Mapping the Structural Landscape of Amyloid Fibrils to Guide Polymorph-Specific Therapeutics

Mapping the Structural Landscape of Amyloid Fibrils to Guide Polymorph-Specific Therapeutics | 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 Mapping the Structural Landscape of Amyloid Fibrils to Guide Polymorph-Specific Therapeutics Ahmed Sadek , Bruno E. Correia , Hilal A. Lashuel doi: https://doi.org/10.1101/2025.05.08.652887 Ahmed Sadek 1 Laboratory of Molecular and Chemical Biology of Neurodegeneration, Institute of Bioengineering, School of Life Sciences , Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland 2 Laboratory of Protein Design and Immunoengineering, Institute of Bioengineering, Ecole polytechnique fédérale de Lausanne , Lausanne, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Bruno E. Correia 2 Laboratory of Protein Design and Immunoengineering, Institute of Bioengineering, Ecole polytechnique fédérale de Lausanne , Lausanne, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: halashuel{at}qf.org.qa Hilal A. Lashuel 1 Laboratory of Molecular and Chemical Biology of Neurodegeneration, Institute of Bioengineering, School of Life Sciences , Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland 3 Weill Cornell Medicine Qatar, Education City, Qatar Foundation , Doha, Qatar 4 Department of Neurology, Weill Cornell Medicine , New York, NY, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: halashuel{at}qf.org.qa Abstract Full Text Info/History Metrics Data/Code Preview PDF Abstract Amyloid fibrils are pathological hallmarks of neurodegenerative diseases and central contributors to their progression, representing promising targets for disease-modifying interventions. However, limited access to patient-derived fibrils and the inability to reproduce pathological folds in vitro hinder the development of fibril-specific ligands. Here, we present FibrilSite, a computational pipeline that identifies geometric and physicochemical similarities across fibril surfaces and demonstrate its utility to identify structural features that distinguish different fibril polymorphs. Our analysis uncovered conserved and polymorph-specific features within alpha-synuclein fibrils. Notably, one site was conserved between ex vivo multiple system atrophy and in vitro H50Q mutant fibrils, suggesting the latter’s potential utility in drug discovery. Druggability predictions further prioritized ligandable sites across fibrils. Together, our findings establish a structure-based framework for identifying disease-relevant features and mapping them onto suitable in vitro models, guiding the rational development of polymorph-specific diagnostics and therapeutics for amyloid-related disorders with improved translational potential. Introduction Neurodegenerative diseases (NDs) are characterized by the progressive loss of neurons, leading to impairments in memory, cognitive, and motor functions 1 . A defining hallmark of NDs is the misfolding and aggregation of soluble proteins into amyloid fibrils 2 , which deposit as extracellular plaques (e.g. Amyloid plaques) 3 or in intracellular inclusions (e.g. Lewy bodies) 4 . Synucleinopathies and tauopathies are two sub-groups of NDs, marked by intercellular inclusions of aggregated alpha-synuclein (aSyn) 5 and hyperphosphorylated tau 6 , respectively. Among NDs, Alzheimer’s disease (AD) and Parkinson’s disease (PD) are the most prevalent 7 , 8 , with the number of affected patients projected to reach 162 million worldwide by 2050 9 . Despite their growing societal impact, no preventive or disease-modifying therapies are currently available 10 . Amyloid fibrils play a key role in the formation and spreading of ND’s pathology through several mechanisms. These include catalyzing monomer aggregation in a nucleation-dependent manner 11 , 12 , cell-to-cell propagation of pathology 13 – 17 and inducing neurotoxicity by disrupting cellular membranes 18 , altering ion homeostasis and cellular proteostasis, and/or promoting oxidative stress 19 – 22 . Therefore, targeting amyloid fibrils offers opportunities to intervene with multiple pathogenic processes that contribute to disease development and progression 23 – 28 . Advances in cryogenic electron microscopy (cryo-EM) have enabled determining the structure of amyloid fibril at near-atomic detail, revealing diverse architectures in both in vitro and ex vivo preparations 29 – 33 . These studies underscore the influence of aggregation conditions on fibril conformation 31 , 34 , demonstrating that a single protein can form fibrils of different structures (i.e., polymorphs) 35 – 37 –a phenomenon known as amyloid polymorphism 38 . Comparative cryo-EM structural analyses have revealed disease-associated fibril polymorphs across NDs 33 . In tauopathies, such as AD 39 , 40 , chronic traumatic encephalopathy (CTE) 41 , and Pick’s disease 42 , a unique tau fibril fold has been resolved for each condition ( Supplementary Fig. 1 A-D). Similarly, in synucleinopathies, including PD, dementia with Lewy bodies (DLB) 43 , multiple system atrophy (MSA) 37 , and juvenile-onset synucleinopathy (JOS) 44 , are each associated with structurally distinct aSyn fibrils ( Fig. 1A-D ). These polymorphs differ in protofilament number, fold and inter-protofilament interfaces, resulting in divergent surface topologies. Such structural features offer opportunities to develop polymorph-selective binders and disease-specific anti-amyloid therapeutics and diagnostics. Download figure Open in new tab Figure 1 Representative alpha synuclein (aSyn) cryo-EM fibril structures. (A-D) Cryo-EM structures of ex vivo aSyn fibrils extracted from postmortem brain tissue of patients with multiple system atrophy 37 (MSA; A, B ), Parkinson’s disease (PD) and dementia with Lewy bodies 43 (DLB; C ) and juvenile-onset synucleinopathy 44 (JOS; D ). MSA fibrils consist of two asymmetric protofilaments, with protofilament A (PF-A) larger than protofilament B (PF-B). A non-proteinaceous density (orange) is present at the inter-protofilament interface and is shared between the MSA and JOS fibrils. (E, F) Cryo-EM structures of in vitro aSyn fibril polymorphs assembled from recombinant, human wild-type aSyn: polymorph 1a (Pol 1a; E ) and polymorph 2a (Pol 2a; F ). Both in vitro fibrils are composed of two symmetrical protofilaments. In Pol 1a, the protofilaments interact via a hydrophobic steric zipper 35 , whereas in Pol 2a the interaction is mediated by electrostatic salt-bridges 36 . Although amyloid proteins readily aggregate in vitro , reproducing pathology-relevant fibril folds remains challenging. For example, recombinant wild-type (WT) aSyn fibrils adopt polymorphs that differ from those derived from synucleinopathy patients ( Fig 1 ). Both in vitro WT and ex vivo MSA fibrils may comprise two protofilaments; however, the symmetric protofilaments in the in vitro fibrils interact via a hydrophobic steric zipper 35 ( Fig 1E ) or electrostatic salt-bridges 36 ( Fig. 1F ). In contrast, ex vivo MSA fibrils exhibit an extended interface between two asymmetric protofilaments, with a cavity that harbors an unidentified, non-proteinaceous density 37 ( Fig. 1A, B ). Additionally, ex vivo MSA fibrils, like other patient-derived fibrils, often contain unidentified co-factors and post-translational modifications (PTM), including acetylation, phosphorylation, and ubiquitination, which may influence fibril formation 37 , 45 – 47 . The absence of these biochemical features in in vitro aggregation conditions likely hinders the replication of pathological fibril polymorphs. Even when seeded amplification assays are employed, ex vivo fibrils give rise to structures resembling in vitro fibrils 48 . Notably, in the case of AD and CTE tau fibrils, their pathological fibril structure could be successfully generated only via the de novo aggregation of truncated tau proteins 49 ( Supplementary Fig. 1 E, F). Small molecules that bind amyloid fibrils hold promise for both diagnostics and therapeutics. Most discovery efforts have relied on high-throughput screening and optimization of know amyloidogenic dyes, such as Congo-red and thioflavin T (ThT) 50 – 52 . While several compounds have been identified, many lack target specificity. For instance, tracers developed for imaging amyloid plaques in AD patients, including BF-227 53 and Pittsburgh compound B (PiB) 54 , also bind aSyn aggregates in vitro 55 , 56 and in MSA patients 51 , 57 , likely due to shared structural motifs in their dye-based scaffolds 50 , 58 . More recently, an aSyn fibril-specific tracer (F0502B) was identified through an intensive screening and optimization process 59 , however, it failed to distinguish between different aSyn fibril polymorphs 60 . Despite these limitations, a few amyloid tracers have advanced to (pre)clinical diagnostic studies, although their selectivity remains a concern 51 , 56 , 61 . Towards addressing the limitations outlined above, we developed a computational pipeline, FibrilSite, to identify shared and distinct surface features across selected fibril sites, based on geometric and chemical properties ( Fig. 2 ). Focusing on sites from ex vivo aSyn fibrils, we assessed their similarity to those found in in vitro aSyn fibrils and in ex vivo fibrils formed by other amyloidogenic proteins. The conserved sites identified through this analysis can then be employed in computational drug-screening workflows, to prioritize candidate molecules for downstream experimental optimization 62 , 63 . To our knowledge, this represents the first comprehensive attempt to determine the extent to which in vitro generated fibrils could serve as reliable tools to discover or validate aSyn fibrils binders. This framework addresses key challenges in developing polymorph-and disease-specific diagnostics and therapeutics, including the rational selection of ligandable sites to achieve polymorph specificity and the strategic use of in vitro fibrils in drug development, particularly when ex vivo fibril folds cannot be reliably reproduced in vitro . Download figure Open in new tab Figure 2 FibrilSite workflow for identifying conserved surface sites across structurally diverse amyloid fibrils. The FibrilSite workflow consists of six steps: (1) Site-forming residues are defined based on structural distinctiveness; (2) their atomic coordinates are mapped onto the fibril’s molecular surface; (3) each site is extracted as a point cloud and featurized with geometric and physicochemical surface properties, including Poisson–Boltzmann continuum electrostatics (PBE), hydrophobicity (HP), shape index (S i ) and hydrogen bond donors/acceptor potential (e - ) (Methods: FibrilSite, Defining fibril sites); (4) a database of 62 sites from 19 cryo-EM fibril structures is compiled; (5) all-vs-all alignments are performed using point cloud registration algorithms, including Random Sample Consensus (RANSAC) and Iterative Closest Point (ICP) (Methods: FibrilSite, Sites alignment); (6) similar sites are identified based on two metrics: surface overlap (SS max ) and surface feature difference (F diff ), which together assess the geometric and physicochemical similarity between aligned sites. Results Computational comparison of amyloid fibril site surfaces To explore whether the distinct structural features of fibril sites can serve as markers for polymorph differentiation, we defined 62 sites from 19 fibril structures, including alpha synuclein (aSyn), tau, amyloid beta (Aβ), prion protein (PrP) and transmembrane protein 106B (TMEM106B). These sites were extracted and analyzed using our computational pipeline, FibrilSite ( Fig. 2 ). Briefly, after defining the site-forming residues, each site was isolated as point cloud and annotated with geometric and physicochemical features, including electrostatics, hydrophobicity, hydrogen bond donors/acceptors, and shape index 64 . To identify similar sites, we performed an all-vs-all alignment using point cloud registration algorithms, including Random Sample Consensus (RANSAC) and Iterative Closest Point (ICP), guided by local surface feature similarity. Site similarity was evaluated using two metrics: (i) SS max , which quantifies the maximum surface overlap between aligned sites while accounting for site size; and (ii) F diff , the Euclidean distance between corresponding surface features of aligned sites. Sites were considered similar if they exhibited both high SS max and low F diff values, indicating substantial surface overlap and minimal feature divergence. Identification of conserved and distinct surface sites in ex vivo amyloid fibrils Given the promiscuity of amyloid-binding small molecules and the limited understanding of their binding sites, we systematically analyzed 27 defined sites in ex vivo fibrils: 15 from aSyn, 4 from tau, 2 from amyloid beta 42 (Aβ 42 ), 2 from transmembrane protein 106B (TMEM106B), and 4 from prion protein (PrP), with a focus on identifying unique aSyn-specific sites ( Fig. 3 , Supplementary Fig. 2 ). Download figure Open in new tab Figure 3 Defined sites from ex vivo alpha-synuclein (aSyn) fibrils. A total of fifteen sites were defined across four ex vivo aSyn fibril structures from patients with multiple system atrophy (MSA) 37 , Parkinson’s disease (PD) and dementia with Lewy bodies (DLB) 43 . Three MSA fibril polymorphs were analyzed: three sites were defined in MSA Type-I (A) , and five in each MSA Type-II variant 37 (B,C) .The asterisk (*) indicates the central cavity site in Type-II fibrils, which is occupied by non-proteinaceous density. The Lewy fold (LF), associated with PD and DLB, contained two defined sites 43 (D) . Site-forming residues are shown as cyan sticks on one fibril layer of each structure. We first analyzed cryo-EM structures of ex vivo WT aSyn fibrils from patients with MSA, PD and DLB, to compare surface similarity between structurally divergent polymorphs with identical sequences. Two distinct folds were observed: a double protofilament fibril in MSA, featuring an N-terminus packed against a three-layered, L-shaped C-terminus; and a single-protofilament fibril–termed the Lewy Fold (LF)–in PD and DLB, characterized by a wave-like N-terminus with the C-terminus folded back onto it ( Fig. 1A-C ). MSA fibrils consist of two asymmetric protofilaments, a larger protofilament A (PF-A) and a smaller protofilament B (PF-B), separated by a non-proteinaceous core. Structural variation within PF-B defined MSA fibril subtypes: in Type I fibrils, residues Lys21-Gly36 were ordered and resolved; in Type-II fibrils, they were disordered. Further conformational differences in the Ala78-Gln99 region subdivide Type-II fibrils into Type-II A and Type-II B ( Fig. 1A, B ). In total, we defined 15 sites: three in MSA Type-I, five in MSA Type II A , five in MSA Type II B and two in LF fibrils ( Fig. 3 ). In MSA structures, two sites could be defined in PF-A across all fibril subtypes. The number of additional sites in PF-B varied by subtype, with one site in Type I, two in Type II A , and two in Type II B . Additionally, the central cavity site–occupied by a non-proteinaceous density–was included in both Type II subtypes. Structural alignments confirmed the distinction between the two fibril folds, with similarities observed only among MSA fibril subtypes ( Fig. 4 ). Two conserved sites with identical sequences were shared across MSA fibrils. The first, located in PF-A ( Ala85-Phe94 ), exhibited greater surface overlap (SS max = 0.89) and lower surface feature difference (F diff = 0.31) between Type-II A (P60) and Type-II B (P64), compared to Type-I (P57), consistent with PF-A conservation within Type-II fibrils ( Fig. 4A ). The second site, located in PF-B ( Ile88–Lys96 ) was conserved between Type-I (P58) and Type-II A (P62) fibrils with SS max = 0.87 and F diff = 0.27 ( Fig. 4B ). Download figure Open in new tab Figure 4 Conserved sites among multiple system atrophy fibril structures. Site matches were identified by aligning 15 sites from ex vivo alpha-synuclein (aSyn) fibril structures from patients with multiple system atrophy (MSA, 13 sites) and Parkinson’s disease (PD)/dementia with Lewy bodies (DLB; Lewy fold [LF], 2 sites), in an all-vs-all manner based on surface feature similarity. Features included electrostatics, hydrophobicity, hydrogen bond donors/acceptor potential, and shape index. For each matched pair, the site surface overlap (SS max , green shading, higher = better) and feature distance (F diff ; Euclidean distance between aligned surface features; purple shading, lower = better) are shown. (A) A conserved site in protofilament A (PF-A) across all MSA fibrils, formed by residues Ala85-Phe94 . (B) A shared site in PF-B of MSA Type-I and Type-II A fibrils, formed by Ile88-Lys96 . (C) A site in PF-A present in all MSA fibrils, defined by Lys34-Glu46 and Lys80-Glu83 , showing extensive overlap in Type-II fibrils and partial overlap with Type-I fibrils (residues: Lys34-Tyr39 ). (D) A site in protofilament B (PF-B) of MSA Type-II fibrils, formed by Val37-Glu46 and Lys80 . (E) A cavity site located in the core of MSA Type-II fibrils, occupied by non-proteinaceous density. Site-forming residues are shown as sticks; shared surface points are rendered in grey. A third conserved site, located in PF-A ( Lys34-Glu46 and Lys80-Glu83 ) was identified across all MSA fibrils. Although this site was highly conserved in Type-II fibrils (II A : P59, II B: P63), its similarity with Type-I fibrils (P56) was limited to the Lys34-Tyr39 region due to conformational differences in PF-A ( Fig. 4C ). Notably, the conformation of residues ( Val37-Glu46 and Lys80 ) was conserved across multiple contexts in Type-II fibrils: between PF-A sites (P59, P63; Fig. 4C ), between PF-B sites (P61, P65; Fig. 4D ), and also between sites across PF-A and PF-B ( Supplementary Fig. 3 A). Another conserved site corresponded to the Type-II fibrils central cavity, which harbors non-proteinaceous matter ( Fig. 4E ). All shared MSA sites had identical amino acid composition and substantial surface alignment, highlighting the internal structural diversity among MSA fibril types while reinforcing their uniqueness relative to LF fibrils. The MSA sites conserved across all fibril subtypes represent potential targets for pan-MSA diagnostic and therapeutic strategies ( Fig. 4A, B ). Additionally, Type-II specific sites ( Fig. 4D, E ) may enable subtype-selective interventions, particularly for monitoring disease progression, as the Type-I to Type-II fibril ratio has been linked to disease duration 55 . Interestingly, some conserved MSA sites included residues such as Y39 and S87, which have been reported to undergo phosphorylation or nitration in disease 37 , 46 , 65 , 66 . However, a few studies suggested that the abundance of these modifications within fibrils is thought to be low 37 , 43 . Sites with identical sequence and conformations helped refine the interpretation of site surface overlap (SS max ) and surface feature difference (F diff ) metrics, establishing a benchmark for site similarity: SS max ≥ 0.5 and F diff ≤ 0.6. Next, we assessed surface similarity between sites from ex vivo aSyn fibrils and those of other amyloidogenic proteins ( Fig.2 , Supplementary Fig. 2 ). Due to differences in protein sequences and fibril polymorph conformation, no cross-protein site matches met our similarity criteria, highlighting the uniqueness of fibril surface features–not only among different amyloidogenic proteins, but also across polymorphs of the same protein (i.e., aSyn). Together, these findings support the potential of structurally defined fibril sites for polymorph- and disease-specific diagnostics and therapeutics. Comparative analysis of in vitro and ex vivo aSyn fibril surface sites Next, we sought to identify sites shared between ex vivo and in vitro aSyn fibrils to assess the suitability of in vitro fibrils for screening pathologically relevant chemical matter. A major challenge in targeting pathological fibrils is the limited availability of patient-derived material, which is likely the most biologically relevant substrate for drug discovery and optimization. Reproducing pathology-relevant fibril folds in vitro has been largely unsuccessful, with the exception of ex vivo AD and CTE tau fibril folds 49 . An alternative strategy that can support drug development efforts involves computationally identifying specific features of brain-derived aggregates that are reproduced in one or more in vitro -generated fibrils, which could provide an intermediate solution for identifying disease-relevant fibril binders. To explore this approach, we defined 35 sites from in vitro aSyn fibril structures assembled from recombinant human wild-type (WT) aSyn, disease-associated familial mutations and post-translationally modified variants ( Supplementary Fig. 4 ). These sites were then compared to those previously defined in ex vivo aSyn fibrils ( Fig. 3 ). Among these, only two sites from the H50Q mutant fibril (P50 and P83) exhibited close structural similarity to a site (P66) in MSA Type-II B fibrils. While minor backbone differences were observed, the side-chain arrangements were highly similar, yielding high surface overlap for P50 (SS max = 0.69; Fig. 5A ). All three sites share an identical sequence; however, the terminal residue Asp98 is unresolved in P83, contributing to a shallower topology and reduced surface overlap with P66 (SS max = 0.55; Fig. 5A ). This match demonstrates that certain in vitro fibril preparations can replicate structural features present in brain-derived fibrils. Download figure Open in new tab Figure 5 Shared sites between ex vivo and in vitro alpha synuclein (aSyn) fibrils. Site matches were identified by aligning 50 sites, including 15 from ex vivo ( Fig. 3 ) and 35 from in vitro aSyn fibril structures ( Supplementary Fig. 4 ). Alignments were performed in an all-vs-all manner based on surface feature similarity. Features included electrostatics, hydrophobicity, hydrogen bond donors/acceptor potential, and shape index. For each matched pair, the site surface overlap (SS max , green shading, higher = better) and feature distance (F diff ; Euclidean distance between aligned surface features; purple shading, lower = better) are shown. (A) Alignment of a site in protofilament B (PF-B) of multiple system atrophy (MSA) Type-II B (P66; formed by Ala85-Asp98 ) with two sites in the in vitro aSyn H50Q mutant fibrils (P50 and P83). (B, C) Alignment of the positron emission tomography tracer F0502B binding site (P84) in in vitro WT polymorph 1a fibrils with putative homologous sites in ex vivo MSA Type-I (P56), MSA Type-IIA (P59) (B) and Lewy Fold (LF; P68; C ) . (D) Alignment of a site in PF-B of MSA Type-I (P58), MSA Type-II A (P62) with a site in the in vitro WT Polymorph 2b fibrils (P36). Site-forming residues are shown as sticks; shared surface points are rendered in grey. We next evaluated the alignments of the aSyn tracer F0502B binding site (P84; Supplementary Fig. 4 D) from in vitro WT aSyn polymorph 1a fibrils with putative homologous regions in ex vivo aSyn MSA Type-I (P56), MSA Type-II A (P59) ( Fig. 5B ) and the Lewy Fold (LF; P68) fibrils 59 ( Fig. 5C ). Although F0502B labels pathological aSyn deposits in PD, DLB and MSA 59 , none of these alignments met our predefined site-similarity thresholds. Among MSA fibrils, P84 showed lower surface feature divergence with P56 in Type-I (F diff = 0.62) than with P59 in in Type-II A (F diff = 1.2), with the latter showing substantial residue mismatches ( Fig. 5B ). Alignment with LF site (P68) resulted in the lowest surface overlap (SS max = 0.35) and high surface feature divergence (F diff = 0.92) ( Fig. 5C ). These findings, together with cryo-EM evidence of F0502B’s conformational adaptability across aSyn fibrils 60 , may explain its broad labelling across synucleinopathies. More broadly, they underscore the value of structural surface feature comparisons for identifying conserved fibril features. Lastly, we evaluated the alignment with the highest surface overlap between in vitro WT and ex vivo aSyn fibrils. This involved P36 from in vitro WT polymorph 2b and two ex vivo MSA sites: P58 (Type-I) and P62 (Type-II A ). Both alignments showed high surface overlap (SS max > 0.8) but considerable surface features differences (F diff = 0.90 and 0.82, respectively; Fig. 5D ). While some residue aligned well–for example, Lys60 in P36 aligned with Lys96 in both MSA sites–others, such as Glu61 in P36 aligning with Phe94 in the MSA sites, introduced pronounced differences in charge and hydrophobicity, underscoring the divergence in surface chemistry despite geometric similarity. Together, these comparisons highlight the structural differences between brain-derived and in vitro -prepared aSyn fibrils. Although shared sites were identified between MSA Type-II B (P66) and the in vitro aSyn H50Q mutant fibril (P50 and P83), no high-confidence matches were found with in vitro WT aSyn fibrils commonly used in research. These findings illustrate the difficulty of reproducing disease-relevant structural features in vitro and support the use of computational strategies to identify conserved surface sites for structure-guided drug design. They further underscore the importance of accelerating efforts to reproduce pathology-associated fibril structures in vitro as essential tools to drive drug discovery and development of disease-specific imaging agents to track amyloid formation in the brain. Druggability assessment of fibril sites To assess the druggability of the defined fibril sites, we employed P2Rank, a machine learning-based ligand-binding site predictor 67 . Given the atypical geometry and periodic topology of amyloid fibrils compared to globular proteins, we modified selected parameters in the default configuration to improve site detection sensitivity (see Methods, Supplementary Table 1 ). Additionally, because the defined sites often exceed the size typically occupied by a single ligand, P2Rank occasionally identified subregions within a given site as discrete pockets. Of the 62 sites defined across 19 cryo-EM fibril structures, P2Rank predicted 41 pockets across 38 sites, spanning 18 fibrils. No pockets were identified in the in vitro aSyn G51D mutant fibril. Identified pockets were subsequently evaluated using P2Rank’s predicted ligand-binding probability score (range: 0 (non-druggable) to 1 (druggable)). Applying a threshold of 0.7, 26 out of 41 pockets were classified as druggable ( Fig. 6 ). View this table: View inline View popup Download powerpoint Supplementary Table 1 Modified P2Rank parameters for amyloid fibril pocket detection Selected parameters in the default P2Rank 67 (v2.5) configuration were adjusted to account for the distinctive geometry of amyloid fibrils. Modifications were made to increase surface sampling density and improve sensitivity to extended or shallow surface grooves characteristic of fibril fold architectures. The full configuration file is provided in the accompanying data repository. Download figure Open in new tab Figure 6 Druggability analysis of defined fibrils sites using P2Rank Druggability of defined fibril sites was assessed using P2Rank 67 . Predicted ligand-binding probabilities (ranging from 0 = non-druggable to 1 = druggable) are shown for 41 identified pockets. To accommodate the atypical topological features of amyloid fibrils, the pocket definition algorithm was modified to improve detection of fibril sites. In some cases, P2Rank identified subregions within a single site, these are denoted by increasing numbers of asterisks (e.g., *, **) following the site name. A probability threshold of 0.7 (dashed line) was used to classify 26 pockets as druggable (blue) and 15 pockets as less-druggable (gray). Fibrils analyzed include ex vivo alpha-synuclein (aSyn) fibrils from MSA patients–Type I (MSA-I, PDB 6XYO), Type II A (MSA-II A , PDB 6XYP), Type II B (MSA-II B , PDB 6XYQ)–and from PD/DLB patients (LF, PDB-8A9L); ex vivo fibrils of other amyloidogenic proteins–Tau CTE-II (PDB 6NWQ), Tau PHF (PDB 7NRV), Aβ 42 (PDB 7Q4M), PrP (PDB 7UMQ), TMEM106B (TMEM, PDB 7QVC); and in vitro aSyn fibrils, including wild-type polymorphs (Pol)–1a (PDB 6CU7, PDB 7WMM), 1b (PDB 6CU8), 2a (PDB 6SSX), 2b (PDB 6SST); familial mutants–E46K (PDB 6UFR), H50Q (PDB 6PES), A53T (PDB 6LRQ), and phosphorylated aSyn at Tyr39 –pY39 (PDB 6L1U). Focusing on ex vivo fibrils, all structures contained at least one druggable site, with the exception of Aβ 42 fibrils. Notably, P78 site in tau paired helical filaments (PHF) was predicted to be druggable, consistent with cryo-EM studies identifying it as a binding site for EGCG 68 . Among ex vivo aSyn fibrils, one of the two defined sites in Lewy fold (LF) fibrils was detected and predicted to be druggable. In contrast, 11 out of 13 defined sites in MSA fibrils were identified by P2Rank, of which 9 were classified as druggable. Among these, the matched sites in Type I (P58) and Type-II A (P62) fibrils ( Fig. 4B ), as well as sites in Type-II A (P59 and P61) and Type-II B (P63 and P65) fibrils ( Fig. 4D , Supplementary Figure 3 ), exhibited similar druggability scores, reinforcing the expectation that structurally and chemically similar sites possess comparable ligand-binding potential. Among in vitro aSyn fibrils, all but WT polymorph 1b and A53T mutant fibrils contained druggable pockets. In WT polymorph 1a, druggable sites included P84, which corresponds to the tracer F0502B binding pocket 59 , as well as P2 and P4, both of which were recently shown by cryo-EM studies to accommodate various small molecules 56 , 60 . These predictions align with experimental findings, providing confidence in the method’s potential for analyzing amyloid fibrils. Despite P2Rank’s overall effectiveness in identifying druggable sites in amyloid fibrils, several discrepancies highlighted the limitations of current pocket prediction tools when applied to non-canonical structures such as amyloid fibrils. For example, among the matched sites in MSA Type I (P57), Type-II A (P60), and Type-II B (P64) fibrils ( Fig. 4A ), only P57 was predicted to be druggable, while P64 scored much lower and P60 was not detected. Similarly, for the matched sites between MSA Type-II B (P66) and the in vitro aSyn H50Q mutant (P50) fibrils ( Fig. 5A ), P50 was predicted to be druggable, while P66 scored below the set threshold. Since these matched sites share identical sequences and similar surface properties, their divergent predictions highlight the need for pocket detection algorithms tailored to the periodicity and surface characteristics of amyloid fibrils. Lastly, although P83 from the in vitro aSyn H50Q mutant fibrils also aligned with P66 from MSA Type-II B ( Fig. 5A ), it was not identified as a pocket, likely due to its shallower topology. Taken together, P50 emerged as the only site from an in vitro aSyn fibril with both high predicted druggability and strong structural similarity to an ex vivo fibril site, underscoring its potential translational relevance. These findings highlight the structural diversity and druggability of many fibril sites and support the feasibility of developing disease-specific molecules based on fibril fold differentiation. Discussion A major challenges in the rational design of amyloid-binding molecules lies in the limited availability of patient-derived fibrils and the structural divergence between in vitro and pathological fibril folds 33 . High-throughput drug screening often relies on in vitro fibrils, which frequently fail to replicate structural features of pathological conformations, leading to molecules with limited specificity 57 , 61 , 69 . Nevertheless, the discovery of F0502B – an aSyn fibril-specific tracer – using in vitro WT aSyn fibrils, demonstrates the potential of fibril-specific drug development through comprehensive screening and optimization 59 . Cryo-EM studies have revealed that F0502B induces a conformational rearrangement in the in vitro fibrils, creating a binding cavity resembling those found in ex vivo aSyn fibrils 59 . Also, its binding adaptability to different site conformations across aSyn fibrils 60 , likely accounts for its ability to label both in vitro and pathological assemblies. These findings underscore the need for strategies that bridge the gap between in vitro systems and disease-relevant fibril conformations. To address this gap, we developed FibrilSite, a computational pipeline for comparing amyloid fibril surface sites based on geometric and physicochemical features ( Fig. 2 ). Using this framework, we analyzed 62 sites from 19 fibril structures spanning multiple amyloidogenic proteins. Site alignments were performed using point-cloud registration algorithms guided by encoded surface properties, enabling quantification of surface overlap (SS max ) and feature divergence (F diff ). The pipeline is modular and expandable, supporting integration of new structures and binding sites. This approach revealed the structural uniqueness of ex vivo aSyn fibril: no sites were shared between fibrils from MSA and PD/DLB patients, or with other amyloidogenic protein fibrils. Within MSA fibrils, we identified conserved sites shared across all polymorphs ( Fig 4A ) , suggesting potential pan-MSA targets. We also identified sites unique to Type-II fibrils ( Fig 4E ) , which could enable subtype-selective targeting. These distinctions may hold clinical relevance, as the Type-I/Type-II ratio is thought to correlate with disease duration 37 . We further identified structurally conserved sites between ex vivo MSA Type-II B fibrils (P66) and the in vitro aSyn H50Q mutant fibrils (P50 and P83; Fig. 5A ), suggesting that certain pathological features can be recapitulated in vitro . These findings underscore the value of identifying shared fibril features between ex vivo and in vitro fibrils to guide ligand discovery. Computational and experimental screening efforts focused on these common features may improve translational relevance, particularly when pathological folds cannot yet be reproduced in vitro . To prioritize fibril sites for ligand design, we evaluated their druggability using P2Rank 67 , a machine learning-based ligand-binding site predictor. We optimized selected parameters to account for the non-globular, periodic geometry of fibrils. Of 62 sites, 41 pockets were identified, 26 of which exceeded a ligand-binding probability of 0.7 and were considered druggable ( Fig. 6 ). Structurally similar site pairs exhibited similar druggability scores, supporting the notion that chemically and geometrically similar surfaces tend to exhibit comparable ligand-binding potential. However, discrepancies were also observed, for example some structurally similar sites with identical sequences yielded differing predictions, underscoring the limitations of current pocket prediction tools when applied to amyloid fibrils and highlight the need for algorithms tailored to amyloid geometries. Beyond surface features, other factors influence druggability include site accessibility and presence of PTMs. Fibril cryo-EM structures resolve only the ordered core, while disordered regions form a dynamic “fuzzy coat” surrounding the core, which may hinder ligand access 33 , 70 – 73 . Furthermore, pathological aggregates often contain PTMs (e.g., phosphorylation and nitration) 4 , 37 , 66 , which can alter the chemical environment of binding sites, potentially influencing ligand interactions and pathogenicity 74 , 75 . Furthermore, Ligand binding modality itself further modulates selectivity and affinity. Cryo-EM studies of ligand-bound fibrils 56 , 59 , 60 , 68 , have revealed that specific ligands, bind diagonally across fibril layers, enabling inter-ligand π–π stabilizing interactions, whereas, less specific ligands, bind horizontally and lack such interactions 56 . These observations emphasize the complexity of ligand-fibril interactions and highlight key structural considerations for designing selective fibril-binding molecules. In summary, our findings demonstrate the utility of computational approaches for identifying structurally conserved and druggable sites in pathological fibrils and mapping them onto in vitro models. This strategy enables the prioritization of targetable sites and the rational selection of appropriate in vitro systems to improve the translational potential of drug development efforts, especially in cases where disease-relevant fibril folds remain experimentally inaccessible. Cross-protein site comparisons facilitate polymorph- and disease-specific targeting while minimizing off-target effects. Combining advances in machine learning drug design against amyloids 76 with structure-based directed targeting and high-resolution structural biology, the precision and effectiveness of fibril-targeting drug design can be substantially enhanced, paving the way for more selective diagnostics and therapeutics for neurodegenerative diseases. Data availability All data for this work are freely accessible on Zenodo at https://doi.org/10.5281/zenodo.15192320 Code availability The code and scripts for FibrilSite, used in site definition, alignment, and analysis, are available online at https://github.com/A-Sadek/FibrilSite Author information Authors and Affiliations Laboratory of Molecular and Chemical Biology of Neurodegeneration, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland Ahmed Sadek, Hilal A. Lashuel Laboratory of Protein Design and Immunoengineering, Institute of Bioengineering, Ecole polytechnique fédérale de Lausanne, Lausanne, Switzerland Ahmed Sadek, Bruno E. Correia Weill Cornell Medicine Qatar, Education City, Qatar Foundation, Doha, Qatar Hilal A. Lashuel Department of Neurology, Weill Cornell Medicine, New York, NY, USA Hilal A. Lashuel Contributions H.A.L., B.E.C., and A.S. conceived the project. A.S. developed FibrilSite algorithm and organized the GitHub repository. A.S. carried out the analysis and created the figures with input from all authors. All authors contributed to writing and revising the manuscript. Corresponding authors Correspondence to Bruno E. Correia and Hilal A. Lashuel Ethics declarations Competing interests H.A.L is the founder and CEO of ND BioSciences, a spinoff from the Lashuel lab focusing on developing novel therapies and diagnostics for neurodegenerative diseases. Methods Computing molecular surfaces and surface features Molecular surfaces and surface features were computed as described in [64]. Briefly, fibril-deposited PDB structures were protonated using Reduce 77 , triangulated with MSMS 78 at a density of 3.0 and a water probe radius of 1.5 Å, then downsampled and regularized to a 1Å resolution using PyMesh 79 . Each vertex was assigned four features: Poisson–Boltzmann electrostatics, hydropathy, shape index, and hydrogen bond potential. Electrostatic properties were computed using APBS 80 , with charge values constrained to ±30 and normalized to ±1. Hydropathy values, based on the Kyte and Doolittle scale 81 , were assigned according to the amino acid identity of the vertex’s closest atom, ranging from −4.5 (hydrophilic) to +4.5 (hydrophobic) and normalized to ±1. The shape index quantified local curvature 82 with values ranging from −1 (highly concave) to +1 (highly convex). Hydrogen bond donor and acceptor potentials were determined using a Gaussian-weighted hydrogen bond potential 83 , with values from −1 (optimal acceptor) to +1 (optimal donor) based on atomic proximity and orientation. FibrilSite, Defining fibril sites Defined sites were manually extracted using a tailored algorithm incorporating point cloud and mesh handling packages, including PyMesh 79 , Open3D 84 , and Biopython 85 . The fibril atomic structure (PDB file) and its corresponding molecular surface (Ply file) were first loaded, and the fibril’s main axis was computed using eigenvector decomposition. Surface points were classified based on their orientation relative to this axis using the dot product, enabling the isolation of the lateral fibril surface. Site-forming residues served as anchor points, and their nearest surface vertices were identified to define the core site region. This region was subsequently expanded by including neighboring surface points within 5Å, retaining only those with similarly oriented surface normals (dot product > 0). Finally, a rim residue was manually designated, and excess peripheral points were trimmed to ensure accurate delineation of each site surface. FibrilSite, Sites alignment Isolated site surfaces, represented as point clouds, were aligned in an all vs all manner using Random Sample Consensus (RANSAC) and Iterative Closest Point (ICP) algorithms implemented in Open3D 84 . First, the source and target sites were centralized in space by subtracting the mean coordinate value from each surface point for both sites. RANSAC then performed 20,000 iterations to align the source site to the target, prioritizing points with minimal surface feature differences and selecting the transformation with the most points within 1Å. Finally, ICP refined the alignment for optimal accuracy. Site Surface overlap metric (SS max ) SS max quantifies the maximum overlap between two aligned site surfaces, providing a measure of structural similarity. It is defined as: where correspondences refer to the number of matched points between the two surfaces after alignment. The metric ensures that the overlap is evaluated relative to both site surfaces, accounting for differences in size. A higher SS max value indicates a greater degree of shared surface between the two sites. Assessment of fibril sites druggability with P2Rank To assess the druggability of defined fibril sites, we used P2Rank (v2.5), a machine learning-based ligand-binding site predictor 67 . Because amyloid fibrils exhibit non-globular, periodic and structurally distinct topologies that diverge from the training data used to develop such models, we modified selected parameters in the default configuration to improve the algorithm’s sensitivity to fibril-specific features. These adjustments, summarized in Supplementary Table 1 , enhanced the tool’s ability to capture the diversity of fibril surface environments and more effectively identify ligandable sites. Predicted pockets were evaluated based on P2Rank’s ligand-binding probability score (range: 0 = non-druggable, 1 = druggable), and classified as druggable if the score exceeded 0.7. Supplementary figures Download figure Open in new tab Supplementary Figure 1 Representative tau fibril cryo-EM structures. (A, B) Ex vivo tau paired helical filaments (PHF) and straight filaments (SF) from Alzheimer’s disease (AD) patient brains 39 , 40 . (C) Ex vivo tau fibrils from chronic traumatic encephalopathy (CTE) patients 41 ; two structural variants were identified, both composed of identical protofilaments with distinct inter-protofilament interfaces–Type-II fibrils are shown. (D) Ex vivo tau fibrils from Pick’s disease patients 42 . Blue densities correspond to potential cofactors, influencing the SF fold in AD and becoming entrapped in the CTE fibril protofilaments. (E, F) Overlay of in vitro generated fibrils 49 (green) with their corresponding ex vivo counterparts (gray): AD-PHF fold (E) and CTE-II fold (F) . Download figure Open in new tab Supplementary Figure 2 Defined sites from ex vivo amyloid fibrils of diverse origins. A total of twelve sites were defined across five ex vivo fibril structures representing different amyloidogenic proteins. (A) Four sites in prion protein (PrP) fibrils isolated from Gerstmann-Straussler-Scheinker disease patients 86 . (B) Two sites in in Type-II amyloid beta 42 (Aβ 42 ) fibrils isolated from familial Alzheimer’s disease (AD) patients 87 . (C) Two sites in transmembrane protein 106B (TMEM106B) fold I fibrils isolated from AD patients 88 . (D) Two sites in type-II tau fibrils isolated from chronic traumatic encephalopathy (CTE) patients 41 . (E) Two sites in tau paired helical filaments (PHF) isolated from AD patients 40 . Site-forming residues are shown as cyan sticks on one fibril layer of each structure. Download figure Open in new tab Supplementary Figure 3 Conserved sites among ex vivo alpha-synuclein (aSyn) MSA fibrils. Site matches were identified by aligning 15 sites from ex vivo aSyn fibrils ( Fig. 3 ) against one another. Alignments were performed based on surface feature similarity, including electrostatics, hydrophobicity, hydrogen bond donors/acceptor potential, and shape index. For each matched pair, the site surface overlap (SS max , green shading, higher = better) and feature distance (F diff ; Euclidean distance between aligned surface features; purple shading, lower = better) are shown. A conserved site formed by Val37-Glu46 and Lys80 in multiple system atrophy (MSA) Type-II fibrils, shared between protofilament A (PF-A) of Type-II A (P59) and protofilament B (PF-B) of Type-II B (P65), as well as between PF-A (P63) and PF-B (P65) of Type-II B . Site-forming residues are shown as sticks; shared surface points are rendered in grey. Download figure Open in new tab Supplementary Figure 4 Defined sites from in vitro alpha-synuclein (aSyn) fibrils. A total of 35 sites were defined across ten in vitro aSyn fibril structures. (A) Seventeen sites were defined in four wild-type (WT) aSyn fibril polymorphs 35 , 36 (Pol): four in Pol 1a, two in Pol 1b, six in Pol 2a, and five in Pol 2b. (B) Thirteen sites were defined in fibrils formed by aSyn variants harboring familial mutations (Mut): four in E46K fibril 89 , three in H50Q 90 , three in G51D fibril 91 , and three in A53T fibril 92 . (C) Four sites were defined in fibrils formed by aSyn phosphorylated at Tyr39 45 . (D) PET tracer F0502B binding site in WT Pol 1a. Site-forming residues are shown as cyan sticks on one fibril layer of each structure. Acknowledgment We thank SCITAS at EPFL for support in running our pipeline and analysis. We thank Evgenia Elizarova and Arne Schneuing for assistance with computational method development (Laboratory of Protein Design and Immunoengineering, Institute of Bioengineering, EPFL, Switzerland). We thank Lucas Burget assistance in running fibril site alignment on the high-performance computing cluster and Danaé Terrien-Ferey for assistance with identifying the fibril sites in amyloid fibrils from other amyloid proteins (Laboratory of Molecular and Chemical Biology of Neurodegeneration, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland). Footnotes https://github.com/A-Sadek/FibrilSite References 1. ↵ Wilson , D. M. et al. Hallmarks of neurodegenerative diseases . Cell 186 , 693 – 714 ( 2023 ). OpenUrl CrossRef PubMed 2. ↵ Iadanza , M. 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