key: cord-0029886-wcp9x7mz
authors: Karamanos, Theodoros K.; Kalverda, Arnout P.; Radford, Sheena E.
title: Generating Ensembles of Dynamic Misfolding Proteins
date: 2022-03-31
journal: Front Neurosci
DOI: 10.3389/fnins.2022.881534
sha: 91b603978419dccc1181f26af00dbe8cefb5c57a
doc_id: 29886
cord_uid: wcp9x7mz

The early stages of protein misfolding and aggregation involve disordered and partially folded protein conformers that contain a high degree of dynamic disorder. These dynamic species may undergo large-scale intra-molecular motions of intrinsically disordered protein (IDP) precursors, or flexible, low affinity inter-molecular binding in oligomeric assemblies. In both cases, generating atomic level visualization of the interconverting species that captures the conformations explored and their physico-chemical properties remains hugely challenging. How specific sub-ensembles of conformers that are on-pathway to aggregation into amyloid can be identified from their aggregation-resilient counterparts within these large heterogenous pools of rapidly moving molecules represents an additional level of complexity. Here, we describe current experimental and computational approaches designed to capture the dynamic nature of the early stages of protein misfolding and aggregation, and discuss potential challenges in describing these species because of the ensemble averaging of experimental restraints that arise from motions on the millisecond timescale. We give a perspective of how machine learning methods can be used to extract aggregation-relevant sub-ensembles and provide two examples of such an approach in which specific interactions of defined species within the dynamic ensembles of α-synuclein (αSyn) and β(2)-microgloblulin (β(2)m) can be captured and investigated.

Although significant recent progress in computational methods has enabled the prediction of the native structure of a protein and of protein complexes given primary sequence information alone (Yang et al., 2020; Jumper et al., 2021) , understanding how a protein misfolds and defining the structural properties of misfolded and aberrantly assembled/aggregated species remain largely a mystery. Protein misfolding represents a critical missing link in our knowledge of protein chemistry as it is represents a fundamental property of the polypeptide chain and is directly linked with numerous human disorders including neurodegeneration, cataract formation, type II diabetes mellitus Chiti and Dobson, 2017; Iadanza et al., 2018; Sawaya et al., 2021) . More than 40 proteins has been identified as the culprits of aggregation in human amyloid diseases (Benson et al., 2020) . Pathological protein self-assembly reactions do not only result in highly ordered amyloid fibrils but also in the formation of amorphous aggregates that lack long range order or a common underlying structure, misfolded oligomers, or phase-separated protein condensates Mathieu et al., 2020) . In this review the term "aggregation" largely refers to protein polymerization on pathway to amyloid unless otherwise stated.

Despite this extraordinary progress, and the stunning advances in structural methods such as cryo-EM, cryo-ET and solid state NMR over the last few years (Bäuerlein and Baumeister, 2021; Reif et al., 2021; Saibil, 2022) , generating high resolution structures of aggregation intermediates remains enormously challenging, and the secrets of protein misfolding remain unveiled. Understanding the early events in protein misfolding that result in large-scale self-assembly into the highly ordered cross-β fibrous assemblies of amyloid is challenging from the physical chemistry view point (Cawood et al., 2021) . Intrinsic protein dynamics play a crucial role in the early stages of the misfolding reaction. These can be manifested in the form of intrinsically disordered proteins (IDPs) (Bondos et al., 2021; Uversky, 2021) that exchange between an array of different conformations, but also partially folded amyloid precursors that retain a dynamic 3D structure, which can loosely self-assemble to generate a pool of low-order oligomers (Figure 1) . Thus, the first main challenge in understanding the principles of protein misfolding is the ability to generate ensembles that capture the dynamics of aggregation precursors that can span the ns to hour timescale. However, the majority of these states may be innocuous in terms of amyloid formation, since they will not possess the physico-chemical properties required to enter the aggregation landscape and will remain monomeric or, if forming inter-molecular interactions in oligomers, will disassemble back to monomers with which they are in dynamic equilibrium (Dear et al., 2020; Michaels et al., 2020; Cawood et al., 2021) . This represents the second main challenge: how do we identify specific sub-ensembles within large pools of interconverting species that show increased propensity to aggregate and/or assemble into amyloid?

Here, we review current computational and experimental methods that can be used to describe the solution properties of highly dynamic proteins, with emphasis on how the kinetics of their formation can influence the structural interpretation of experimental observables. We then discuss how clustering of these ensembles may be performed using machine learning methods in order to identify aggregation-prone vs. aggregationresilient states. Finally, we show how these methods/concepts can be used to describe the misfolding of two example systems: a protein that aggregates from an IDP state (αSyn) or from a dynamic, yet topologically well-defined species (β 2 m).

Proteins with an enhanced propensity to aggregate into amyloid can be (1) disordered (IDPs) or contain intrinsically disordered regions (IDRs), (2) structured, but unstable thermodynamically or kinetically, or (3) combinations of these traits. Examples include variants of immunoglobulin light chain associated with light chain amyloidosis (thermodynamically unstable) (Morgan et al., 2021) , β 2 m (both kinetically and thermodynamically FIGURE 1 | Protein misfolding and flexibility. Examples of proteins with different degrees of flexibility (α-synuclein-IDP, prion protein -folded and IDR, and β 2 m-folded), each of which aggregate to form amyloid fibrils. For each class of protein, its structure cannot be represented by a single conformation, as each interconverts between various conformers on different timescales. Hence, the conformational properties of these proteins are best described using an ensemble of protein states guided by different types of experimental restraints. Oligomers that form from these precursors may retain the structure of the monomer, convert to a different structure, or form new structures not accessible to their precursors. All eventually form the cross-β fold of amyloid which, whilst containing a canonical parallel in-register β-strand structure can adopt a variety of different structures (127 different amyloid structures have been collated in the amyloid atlas; Sawaya et al., 2021). unstable; , amyloid-β (Aβ), α-synuclein and islet associated polypeptide (IAPP) that are IDPs (Chiti and Dobson, 2017) and prion protein (Singh and Udgaonkar, 2015) or poly-glutamine-containing proteins such as ataxin 3 and huntingtin (contain both structured and disordered regions) (Lieberman et al., 2019) .

For IDPs, disorder serves as a means to explore a vast conformational landscape in their monomeric form that may, or may not, be related to their function and/or propensity to aggregate. Thermodynamically, for disordered proteins to aggregate into amyloid, the gain in enthalpy from the formation of the repetitive cross-β interactions (main-chain hydrogen bonding and interactions between stacked side-chains) of the ubiquitous amyloid fold compensates for the entropy loss arising from the ordering of a disordered/unstructured polypeptide chain. Disorder that leads to misfolding can also be generated by other mechanisms, including proteolytic cleavage of larger precursors that may be otherwise folded/aggregation-resilient (serum amyloid A, antibody light chains, transthyretin, Aβ, and others) (Adams et al., 2019; Lewkowicz and Gursky, 2022; Lichtenthaler et al., 2022) or even aggregation of the nascent polypeptide chain as it exists the ribosome (Willmund et al., 2013; Deuerling et al., 2019; Cassaignau et al., 2020) .

For protein precursors that are initially folded (e.g., β 2 m, light chains, transthyretin; Iadanza et al., 2018) , local protein motions which lead to exposure of hydrophobic/aggregationprone regions (APRs) (Beerten et al., 2012; Houben et al., 2022) that are normally buried in the native structure, have been suggested as the drivers of self-assembly. For misfoldingprone proteins that contain long disordered regions (IDRs) dispersed within, or at the termini, of folded domains (e.g., prions and polyQ-containing proteins), the initiating stages of aggregation may be dominated by the IDR, by interactions involving the folded domain, or both (Scarff et al., 2013; Singh and Udgaonkar, 2015; Sicorello et al., 2018 Sicorello et al., , 2021 Lieberman et al., 2019) . And, while it is now straightforward to predict the presence of APRs in protein sequences (Tsolis et al., 2013) , these regions cannot be solely responsible for driving the initial stages of aggregation, since it is well-known that regions that flank these sequences can play a pivotal role in controlling assembly . The small oligomeric species that self-assemble from aggregation-prone monomers can have "memory" of the structural properties of their corresponding precursors, thus creating pools of native, partially folded or unfolded oligomers (Cawood et al., 2021) . Alternatively, selfassembly may generate new structures not accessible/populated in their monomeric precursors (Figure 1) . Initially formed small oligomers can continue to grow in size, without further conformational change to generate larger amorphous aggregates, or they can undergo a transition to a cross-β structure which is followed by elongation processes that result in the formation of the large fibrillar aggregates classic of amyloid (Xue et al., 2008; Knowles et al., 2009) .

In summary, therefore, even by focusing on the earliest stages of misfolding and aggregation a complicated picture emerges that involves numerous, structurally distinct precursors that lead to aggregate formation via a range of kinetic mechanisms. Nonetheless, the vast diversity of protein structures of unrelated sequence and function that can form fibrillar aggregates suggests the presence of common, fundamental underlying mechanisms that are yet to be discovered and understood.

The conformational heterogeneity of IDPs and proteins that contain a significant portion of IDRs precludes the conventional investigation of these species using methods able to determine high resolution structures, such as cryo-EM and X-ray crystallography (Thomasen and Lindorff-Larsen, 2022) . For amyloid precursors that are initially folded, even though the native monomeric state may be populated to an extent that allows its characterization by structural approaches, these methods cannot capture the rarely populated, partially folded species that can be crucial for aggregation (Radford et al., 1992; Dhulesia et al., 2010; Buell et al., 2011; Karamanos et al., 2016) , or the loosely associated oligomeric species that form early during assembly (Laganowsky et al., 2012; Karamanos et al., 2014 Karamanos et al., , 2019 Fusco et al., 2017) . Methods able to capture both local and global properties of the polypeptide chain, and to detect rare and transiently populated species, are needed in order to describe the conformational properties of these dynamic protein states. Since the equilibria that lead to the formation of these lowly populated species are uniquely sensitive to factors such as pH and salt concentration, and hence the rate of amyloid aggregation is also highly dependent on the solution conditions (Buell et al., 2014) , experimental restraints should ideally be collected in solution. Experiments need to be carefully planned so that these early species are resident for long enough to enable their detection and characterization, holding off the inevitable downhill thermodynamic cascade to the amyloid fold (Karamanos et al., 2015) . If such conditions can be found, a range of powerful solution techniques can be used to yield restraints used in ensemble calculations (Cawood et al., 2021) . These include small angle X-ray scattering (SAXS)/NMR (Mertens and Svergun, 2017 ) (generating R h ), hydrogen exchange (HX) monitored by NMR or mass spectrometry (MS) (Radou et al., 2014; Wan et al., 2020) (yielding information on solvent accessibility of the main-chain/hydrogen bond stability); single molecule fluorescence energy transfer (smFRET) or fluorescence correlation spectroscopy (FCS) (Naudi-Fabra et al., 2021) (interatomic distances and distance distributions), and chemical cross-linking (Faull et al., 2019) (inter-residue contacts). Alternatively, in favorable cases, restraints collected in the gas phase by electrospray ionization mass spectrometry (ESI-MS) (Politis et al., 2014; Rajabi et al., 2015; Österlund et al., 2019) (ion mobility, mass distribution) or in the frozen state using electron paramagnetic resonance (EPR) (Jeschke, 2018; Kapsalis et al., 2019 ) (distance distributions) can provide additional information, as long as the ionization/freezing process can be ensured not to change the conformational equilibrium.

While each of these methods alone cannot deal with the vast heterogeneity of protein ensembles in terms of the array of different protein conformations and oligomeric states present, when applied together the properties of these complex systems can begin to be revealed Naudi-Fabra et al., 2021) . A prerequisite for an experimental restraint to be used in the generation of a conformational ensemble is that its value must be able to be directly back-calculated from the atomic coordinates of the species present. This is not always a simple task, as it generally requires a robust theoretical model that can take into account the extreme averaging that takes place in highly dynamic proteins. In the next paragraphs we give a brief overview of some of the techniques that have been used to generate ensemble representations of misfolding proteins. For a more technical description of how these methods work we refer the reader to a number of excellent reviews (Roy et al., 2008; Clore and Iwahara, 2009; Jeschke, 2012; Politis et al., 2014; Chiliveri et al., 2021) .

A technique that naturally ticks all the boxes for analysis of dynamic protein ensembles in solution is NMR spectroscopy. NMR is the go-to method when disordered proteins or proteins with IDRs are involved (Meier et al., 2008; Jensen et al., 2013; Arai et al., 2015; Salvi et al., 2016; Dyson and Wright, 2021) . Its unique ability to provide residue-specific information in solution (using 1 H, 13 C, and/or 15 N labeled proteins) is one of the main advantages that make NMR stand out from other biophysical techniques (Alderson and Kay, 2021) . Solution NMR can be used to provide numerous experimental observables that report on local (chemical shifts, short range nuclear Overhauser effects (NOEs), 3-bond J couplings) or global [residual dipolar couplings (RDCs), paramagnetic relaxation enhancements (PREs)] properties of a proteins' structure. Importantly, NMR spins are sensitive to the overall tumbling of the molecule and also to local motions, and thus sophisticated NMR relaxation methods can be used in order to study protein motion directly (Lipari and Szabo, 1982a,b) . Of the many NMR methods available, the ones that report on global, slower timescale motions (such as RDCs and PREs) are perhaps more useful in order to capture the large-scale dynamics of misfolding proteins and thus we will focus our discussion on those (see Table 1 for a more comprehensive list). The well-known molecular weight limitation of NMR which make the study proteins > 30 kDa in size difficult, unless specific labeling (e.g., 13 C methyl) is used (Tugarinov and Kay, 2004) , is not prohibitive for IDPs (even if these consist of more than 300 residues; Mamigonian et al., 2022) , since local disorder causes long transverse relaxation (T 2 ) times and therefore NMR signals do not decay rapidly. For natively folded proteins that interconvert with misfolded monomeric or oligomeric states, the properties of the misfolded/aggregated state can also be investigated by adjusting the solution conditions such that misfolded states represent a small fraction of the molecules in solution, allowing powerful NMR methods to characterize excited, rarely populated (<5%) protein states (Anthis and Clore, 2015) . When these experiments are performed and data successfully obtained, calculating NMR observables from structure can be straightforward. This is certainly the case for distance-based measurements (NOEs) which are often calculated as an r −6 weighted average of the interatomic distances r. However, other NMR observables, such as chemical shifts, do not have analytical expressions to describe their relationship with atomic coordinates, and empirical models are often used (Shen et al., 2008; Robustelli et al., 2010 Robustelli et al., , 2012 . It is important to keep in mind that the timescales of exchange between the various protein states, which could represent transiently folded regions of IDPs or IDRs, or monomer-oligomer equilibria, also affect the NMR observables, and how the kinetics of exchange affect a particular NMR parameter has to be taken into account for a quantitative interpretation of the data (Salvi et al., 2016 ) (see following section).

The atom-specific information obtained from NMR studies is even more powerful if it can be combined with other techniques that provide complementary information such as smFRET or SAXS (Krzeminski et al., 2013; Lincoff et al., 2020; Naudi-Fabra et al., 2021) . smFRET measures the proximity of individual pairs of fluorescence dyes over time (in TIRF mode) or population (in confocal mode) and thus can inform on conformations of individual molecules and the kinetics of their interconversion in a quantitative manner (Roy et al., 2008; Schuler and Eaton, 2008) . In smFRET studies, care must be taken to ensure that the fluorescent dyes do not alter the proteins' properties which is of key concern for IDPs/IDRs (Borgia et al., 2016) . Small angle X-ray scattering (SAXS), on the other hand, is a dye-free ensemble technique that reports on the overall shape of the protein under investigation and can be used to derive the overall compactness of the ensemble by weighting various conformations present in solution (Różycki et al., 2011; Ahmed et al., 2021) . Both techniques have been used extensively to generate ensemble representations of IDPs or multidomain proteins which contain a significant portion of IDRs (Bernadó et al., 2005b; Merchant et al., 2007; Holmstrom et al., 2018) . In terms of aggregating proteins, integrative studies have been performed in order to describe ensembles of ataxin (Sicorello et al., 2021) , α-syn (Schwalbe et al., 2014; Chen et al., 2021) , amyloid β (Sgourakis et al., 2007) and tau (Chen et al., 2019; Stelzl et al., 2022) among others (Strodel, 2021) .

A technique that is powerful, but perhaps under-utilized, when it comes to dynamic proteins is ESI-MS. Bottom up ESI-MS experiments can provide restraints captured in solution and analyzed subsequently using liquid chromatography MS (LC-MS) (such as cross-linking or HX studies) (Belsom and Rappsilber, 2021) or native ESI-MS that is performed on intact molecules in the gas phase (Beveridge and Calabrese, 2021) . Ion mobility MS that reports on the collision cross section (CCS) of a protein can separate species based on mass (monomer, dimer etc.), but can also resolve species of the same mass, but different CCS (e.g., compact vs. expanded versions of isobaric species) (Beveridge et al., 2019; Moons et al., 2020) . The ability of native ESI-MS to detect small populations of protein conformers and separate them based on size (resolution of a few Da) and shape (CCS) has been powerful in the investigation of folding/misfolding and aggregation pathways (Benesch et al., 2006; Smith et al., 2006 Smith et al., , 2007 Woods et al., 2013; Young et al., 2014; Britt et al., 2021) and in the assembly of dynamic chaperone assemblies (Young et al., 2018) . Theoretical models that allow the calculation of MS-derived restraints such as CCS are perhaps lacking, although significant progress in this area has been made recently (Kulesza et al., 2018) . Concerning IDPs or IDRs, it is important to ensure that the compaction or extension of the polypeptide chain observed is not the result of the electrospray ionization/desolvation process itself (Vahidi et al., 2013; Borysik et al., 2015; Devine et al., 2017) .

To avoid ionization issues, experiments that capture protein motions can be performed in solution and subsequently analyzed by MS methods. Zero-length cross-linkers (such as EDC (1-ethyl-3-(3-dimethylaminopropyl) carbodiimide hydrochloride) and DMTMM (4-(4,6-dimethoxy-1,3,5-triazin-2-yl)-4-methylmorpholinium chloride)) allow adjacent carboxyl and amine-carboxyl sidechain to be covalently linked and identified using proteolysis and tandem MS (LC-MSMS). An array of cross-linkers with different chemistry (free-radical, maleimide, NHS ester, and others) and cross-linker length, can provide additional information on sidechain-sidechain distance, albeit averaged over the timescale of the cross-linking experiment (Sinz, 2018) . Using lasers or LEDs the timescale needed for photo-crosslinking can be reduced from tens of minutes to less than seconds (Russmann et al., 1998) , providing a clearer snap-shot of the interactions by reducing averaging (Horne et al., 2018) . These experiments capture the dynamic Zamel et al., 2021) . Even though in some cases crosslinking restraints have been treated as NMR-derived distances, care has to be taken when dealing with ensembles of structures, since the nature of the two distances is fundamentally different. Once an irreversible crosslink has formed, the two atoms are not available for any further additional reactions, whereas in an NOE experiment one atom may give rise to multiple distance restraints. Differential hydrogen-deuterium exchange that measures the solvent accessibility/hydrogen bond stability of the protein under investigation is another technique that combined with ESI-MS analysis can be used to investigate large/dynamic states at the peptide/single residue level (by rapid quenching, proteolysis and LC-MSMS analysis) (Faull et al., 2019; Calabrese et al., 2020; Wang et al., 2022) . Recent innovations have also increased the time resolution of HX-MS to ms (Hu et al., 2013; Seetaloo et al., 2022) . These data can be converted to protection factors and can be used for ensemble generation (Wan et al., 2020) . Using sophisticated pulse schemes, hydrogen exchange with solvent can be followed by NMR that allows ultra-fast, subms rates to be measured without the need of dedicated HDX hardware (Skrynnikov and Ernst, 1999; Kateb et al., 2007; Segawa et al., 2008; Dass et al., 2021) . One drawback of hydrogen exchange methods that limits their application toward ensemble generation is that accurate models that describe the crucial role of electrostatics to the measured exchange rates are lacking ( Table 1) .

We note that although the techniques mentioned in the previous paragraphs are excellent in capturing the soluble species formed in the early stages of protein aggregation, the reduced solubility of aggregates formed later in assembly may limit the repertoire of solution techniques available to characterize them. Such states are perhaps best captured by techniques such as cryo-EM (Bäuerlein and Baumeister, 2021; Saibil, 2022) , solid-state NMR (Reif et al., 2021) and/or atomic force microscopy (AFM) (Aubrey et al., 2020) . Despite recent advances, sample heterogeneity still poses significant challenges in the characterization of partially soluble states (Cawood et al., 2021) . Overall, it is clear that many experimental techniques must be used to generate complementary restraints that together have the potential to visualize the dynamics that are in play.

For most of the experimental techniques mentioned above, theoretical frameworks that allow the back-calculation of the experimental restraints from the molecular structure exist. However, when dealing with highly dynamic proteins such as those involved in protein misfolding and aggregation, these restraints need to be averaged appropriately in order to generate an accurate representation of the solution properties of the entire ensemble. It is often the case that the different protein states within the ensemble are assumed to be in fast exchange between each other. This essentially means that the exchange between these species is faster than observation of the experimental variable, and thus the experimental restraints correspond to the population-weighted average between all the conformers. Fast exchange is supported by the poor chemical shift dispersion of IDPs (IDRs) in NMR studies, and is usually a safe approximation for these types of proteins, but it may not always be the case. Protein self-oligomerization that occurs in the early stages of aggregation, or even the formation of local secondary structural elements in IDPs, can occur on slower timescales. In the case of NMR observables, the kinetics of the conformation exchange can significantly affect the measured values (Iwahara and Clore, 2006; Cavalli et al., 2013; Janowska and Baum, 2016) . Figure 2A shows how PREs are affected by the kinetics of exchange between an extended (state A) (95% populated) and a rarely populated (5%) compact state B in which a hypothetical C-terminal helix is interacting with the N-terminal segment of a protein. In the compact state (state B) the distance (r) between the spin label [usually S-(1-oxyl-2,2,5,5-tetramethyl-2,5-dihydro-1H-pyrrol-3yl) methyl methanesulfonothioate (MTSL)] and the helix is 7 Å, giving rise to a high PRE value (or 2,B ) rate (6,750 s −1 ), while the PRE rate for state A, in which the spin label and N-terminus is > 15 Å away, is low ( 2,A = 5 s −1 ). In the fast exchange limit, wherein the rate of exchange k ex 2,B the observed PRE rate approximates the population weighted average of 2,A and 2,B (Clore and Iwahara, 2009 ; dashed line in Figure 2A) . However, if k ex ∼ 2,B or k ex < 2,B the observed PRE rate is much smaller than the population weighted average (Figure 2A) . In this hypothetical case the rate of N-C association could be determined, in principle, from the rate of helix formation (assuming that helix formation can only occur when the termini come into close contact), but of course, in reality helix formation could be slower than the rate of binding. Clearly, for four out of the five curves in Figure 2A the fast exchange assumption would lead to overestimation of r and the generation of a more expanded ensemble that could fit the experimental data equally well.

RDCs can also provide useful information about protein structure and are powerful when using NMR to calculate structures and dynamics of proteins (Chiliveri et al., 2021) . For dynamic systems, RDCs are normally averaged following two assumptions: (1) That all possible conformations can be sampled during the measurement time and (2) that interconversion between states is slower than the event that leads to reorientation of the molecule in the alignment medium [related to the correlation time (τ c ) of the molecule] (Meier et al., 2008) . If both assumptions are satisfied, transformation from the time average to the ensemble average is straightforward, and the observed RDC will be equal to the average over all molecular conformations. In general, assumption 2 is normally a safe assumption, as molecular reorientation should be very fast and comparable to the molecular tumbling time (on the ns timescale), unless association of the protein with the alignment medium takes place. For highly dynamic IDPs, assumption 1 should also be satisfied, but this might not be the case if transient interactions are formed that result in conformational exchange on a slower timescale (Figures 2B,C) . Imagine a scenario in which an IDP (state A) exchanges with a transiently folded state (state B) that may be related to misfolding. Alignment of state A may be weak (as it is normally the case for IDPs), giving rise to an RDC for that state, D A = 11.2 Hz, while the folded state B gives rise to D B = 67 Hz. As observed in Figure 2A for PREs, the measured RDC for both states depends on the exchange rate between them. For simplicity we will discuss only state A, as state B is populated only to 20% in this example, and may not be directly observable (Figure 2B ). In the slow exchange limit on the chemical shift timescale (k ex < 100 s −1 ) the observed RDC for state A equals D A , while when exchange approaches the fast exchange regime (k ex > 8,000 s −1 ) the observed RDC approximates the population-weighted average of the two states, as expected (Lorieau et al., 2012 ; Figure 2C ). However, it is evident from Figure 2C that in the intermediate exchange regime (100 < k ex < 8,000 s −1 ) the observed RDC shows a complex behavior that, if not correctly taken into account, may lead to erroneous conclusions about presence/absence of local secondary structure, for instance, in a dynamically interconverting ensemble of states.

In conclusion, treating NMR-derived restraints as populatedweighted averages over all ensemble members is able to capture FIGURE 2 | Dependence of NMR observables often used to drive ensemble generation on the kinetics of chemical exchange. (A) A spin-labeled IDP undergoes intramolecular exchange between an expanded state A (p A = 95%) and a more compact state B (p B = 5%) that involves transient helix formation (red box). The 7Å distance between the spin label (placed on residue 20 of this hypothetical 200 residue protein) and the helix in residues 157-163 in state B gives rise to a PRE rate for that state, 2,B = 6,750 s −1 , while the PRE rate for state A where these residues are > 15 Å apart is low (Continued) FIGURE 2 | ( 2,A = 5 s −1 ). Only when exchange is fast on the PRE timescale (k ex 2,B ) does the observed PRE rate approximate the population-weighted average (dashed line). (B) Simulated 1D NMR spectra of a 2-spin coupled (coupling constant J = 90 Hz) system that undergoes 2-site exchange. State A is highly populated p A = 80% and gives rise to an RDC D A = 11.2 Hz, while the RDC of state B, D B is 67 Hz. The chemical shift of state A was set to ω A = 200 Hz, giving rise to a doublet, separated by J+D A . For state B, ω B = 600 Hz. Simulated spectra at different exchange rates (k ex , colored bar) were generated using 5,000 points, apodised and Fourier transformed. Owing to the small value of p B = 20%, the doublet corresponding to state B (separated by J+D B ) is only visible in the first spectrum. The state A doublet moves toward the average chemical shift position with increasing k ex . Peak positions and linewidths were extracted using a Gaussian fitting procedure. The RDC of state A was measured as the difference in frequency of the state A doublet after J was subtracted, and is plotted as a function of k ex in ( the time averaging that happens in solution when exchange between the various states is fast. This has led to some elegant examples including the generation of ensembles of misfolding IDPs able to quantitatively describe the experimental restraints (Iwahara et al., 2004; Bernadó et al., 2005a; Dedmon et al., 2005; Huang and Grzesiek, 2010; Salmon et al., 2012; Janowska et al., 2015; Salvi et al., 2016; Karamanos et al., 2019; Naudi-Fabra et al., 2021; Sicorello et al., 2021; Mamigonian et al., 2022) . However, when/if motions on slower timescales occur, these have to be taken into account in order to avoid data misinterpretation.

Different computational approaches have been developed that enable measured experimental restraints to be converted into structural ensembles. The two main approaches involve (1) biasing molecular dynamics (MD) simulations by the addition of energy terms that minimize the difference between the observed and calculated restraints (Jaynes, 1957; Roux and Weare, 2013) , or (2) reweighting ensembles that have been initially generated with no experimental information (Różycki et al., 2011; Cavalli et al., 2013) . In both cases overfitting is avoided using maximum entropy or Bayesian techniques. Approach 1 requires that the theoretical models used to calculate the experimental observables from structural models are also differentiable, which sometimes is not straightforward, especially for some of the MS-derived restraints (such as CCS). Approach 2, on the other hand, assumes that all relevant protein states are already present in the initial ensemble and may not be appropriate in cases where conformational sampling is not efficient. A detailed description of these computational protocols is beyond the scope of this review and we refer the reader to some excellent recent reviews on the topic (Hummer and Köfinger, 2015; Bonomi et al., 2017; Bottaro and Lindorff-Larsen, 2018; Pietrek et al., 2020; Thomasen and Lindorff-Larsen, 2022) . We note that the computer-generated ensembles are only a true reflection of the experimental data that were used for their generation. Parameters such as the number of ensemble members or even their weights can vary depending on the nature and quantity of the experimental input. Hence, the more complex and broad the number of conformers, the greater the number of experimental data of different type is needed to best define the ensemble. Thus, a plethora of different, unrelated experimental methods are needed in order to obtain an unbiased representation of the dynamics that take place in solution. We note that the recent developments in deep learning algorithms able to accurately predict the structure of folded proteins from their amino-acid sequence opens the window for a future extension of these methods to capture hidden structural motifs/propensities in IDPs. In order for this to happen, a large, high quality dataset of experimentally determined ensembles (using the methods described here) is necessary in order to train accurate deep learning networks. Although this is not available at the moment, the fast progress in the field of protein chemistry holds for an exciting future in this research area (Serpell et al., 2021) .

Of the vast number of species contained in an ensemble of monomeric aggregation-prone IDPs, and oligomeric ensembles of folded/unfolded precursors, only a tiny minority of conformers may possess the properties required for further aggregation. Of all possible conformers, only specific sub-ensembles will be able to transition into the aggregation landscape and eventually push the equilibrium toward fibrillar species that lie at a thermodynamic energy sink (Figure 1) . How can one then search for, or teaseout, aggregation-relevant members of the ensemble from their aggregation-resilient counterparts? The answer to this question is not obvious currently, but its solution would represent a key step forward in understanding how, and why, proteins aggregate. Building on recent advances in the field of machine learning, we discuss below how such techniques can be used to generate new insights into aggregation-relevant conformers buried within a myriad of alternative species unrelated to an aggregation pathway into amyloid.

The problem of sub-clustering of structures based on common properties is not a new one, and techniques such as principal component analysis (PCA) are elegant ways to generate subclusters based on overall similarities in one or more structural properties (Papaleo et al., 2009) . In many ways, ensemble subclustering resembles problems that are ideal for unsupervised machine learning methods, that are typically described as an unbiased method to identify patterns in "unlabeled" data (unlabeled here refers to the fact that each structure is not tagged a priori with a label that includes it to cluster X). In its simplest form unsupervised clustering can be performed by Gaussian mixture models (GMM) that, given a number of normal distributions, will try and determine to which distribution each point belongs. The number of normal distributions the model has access to is usually not known and may affect the clustering results. Thus, these models are often combined with Bayesian approaches to keep the number of distributions to a minimum (Roberts et al., 1998) .

To illustrate the power of clustering methods based on machine learning we use here an ensemble of αSyn structures that was generated using molecular dynamics simulations guided by 595 NMR PRE-derived intramolecular distances (Allison et al., 2009) . Figure 3 shows the performance of a simple GMM in clustering of αSyn structures based on their end-to-end distance and surface accessible surface area (SASA). Four partially overlapping clusters are evident, although there is definitely room for improvement. Instead of performing clustering analysis using global features as shown in Figure 3 , we can extend these ideas to include local features. Due to the complex nature of the problem, in many cases information about which residues/regions of the protein are important/irrelevant for misfolding/aggregation is sparse (Aguirre et al., 2022; Seetaloo et al., 2022) . Perhaps the most informative results come from mutational studies that assess the effect of mutations on misfolding/aggregation rate in a rigorous way. For instance, we have recently shown that a 7residue segment (residues 36-42), termed P1, in the N-terminal region of αSyn acts as a "master regulator" of aggregation (Doherty et al., 2020) . Deletion or substation of the seven residues in P1 prevents aggregation of αSyn at neutral pH in vitro (up to the experimental time of 100 h) and also prevents amyloid formation and proteotoxicity in C. elegans (Doherty et al., 2020) . NMR PRE experiments showed that residues in P1 make extensive intramolecular contact with the NAC region that this region flanks, as well as the acidic C-terminal region of the protein (Doherty et al., 2020) . Yet, how these contacts alter or refine the structural ensemble, and how these changes "turn on" aggregation of the protein remains obscure at a molecular level. Do residues in P1 show specific intra-molecular interaction hidden within the broad ensemble of conformers shown in Figure 3 , and do these interactions result in compaction/other alterations of the chain? To answer these questions, we trained another simple Bayesian GMM to cluster the αSyn ensemble based on the number of contacts made by residues in P1 and the SASA. The four clusters shown in Figure 4 range between expanded conformations with very few P1 contacts (cluster A) to more compact states with more contacts made by residues in P1. All four clusters show differences in their contact maps, with clusters A and D being most different. Even though this analysis is used here only for illustration purposes, it highlights the type of information that can be gained. For instance, interactions between residues in the important NAC region (residues 61-95) are only present in cluster D when P1 is also involved in numerous contacts with the NAC and C-terminal regions, while in cluster A NAC seems to be shielded by the C-terminus (Figure 4) . Although the use of machine learning described here is solely to unpick already available dynamic ensemble, other uses of these powerful methods can be envisaged, such as in molecular dynamics simulations used to generate the initial ensemble (Noé et al., 2020) . In general, we expect that these types of analyses, extended to deep convolutional neural networks, will reveal hidden patterns and propensities for IDPs, much like they were able to revolutionize structure prediction for folded proteins.

Many of the ideas described above for defining and subclassifying the monomeric ensembles of IDPs, are equally well applicable to address the challenges with understanding early oligomeric species formed by specific assembly of partially folded protein conformers, as such species are also often highly heterogeneous, dynamically interconverting and short-lived. Structural information for several of oligomeric intermediates of amyloid assembly is available, in cases where these species have been trapped/enriched by specifically designed chemical tools or caught by NMR, MS or single molecule methods (Cawood et al., 2021) . However, these examples are far less numerous than those of IDPs. This reflects the difficulty in finding conditions wherein stable populations of oligomeric species are present, without further polymerization into amyloid fibrils. One such system with favorable properties for biophysical analysis is wild-type human β 2 m (hβ 2 m), the culprit protein of dialysis related amyloidosis (Allison et al., 2009 ) that consists of 400 structures shown in Figure 3 , based on the number of Cα contacts made by residues in P1 and the solvent accessible surface area. For clustering a contact is defined if two Cα atoms are within 8 Å. The four ellipses correspond to the four clusters identified and are labeled (A-D). The corresponding contact maps are shown below. For the contact maps the definition of contacts is more generous and includes all atoms of two given residues. The P1 region is highlighted in a green box and n denotes the number of structures in each cluster. (Gejyo et al., 1985) . hβ 2 m is highly resistant to aggregation in vitro, and its polymerization in vivo is thought to be initiated by partial unfolding on the surface of collagen filaments (Relini et al., 2006; Hoop et al., 2020) . The propensity of hβ 2 m to aggregate into amyloid is also enhanced dramatically by proteolytic cleavage of six amino acids from its N-terminus, which generates a (Karamanos et al., 2014) . N6 is shown as a Cα trace with the BC (green), DE (yellow) and FG (blue) loops highlighted (space fill). Note that the BC loop contains the trans Pro32. Hβ 2 m and mβ 2 m are shown in a surface representation (gray) bound to N6. Ensembles of 100 complexes (aligned on N6) are shown. (B) A small molecule stabilized tetramer of N6 (7AFV) (Cawood et al., 2020) . N6 subunits are shown as cartoons and the four copies of the covalent small molecule (S54; Cawood et al., 2020) are highlighted as spheres. The DE loop that is involved in one of the tetramer interfaces is shown in yellow and a schematic of the subunit arrangement in the tetramer is shown on the right. highly aggregation-prone and partially folded variant, N6 (Esposito et al., 2000; Karamanos et al., 2014) . While N6 retains a native-like immunoglobulin fold, the protein is far from native; it is dynamic and weakly protected from hydrogen exchange, contains a non-native trans Pro32 essential for aggregation into amyloid , and possess a re-packed hydrophobic core as a consequence of the loss of the N-terminal six amino acids (Figure 1 ; . These unique features of the N6 amyloid precursor imply specificity in the early stages of assembly, in that this species, and no other, more highly unfolded states is the most amyloidogenic species in the folding energy landscape (Karamanos et al., 2016) . For β 2 m, there is no simple relationship between thermodynamic stability and amyloid aggregation, as exemplified by the murine protein, mβ 2 m, which is less stable than N6, yet does not readily aggregate into amyloid, at least under most conditions in vitro (Karamanos et al., 2016) . An interesting property of this system is that the interaction of the N6, hβ 2 m and mβ 2 m variants in different combinations has different effects on the timecourse of aggregation, with the N6-mβ 2 m interaction inhibiting the aggregation of N6, while the N6-hβ 2 m interaction promotes the self-assembly of hβ 2 m (Karamanos et al., 2014) . The affinities of both complexes are low (K d ∼50 and 500 µM, respectively), yet clear evidence for a 1:1 interaction between the proteins can be detected by NMR chemical shift perturbation and by NMR PRE studies (Karamanos et al., 2014) . Using this information, ensembles were generated using intermolecular PRE values that describe the association of these protein pairs in a quantitative manner using simulated annealing docking calculations as shown in Figure 5A (Karamanos et al., 2014) . The resulting ensembles showed that although similar parts of the proteins involving the loops surrounding the important trans Pro32, are involved in both interfaces, the structural ensembles are distinct: the interface for the inhibitory N6-mβ 2 m interaction is less diffuse than that of the N6-hβ 2 m complex and involves more hydrophobic interactions than its amyloid-competent counterpart.

The visualization of these transient interactions is not only a neat biophysical experiment that demonstrated a surprising specificity to the transient ensembles that drive or inhibit amyloid assembly, but it also led to the development of new strategies to inhibit assembly of N6, by targeting the early protein-protein interactions that drive assembly (Cawood et al., 2020) . Specifically, by taking advantage of the interfaces identified, screening for a small molecule inhibitor of assembly was performed using disulfide tethering, in which a unique Cys was placed in the interface of interest and a library of small molecules (each as a symmetrical disulfide) was screened using ESI-MS (Cawood et al., 2020) . The result was a fragment that covalently binds to the interface region and inhibits assembly by stabilizing an off-pathway tetramer (Cawood et al., 2020 ; Figure 5B) . Remarkably, the ligand-bound tetramer was crystallized, providing an atomic-level view of a trapped oligomer and a complete understanding of why this structure is incompatible with the on-pathway dimer fold ( Figure 5B ). This finding opens up opportunities to target heterogenous/transient interactions that are normally considered undruggable in these dynamic proteins, since the covalent tethering approach is generic, does not require prior structural information and the proteins involved lack a well-defined pocket. This contrasts with the design principles of tafamidis that inhibits the aggregation of transthyretin and is now in clinical use (Ruberg et al., 2019) .

Dynamic protein states such as those involved in protein misfolding and aggregation represent a challenge to structurally characterize using X-ray crystallography and cryo-EM. Generating realistic representations of these dynamic protein systems requires measurement of a plethora of restraints using an array of experimental methods that report on long-and shortrange interactions. Detailed understanding and appreciation of how the timescale of protein conformational exchange affects the interpretation of the experimental data is needed to generate restraints that realistically describe the experimental parameters. However, when these restraints are properly averaged to reflect the time averaging of events occurring in solution, detailed structural ensembles can be generated. Clustering of these ensembles using powerful machine learning techniques holds promise in understanding the structural propensities that cause only a few of these molecules to self-assemble to pathological aggregates and why other disordered species are aggregationresilient. With the progress in machine learning, combined with proper treatment of experimental restraints, we may soon be able to visualize dynamic protein ensembles in intricate detail and pick out individual conformers able to drive or arrest protein aggregation, including the downhill cascade into amyloid fibrils.

TKK wrote the first draft. TKK, APK, and SER contributed to manuscript revision and conceptualization, read, and approved the submitted version. All authors contributed to the article and approved the submitted version. 

Hereditary transthyretin amyloidosis: a model of medical progress for a fatal disease

Conformational change in the monomeric alpha-synuclein imparts fibril polymorphs

Refinement of α-synuclein ensembles against saxs data: comparison of force fields and methods

Nmr spectroscopy captures the essential role of dynamics in regulating biomolecular function

Determination of the free energy landscape of alpha-synuclein using spin label nuclear magnetic resonance measurements

Visualizing transient dark states by nmr spectroscopy

Conformational propensities of intrinsically disordered proteins influence the mechanism of binding and folding

Quantification of amyloid fibril polymorphism by nanomorphometry reveals the individuality of filament assembly

Towards visual proteomics at high resolution

Aggregation prone regions and gatekeeping residues in protein sequences

Anatomy of a crosslinker

Tandem mass spectrometry reveals the quaternary organization of macromolecular assemblies

Amyloid nomenclature 2020: update and recommendations by the international society of amyloidosis (ISA) nomenclature committee

Defining long-range order and local disorder in native alphasynuclein using residual dipolar couplings

A structural model for unfolded proteins from residual dipolar couplings and small-angle x-ray scattering

Structural proteomics methods to interrogate the conformations and dynamics of intrinsically disordered proteins

Ion mobility mass spectrometry uncovers the impact of the patterning of oppositely charged residues on the conformational distributions of intrinsically disordered proteins

On the roles of intrinsically disordered proteins and regions in cell communication and signaling

Principles of protein structural ensemble determination

Consistent view of polypeptide chain expansion in chemical denaturants from multiple experimental methods

Ensemble methods enable a new definition for the solution to gas-phase transfer of intrinsically disordered proteins

Biophysical experiments and biomolecular simulations: a perfect match

Integration of mass spectrometry data for structural biology

Population of nonnative states of lysozyme variants drives amyloid fibril formation

Solution conditions determine the relative importance of nucleation and growth processes in α-synuclein aggregation

Inter-domain dynamics in the chaperone sura and multisite binding to its outer membrane protein clients

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Molecular dynamics simulations with replica-averaged structural restraints generate structural ensembles according to the maximum entropy principle

Modulation of amyloidogenic protein self-assembly using tethered small molecules

Visualizing and trapping transient oligomers in amyloid assembly pathways

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Advances in nmr spectroscopy of weakly aligned biomolecular systems

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The contribution of electrostatics to hydrogen exchange in the unfolded protein state

Kinetic diversity of amyloid oligomers

Mapping long-range interactions in alpha-synuclein using spin-label nmr and ensemble molecular dynamics simulations

Chaperone interactions at the ribosome

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A short motif in the n-terminal region of α-synuclein is critical for both aggregation and function

Nmr illuminates intrinsic disorder

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A diversity of assembly mechanisms of a generic amyloid fold

Conformational conversion during amyloid formation at atomic resolution

Removal of the n-terminal hexapeptide from human β2-microglobulin facilitates protein aggregation and fibril formation

Structural basis of cullin 2 ring e3 ligase regulation by the cop9 signalosome

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A new form of amyloid protein associated with chronic hemodialysis was identified as β2-microglobulin

Conformational ensembles of an intrinsically disordered protein consistent with nmr, saxs, and single-molecule fret

Accurate transfer efficiencies, distance distributions, and ensembles of unfolded and intrinsically disordered proteins from single-molecule fret

Collagen i weakly interacts with the β-sheets of β2-microglobulin and enhances conformational exchange to induce amyloid formation

Rapid mapping of protein interactions using tagtransfer photocrosslinkers

Protein structure and aggregation: A marriage of necessity ruled by aggregation gatekeepers

Stepwise protein folding at near amino acid resolution by hydrogen exchange and mass spectrometry

Ensemble calculations of unstructured proteins constrained by rdc and pre data: a case study of urea-denatured ubiquitin

Bayesian ensemble refinement by replica simulations and reweighting

A new era for understanding amyloid structures and disease

Detecting transient intermediates in macromolecular binding by paramagnetic nmr

Ensemble approach for nmr structure refinement against 1h paramagnetic relaxation enhancement data arising from a flexible paramagnetic group attached to a macromolecule

Amyloid formation under physiological conditions proceeds via a native-like folding intermediate

Intermolecular paramagnetic relaxation enhancement (pre) studies of transient complexes in intrinsically disordered proteins

Unveiling transient proteinprotein interactions that modulate inhibition of alpha-synuclein aggregation by beta-synuclein, a pre-synaptic protein that co-localizes with alpha-synuclein

Information theory and statistical mechanics

Describing intrinsically disordered proteins at atomic resolution by nmr

Deer distance measurements on proteins

The contribution of modern epr to structural biology

Highly accurate protein structure prediction with alphafold

Allosteric activation of an ion channel triggered by modification of mechanosensitive nano-pockets

Structural mapping of oligomeric intermediates in an amyloid assembly pathway

Visualization of transient protein-protein interactions that promote or inhibit amyloid assembly

Mechanisms of amyloid formation revealed by solution nmr

A population shift between sparsely populated folding intermediates determines amyloidogenicity

Measuring fast hydrogen exchange rates by nmr spectroscopy

The amyloid state and its association with protein misfolding diseases

An analytical solution to the kinetics of breakable filament assembly

Characterization of disordered proteins with ensemble

Bringing molecular dynamics and ion-mobility spectrometry closer together: shape correlations, structure-based predictors, and dissociation

Atomic view of a toxic amyloid small oligomer

Dynamic protein structures in normal function and pathologic misfolding in systemic amyloidosis

Secretases in alzheimer's disease: novel insights into proteolysis of app and trem2

Polyglutamine repeats in neurodegenerative diseases

Extended experimental inferential structure determination method in determining the structural ensembles of disordered protein states

Model-free approach to the interpretation of nuclear magnetic resonance relaxation in macromolecules. 1. Theory and range of validity

Model-free approach to the interpretation of nuclear magnetic resonance relaxation in macromolecules. 2. Analysis of experimental results

Ph-triggered, activated-state conformations of the influenza hemagglutinin fusion peptide revealed by nmr

The intrinsically disordered sars-cov-2 nucleoprotein in dynamic complex with its viral partner nsp3a

Beyond aggregation: pathological phase transitions in neurodegenerative disease

Conformational distributions of unfolded polypeptides from novel nmr techniques

Characterizing the unfolded states of proteins using single-molecule fret spectroscopy and molecular simulations

Combining nmr and small angle x-ray scattering for the study of biomolecular structure and dynamics

Dynamics of oligomer populations formed during the aggregation of alzheimer's aβ42 peptide

Metal ions shape α-synuclein

Light chain stabilization: a therapeutic approach to ameliorate al amyloidosis

Quantitative description of intrinsically disordered proteins using singlemolecule FRET, NMR, and SAXS

Machine learning for molecular simulation

Cross-linking mass spectrometry: methods and applications in structural, molecular and systems biology

Native ion mobility-mass spectrometry reveals the formation of β-barrel shaped amyloidβ hexamers in a membrane-mimicking environment

Freeenergy landscape, principal component analysis, and structural clustering to identify representative conformations from molecular dynamics simulations: the myoglobin case

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A mass spectrometry-based hybrid method for structural modeling of protein complexes

The folding of hen lysozyme involves partially structured intermediates and multiple pathways

Functional dynamics of hexameric helicase probed by hydrogen exchange and simulation

Mass spectrometric methods to analyze the structural organization of macromolecular complexes

Solid-state nmr spectroscopy

Collagen plays an active role in the aggregation of beta2-microglobulin under physiopathological conditions of dialysis-related amyloidosis

Bayesian approaches to gaussian mixture modeling

Using nmr chemical shifts as structural restraints in molecular dynamics simulations of proteins

Interpreting protein structural dynamics from nmr chemical shifts

On the statistical equivalence of restrainedensemble simulations with the maximum entropy method

A practical guide to single-molecule fret

Saxs ensemble refinement of escrt-III chmp3 conformational transitions

Transthyretin amyloid cardiomyopathy: JACC state-of-the-art review

Two wavelength femtosecond laser induced DNA-protein crosslinking

Cryo-em in molecular and cellular biology

Multi-timescale conformational dynamics of the sh3 domain of cd2-associated protein using nmr spectroscopy and accelerated molecular dynamics

Multi-timescale dynamics in intrinsically disordered proteins from nmr relaxation and molecular simulation

The expanding amyloid family: structure, stability, function, and pathogenesis

A tale of a tail: structural insights into the conformational properties of the polyglutamine protein ataxin-3

Protein folding studied by single-molecule fret

Predictive atomic resolution descriptions of intrinsically disordered htau40 and α-synuclein in solution from nmr and small angle scattering

Local structural dynamics of alpha-synuclein correlate with aggregation in different physiological conditions

Exchange rate constants of invisible protons in proteins determined by nmr spectroscopy

Alphafold: a special issue and a special time for protein science

The alzheimer's peptides aβ40 and 42 adopt distinct conformations in water: a combined md / nmr study

Consistent blind protein structure generation from nmr chemical shift data

The structural properties in solution of the intrinsically mixed folded protein ataxin-3

Capturing the conformational ensemble of the mixed folded polyglutamine protein ataxin-3

Molecular mechanism of the misfolding and oligomerization of the prion protein: current understanding and its implications

Cross-linking/mass spectrometry for studying protein structures and protein-protein interactions: where are we now and where should we go from here?

Detection of intermolecular chemical exchange through decorrelation of two-spin order

Direct observation of oligomeric species formed in the early stages of amyloid fibril formation using electrospray ionisation mass spectrometry

Monitoring copopulated conformational states during protein folding events using electrospray ionization-ion mobility spectrometry-mass spectrometry

Global structure of the intrinsically disordered protein tau emerges from its local structure

Energy landscapes of protein aggregation and conformation switching in intrinsically disordered proteins

Conformational ensembles of intrinsically disordered proteins and flexible multidomain proteins

A consensus method for the prediction of 'aggregation-prone' peptides in globular proteins

An isotope labeling strategy for methyl trosy spectroscopy

Looking beyond the core: the role of flanking regions in the aggregation of amyloidogenic peptides and proteins

Recent developments in the field of intrinsically disordered proteins: intrinsic disorder-based emergence in cellular biology in light of the physiological and pathological liquid-liquid phase transitions

Partially disordered proteins studied by ion mobility-mass spectrometry: implications for the preservation of solution phase structure in the gas phase

Reconciling simulated ensembles of apomyoglobin with experimental hydrogen/deuterium exchange data using bayesian inference and multiensemble markov state models

Pocket delipidation induced by membrane tension or modification leads to a structurally analogous mechanosensitive channel state

The cotranslational function of ribosome-associated hsp70 in eukaryotic protein homeostasis

Advances in ion mobility spectrometry-mass spectrometry reveal key insights into amyloid assembly

Systematic analysis of nucleation-dependent polymerization reveals new insights into the mechanism of amyloid self-assembly

Improved protein structure prediction using predicted interresidue orientations

Quantitative mass imaging of single biological macromolecules

Ion mobility spectrometry-mass spectrometry defines the oligomeric intermediates in amylin amyloid formation and the mode of action of inhibitors

Structural and dynamic insights into α-synuclein dimer conformations

Conflict of Interest: The authors declare that the research was conducted in the

We thank our research groups and colleagues in the Astbury Centre for many enlightening discussions, especially Anton Calabrese for discussions on MS methods, Christos Pliotas for discussion on EPR methods, and our group and collaborators for the work on β 2 m and αSyn which we highlight here.