Energetic profiling reveals thermodynamic principles underlying amyloid fibril maturation

Energetic profiling reveals thermodynamic principles underlying amyloid fibril maturation | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Energetic profiling reveals thermodynamic principles underlying amyloid fibril maturation View ORCID Profile Ramon Duran-Romaña , View ORCID Profile Joost Schymkowitz , Frederic Rousseau , View ORCID Profile Nikolaos Louros doi: https://doi.org/10.1101/2025.05.14.653959 Ramon Duran-Romaña 1 Switch Laboratory, VIB Center for Brain and Disease Research , Herestraat 49, 3000 Leuven, Belgium 2 Switch Laboratory, Department of Cellular and Molecular Medicine , KU Leuven, Herestraat 49, 3000 Leuven, Belgium Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ramon Duran-Romaña Joost Schymkowitz 1 Switch Laboratory, VIB Center for Brain and Disease Research , Herestraat 49, 3000 Leuven, Belgium 2 Switch Laboratory, Department of Cellular and Molecular Medicine , KU Leuven, Herestraat 49, 3000 Leuven, Belgium Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Joost Schymkowitz For correspondence: Nikolaos.louros{at}utsouthwestern.edu Frederic.Rousseau{at}kuleuven.vib.be Joost.Schymkowitz{at}kuleuven.vib.be Frederic Rousseau 1 Switch Laboratory, VIB Center for Brain and Disease Research , Herestraat 49, 3000 Leuven, Belgium 2 Switch Laboratory, Department of Cellular and Molecular Medicine , KU Leuven, Herestraat 49, 3000 Leuven, Belgium Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: Nikolaos.louros{at}utsouthwestern.edu Frederic.Rousseau{at}kuleuven.vib.be Joost.Schymkowitz{at}kuleuven.vib.be Nikolaos Louros 1 Switch Laboratory, VIB Center for Brain and Disease Research , Herestraat 49, 3000 Leuven, Belgium 2 Switch Laboratory, Department of Cellular and Molecular Medicine , KU Leuven, Herestraat 49, 3000 Leuven, Belgium 3 Center for Alzheimer’s and Neurodegenerative Diseases, Peter O’Donnell Jr. Brain Institute, University of Texas Southwestern Medical Center , Dallas, TX, 75390, USA 4 Department of Biophysics, University of Texas Southwestern Medical Center , Dallas, TX, 75390, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Nikolaos Louros For correspondence: Nikolaos.louros{at}utsouthwestern.edu Frederic.Rousseau{at}kuleuven.vib.be Joost.Schymkowitz{at}kuleuven.vib.be Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract Amyloid fibrils adopt diverse structural polymorphs that underlie disease-specific phenotypes, but the thermodynamic principles guiding their formation and maturation remain poorly understood. Here, we apply energetic profiling to structural time series from cryo-EM datasets of IAPP, tau, and α-synuclein to decode the principles governing fibril maturation, stability and polymorphic divergence. By mapping residue-level free energy contributions across experimentally resolved assembly pathways, we reconstruct the complex maturation trajectories of distinct amyloid systems. We find that amyloid assembly is driven by aggregation-prone regions that act as sequence-encoded stabilizing motifs. As assembly progresses, these motifs are reorganized and expanded, while additional regions introduce structural frustration that enables conformational flexibility. In several cases, environmental cofactors such as metal ions or polyanions are observed in association with regions of structural remodeling, where they may act to compensate for otherwise energetically strained conformations. Notably, structurally distinct polymorphs can exhibit similar energetic profiles, while polymorphs with similar folds may differ thermodynamically depending on their assembly environment. This framework connects polymorph structure to both encoded sequence features and extrinsic modifiers, offering mechanistic insight into how amyloid strains mature and diversify over time. Introduction Amyloid fibrils are a common form of protein aggregation 1 , linked to over fifty human diseases, including Alzheimer’s, Parkinson’s, and type 2 diabetes 2 . Despite arising from proteins with widely different sequences and biological roles, these aggregates share a hallmark structural feature: the cross-β architecture 3 . In this configuration, protein monomers form β-strands that stack into extended sheets, stabilized by backbone hydrogen bonds along the fibril axis. These sheets are further packed together through tightly interlocking side chains, forming so-called steric zippers that define the protofilament core and mediate interactions between protofilaments 4 . Notably, many amyloid fibrils are stabilized by short, hydrophobic sequence motifs known as aggregation-prone regions (APRs), which provide nucleation sites for fibril growth and often form the energetic spine of the fibril core 5 - 8 . In addition to structural stability, the cross-β structure also enables amyloid fibrils to propagate in a prion-like manner. In this process, existing fibrils act as templates that induce the misfolding and incorporation of soluble monomers. This seeding mechanism supports sustained fibril growth, transmission between cells, and, in some cases, even spread between organisms 9 - 12 . Crucially, it also allows fibrils to replicate their specific conformations, giving rise to distinct structural strains, or polymorphs, with potentially different biological effects 13 , 14 . Recent advances in high-resolution cryo-electron microscopy (cryo-EM) have provided atomic-level insight into these structures, revealing near-atomic models for hundreds of amyloid fibrils and uncovering an unexpected diversity of structural polymorphs. 15 - 17 . Strikingly, a single protein can adopt multiple discrete fibril folds that correlate with specific clinical phenotypes. For example, structurally distinct tau fibrils have been isolated from brains of patients with different tauopathies, yet each disease consistently exhibits a conserved fold across individuals 18 . Similar correlations have been observed for amyloid-β 19 - 21 , α-synuclein 22 , 23 , TDP-43 24 , 25 , and other amyloid-forming proteins 26 , 27 . However, fibrils formed in vitro frequently fail to replicate the structures found in patient tissue 17 , 28 , 29 , pointing to important, but still poorly understood, roles for cellular cofactors 29 , interactions with other biomolecules 16 , 30 , and post-translational modifications in shaping fibril structure 31 . This growing structural diversity has raised new questions about how sequence and environment determine amyloid fibril structure, and how these polymorphs form and evolve over time 6 , 32 - 34 . To address this, we recently developed an in silico method to calculate residue-level energetic stability of fibril structure 6 , enabling systematic classification of disease-associated polymorphs based on their energetic profile 33 . This method was later independently validated by its ability to predict experimental measures of fibril stability and assembly 35 . Building on this method, we now apply energetic profiling to published structural time series of amyloid fibrils formed by IAPP 36 , tau 37 , and α-synuclein 38 . Remarkably, our approach reconstructs the complex maturation timelines of each protein, uncovering the energy landscape that governs their folding, assembly, and polymorphic divergence. We find that fibril assembly is anchored by APRs that act as sequence-encoded stabilizing motifs. As assembly progresses, these motifs undergo reorganization, while other segments introduce structural frustration that enables conformational diversification. In several cases, environmental cofactors, such as metal ions or polyanions, are found in conjunction with structural remodeling, where they appear to compensate for regions of local energetic strain and enable otherwise unfavorable conformations. Together, these findings reveal a unifying thermodynamic framework in which intrinsic sequence features and extrinsic factors collectively shape the structural diversity of amyloid strains observed in disease. Results APR stabilization underlie the emergence of distinct IAPP fibril polymorphs Time-resolved cryo-EM of fibrils assembled in vitro from the human islet amyloid polypeptide (IAPP) mutant S20G recently revealed notable structural diversity during assembly 36 . Early-stage fibrils begin as a shared protofilament with a P-shaped cross-section, which later branches into two distinct lineages: one forming C-shaped and the other L-shaped protofilament cores ( Fig. 1A ). To understand the forces underlying their structural maturation, energetic profiling reveals that the late-stage fibril cores of both lineages become significantly more stable than their shared P-shaped intermediate ( Fig. 1B, C ), nearly doubling the average per-residue stabilizing free energy, while the added peripheral protofilaments remain comparatively unstable. This increased stability of the fibril cores is not uniformly distributed. Instead it is concentrated within three APRs previously identified as critical for IAPP aggregation, spanning residues 12-17 39 , 40 , 22-27 41 , 42 , and 30-36 43 , 44 , and located within β-strand regions that maximize backbone hydrogen bonding ( Fig. 2A and Fig. 1D ). As fibrils mature, these APRs not only become more stabilizing but also expand their structural contribution to the fibril core, a process facilitated by the formation of additional hydrogen bonds that reinforce β-sheet stacking ( Fig. 1D, E and Fig. S1). This is further reinforced by strengthened side-chain packing, enhanced hydrophobic burial, and more favorable solvation ( Fig. 1F-H ). In contrast, the sequences between APRs remain relatively unstable or become more frustrated, contributing little or negatively to the fibril’s overall stability ( Fig. 2A ). This produces a biphasic energy landscape composed of localized stabilizing hotspots embedded within frustrated or neutral regions ( Fig. 1C ). Consistent with thermodynamic expectations, these patterns suggest that fibril maturation proceeds along an energetically downhill trajectory, with increasing stabilization concentrated in key APR motifs as polymorphs evolve toward distinct yet thermodynamically favorable endpoints. Download figure Open in new tab Figure 1. Structural assessment of IAPP intermediate protofilaments. (A) Graphical representation of amyloid fibrils formed by the S20G IAPP mutant. Following the formation of an early intermediate (shown in green), two separate structural lines are generated as kinetics of aggregation progress (L- and C-lineage). In each structure, colored protofilaments represent the core structural elements that define the fibril polymorph, while grey protofilaments denote later-added peripheral elements that contribute less to the overall stability (B) Total stability measurements of protofilament structures. (C) Distribution of individual residue energy contributions to protofilament stabilities. Percentage of residues contributing to protofilament stability are shown. (D) Backbone hydrogen-bond energies of stabilizing (shown in cyan) and neutral or destabilizing residues (shown in light green). Statistics: t-test comparison of means. (E-H) Distribution of per residue contributions to (E) backbone H-bonds, (F) van der Waals interactions, (G) solvation energies, as well as (H) total side-chain burial values. All plots are split per lineage and color-coded as in A. Download figure Open in new tab Figure 2. Thermodynamic profiling of IAPP S20G intermediate protofilaments. (A) Individual residue contributions (residue numbers shown in x-axis) to the stability of protofilaments. Protofilament profiles are clustered based on evolution of both lineages (shown left). Experimentally determined APRs are highlighted at the top. (B-D) Different favorable interactions between common residues stabilize the different protofilament cores (highlighted in grey) and cross-protofilament interfaces (highlighted in orange). (E) Principal component analysis and k-means clustering of IAPP protofilament thermodynamic profiles reconstructs the C-lineage (pink cluster) and L-lineage (orange cluster). The common early P-shaped intermediate is located between the two clusters in the eigenspace (green cluster), with peripheral fibrils forming separate distant clusters (grey-shaded clusters). Protofilaments are color-coded as in Figure 1A . (F) Per residue contributions to the main principal components. Despite their morphological divergence, the mature C- and L-folds are anchored by the same aggregation-prone regions, but each lineage reinforces different parts of the sequence ( Fig. 2A ). The L-fold enhances stability in the N-terminal APR, while the C-fold draws more stabilizing energy from the central APR, consistent with its known role in IAPP nucleation. These findings indicate that sequence-encoded stabilizing motifs can be differentially deployed across lineages, producing distinct structural outcomes that are thermodynamically comparable but stabilized through alternative energetic routes. Further structural analysis revealed that a set of eight hydrophobic residues (L12, F15, V17, F23, I26, L27, V32, and F36) recurs across all folds and plays a central role in core stabilization. In the P- and L-folds, a subset of these residues bridge the N-terminal and central APRs through steric zippers packed within the protofilament core, with the rest stabilizing protofilament contacts. In the C-fold, however, the same residues shift to stabilize the core and inter-protofilament contacts ( Fig. 2B–D ). This flexible reuse highlights how a single sequence can support multiple stable folds by adapting its packing interfaces to different structural contexts. To assess global energetic trends, principal component analysis (PCA) of residue-level energy profiles confirms these relationships. The intermediate P-shaped fold occupies a position between the mature C and L clusters in the energy space, while more peripheral protofilaments form outliers ( Fig. 2E ). The first PCA component, driven by changes in the central APR, clearly separates the two lineages. Analysis of variance confirms that APRs account for most of the energetic differentiation observed during fibril evolution ( Fig. 2F ). Backbone strain accumulates during tau fibril maturation despite APR-driven stabilization We next asked whether the patterns observed for IAPP fibril maturation extend to other amyloid systems. Tau fibrils assembled in vitro from residues 293–391 can adopt disease-relevant folds resembling those found in Alzheimer’s disease (AD) and chronic traumatic encephalopathy (CTE). Remarkably, both polymorphs can be reproduced under simplified conditions by varying only the buffer composition, specifically using Mg 2+ to promote AD-like folds and NaCl to favor CTE-like structures 37 , 45 . This minimal system provides a rare opportunity to dissect how intrinsic thermodynamic forces govern amyloid maturation, independent of cellular cofactors or post-translational modifications. Recent time-resolved cryo-EM reconstructions of tau protofilaments assembled under these conditions offer direct insight into the conformational transitions and energetic constraints that shape their maturation pathways (Fig. S2). Energetic profiling of the tau time-series reveals a similar biphasic pattern ( Fig. 3A, B , Fig. S3B and Fig. S4B). As assembly progresses, however, tau fibrils show a progressive loss of overall stability that contrasts with the IAPP behavior: the number of destabilizing residues steadily increases across both AD and CTE lineages ( Fig. 3C, D ). This thermodynamic penalty originates almost exclusively from backbone hydrogen bonds that become increasingly strained ( Fig. 3E, F , Fig. S3A and Fig. S4A), leading to a progressive loss of stabilizing energy contributions. As a result, we observe a strong negative correlation between fibril maturation stage and the magnitude of backbone hydrogen-bond energy ( Fig. 3G, H ). Other interactions, such as hydrophobic burial and solvation, remain largely unchanged (Fig. S3D, E and Fig. S4D, E), indicating that the expanding tau fibril cores tighten at the cost of local backbone geometry rather than through loss of side-chain packing. Download figure Open in new tab Figure 3. Thermodynamic profiling of recombinant tau intermediate protofilaments. (A, B) Density plots of residue-wise folding energy contributions (ΔG in kcal/mol) for selected structures across the AD (A) and CTE (B) trajectories. (C, D) Percentage of highly destabilizing residues (ΔG > 1.5 kcal/mol) across all modeled structures in the AD (C) and CTE (D) trajectories plotted against the maturation order, with each point representing one structure. A significant positive correlation is observed in both AD and CTE, indicating increasing energetic destabilization with structural progression. Dashed line indicates linear regression. Pearson correlation coefficients and P values are indicated. (E, F) Density plots of backbone hydrogen bond energies (kcal/mol) across a representative subset of structures from AD (E) and CTE (F) trajectories. (G, H) Correlation between average backbone H-bond energy and maturation stage in the AD (G) and CTE (H) trajectories. Progressive weakening of backbone H-bonding is observed in both cases. Dashed line indicates linear regression. Pearson correlation coefficients and P values are indicated. (I-J) Thermodynamic profiling of (I) AD and (J) CTE recombinant tau protofilaments. (K-M) Principal component analysis recapitulates the timeline of protofilaments formed in conditions that reproduce the (K-L) AD and (M-N) CTE fold. In plots A-H, K and M, timepoints are color-coded according to the AD (Figure S2 A) and CTE (Figure S2 C) maturation trajectories. Energetic maps of intermediate (J-shaped) and mature (C-shaped) protofilaments show that stabilizing energy remains concentrated in motifs such as 306 VQIVYK 311 and 350 PAM4 362 , which are well-known APRs implicated in tau aggregation 46 ( Fig. 3I, J ). In contrast, structural frustration accumulates in neighboring sequences, particularly in flexible turn-forming motifs like the PGGG repeats. Importantly, dimensionality reduction analysis of the maps using PCA captures the chronological progression of structural maturation for both AD ( Fig. 3K ) and CTE fibril trajectories ( Fig. 3M ). Variance decomposition, which allows identifying the most influential residues, further highlights key stabilizing residues within APRs and destabilizing ones in adjacent PGGG-containing turns as primary contributors to energetic divergence during maturation ( Fig. 3L, N ). Taking together, these results suggest that tau maturation, like IAPP, is anchored by APRs. However, unlike IAPP, it is accompanied by a buildup of internal strain in regions flanking these stabilizing cores. Given that fibril formation must ultimately proceed downhill in free energy, this paradox implies the presence of missing stabilizing contributions that the current energetic model fails to capture and would potentially offset the accumulating strain, allowing these polymorphs to fully mature. AD and CTE strains emerge from a common pathway via local energetic reorganization To investigate how polymorphic diversity arises from tau’s maturation pathway, we performed PCA on the residue-level energy profiles of both J-shaped and C-shaped protofilaments. This analysis revealed three distinct clusters: a shared set of early intermediates, and two separate groups corresponding to the mature AD and CTE folds ( Fig. 4A ). These findings suggest that both tau strains follow a common initial maturation trajectory before diverging into distinct polymorphs late in assembly. Energetic differences between the mature AD and CTE structures are concentrated in the 343–358 region ( Fig. 4B ). For example, K343 lies in a loop that is considerably tighter in the CTE fold, forcing its side chain into a more destabilizing orientation than in the AD fold. Conversely, R349 adopts a more constrained conformation in the AD fold, generating an unfavorable local energy that is not present in CTE. The most prominent difference centers on an intra-sheet interaction unique to the AD fold: K353 forms a stabilizing salt bridge with D358, supported by exposure of S356 to the solvent. In contrast, this interaction is absent in CTE protofilaments, where D358 remains unsatisfied and S356 is buried within the fibril core 47 . These subtle shifts redistribute local strain, contributing to fold-specific stabilization patterns ( Fig. 3I, J ). Download figure Open in new tab Figure 4. Defining features of the AD and CTE maturation pathways. (A) Principal component analysis of their energetic profiles reveals separate clusters for AD-shaped and CTE-shaped protofilaments and validates a common cluster of intermediate J-folds. Points represent individual protofilaments (PF1, PF2, or PF3) and are colored by structural type. Dashed lines highlight hierarchical clustering of the individual energy profiles, which was performed on the embedding coordinates, and the dendrogram was cut to define three clusters. Labeled protofilaments represent outliers that were misclassified relative to their structural type. (B) Mapping of the second principal component loadings that separate the AD and CTE clusters reveals key residues. (C-E) Prediction of Mg ion binding for fibrils of the AD timeline. Maturation of (C) J-fold, (D) C-fold, and (E) all protofilament structures together, shows a strong correlation of increased metal binding as kinetics progress. Points represent individual protofilaments (PF1, PF2, or PF3), while dashed line indicates linear regression. Pearson correlation coefficients and P values are indicated. Timepoints are color-coded according to the AD maturation trajectory (Figure S2 A). (F) Graphical representation of key metal binding sites in the tau AD C-fold. (G) Metal binding significantly improves the local energetics of the binding sites. Importantly, these features begin to stratify early-stage intermediates by lineage. In particular, the exposure or burial of S356 appears to act as an early thermodynamic bifurcation point: S356 is mostly exposed in AD-like J-shaped protofilaments, whereas it is largely buried in their CTE-like counterparts (Fig. S5). This suggests that the divergence of AD- and CTE-like fibrils arises gradually from a common maturation pathway, driven by localized energetic reorganization rather than a discrete conformational switch. Cofactors redistribute and compensate for protofilament energetic frustration Because tau fibril maturation is thermodynamically constrained, the observed buildup of internal frustration in both the AD and CTE fold, especially in late-stage structures, implies the presence of stabilizing contributions not captured by protein-only models. Notably, the AD and CTE fibrils analyzed here were assembled under distinct buffer conditions: AD fibrils in the presence of Mg 2+ , and CTE fibrils with NaCl 37 , 45 . This raised the possibility that extrinsic cofactors such as metal ions help stabilize energetically strained conformations. To explore this, we predicted metal-binding sites across the tau maturation series 48 . In AD fibrils, Mg 2+ coordination becomes increasingly favorable as maturation proceeds ( Fig. 4C– E ). Two key binding sites emerge: one bridging the N- and C-terminal regions of the core, and the other centers on residues S356 and E358 ( Fig. 4F ). Notably, the latter site only emerges in the final C-shaped AD protofilament and involves S356, the same residue that differentiates the AD and CTE folds ( Fig. 4B ). This suggests that Mg 2+ may either stabilize a conformation that emerges with increasing strain, or alternatively, guide the maturation pathway toward that conformation by redistributing energetic strain as it accumulates. Consistent with this model, energetic re-evaluation of the AD mature fibrils with Mg 2+ ions modeled into these predicted sites confirms that local backbone strain is reduced, and overall energetic profiles improve ( Fig. 4G ). In contrast, our analysis did not predict Na + binding sites in the CTE-like fibrils. This may reflect both the monovalent and diffuse nature of Na + ions, which are less likely to form stable, geometry-specific coordination complexes. In comparison, Mg 2+ , as a bivalent cation, has a stronger and more predictable binding profile, enabling more confident identification of stabilizing interactions. These differences suggest that, under the present in vitro conditions, AD stabilization likely involves direct metal coordination, whereas CTE stabilization may rely more on nonspecific ionic screening. To further examine how external cofactors influence fibril energetics, we analyzed a recent structural time series capturing the remodeling of α-synuclein fibrils upon heparin binding 38 (Fig. S6). Heparin is frequently used in vitro to accelerate α-synuclein fibril formation and promote the emergence of defined polymorphs. Thus, this dataset provides a parallel opportunity for investigating how polyanions influence energetic remodeling during assembly. Early heparin-bound polymorphs (Hep-remod-1 and Hep-remod-2) show enhanced β-structure and improved energetic profiles compared to the apo state ( Fig. 5A ). However, prolonged heparin exposure leads to the formation of a more frustrated polymorph (Hep-remod-3), as evidenced by a drop in hydrogen-bond stability and a decrease in the fraction of stabilizing residues ( Fig. 5B, C ). PCA of energy profiles separates early and late heparin-bound states ( Fig. 5D ), with energetic shifts mapping to known heparin-binding regions (residues 38–45 and 58– 67) ( Fig. 5E, F ). Since our computational framework cannot directly model polyanions like heparin, the apparent destabilization of the late heparin-induced polymorph likely reflects missing stabilizing contacts that heparin would normally provide. Download figure Open in new tab Figure 5. Analysis of heparin-induced aS protofibril restructuring. (A) Ramachandran density plots of aS fibril structures. (B) Backbone hydrogen bond comparison of aS fibril structures. Statistics: t-test comparison of means. (C) Thermodynamic profiling of aS protofilaments. (D) Principal component analysis of aS thermodynamic profiles. (E) Variables plot and (F) mapping of loadings on the aS protofilament core. Together, these observations show that amyloid fibril maturation, while ultimately thermodynamically downhill, can involve increasing internal frustration that must be offset by stabilizing cofactors. Metal ions and polyanions act as external energetic supports, allowing strained polymorphs to persist by redistributing free energy across the fibril structure. Discussion Amyloid fibrils are defined by their shared cross-β architecture but exhibit remarkable structural diversity, giving rise to polymorphs with distinct biological and pathological properties. Despite the accumulation of many high-resolution cryo-EM structures, the thermodynamic principles that govern how specific polymorphs emerge, evolve, and stabilize over time remain poorly understood. This knowledge gap is especially pronounced in the context of fibril maturation, where assembly occurs over time and is shaped not only by sequence-encoded features but also by environmental cofactors that are often overlooked in structural models. Here, we address this gap by combining energetic profiling 6 , 33 with time-resolved cryo-EM datasets from three amyloid-forming proteins: IAPP, tau, and α-synuclein. Across more than 110 protofilament structures, we uncover a set of unifying thermodynamic features that shape fibril assembly, and we identify system-specific deviations that reveal how intrinsic and extrinsic factors interact to produce diverse polymorphic outcomes. A central finding is the consistent role of APRs 49 as thermodynamic anchors during amyloid maturation 5 , 50 - 55 . In all systems studied, these short segments dominate the stabilizing energy landscape and expand their structural influence as fibrils mature. This pattern is particularly striking in IAPP, where progressive stabilization of three APRs drives divergence into distinct structural polymorphs that, despite their morphological differences, achieve equivalent overall thermodynamic stability. This convergence echoes classical structure–function studies showing that targeted substitutions at L12,⍰F15,⍰V17, and⍰I26 markedly reduce β-sheet content, suppress fibril growth, and alleviate β-cell cytotoxicity in vitro and in rodent models 39 , 42 , 43 , 56 - 58 . Comparative genomics strengthens the point: rodents, whose IAPP is naturally non-amyloidogenic, harbor exactly such protective substitutions⍰ 59 , and engineering the same changes into human IAPP converts it into a potent, non-aggregating dominant-negative inhibitor of the wild-type peptide 60 , 61 . These insights have already been translated into clinic-approved analogues that retain glycemic control yet resists fibrillation 62 . Our findings support a model in which APRs act as reusable energetic cores, adaptable across folds and conformational contexts, and essential for nucleating and sustaining ordered amyloid assembly. In contrast, tau fibrils follow a distinct trajectory. While APRs remain key stabilizing features, the overall maturation process is accompanied by increasing backbone strain and growing energetic frustration, especially in flexible regions such as PGGG-containing turns. This creates a thermodynamic paradox: if amyloid maturation must proceed downhill in free energy, how can it culminate in more frustrated structures? The answer, we propose, lies in stabilizing interactions not accounted for in a protein-only model. This interpretation is supported by our analysis of cofactor interactions. In tau fibrils assembled under Mg 2+ conditions, predicted metal-binding sites are located in regions already stabilized by APRs, including a segment shown to impart stability specifically to AD-folded tau filaments 63 , and implicated in promoting the prion-like propensity of C-shaped tau strains 64 and tau polymorph divergence 46 . While these regions are not themselves highly frustrated, metal coordination may modulate the energetic balance of adjacent frustrated segments, helping the structure accommodate strain without global destabilization. Conversely, tau fibrils formed in NaCl do not exhibit structured ion binding, consistent with a reliance on nonspecific ionic screening or other unmodeled interactions. Interestingly, we identified K343 and R349 as key residues that stratify tau intermediates across maturation and the AD versus CTE timelines. Both residues are consistently destabilized in the two end-state folds. However, they reside in regions where undefined cryo-EM densities have been observed in structures derived from patient tissue, pointing to the possible involvement of unmodeled cofactors or modifications. These sites have the potential to be further stabilized in vivo through post-translational modifications 31 and lipid-based anionic interactions 65 , which may help alleviate local frustration and contribute to polymorphic divergence. Similarly, S356, a residue that already distinguishes early J-shaped protofilaments by lineage, is often found phosphorylated and serves as a biomarker of pre-tangle soluble tau assemblies in AD 63 , 66 , 67 . In the same light, we found that heparin restructures local hydrogen bonding and alters the fractions of residues adopting stabilizing versus frustrated conformations in aS fibrils, with strain-classifying thermodynamic changes observed in previously identified APRs that mediate aS self-assembly 68 . Whether cofactors stabilize strained structures after they emerge or help guide folding toward them during assembly remains an open question. However, our results clearly show that cofactors shift the distribution of free energy across the fibril and help determine which conformational states are accessible under specific conditions. We propose a general thermodynamic framework in which sequence-encoded APRs define the energetic backbone of fibril structures, while environmental factors modulate local frustration to favor one polymorph over another. This model explains how structurally distinct polymorphs can emerge from a single sequence, why in vitro and in vivo fibrils often differ, and how disease-specific strain properties may arise from context-dependent stabilization. It also offers a conceptual basis for understanding how small environmental changes, such as ionic conditions, cofactors, and polyanions, can exert outsized effects on amyloid structure, propagation, and toxicity. Methodologically, this work demonstrates the power of combining structural time series with energetic modeling to reconstruct the complex maturation timelines of diverse amyloid systems. The ability to map stabilizing and frustrating residues along assembly trajectories provides a powerful tool for pinpointing residues that act as conformational switches or sites of cofactor dependence. These residues represent promising targets for therapeutic strategies aimed at controlling strain emergence through mutation, ligand binding, or cofactor modulation. In conclusion, this study provides a thermodynamic blueprint for amyloid polymorphism: a model in which structure emerges from the dynamic balance of intrinsic stability and environmental influence, offering a unified framework to explain polymorphic diversity and its relevance to disease. Materials & Methods Collection of amyloid fibril structures We collected all available cryo-EM structures of amyloid fibrils from, to the best of our knowledge, the only three published studies that investigate amyloid formation, maturation and remodeling using a time-course approach. These include: (i) the in vitro maturation of IAPP-S20G over time 36 , (ii) the in vitro assembly of tau into paired helical filaments (PHFs) and chronic traumatic encephalopathy-like filaments 37 , and (iii) the time-dependent remodeling of mature α-synuclein fibrils upon heparin binding 38 . Energy profiling of amyloid structures To assess the thermodynamic stability of amyloid fibrils, we used the FoldX force field 69 . FoldX estimates the free energy of a protein structure by calculating the contribution of each atom based on its interactions with neighboring atoms. These atomic contributions are first summed at the residue level and later at the level of the entire protein. This allows for mapping the per-residue contribution to the total free energy (called ΔG contrib ), along with detailed breakdowns of individual energetic components. These include van der Waals interactions (ΔG vdw ), solvation energies for polar and apolar groups (ΔG solvP and ΔG solvH ), electrostatic interactions (ΔG el ), hydrogen bonding (ΔG Hbond ), entropic penalties for main and side chains (ΔS mc and ΔS sc ), and water-mediated hydrogen bonding (ΔG wb ). Since cryo-EM-derived fibril structures vary in the number of stacked monomer layers, all structures were extended to a uniform depth of 10 monomers prior to analysis. Structures were then subjected to side-chain energy minimization using the FoldX RepairPDB command. This procedure optimizes side-chain conformations by sampling from a rotamer library derived from high-resolution X-ray structures, selecting the lowest-energy rotamer for each residue. Subsequently, we used the SequenceDetail function in FoldX to compute per-residue free energy contributions (ΔG contrib ) and the associated thermodynamic components. To avoid boundary artifacts caused by missing head-to-tail stacking interactions at the fibril termini, monomers located at both ends of the extended fibril were excluded from downstream analysis. Removal of low-quality amyloid fibrils structures The accuracy of the free energy estimation by FoldX correlates with structure quality parameters, such as resolution. Therefore, we defined an empirical partitioning boundary based on a linear relationship between structural resolution and van der Waals (VDW) clashes (Fig. S2). Specifically, structures were excluded if they fell above the line defined by the equation: This criterion effectively removes models with both poor resolution and high steric clashes, retaining only high-quality structures for downstream analysis. Resolution values were obtained from the original studies using the Fourier Shell Correlation (FSC) 0.143 criterion 70 , which estimates the resolution of the reconstructed cryo-EM maps. Van der Waals clashes were identified using FoldX. As a result of this filtering strategy, the following models were excluded from the analysis: MIA-5, MIA-8, and LIA-4 (from the in vitro assembly of tau into paired helical filaments), and MIA-1, MIA-3, and MIA-14 (from the in vitro assembly of tau into CTE-like filaments). Additionally, the first protofilament of LIA-7 and of THF were removed due to excessive steric clashes, while the remaining protofilaments in these structures were retained for analysis. Dimensionality reduction analysis To investigate which residues are energetically driving the maturation and remodeling of amyloid fibrils over time, we performed principal component analysis (PCA) on the per-residue free energy contributions (ΔG contrib ) calculated by FoldX. These values were compiled into a matrix where rows represented individual protofilaments and columns corresponded to aligned residue positions. Prior to dimensionality reduction, the data was normalized by row-wise standardization to account for differences in absolute energy scales across protofilaments. Then, missing values, which occurred in some protofilaments due to unresolved residues, were imputed using the imputePCA function from the missMDA R package with regularized iterative PCA 71 . PCA was then performed on the imputed, standardized matrix using the prcomp function from the stats R package. Clustering was subsequently applied to the first three principal components using hierarchical clustering via the hclust function. Metal binding analysis To investigate the potential contribution of metal ions to the in vitro assembly of tau fibrils, we used a previously established high-accuracy algorithm implemented in FoldX 48 to predict metal binding sites in each protofilament. This method identifies positions on the protein surface where metal ions can form favorable electrostatic interactions with multiple protein atoms in geometrically optimal configurations. This approach has been shown to predict more than 90% of the metal binding sites in globular proteins with a positional accuracy of ≤0.6 Å 48 . Given that the tau (297–391) construct forms paired helical filaments (PHFs) when incubated in phosphate buffer supplemented with magnesium chloride 37 , 45 , we predicted magnesium binding sites in all protofilaments derived from the PHF reaction. In contrast, for structures derived from the CTE-like reaction, which uses sodium chloride instead of magnesium chloride 37 , 45 , we predicted sodium binding sites (although no binding sites with high affinity could be inferred). To assess the impact of predicted metal binding on structural energetics, we incorporated the identified metal ions into each structure and recalculated per-residue free energy contributions (ΔG contrib ) using the FoldX SequenceDetail function. Quantification, statistics and visualizations The methods of statistical analysis are provided in detail in the corresponding figure legends. Visualizations were performed with GraphPad prism or custom R scripts using the packages ggplot2 and plotly. ChimeraX was used to visualize protein structures 72 . Data Availability All data generated in this study are available as Source data and are also available on the Zenodo database under accession code XXXXX. Funding The Switch Laboratory was supported by the Flanders Institute for Biotechnology (VIB, grant no. C0401 to FR and JS), KU Leuven (postdoctoral grant PDMT2/24/084 to RD-R.), the Fund for Scientific Research Flanders (FWO, project grant G0A6724N to JS), and the Stichting Alzheimer Onderzoek / Fondation Recherche Alzheimer (project grant SAO-FRA 2023/0005 to JS). NL was supported by a Thomas O. Hicks Endowed Scholarship. Computational resources were provided by the BioHPC cluster supported by the Lyda Hill Department of Bioinformatics at UTSW. Declaration of interests None declared. Funder Information Declared Vlaams Instituut voor Biotechnologie, https://ror.org/03xrhmk39 , C0401 KU Leuven , PDMT2/24/084 Stichting Alzheimer Onderzoek , SAO-FRA 2023/0005 Fund for Scientific Research Flanders , G0A6724N References 1. ↵ Chiti , F. & Dobson , C.M. Protein Misfolding, Amyloid Formation, and Human Disease: A Summary of Progress Over the Last Decade . Annu Rev Biochem 86 , 27 – 68 ( 2017 ). doi: 10.1146/annurev-biochem-061516-045115 OpenUrl CrossRef PubMed 2. ↵ Buxbaum , J. N. et al. 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Share Energetic profiling reveals thermodynamic principles underlying amyloid fibril maturation Ramon Duran-Romaña , Joost Schymkowitz , Frederic Rousseau , Nikolaos Louros bioRxiv 2025.05.14.653959; doi: https://doi.org/10.1101/2025.05.14.653959 Share This Article: Copy Citation Tools Energetic profiling reveals thermodynamic principles underlying amyloid fibril maturation Ramon Duran-Romaña , Joost Schymkowitz , Frederic Rousseau , Nikolaos Louros bioRxiv 2025.05.14.653959; doi: https://doi.org/10.1101/2025.05.14.653959 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Biophysics Subject Areas All Articles Animal Behavior and Cognition (7619) Biochemistry (17639) Bioengineering (13865) Bioinformatics (41854) Biophysics (21405) Cancer Biology (18544) Cell Biology (25432) Clinical Trials (138) Developmental Biology (13356) Ecology (19863) Epidemiology (2067) Evolutionary Biology (24287) Genetics (15587) Genomics (22465) Immunology (17701) Microbiology (40300) Molecular Biology (17142) Neuroscience (88440) Paleontology (666) Pathology (2825) Pharmacology and Toxicology (4814) Physiology (7633) Plant Biology (15107) Scientific Communication and Education (2042) Synthetic Biology (4285) Systems Biology (9809) Zoology (2268)