figures \n12-15) and further examined elongation of single KQ cluster variants (n=7) on the other two \nseed types to assess the influence of fibril structure  (Figure 4b, Supplementary Figure 16). \nRelative elongation rates were then used to derive the changes of the energy barriers of \nelongation (ΔΔGǂ), as described above (Figure 4c).  \nElongation of the WT - Fm seeds by mutant monomers was slower compared to the WT \n(Figure 4c), except for a few repeats with KQ4. The average increase in energy barrier of \nelongation was 4.0 ± 1.2 kJ.mol-1, 3.8 ± 3.9 kJ.mol-1, and 5.4  ± 2.8 kJ.mol-1 for single-point \nmutations, KQ cluster variants (1-3 mutations), and double KQ cluster variants (3-6 mutations), \nrespectively (Figure 4c, Supplementary Tables 5 and 6). Linear regression analysis showed \nthat the monomer net charge (i.e., number of  mutations) accounted for only ~ 35% of the \nvariance in ΔΔG ‡ values, indicating a strong sequence- specific component to elongation  \n(Equation 4, Supplementary figure 17).  \nEquation 4  𝛥𝛥𝛥𝛥𝐺𝐺ǂ =  𝛽𝛽0 + 𝛽𝛽1(#𝑚𝑚𝑚𝑚𝑡𝑡) + 𝜀𝜀 \nE\nquation 5  𝛥𝛥𝛥𝛥𝐺𝐺ǂ =  𝛽𝛽0 + ∑ 𝛽𝛽𝑗𝑗𝐾𝐾𝑄𝑄𝑗𝑗\n7\n𝑗𝑗=1 + 𝜀𝜀 \nW\nhere β0 is the global intercept (expected to be zero since ΔΔG ǂ values are defined relative \nto WT), β1 is the global slope coefficient for the number of mutations (#mut), βj are the variant-\nspecific coefficients for mutations in cluster j (j=1-7), KQj is the binary encoding for mutations \nin cluster j, and ε is the residual error between fitted and experimental data  representing \nunexplained variance (Supplementary figure 17).  \nI\nnstead, we used cluster-specific coefficients representing the average contribution from each \nKQ cluster (i.e., either single mutation or whole cluster mutated) (Equation 5, adjusted R² = \n0.69, Figure 4d). Among these, clusters KQ1, KQ2, KQ3, and KQ5 showed the most \npronounced inhibitory effects on elongation of WT seeds , whereas the other clusters had \nweaker or statistically insignificant contributions (p > 0.05, Figure 4e). The model (Equation 5) \ncaptured well the main trends observed for the ΔΔG‡ values. First, the effects of single-point \nmutations within individual clusters were non-additive, as shown by comparison of their ΔΔGǂ \nvalues with those obtained when the entire KQ cluster was mutated (Figure 4c). Second, the \ncombined effect of mutating two distinct clusters was well approximated by the mean of their \nindividual effects, indicating near-additive behaviour between different clusters. \nThe strong effects of K to Q mutations outside of the fibril core (e.g., K6Q, K102Q, Figure 4c) \nunderscore the important role of the electrostatic interactions of the fuzzy coat in the \nelongation kinetics of αSyn fibrils. (56,89,90) In comparison, the variants with mutations near \ncore (KQ4 and KQ6), showed unusually large variation in ΔΔ Gǂ values. (Figure 4c). Further \nanalysis using WT-Fm seeds of different maturation ages revealed that their elongation rates \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted November 3, 2025. ; https://doi.org/10.1101/2025.11.01.685997doi: bioRxiv preprint \n\n11 \n \nwere highly dependent on seed age (Supplementary Figure 18). Given that  polymorph \ncomposition evolves during fibril maturation (74) , the pronounced variance in  ΔΔG‡ values \nlikely arises  from fibril polymorph heterogeneity combined with variant -specific polymorph \nselectivity, consistent with the distinct aggregation behaviours of KQ4 and KQ6 observed in \nother assays. This indicates that K43+K45 and K80 are critical for recognizing specific α Syn \nfibril folds in line with other reports. (48,54)  \nThese conclusions are consistent with the AFM analysis  of the fibrils elongated by different \nmonomers (Figure 4f). We observed WT-like fingerprints for monomer variants with mutations \nin the fuzzy coat (KQ1, KQ2, KQ3, and KQ7), whereas elongation by variants with mutations \nin the core resulted in only subpopulations of fibrils with short apparent pitch (KQ5) or mostly \nflat fibrils (KQ4, KQ6) (Figure 4f, Supplementary Figure 9). \nComparable results of the KQ cluster variants were obtained when elongation was studied \nusing WT–Ri and α Syn1–125 fibrils as seeds. The ΔΔG‡ values did not show significantly \ndifferent trend across all three polymorphs, but the different seed types varied in the saturation \nbehaviour of the elongation rates as a function of monomer concentration, perhaps reflecting \ntheir distinct surface properties (Supplementary Figure 16, Supplementary Table 5). (91) N-\nterminal variants (KQ1– 3) show slower kinetics and moderate -to-high saturation elongation \nconstants (Ke) across all three polymorphs, consistent with weakened initial monomer -fibril \ninteractions. Variants with mutations near the core (KQ4–6) display lower K e values and \npolymorph-dependent kinetics, possibly suggesting rapid monomer attachment with  rate-\nlimiting rearrangement step or increased propensity of monomers  to attach in an out -of-\nregister conformation. (92) The KQ7 variant exhibits WT-like kinetics and saturation. \nTogether, our results show that templated aggregation is influenced by both the intrinsic \nproperties of the fibril and the sequence features of the monomer, with the latter playing a \ndominant role in defining elongation kinetics.  The sequence-dependent effects of K-to-Q \nmutations highlight critical roles of lysines in  shaping the complex polymorphic landscape of \nαSyn. However, it remains uncertain whether the observed effects genuinely reflect the \nremoval of interactions facilitated by the lysines in the wild-type protein, or if they instead result \nfrom newly enabled interactions specific to glutamine residues. (56) \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted November 3, 2025. ; https://doi.org/10.1101/2025.11.01.685997doi: bioRxiv preprint \n\n12 \n \n \nFigure 4: Effects of KQ mutations on αSyn WT fibril elongation. (a) Schematic overview of elongation as \nthe dominant aggregation process. Either WT (grey) or mutant (gold) monomers elongate pre-formed WT fibrils \nwith different efficiencies. (b) Example initial rate analysis from aggregation kinetics of WT (black) and KQ4 \n(yellow) monomers seeded by different WT ( Fm - top, Ri - middle), or C-terminally truncated (aS125 - bottom) \nfibrils. The initial rates were obtained from linear fitting of the raw ThT data in the 0 – 2.5 h range. Fit to the equation \ndescribing saturating elongation (or linear function in case of the WT monomer on Fm) are shown as dotted lines. \nThe raw data for all KQ variants elongation of  Ri and aS125 are shown in Supplementary Figure 16.  Fitting \nparameters from the initial rate analysis are provided in Supplementary Table 5. (c) Mutational effects on the \nenergy barrier of αSyn elongation. (top) Schematic profile of αSyn sequence showing frequency of solved fibril \nstructures where given residue was resolved (grey). Position of lysine clusters is highlighted by coloured boxes. \n(middle) ΔΔGǂ of single-point mutant and KQ cluster variants, (bottom) double cluster variants. Negative values \n(below dashed line) correspond to mutant monomer elongating WT fibrils faster than WT. The mean and median \nvalues are depicted by open squares and lines, respectively. The boxes represent 25 to 75 percentiles of the mean; \noutlier values are marked by whiskers . Examples of raw datasets and the ΔΔG ǂ values are provided in \nSupplementary figures 12-16 and Supplementary Table s 5 and 6, respectively . (d) Weighted least square \nregression of ΔΔGǂ values using equation 5. Individual points correspond to the mean values from panel (c) with \n1:1 line depicted in red. Residuals (observed-predicted) are shown on top.  (e) Variant-specific coefficients βj \nfrom equation 5 representing the average contribution when  single mutation, or whole cluster is mutated. Values \ncorrespond to the mean +/ - standard errors. Asterisks denote levels of statistical significance: * p < 0.05; ** p < \n0.01; *** p < 0.001. (f) AFM analysis of products from elongation of WT-Fm seeds. The apparent pitch lengths \n(i.e., 360 ° turn) and heights were extracted from the manually selected fibril profiles using an automated python \nscript. (59,72) Kernel density estimation was applied to the morphological fingerprint (i.e., height vs pitch plot) of \nWT seeds elongated by WT monomer for better visualization. Each  red dot corresponds to the profile of a single \nfibril. The height profile is color-coded according to the bar next to the WT image. White scale bars correspond to \n1 μm. The sequence profile in the middle corresponds to the one in (c). \n  \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted November 3, 2025. ; https://doi.org/10.1101/2025.11.01.685997doi: bioRxiv preprint \n\n13 \n \nSecondary Nucleation and Polymorph Specificity of KQ Variants in Seed Amplification \nAssay at low pH. \nFinally, we investigated whether the same principles govern α Syn aggregation under \nconditions where secondary nucleation  plays a dominant role  ( Figure 5 a). We used the \nsolution conditions established in our recently developed seed amplification assay  (SAA) in \nwhich aggregation is dominated by secondary nucleation (i.e., low seed concentration, \nquiescent conditions, pH 3, 250 mM Na2SO4, Table 5) (93) and followed aggregation of WT or \nmutant monomers in the presence 1 nM WT-Fm or WT-Ri seeds (Figure 5b, Supplementary \nFigures 19 and 20). To dissect the effects of fibril structure from monomer properties, we \nmodelled the log-transformed aggregation half-times of 26 αSyn single and double cluster KQ \nvariants and WT (Figure 5c, Supplementary Table 7) by linear regression using Equation 6. \nAnalogously to relative  growth rates at neutral pH, the mutational effects on ln(t h) were \nsequence-dependent and could be modelled reasonably well  by a linear combination of \ncluster-specific coefficients (Equation 6, adj R2=0.66, Figure 5d and e). Interestingly, the trend \nof variant specific contributions was  similar compared to the relative growth rates at neutral \npH; mutations in N- terminal (KQ1–KQ3) and KQ5  clusters prolonged the aggregation half -\ntime, while KQ4 and KQ6 had effects similar to WT, and KQ7 exhibited moderately accelerated \naggregation. This result suggests that secondary nucleation under low-pH and elongation at \nneutral-pH are driven by interactions between similar sequence regions. \nEquation 6  ln(𝑡𝑡ℎ) =  𝛽𝛽0 + 𝛽𝛽1𝐹𝐹𝑉𝑉𝐹𝐹𝑉𝑉 𝑉𝑉 𝐹𝐹+ ∑ 𝛽𝛽𝑗𝑗𝐾𝐾𝑄𝑄𝑗𝑗\n7\n𝑗𝑗=1 + 𝜀𝜀 \nW\nhere Fibril is the binary encoding for fibril type (i.e., WT-Fm or WT-Ri).  \nIn contrast to the analysis of the relative growth rates at neutral pH, including the fibril type \ninto the model significantly improved the quality of the fit (Equation 6). The positive coefficient \nfor Ri fibrils ( βRi = 0.4) compared to the Fm baseline reflects the overall slower kinetics \nobserved for this polymorph. Although this difference may arise from distinct surface properties \nof the two fibril types, we cannot exclude the possibility of a minor systematic deviation due to \nconcentration differences introduced during fibril preparation and handling (estimated at \n4 – 10%).  \nNotably, amplification of the seeds by a handful of variants was specific to the polymorph type. \nSpecifically, variants KQ1 and KQ5 amplified Fm, but not Ri fibrils, whereas the opposite trend \nwas observed for the variant KQ2 (Figure 5c). Interestingly, KQ2 is a double-point mutant that \nincludes K23Q, a mutation commonly used as a substrate in the state-of-the-art SAA \nprotocols. (94,95). To investigate whether the polymorph specificity is more general  \nphenomenon, we used  KQ cluster variants to  amplify seeds from brain homogenates of \npatients with PD and MSA  ( Figure 5f ). The addition of minute amounts of brain -derived \nsamples (105- fold buffer dilution) decreased the lag time of aggregation compared to buffer-\nonly control in most cases (Supplementary Figure 21). Importantly, variants KQ1, KQ2, and to \nlesser degree KQ4 and KQ5 showed significantly higher sensitivity towards PD  samples \ncompared to those from MSA patients (p<0.05, Figure 5f). Thi s finding corroborates our results \nwith in vitro generated fibrils and highlights the potential of using selected variants in seed \namplification assays to distinguish between disease- specific polymorphs from patient \nsamples. \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted November 3, 2025. ; https://doi.org/10.1101/2025.11.01.685997doi: bioRxiv preprint \n\n14 \n \n \nFigure 5. Effect of KQ mutations on seed amplification of two WT polymorphs at low pH. (a) Schematic \noverview of dominant mechanism probed in the assay. At low pH (pH = 3), amplification of pre- formed seeds \nis dominated by the secondary nucleation (yellow) of monomers on the fibril surface. (b) ThT kinetics of WT and \nKQ cluster variants in the presence of 1 nM WT - Fm (left) or WT - Ri (middle) seeds. The seeded aggregation \nkinetics of all variants carried out in the presence of 0, 1 nM, and 1 μM of sonicated WT seeds in conditions \ndescribed in our previous study are show in Supplementary Figures 19 and 20. (93) (c) Correlation between \naggregation half-times in the presence of 1 nM Fm and Ri seeds. The values correspond to the mean half-time \nvalues obtained from fitting the raw data from experiments carried out in triplicates in two independent \nmeasurements (circles, diamonds). The grey zones correspond to the endpoints of the measurements and points \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted November 3, 2025. ; https://doi.org/10.1101/2025.11.01.685997doi: bioRxiv preprint \n\n15 \n \nwithin these zones were derived with lower confidence. The points outside of the red lines correspond to the \nvariants whose ThT signal did not plateau during the experiments and are shown for illustration. The black line has \nslope of 1 to easily visualize the specificity of the mutant monomers to different WT polymorphs. d) Weighted least \nsquare regression of log transformed half -times using equation 6. Individual points correspond to the mean \nvalues from panel (c) with 1:1 line depicted in red. Residuals ( observed-predicted) are shown on top. (e) Variant-\nspecific coefficients βj from equation 6 representing the average contribution when a whole cluster is mutated. \nValues correspond to the mean +/- standard errors. Asterisks denote levels of statistical significance: * p < 0.05; ** \np < 0.01; *** p < 0.001. (f) Seed amplification of brain homogenates from  patients with Parkinson’s (PD, \nviolet) and multiple system atrophy (MSA, green ). The amplification was carried out using 10 μM WT (left) or \nvariant (e.g., KQ1, middle) monomer in the presence of PD (violet), MSA (green), or healthy (control, grey)  brain \nhomogenate diluted 105 times into the assay buffer. The reactions carried out in quadruplicates were monitored by \nThT fluorescence. The time to reach a threshold (TTT, right) was calculated as a 10 x SD of the mean signal \nbetween first and fifth hour of each dataset (Supplementary Table 8). For datasets where TTT could not be detected \nduring the experiment, the end time of the experiment  was used instead. Statistical significance between groups \nwas assessed using two-sided Mann–Whitney U tests, with significance levels indicated by stars (* p < 0.05, ** p \n< 0.01, *** p < 0.001). \nDiscussion and conclusions \nIn this study, we carried out a comprehensive mutational analysis of α-synuclein (αSyn) to (i) \nquantify how electrostatic interactions contribute to the kinetics and thermodynamics of its \nassembly, and (ii) assess the broader applicability of this sequence-perturbative approach for \nprobing the degenerate  energy landscapes characteristic of self -associating intrinsically \ndisordered proteins (IDPs).  \nSeveral systematic mutational studies aiming to elucidate sequence determinants of α Syn \naggregation have been carried out (see, for example,  (96) for their review). However, their \nglobal mechanistic interpretation is often difficult, due to the reported aggregation half-times \nor rates stemming from experiments that have been conducted under conditions influenced \nby many  variables—such as shaking speed, the presence of beads, buffer composition, \nreaction vessel size, and, importantly, the existence of multiple aggregation pathways. These \nfactors can significantly impact the observed aggregation behaviour, making it difficult to \nisolate sequence-specific effects and carry out quantitative comparison between results of \ndifferent studies.  \nWe circumvented the considerable variability inherent to α Syn aggregation data by studying \nthe effects of mutations in (i) large number of experiments , (ii) under controlled conditions, \nwhere one or a few well-defined microscopic steps dominate the aggregation process, and (iii) \nusing WT seeds where possible to constrain the structural changes to a minimum. We recently \ndemonstrated that amyloid fibril elongation, which can be viewed as a templated -folding \nreaction, can be studied using conservative mutations and using WT seeds in all cases. (59) \nUnder well-controlled conditions, this approach provides structural insights into the transition \nstate ensemble similar to studies of protein folding.  \nHere, we investigated whether this modelling approach can be extended to different types of \nmutations and aggregation steps beyond elongation, within a more complex and polymorphic \nenergy landscape. We find that, with few exceptions, K -to-Q mutations impair or slow down \nthe aggregation of αSyn under  all conditions studied. Aggregation from homogeneous \nmonomer solutions can be modelled reasonably well ( R2=0.53) by global scaling of intrinsic \n(net charge, protein concentration) and extrinsic (ionic strength) properties. In contrast, \nsequence-specific contributions become significant under conditions where aggregation is \ngoverned by monomer–fibril interactions, and the energy landscape is dictated by the available \nfibril structure. We observe similar magnitudes  of energy changes (relative to the WT \nreference) for the fibril growth and fibril stability of the mutant variants (Figure 3), most of which \ndisplayed distinct morphological features compared to WT fibrils. Unlike Φ-value analysis, the \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted November 3, 2025. ; https://doi.org/10.1101/2025.11.01.685997doi: bioRxiv preprint \n\n16 \n \nstructure—and thus the stability —of the KQ fibrils was  not imposed by seeded growth using \nWT fibrils. Instead, we interpret the observed correlation such that the loss of electrostatic \ninteractions important for WT seed growth promotes the formation of new interaction networks, \nstabilizing distinct fibril polymorphs in some of the KQ variants.  \nIn another set of experiments, where we imposed the structure of WT fibrils by using them as \nseeds, we were able to robustly quantify the increase of the energy barrier of elongation to be \naround 4-6 kJ/mol which is in  a range comparable to disruption of a surface-exposed salt \nbridge of folded proteins. (97) We find that changes on the order of 8-10 kJ/mol (corresponding \nto approx. two-thirds of the overall height of the energy barrier  (98)) are sufficient to render \nvariants essentially incompatible for elongation of WT seeds and vice versa ( Figure 3Figure \n4). The changes to the energy barrier are higher compared to stability perturbations predicted \nfor single point mutations by FoldX on 47 representative WT polymorph structures (mean ΔG0 \n= 1.6 ± 0.9 kJ/mol ). (Supplementary Figure 22 , Supplementary Table 9) Both experimental \nand in silico data exhibit substantial variability, consistent with the pronounced polymorphism \nof αSyn fibrils, which hinders determining whether contact changes arise during formation of \nthe transition state or exclusively within the fibrillar state. \nTo gain further insights into how individual KQ cluster mutations  shape the global αSyn free \nenergy landscape, w e grouped them based on their relative effects (compared to WT)  \nobserved in all our assays ( Figure 6a). The results reveal that mutations found in the fibril \ncores (Figure 6b),  K43Q+K45Q (KQ4) and K80Q (K Q6), have the largest and most distinct \neffects across all assays, specifically in  seeding, cross -seeding, and fibril stability. These \nmutations are examples of perturbations that significantly modify the free energy landscape of \nthe WT sequence, creating pathways and energy minima not accessible to the WT under the \nsame experimental conditions  ( Figure 6c) . These observations are consistent with other \nstudies showing that acetylation of lysines K43 and K80, or modifications of adjacent regions, \nsignificantly alter aggregation and seeding, identifying them as key modulators of αSyn \naggregation. (48,54,58,99). In contrast, mutations of lysines found outside of the fibril cores  \n(KQ1-3, KQ7, Figure 6b) negatively affected energy barriers whilst overall maintaining a WT-\nlike free energy landscape minima (Figure 6c).  \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted November 3, 2025. ; https://doi.org/10.1101/2025.11.01.685997doi: bioRxiv preprint \n\n17 \n \n \nFigure 6. (a) Hierarchical clustering of mutations based on the magnitudes of mutational effects. Relative \neffects of mutations relative to WT across different assays were log2 transformed to have comparable amplitudes \n(see materials and methods for details) . The assay data include differences of energy barriers between WT and \nmutant on respective KQ seeds (ddG KQ , Figure 3d), fibril stability (Figure 3b), cluster specific coefficients from \nwls regression of elongation on WT-Fm (ddG WT, Figure 4e), cluster specific coefficients from wls regression of \nhalf-times from unseeded experiments (De novo, Figure 2f), and cluster specific coefficients from wls regression \nof half-times from assays at low pH  and low seed concentrations  (2nd Nucleation, Figure 5e). (b) Sequence and \nstructural context of the mutational effects. The mutated lysine clusters are depicted in simplified color -coding \nscheme according to their clustering in (a). The graph above depicts frequency of the lysine residues resolved in \navailable αSyn fibril structures. (c) Schematic illustration of mutational effects on α Syn energy landscape.  \n(Top) WT energy landscape showing high barrier of de novo aggregation leading to multiple co-existing fibril \npolymorphs. Addition of WT seeds (right) leads to accelerated aggregation. (Middle) WT-like energy landscape of \nvariants with KQ mutations of residues found in fuzzy coats of αSyn fibrils. These mutations slow-down aggregation \n(higher barrier) leading to polymorphs that are efficiently elongated by WT monomers. Conversely, mutant \nmonomers can elongate WT fibrils seeds with generally slower kinetics. (Bottom) Altered energy landscape with \nKQ mutations of residues found near cores of α Syn fibrils. These mutations aggregate with kinetics similar to WT  \nleading to polymorphs that are elongated by WT monomers with low efficiency. Conversely, mutant monomers are \ninefficient in elongating WT seeds, indicating selectivity to specific polymorphs.  \nThe work presented here is an attempt to unify commonly used aggregation assays to assess \nthe impact of sequence perturbations in a formalized and systematic manner. It highlights \nmutational studies as a tool to probe the αSyn energy landscape. Our results demonstrate \nthat it can yield quantitative information, provided it is carried out under well controlled \nconditions and complemented by structural, or morphological analysis to ensure meaningful  \nand interpretable results. The clustering in Figure 6 provides a compelling framework for \nunderstanding how specific mutations, in this case those that alter electrostatic interactions, \nmodulate the aggregation landscape. However, expanding the mutational space will be \nessential for further validation and generalizing the observations made here. In this study, we \nselected mutations with the same chemistry to demonstrate the feasibility  and scalability of \nsuch an approach. The scaled-down purification protocol developed here allows to obtain ca \n30 αSyn variants  within 10 days in sufficient amount (1 -3 milligrams) and purity ( >95%) to \nperform all assays presented here. Extending the dataset by including (i) different mutations \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted November 3, 2025. ; https://doi.org/10.1101/2025.11.01.685997doi: bioRxiv preprint \n\n18 \n \nof the same residues (e.g., alanine, glutamate), (ii) mutations of negatively charged residues \nwithin the imperfect repeats (e.g. E -to-Q mutations), (iii) and mutations targeting different \nphysio-chemical properties (e.g., aromaticity, hydrophobicity) within specific sequence regions \nwill provide more complex and comprehensive insights. Moreover, the framework could be \nextended by other assays e.g. formation of oligomers, and, importantly, cellular assays that \nwould help bridge the observation of mutational effects in vitro to more physiologically relevant \nenvironments. Together, these efforts will help clarify the pathways and sequence regions \nresponsible for the transition from physiological to pathological αSyn conformations. They will \nalso help identify in vitro  assays with readouts that are directly translatable to biologically \nrelevant outcomes, that can be used for efficient and high-throughput drug screening. Finally, \nwe demonstrate that certain mutant variants exhibit selectivity in seed amplification assays, \nenabling discrimination between disease-associated protein conformations, highlighting their \npotential for diagnostic applications. \nMaterials and methods \nMutagenesis \nLysine-to-glutamine (KQ) variant s were prepared starting from pET29a_ αSyn WT plasmid \nusing Golden gate mutagenesis protocol. The primers were ordered from TAG Copenhagen \nA/S (Denmark), all chemicals, restriction enzymes and buffers from New England Biolabs \n(USA) unless stated otherwise. Protocol utilizes set of primers provided in Table 1 and consists \nof (i) amplification ( Table 2  andTable 3), (ii) purification, (iii) restriction/ligation, and (iv) \ntransformation. Linear products of amplification were purified from agarose gel after \nelectrophoresis in 1 % TAE agarose (120 V, 30 min) using GFX PCR DNA and Gel Band \nPurification Kit (Cytiva, USA). The Golden gate assembly mixture was prepared according to \nTable 2 and incubated at 37 °C for 16  h. The reaction was stopped by 10 min incubation at \n85 °C. Residual template DNA was digested DpnI (1U, 1.5 h, 37 °C incubation) that was then \ndeactivated by heating (85 °C, 10 min). The individual reactions were pooled together, cleaned \nusing the GFX PCR DNA and Gel Band Purification Kit (Cytiva, USA), transformed into the \nBL21(DE3) competent E. coli which were plated on LB-agar containing kanamycin as selection \nmarker (50 μg/ml), and incubated for 12 h at 37 °C. Single colonies were transferred into the \n96-well plate containing 50 μl of TE buffer using sterile toothpick and send for sequencing \n(Eurofins Genomics, Germany). The same colonies were simultaneously transferred to a 96-\nDW plate containing 1 mL of LB (kanamycin) which was then used to create respective glycerol \nstocks. KQ cluster variants created in the first round of mutagenesis (together with single-point \nmutants) were used as templates for preparation of the double -cluster variants, which were \nsubsequently used as templates for the triple-cluster variants.  \n \n \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted November 3, 2025. ; https://doi.org/10.1101/2025.11.01.685997doi: bioRxiv preprint \n\n19 \n \nTable 1. List of primers used for mutagenesis. Regions of complementarity between the pair of primers are \nshown in green and are followed by the sequences complementary to the plasmid. The mutated codons and \nnucleotides are highlighted in bold and red, respectively. Sequence upstream of the green contains random \nflanking sequence (lower case), BsaI recognition site (GGTCTC), and an extra base next to which BsaI cleaves. \nReverse (R) and forward (F) direction of primer is encoded in the primer name.  \nPrimer \nn. Primer name Mutations Sequence (5'-3') \n1 αSyn _F_8-15 - ggctacGGTCTCaTGTCAAAAGCCAAGGAAGGAGTGG \n2 αSyn _R_K6Q K6Q ggctacGGTCTCaGACAGACCTTgCATGAAGACGTCCATATGT \n3 αSyn _R_8-15 - ggctacGGTCTCaGACAGACCTTTCATGAAGACGTCC \n4 αSyn _F_K10Q K10Q ggctacGGTCTCaTGTCAcAAGCCAAGGAAGGAGTGGTGG \n5 αSyn _F_K12Q K12Q ggctacGGTCTCaTGTCAAAAGCCcAGGAAGGAGTGGTGG \n6 αSyn \n_F_K10Q+K12Q K10Q+K12Q ggctacGGTCTCaTGTCAcAAGCCcAGGAAGGAGTGGTGGCAGC \n7 αSyn _F_26-31 - ggctacGGTCTCaTTGCGGAAGCAGCGGG \n8 αSyn _R_K21Q K21Q ggctacGGTCTCaGCAACACCCTGTTTGGTCTgTTCCGC \n9 αSyn _R_K23Q K23Q ggctacGGTCTCaGCAACACCCTGTTgGGTCTTTTCCGC \n10 αSyn \n_R_K21Q+K23Q K21Q+K23Q ggctacGGTCTCaGCAACACCCTGTTgGGTCTgTTCCGCG \n11 αSyn _F_39-46 - ggctacGGTCTCaGTACGTAGGTTCGAAGACGAAGGA \n12 αSyn _R_K32Q K32Q ggctacGGTCTCaGTACAAAACTCCCTCTTTTGTTTgCCCCGC \n13 αSyn _R_K34Q K34Q ggctacGGTCTCaGTACAAAACTCCCTCTTgTGTTTTCCCCGC \n14 αSyn \n_R_K32Q+K34Q K32+K34Q ggctacGGTCTCaGTACAAAACTCCCTCTTgTGTTTgCCCCGCT \n15 αSyn _R_30-39 - ggctacGGTCTCaGTACAAAACTCCCTCTTTTGTTTTCCCC \n16 αSyn _F_K43Q K43Q ggctacGGTCTCaGTACGTAGGTTCGcAGACGAAGGAAGGC \n17 αSyn _F_K45Q K45Q ggctacGGTCTCaGTACGTAGGTTCGAAGACGcAGGAAGGC \n18 αSyn \n_F_K43Q+K45Q K43+K45Q ggctacGGTCTCaGTACGTAGGTTCGcAGACGcAGGAAGGCGT \n19 αSyn _R_63-69 - ggctacGGTCTCaTCACAAATGTGGGTGGAGCTG \n20 αSyn _R_K58Q K58Q ggctacGGTCTCaGTGACTTGCTCTTTTGTCTgTTCTGCTACG \n21 αSyn _R_K60Q K60Q ggctacGGTCTCaGTGACTTGCTCTTgTGTCTTTTCTGCTACG \n22 αSyn \n_R_K58Q+K60Q K58Q+K60Q ggctacGGTCTCaGTGACTTGCTCTTgTGTCTgTTCTGCTACGG \n23 αSyn _F_85-89 - ggctacGGTCTCaCGCGGGCTCAATTGCTG \n24 αSyn _R_K80Q K80Q ggctacGGTCTCaCGCGCCCTCTACAGTCTgTTGCG \n25 αSyn _F_99-105 - ggctacGGTCTCaCAGCTTGGCAAGAACGAAGAGG \n26 αSyn _R_K96Q K96Q ggctacGGTCTCaGCTGGTCTTTCTgGACGAATCCGG \n27 αSyn _R_K97Q K97Q ggctacGGTCTCaGCTGGTCTTgCTTGACGAATCCGG \n28 αSyn \n_R_K96Q+K97Q K96Q+K97Q ggctacGGTCTCaGCTGGTCTTgCTgGACGAATCCGGTCG \n29 αSyn _R_93-100 - ggctacGGTCTCaGCTGGTCTTTCTTGACGAATCCG \n30 αSyn _F_K102Q K102Q ggctacGGTCTCaCAGCTTGGCcAGAACGAAGAGGGCG \n \n  \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted November 3, 2025. ; https://doi.org/10.1101/2025.11.01.685997doi: bioRxiv preprint \n\n20 \n \nTable 2. Composition of the reaction mixtures for mutagenesis (PCR 1) and Golden Gate Assembly (PCR \n2). Phusion MM – master mix containing HF-Phusion DNA Polymerase, dNTPs, and buffer components.  \nPCR 1 – amplification mixture PCR 2 –Golden Gate Assembly  \nComponent Stock Final Component Stock Final \n2x Phusion \nMM 2x 1x T4 buffer 10x 1x \nFwd_primer 10 μM 0.5 μM T4 ligase 400 kU/ml 400 U/mL \nRev_primer 10 μM 0.5 μM BsaI-HF 20 kU/mL 1.2 kU/mL \nPlasmid DNA 50-200 ng/μL 100 ng PCR mix 50-200 ng/μL 100 ng \nmqH2O - - mqH2O - - \n \nTable 3. Thermocycler protocol used for the PCR 1 mutagenesis. Times and temperatures were adjusted \nbased on the size of the plasmid (approx. 5.8 kb) and fidelity of the HF-Phusion polymerase. \nStep Temperature \n(°C) \nTime \n(s) n. cycles \nInitial denaturation 98 30 1 \nDenaturation 98 5 \n28 Annealing 55 15 \nExtension 72 180 \nFinal Extension 72 600 1 \nProtein expression \nLarge scale protein expression and purification \nWild type, KQ cluster variants, and truncated variant ( αSynS1-125) of αSyn were expressed \nin E. coli BL21 (DE3) cells transformed by the pET29a plasmid carrying the respective gene. \nThe transformed cells were used to inoculate 1 L of LB medium containing kanamycin (30 \nμg.mL-1) as selection marker. Following the 3 -hour incubation at 37 °C (OD 600∼ 0.6–0.8), \nprotein expression was induced by addition of IPTG (1 mM final concentration) and carried \nout for 4 hours at 37 °C. The cells were harvested by centrifugation (5,000×g, 20 minutes) and \nthe resulting pellet resuspended in 20 mL of Tris buffer (10 mM Tris–HCl, 1 mM EDTA, pH 8.0) \nwith 1 mM PMSF (phenylmethylsulfonyl fluoride). Cells were sonicated with a probe \nultrasonicator for 8 min (10 s on time, 30 s off time, 12 rounds with 40% amplitude). 1 μL of \ncommercial DNAse (Benzonase®) was added to the cell lysate and the insoluble fraction was \nremoved by centrifugation (20 000×g, 30 min at 4 °C). Cell-free extract was boiled for 20 min \nand the heat-precipitated proteins removed by centrifugation (20 000×g for 20 min at 4 °C). \nαSyn was precipitated by addition of saturated (NH\n4)2SO4 (4 mL per 1 mL of supernatant). The \nsolution was incubated on a rocking platform at 4 °C for 15 min and then centrifuged \n(20 000×g, 20 min, 4 °C) to obtain a protein pellet. The pellet was dissolved in 7 mL of 25 mM \nTris–HCl pH 7.7 with 1 mM DTT. Protein was dialyzed against the same buffer for 16– 18 h \nwith a buffer exchange after 12 h of dialysis at 4 °C. The dialyzed protein was then subjected \nto anion exchange chromatography (AEC) (HiTrap Q Hp 5 ml, GE healthcare) followed by size \nexclusion chromatography (SEC) (HiLoad 16/600 Superdex 200 pg. column). The monomeric \nfraction of αSyn eluted in 10 mM of sodium phosphate buffer (pH 7.4) was collected, and the \nprotein concentration determined by UV-absorption at 280 nm with theoretical molar extinction \ncoefficients calculated from the protein sequence using ProtParam80 (Expasy, Switzerland). \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted November 3, 2025. ; https://doi.org/10.1101/2025.11.01.685997doi: bioRxiv preprint \n\n21 \n \nSmall-scale protein expression and purification  \nThe small -scale expression of αSyn KQ variant s was carried out  in 90 mL LB medium \nanalogously to the large-scale expression. Pellets from the harvested cells were resuspended \nin 100 mM MES, 750 mM NaCl, 1 mM EDTA, 1 mM PMSF pH 7 and heated to 80 °C for 30 \nminutes. Next, acetic acid (cfinal = 1% v/v) and streptomycin (cfinal = 1% w/v) were added, and \ninsoluble fraction removed by centrifugation (20 000×g, 20 min, 4 °C). Resulting cell -free \nextract was precipitated by addition of saturated (NH 4)2SO4 (4 mL per 1 mL of supernatant). \nThe solution was incubated on a rocking platform at 4 °C overnight and then centrifuged \n(20 000×g, 30 min, 4 °C). Resulting protein pellet was dissolved in 4 mL of 10 mM NaP buffer \npH 7.4 and dialyzed twice to the same buffer. Proteins were purified by AEC using AcroPrep \n96- well filter plates (Cytiva, USA) and Multi-well Plate Vacuum Manifold (Cytiva, USA). Each \nwell was loaded with 350 μL of Nuvia HP -Q strong anion exchange resin (Bio- Rad, USA). \nResins were washed by 10 mM NaP buffer pH 7.4 before samples were applied (1 mL /well, \n4 wells/sample). Unbound proteins were washed with 5 x 0.6 mL of 10 mM NaP buffer and 5 \nx 0.6 mL of 10 mM NaP buffer with 100 mM NaCl. Single-point mutants, KQ clusters variants, \nand double KQ cluster variants were eluted by10 mM NaP buffer supplemented with 250, 300, \nor 350 mM NaCl, respectively. Proteins were concentrated and buffer -exchanged using \nAmicon ® Ultra Centrifugal Filters with 3 kDa Mw cut-off (Merck, USA), aliquoted, flash-frozen \nin liquid nitrogen, and stored at -80 °C. Their concentration, purity and size distribution were \ndetermined using UV -absorbance ( ε\n280 = 5,960 cm -1M-1), SDS -PAGE analysis and flow-\ninduced dispersion (FIDA) analysis. \nFibril preparation \nA 100 or 200 μM αSyn WT, KQ cluster variants, or αSyn-C1-125 monomers were buffer \nexchanged into the corresponding assembly conditions (Table 4, (71,88)). After the incubation, \nfibrils were pelleted by centrifugation (16,000 x g, 6 0 min, 25 °C ) and supernatant carefully \nremoved. The residual monomer concentration was quantified from the isolated soluble \nfraction using UV-absorbance, SDS-PAGE and FIDA . The fibrils were resuspended in the \nrespective buffer to final concentration of 100 or 200 μM (in monomer equivalents), flash-\nfrozen in liquid nitrogen and stored at -20 °C. Prior to the experiments (seeding assays, \nchemical depolymerization), fibrils were thawed and sonicated using an ultrasonic probe \n(Hielscher UP200St). Sonication was carried out in repeating 1s- pulses of 100% amplitude \nseparated by 1 second pauses for 4 minutes (two minute total sonication time). \nTable 4. Conditions used for assembly of different fibril polymorphs used in this work. \nFibrils Buffer T (°C) Shaking \n(rpm) \nTime \n(days) \nWT - Fm 50 mM Tris-HCl 150 mM KCl pH 7.4 37 600 7-28 \nWT - Ri 5 mM Tris-HCl pH 7.4 37 600 14 \nαSynS1-125  50 mM Tris-HCl 150 mM KCl pH 7.4 37 600 14 \nKQ 50 mM Tris-HCl 150 mM KCl pH 7.4 37 600 14-28 \n \n  \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted November 3, 2025. ; https://doi.org/10.1101/2025.11.01.685997doi: bioRxiv preprint \n\n22 \n \nThioflavin T assays \nAll ThT kinetic measurements were carried out using the FLUOstar Omega plate reader (BMG, \nGermany) using 440/488 excitation and emission and bottom reading every 5 or 10 minutes. \nAll reactions contained 50 μM ThT (final concentration) and were carried out in triplicates with \nreaction volumes of 15 μL per well unless  stated otherwise. The conditions of  specific ThT \nassays are provided in Table 5. \nTable 5. Overview of the conditions for different ThT assays used in this study. * Each  well \ncontained a single glass bead (d = 1 mm). \nAssay Buffer T \n(°C) \nShaking \n(rpm) \nCorning \nplate \nDe novo – \nmonomer  \n10 mM sodium phosphate, 150 mM \nNaCl, pH 7.4  37 Double orbital \n(300) * \nNon-treated \n3540 \nDe novo – pH 50 mM sodium citrate,  \npH 4.6, 5.4, 6.2, and 7.4 37 Double orbital \n(300) * \nNon-treated \n3540 \nDe novo – salt 10 mM sodium phosphate, pH 7.4, \nvarying salts 37 Double orbital \n(300) * \nNon-treated \n3540 \nSeeded - \nelongation \n50 mM Tris-HCl, 150 mM KCl, pH \n7.4 37 Quiescent Non-binding \n3544 \nSeed \namplification \n50 mM sodium citrate, 250 mM \nNa2SO4, pH 3 37 Quiescent Non-binding \n3544 \nDe novo aggregation assay \nProtein samples were buffer exchanged into the assay conditions ( Table 5), diluted to final \nconcentrations of 5 – 200, 40, and 50 μM for monomer dependence, salt, and pH screening, \nrespectively, and supplemented by 50 μ M ThT. Resulting kinetic curves were fitted to the \nsigmoidal function described by Equation M1: \nEquation M1 𝑦𝑦 =  𝑦𝑦0 + 𝐴𝐴�1 + 𝑒𝑒𝑒𝑒𝑒𝑒�−𝑘𝑘(𝑡𝑡− 𝑡𝑡0.5)���  \nThe y0 is the pre-transition baseline, A is the signal amplitude, k is the apparent growth rate, \nand t0.5 is the midpoint of the transition, i.e., half-time. (63) \nThe theoretical net charges of different variants at varying pH conditions were estimated using \nthe Henderson-Hasselbach equation based on the protein sequence as \nE\nquation M2 ∑ −1 (1 + 10𝑝𝑝𝑝𝑝𝑛𝑛−𝑝𝑝𝑝𝑝)⁄𝑛𝑛𝑛𝑛𝑛𝑛𝑣𝑣𝑚𝑚𝑛𝑛𝑣𝑣 𝑛𝑛\n𝑛𝑛=1 + ∑ 1 (1 + 10−𝑝𝑝𝑝𝑝𝑝𝑝+𝑝𝑝𝑝𝑝)⁄𝑝𝑝𝑝𝑝𝑝𝑝𝑛𝑛 𝑚𝑚𝑛𝑛𝑣𝑣𝑛𝑛\n𝑗𝑗=1  \nW\nhere the pKn and pKp are dissociation constants of negatively and positively charged amino \nacid groups, respectively.   \nSeeded aggregation assay \nA dilution series of the αSyn variants was prepared in 5-80 μM monomer range and sonicated \nseeds were added (final concentration 2.5 μM) just prior the measurement. A control reaction \nwithout ThT containing 40 or 80 μM of monomer was included for each series and used for \nquantification of the residual monomer concentration at the end of the reaction. A linear curve \nwas fitted to the first 2.5 hours of the data and resulting slopes plotted against the initial \nmonomer concentration ([M]\n0). The apparent elongation rate constants were extracted as the \nslopes in the linear data range of the plots ([M]0 ~ 0-40 μM). To cancel out the contribution of \nthe number of seeding- competent fibril ends ([S]), the effect of mutations was related to the \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted November 3, 2025. ; https://doi.org/10.1101/2025.11.01.685997doi: bioRxiv preprint \n\n23 \n \nelongation of WT measured on the same plate with the same batch of seeds according to \nEquation M3. \nE\nquation M3  ∆∆𝐺𝐺ǂ =  ∆𝐺𝐺ǂ(𝑚𝑚𝑚𝑚𝑡𝑡) − ∆𝐺𝐺ǂ(𝑊𝑊𝑊𝑊) =  −𝑅𝑅 𝑊𝑊l n(𝑘𝑘+𝑚𝑚𝑚𝑚𝑚𝑚[𝑆𝑆]) + 𝑅𝑅 𝑊𝑊l n(𝑘𝑘+𝑤𝑤 𝑚𝑚[𝑆𝑆])  \n  =  −𝑅𝑅\n𝑊𝑊l\nn(𝑘𝑘+𝑚𝑚𝑚𝑚𝑚𝑚𝑘𝑘+𝑤𝑤 𝑚𝑚⁄ ) \nW\nhere ΔGǂ is the Gibbs activation energy of elongation, R the universal gas constant, T the \nthermodynamic temperature, and k+ the microscopic elongation rate constant of wild type (wt) \nor mutant (mut).  \nIn cases where saturation elongation was observed, initial rates were fitted to Equation M4. \nE\nquation M4  𝑣𝑣= 𝑣𝑣𝑚𝑚𝑣𝑣 𝑚𝑚[𝑀𝑀]/([𝑀𝑀] + 𝐾𝐾𝑛𝑛)  \nW\nhere v and v max is the observed and maximum rate, respectively, K e is the elongation \nsaturation constant, and [M] is the monomer concentration. (91) \nSeed amplification assay at low pH \nWT or mutant monomers (10 μ M) were brought to the assay conditions by mixing from high \nprotein concentration stock. Sonicated Fm or Ri fibrils were added to the samples to final \nconcentrations of 0, 0.001, or 1 μM. The kinetic curves in the presence of 1 nM were fitted by \nEquation M1 to extract the aggregation half-times used for further analyses.  \nBrain samples were acquired from the Bispebjerg Brain Bank at Bispebjerg- Frederiksberg \nHospital (University Hospital of Copenhagen, Denmark; Ethical approval: j.no.: H -15016232, \ndata protection agency: j.no.: P-2020-937, Table 6). Brain tissue homogenates were prepared \nas follows: Approximately 50 mg of tissue samples were homogenized using a bead \nhomogenizer (Precellys, Bertin Technologies) with 2 cycles of 45 seconds at 4,600 rpm in \nbuffer containing 1x dPBS (Gibco), 1x HALTTM protease and phosphatase inhibitor cocktail \n(cat no.: 78444, Thermo ScientificTM) to a final concentration of 10% w/v. Aliquoted \nhomogenates were stored at -80℃ for further use. For the plate assay, the brain homogenates \nwere diluted 1000-fold further to the condition of seed amplification assay described in Table \n5 with 10 μM monomer of different α-Syn variants. \nTable 6. Overview of the patients derived samples used for seed amplification assay.  \nGroup Number Age Sex Disease duration Subtype \nMSA 1 69 Female 8 MSA-P \nPD 1 64 Male 13 - \nControl 1 77 Male - - \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted November 3, 2025. ; https://doi.org/10.1101/2025.11.01.685997doi: bioRxiv preprint \n\n24 \n \nWeighted least-square regression of the data from aggregation assays. \nThe data from unseeded experiments, elongation experiments with WT seeds at neutral pH, \nand mildly seeded experiments at low pH were compiled into three datasets. Variants were \none-hot encoded at the single- mutation (elongation) or KQ -cluster (unseeded and low -pH) \nlevel, and each data point was parameterized by the number of mutations, pH, ionic strength, \nand net charge (Equation M2)  derived from the experimental conditions.  Experimental \nreplicates were grouped by variant and fibril type to calculate mean ΔΔ G values and \ncorresponding standard errors of the mean (SEM), with each group assigned an inverse -\nvariance weight (1/SEM²). For the unseeded and low -pH datasets, mean and standard \ndeviation (SD) of log-transformed half-times from each triplicate measurement were used as \na single data point and weights (1/SD), respectively. The noise level was estimated from the \naverage correlation between replicate measurements, computed using Fisher’s z-transformed \nmean. Weighted least squares (WLS) regression and statistical analyses were performed in \nPython (v3.11) using statsmodels (v0.14). \nQuantification of residual monomer \nUV absorbance \nSamples without ThT were withdrawn from the plate and centrifuged to pellet down the fibrils \n(16,000 x g, 60 min, 25 °C). The concentration of monomer was determined by UV absorbance \nusing NanoDrop (ThermoFisher, USA) and the extinction coefficient of α Syn \n(ε280 = 5,960  cm- 1M-1) calculated from the sequence using Expasy webserver.  \nFlow-induced dispersion analysis (FIDA) \nThe oligomeric state analysis of the supernatant and monomer quantification were further \ndetermined using the FIDA1 instrument (FidaBio, Denmark). Samples were analyzed using \nthe method provided in Table 7.\n \nTable 7. FIDA method used for analysis of soluble αSyn fraction. \nStep Component Time (s) Pressure (mbar) Temperature (°C) \nWash 1 1 M NaOH 45 3500 25 \nWash 2 Water 45 3500 25 \nEquilibration Buffer 40 3500 25 \nSample Protein 20 75 25 \nMeasurement Buffer 75 1500 25 \n \nThe monomer concentration was quantified from the areas under the peak (obtained by fitting \na Gaussian function to the Taylorgrams by the in- built software) using calibration curve of \nknown monomer concentrations. \n  \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted November 3, 2025. ; https://doi.org/10.1101/2025.11.01.685997doi: bioRxiv preprint \n\n25 \n \nSDS-PAGE analysis \nThe samples were collected from the assay plate and centrifuged to pellet down the fibrils \n(16,000 x g,  60 min, 25 °C ).The supernatant was mixed with the NuPAGE ™ LDS Sample \nBuffer (ThermoFisher) in 1 to 1 ratio and applied to NuPAGE™ Bis-Tris Mini Protein Gels, 4–\n12% (ThermoFisher). Calibration samples of SEC -isolated αSyn  monomer of known \nconcentrations were prepared in the same way. The electrophoresis was carried out at \nconstant 200 V for 35 minutes, followed by staining using InstantStain Coomassie Stain (Kem-\nen-tec-nordic). Upon destaining in distilled water, the gels were imaged using ChemiDoc \nimaging system (BioRad), and intensity of bands corresponding to αSyn  was analyzed using \nImage Lab software (BioRad). The concentration of residual monomer was calculated based \non the calibration curve made using the monomer standards of known concentrations.  \nQuartz crystal microbalance analysis of fibril growth \nElongation of WT or KQ fibrils were measured by their immobilization on a QCM sensor (Biolin \nScientific, Gothenburg, Sweden) and measuring changes in mass upon subsequent \nincubation with WT or KQ monomer solution (100). Sonicated fibrils (65 μL, 100 μM monomer \nequivalent) were mixed with 10 μL of 1  mg.mL\n-1 of Traut’s reagent (2- Iminothiolane, \nThermoFisher) and spotted on the QCM sensor following 1 hour incubation at room \ntemperature. Next, the solution was pipetted out and the chip surface was blocked by addition \nof 1% mPEG and incubation for 30 min. The sensor with immobilized fibrils was then \nthoroughly washed by miliQ H\n2O and dried under gentle nitrogen stream. The measurements \nwere performed with a QSense Pro QCM -D instrument (Biolin Scientific, Gothenburg, \nSweden) by measuring the elongation rate as change in the resonant frequency over time. \nThe sample chamber equilibrated to 37 °C was filled automatically by 3 cell volumes (60 μL) \nof WT monomer (50 μ M) and the measurement proceeded until stable linear slope was \nachieved. Next, the sensor was cleaned using buffer (50 mM Tris -HCl 150 mM KCl pH 7.4) \nand mutant monomeric solution was injected until a stable slope was achieved. The elongated \nrates of WT and mutant variants were measured as the slopes of the third overtone frequency \nafter the first and second injections and used to calculate ΔΔG\nǂ according to the Equation M3. \nFor the KQ fibrils, the sequence of WT and KQ mutant monomer addition was reversed.  \nAFM analysis of the fibrils \nFibrils were diluted to 2.5 µM monomer equivalent concentration and 20 µL of the solution was \ndeposited onto freshly cleaved mica substrates. Following 2min of incubation, the substrates \nwere cleaned extensively with miliQ water and dried under nitrogen ga s flow. All fibrils were \nimaged in tapping mode in air using a DriveAFM (Nanosurf, Liestal, Switzerland) using PPP-\nNCLAuD cantilevers (Nanosensors, Neuchatel, Switzerland). The amyloid fibrils were \ncharacterized by their apparent twist (assuming 2 1 helical symmetry as described for Fm \npolymorphs in (71)) and height extracted using automated python script (59,72).  \nTransmission electron microscopy of KQ4 and KQ6 fibrils \nSamples were prepared on a glow discharged, formvar/carbon-coated 400 mesh grid for 20 \nseconds. The grids were then washed with two drops of double- distilled water and stained \ntwice with 2% uranyl acetate. Excess stain was blotted, and the grids were air -dried for 30 \nminutes before imaging. A 200 kV Tecnai T20 G2 electron microscope (FEI, USA) was used \nto analyze the fibrils. Images were captured with a TVIPS XF415 CMOS 4K camera using \nTVIPS EMplify v0.4.5 software.  \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted November 3, 2025. ; https://doi.org/10.1101/2025.11.01.685997doi: bioRxiv preprint \n\n26 \n \nThermodynamic stability of fibrils \nThe thermodynamic stability of WT and mutant fibrils was measured in 50 mM Tris -HCl 150 \nmM KCl buffer pH 7.4 using chemical depolymerization and FIDA analysis of residual \nmonomer as described in (72) . In short, sonicated fibrils (40 μM monomer equivalent) were \nincubated in series of buffers containing increasing concentrations of urea (0-5 M) for 3 days \nat 25 °C. Samples were analyzed using by FIDA using the method described above. The \nmonomer concentration was extracted from the elution profiles after correction for the viscosity \nat different urea concentrations as previously described. (72) The chemical depolymerization \ncurves were analyzed using NumPyro to sample posterior distributions of the isodesmic model \n(Equation M5) parameters using the No U-Turn Sampler (NUTS). (73,74,101) \nE\nquation M5- 𝐴𝐴= 2 �1 + 2𝑀𝑀\n𝑒𝑒𝑒𝑒𝑒𝑒(− (∆𝐺𝐺+ 𝑚𝑚𝑚𝑚) 𝑅𝑅\n𝑊𝑊⁄ ) + � 1 + 4\n𝑀𝑀\n𝑒𝑒𝑒𝑒𝑒𝑒(− (∆𝐺𝐺+ 𝑚𝑚𝑚𝑚) 𝑅𝑅\n𝑊𝑊⁄ )�⁄  \nWhere A is the area under the Gaussian peak from FIDA, M is the protein concentration in \nmonomer equivalents, ΔG is the thermodynamic stability, m is the m -value, R the universal \ngas constant, and T is the temperature.  \nWe fit a quadratic equation (Equation M6) to the correlation between denaturant m-value (m) \nand fibril diameter (d).  \nE\nquation M6   𝑚𝑚= 𝑉𝑉𝑑𝑑2 \nW\nhere a is a proportionality constant corresponding to π/4T with T representing the average \nchain thickness (4.10-10 m for protein backbone). The quadratic dependence arises naturally \nfrom a “Clackson scroll” geometry, in which a rope or sheet is rolled into a cylindrical form. \nThis configuration approximates the change in solvent -accessible surface area that occurs \nwhen a disordered protein chain assembles into a fibril. We assume that the m-value is directly \nproportional to this change in exposed surface area, consistent with the relationship observed \nfor globular protein folding. (76) \nFoldX analysis of mutational changes on fibril stability \nFoldX calculations were performed on a curated set of aSyn fibril structures selected from the \nAmyloid Atlas  (v2024). (75) We curated the full set of aSyn structures  by excluding (i) \nstructures formed from conditions significantly different from physiological conditions used in \nthis study, (ii) containing other compounds – such as lipids – or (iii) formed from mutant \nvariants. Subsequently, the structures were aligned and manually investigated for similarity \nand structures with extensive overlap were removed to minimize bias from a single motif being \nrepresented multiple times. This curated set of 47 structures (Supplementary Table 9)  \nrepresent the two major structure families recently identified in a meta-analysis of the full aSyn \nstructure library, as well as the largest of the minor groups.(102)  \nFoldX calculations were performed using the FoldX4 suite as described in (59). (103) Briefly, \nbefore modelling any mutations in the structures, all PDB files were repaired using the \nREPAIRPDB command. Subsequent commands were only performed on the repaired \nstructures. The ΔΔG upon mutagenesis was calculated using the BUILDMODEL command, \nensuring that the mutation was introduced in all chains of the PDB file. The total ΔΔG is divided \nby the number of chains in the structure to evaluate a per chain ΔΔ G. This was done for all \npossible single KQ mutations in the curated structure set. \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted November 3, 2025. ; https://doi.org/10.1101/2025.11.01.685997doi: bioRxiv preprint \n\n27 \n \nClustering of mutational effects \nFibril stability, ΔΔGǂ values on KQ seeds, and cluster -specific coefficients from WLS \nregression of data from unseeded experiments, elongation experiments on WT at neutral pH, \nand mildly seeded experiments at low pH were log2-transformed. Biclustering of the resulting \nvalues was performed using seaborn.clustermap (Python 3.11) with hierarchical clustering \napplied to both assays and KQ variants, using the default settings of Euclidean distance and \naverage linkage. \nMolecular dynamics simulations of αSyn monomers \nA\nll simulations were prepared and executed with the CALVADOS Python interface and  \nthrough the publicly available Google Colab.  (70) A single protein chain was simulated with \nboth N- and C-terminal charges. The initial configuration was ce ntered in a cubic box with a \nside length of 40 nm.  Simulations were performed at 310.15 K, pH 7.5, and a range of ionic \nstrengths (0, 5, 50, and 150 mM). Trajectories were saved every 100,000 integration steps \n(equivalent to 1 ns per saved frame). A total of 1000 frames were saved per replicate, \ncorresponding to 100,000,000 integration steps and an aggregate production length of 1 μs.  \nFrom each trajectory, ensemble observables including the radius of gyration (Rg), end-to-end \ndistance (Ree), Flory scaling parameter (ν) , and energy interaction map were compu ted \nautomatically. Reported values represent averages across three independent replicates for \neach condition. Residue –residue contacts were calculated using a 9 Å cutoff applied to \ncoarse-grained bead distances, excluding pairs with \n𝑉𝑉−𝑗𝑗\n ≤ 2 to remove bonded and next -\nnearest neighbour contributions. Contact probabilities and per-residue profiles were averaged \nacross all three replicates. To connect simulations to experiment, Rg values were correlated \nwith the aggregation half-times measured experimentally and averaged across salts for each \nionic strength (for 150 mM, aggregation half -times at 100 and 200 mM NaCl, KCl, and NaI \nwere averaged together).  \n  \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted November 3, 2025. ; https://doi.org/10.1101/2025.11.01.685997doi: bioRxiv preprint \n\n28 \n \nAuthors contributions \nA.K.B. supervised the work. A.K., S.F., R.K.N., and A.K.B. conceptualized the work. R.K.N., \nS.F., and A.K. designed the mutant primers, A.K.  designed and carried out the experiments, \nanalyzed the data, prepared the graphics, and wrote the manuscript. A.K. and S. F., carried \nout the seed amplification assay experiments, J.A.L and A.K. performed the QCM \nmeasurements, A.K., F.S., and C.F., expressed and purified the mutants, H.M.B.  and A.S. \ncarried out TEM analysis of the KQ fibrils, R.K.N. and C.F. wrote the python code for the \nanalysis of urea depolymerization experiments. C.F. help with preparation of the graphics. J.F. \nand S.A. prepared the samples for seed amplification assay. All authors contributed to the \npreparation of the manuscript and agree with its content. \nAcknowledgements \nA.K.B thanks the Novo Nordisk Foundation for funding (NNF17SA0028392 and \nNNF21OC0065495). This research was co- funded by the European Union (ERC CoG \n101088163 EMMA to A.K.B.), Lundbeck foundation (grant number R366- 2021-169 STADIC \nto A.K.B.) . 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