Kinetics of Amyloid Fibrillar Aggregation of Uperin 3.5 is Directed by

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Kinetics of Amyloid Fibrillar Aggregation of Uperin 3.5 is Directed by the Peptide’s Secondary Structure Torsten John, Tiara J. A. Dealey, Nicholas P. Gray, Nitin Patil, Mohammed Akhter Hossain, Bernd Abel, John A. Carver, Yuning Hong, and Lisandra L. Martin Biochemistry, Just Accepted Manuscript • DOI: 10.1021/acs.biochem.9b00536 • Publication Date (Web): 06 Aug 2019 Downloaded from pubs.acs.org on August 9, 2019

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Kinetics of Amyloid Fibrillar Aggregation of Uperin 3.5 is Directed by the Peptide’s Secondary Structure Torsten John,†,‡,§ Tiara J. A. Dealey,† Nicholas P. Gray,† Nitin A. Patil,∥,# Mohammed A. Hossain,∥ Bernd Abel,‡,§ John A. Carver,⊥ Yuning Hong,∇ and Lisandra L. Martin,*,† †School

of Chemistry, Monash University, Clayton, Victoria 3800, Australia

‡Leibniz

Institute of Surface Engineering (IOM), Permoserstraße 15, 04318 Leipzig, Germany

§Wilhelm-Ostwald-Institute

for Physical and Theoretical Chemistry, Leipzig University, Linnéstraße 3, 04103

Leipzig, Germany ∥Florey

Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, Victoria 3010,

Australia ⊥Research

School of Chemistry, The Australian National University, Acton, Australian Capital Territory 2601,

Australia ∇Department

of Chemistry and Physics, La Trobe Institute for Molecular Science, La Trobe University, Melbourne,

Victoria 3086, Australia

1 ACS Paragon Plus Environment

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ABSTRACT: Many peptides aggregate into insoluble β-sheet rich amyloid fibrils. Some of these aggregation processes are linked to age-related diseases, such as Alzheimer’s disease and type 2 diabetes. Here, we show that the secondary structure of the peptide uperin 3.5 directs the kinetics and mechanism of amyloid fibrillar aggregation. Uperin 3.5 variants were investigated using thioflavin T fluorescence assays, circular dichroism spectroscopy and structure prediction methods. Our results suggest that those peptide variants with a high propensity to form an α-helical secondary structure under physiological conditions are more likely to aggregate into amyloid fibrils than peptides in an unstructured or ‘random coil’ conformation. This conclusion is in good agreement with the hypothesis that an α-helical transition state is required for peptide aggregation to amyloid. Specifically, uperin 3.5 variants in which charged amino acids were replaced by alanine were richer in α-helical content leading to enhanced aggregation compared to the uperin 3.5 wild type. However, the addition of 2,2,2-trifluorethanol as a major cosolute or membrane-mimicking phospholipid environments (DMPG) locked uperin 3.5 to the α-helical conformation preventing amyloid aggregation. Strategies to stabilize peptides into their α-helical conformation could provide therapeutic approaches to overcome peptide aggregation-related diseases. The impact of the physiological environment on the peptide secondary structure could explain aggregation processes in a cellular environment.

INTRODUCTION Peptides and proteins can form insoluble amyloid fibrillar aggregates1,2 that are associated with the development of various age-related diseases, such as Alzheimer’s disease (amyloid-beta peptide) and type 2 diabetes (human islet amyloid polypeptide).1,3,4 Research efforts to develop effective agents to combat these diseases have had limited success.5,6 A major reason for this is an inadequate understanding of the biomolecular mechanisms involved in amyloid fibril formation and disease development.7–9 In addition to the link to multiple diseases, peptide aggregates are functional in various species, such as curli fibers in bacteria or hydrophobins in fungi.7,10 Moreover, peptide fibrillar structures have been specifically designed as novel supramolecular scaffolds and bionanomaterials for functional applications in medicine and engineering.11– 13

Peptides can adopt a variety of secondary structures, depending on the physicochemical properties of the solvent or cellular environment, i.e. its polarity, pH or ionic strength.14,15 Thus, peptides can form α-helices or β-sheets or exhibit a wide range of dihedral angle space, including unstructured forms in which intrinsically disordered or ‘random coil’ structures exist. The aggregation process from soluble peptide monomers into insoluble fibril structures proceeds via


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Biochemistry

intermediates.16 These intermediates include oligomeric and protofibrillar species that are presumed to be the toxic species in disease progression.17–19 Peptide aggregation is initiated by the formation of critically sized β-sheet rich oligomeric nuclei during the lag phase of amyloid aggregation. These nuclei can then elongate and mature to form larger protofibrils and fibrillar structures (growth phase) (see Figure 1).20–23 Notably, hydrophobic interactions are an important driving force for the formation of β-sheet conformations.24

Figure 1. Schematic of the kinetics of amyloid peptide aggregation with initial nucleation of unstructured ‘random coil’ peptide monomers into β-sheet rich oligomers (lag phase) that then elongate into mature fibrils (β-strands/sheets) (growth phase). The nucleation step might be accelerated by the presence of an α-helical intermediate. (Structures predicted25–29 and visualized in VMD.30 Figure adapted from31.)

The α-helical peptide conformation has been discussed as a transient intermediate in the peptide fibrillar aggregation process.32–37 It might be involved in, or directly initiates, the formation of nuclei that then transform into β-sheets during the lag phase of peptide aggregation (see Figure 1).34,38 The concept of an α-helical transient intermediate led to the idea of inhibiting fibril formation by targeted stabilization and locking of the peptide in an α-helical conformation.34,39,40 Peptide aggregation is expected to be faster for peptides with a low energy barrier for the formation of an α-helix. However, if the α-helical conformation is highly stabilized and the energy barrier for the α-helix to β-sheet transition is increased, then the peptide will be trapped in its α-helical conformation.32 In many cases, an α-helix is the major secondary structural entity adopted by a peptide when in contact with a membrane surface, as common for antimicrobial activities.41,42 In this work, we have studied the kinetics of the 17-amino acid uperin 3.5 peptide (U3.5, GVGDLIRKAVSVIKNIV-NH2) and some variants. U3.5 is an antimicrobial peptide (AMP)43–45 from the Australian toadlet Uperoleia mjobergii43 that undergoes amyloid fibrillar aggregation.46,47 We systematically investigated the aggregation of U3.5 variants and found a


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relationship between the peptide secondary structure and the tendency of the peptide to aggregate. U3.5 variants with high α-helical content aggregated faster into β-sheet rich fibrils than variants that were unstructured in water and buffer. As a result, it was possible to distinguish between U3.5 variants that rapidly aggregated and U3.5 variants that aggregated after longer lag times. U3.5 variants in which charged amino acids were replaced by apolar alanine exhibited an enhanced propensity to form an α-helical conformation and thus aggregated faster than the wild type (wt) peptide.

MATERIALS AND METHODS Peptide synthesis, purification and characterization. The uperin 3.5 variants were synthesized using Solid Phase Peptide Synthesis (SPPS) or were purchased from GenicBio Limited (Shanghai, China) and Peptide 2.0 (USA). For the peptides synthesized in our group, the following protocol was used. The first amino acid was manually coupled as Fmocprotected amino acid (4 eq.) in the presence of HCTU (4 eq.) and DIEA (6 eq.) to ensure the resin loading. Further peptide chain elongation was carried out on a LibertyTM microwave peptide synthesizer, using Fmoc-protected amino acids (4 eq.) in the presence of HCTU (4 eq.) and DIEA (6 eq.). Fmoc removal was carried out with a 20 % piperidine solution in DMF. Side chain protecting groups and peptide cleavage from solid support was achieved using a solution of TFA/TIS/H2O (96/2.5/2.5) for 2 hours at room temperature. After cleavage, the resin was removed by filtration, the filtrate was concentrated under a stream of nitrogen and the peptide products were precipitated in ice cold Et2O and washed by centrifugation three times. The peptides were analyzed by an Agilent-1200 series RP HPLC system and purified by a Waters RP-HPLC system, using water (Buffer A) and acetonitrile (buffer B) with 0.1 % TFA. The peptides were characterized by RP HPLC, MALDI-TOF MS (minimum purity: 90 %, see Table S1). Preparation of buffer solutions and lipids. Potassium phosphate monobasic (≥ 99.0 %), potassium phosphate dibasic (≥ 98.0 %) and sodium chloride (≥ 99.5 %) were used for buffer preparation. Ultrapure water (18.2 MΩ cm) was used for all experiments. Phosphate buffered saline (PBS) solutions were prepared as 200 mM phosphate and 1000 mM sodium chloride solutions. The pH was adjusted to 7.40 ± 0.05 using diluted sodium hydroxide (≥ 97.0 %, pellets) or hydrochloric acid (32 %). The highly concentrated buffer solution was filtered through hydrophobic polypropylene membrane filters (GH Polypro, 0.2 μm, PALL Life Sciences, USA) and stored in a chemical fridge. For all experiments, the PBS buffer was added as 10x concentrate to obtain a final concentration of 20 mM phosphate and 100 mM sodium chloride. The lipids 1,2-dimyristoyl-sn-glycero-3-phosphocholine (DMPC, 99%), 1,2-dimyristoyl-sn-glycero-3-phospho-(1′-racglycerol) (DMPG, 99%) and cholesterol (>98%) were obtained from Avanti Polar Lipids (USA). DMPC and cholesterol were each dissolved in pure ethanol-free chloroform (≥99%, Sigma-Aldrich, USA) and DMPG was dissolved in a 2:1 (v/v) mixture


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of chloroform and methanol (≥99.8%, Merck, Germany) to prepare 5 mM stock solutions. Aliquots of 100 μL were transferred into test tubes, the solvent evaporated and dried under vacuum as previously described.48 To form liposomes, the lipids were resuspended in 0.5 mL PBS buffer (1 mM lipid), incubated at 37 °C for at least 30 minutes, vortexed and finally extruded through polycarbonate membranes (0.1 μm, Avanti Polar Lipids, USA). Thioflavin T fluorescence assays. a) Peptide aggregation in water and buffer. All U3.5 variants were prepared as 1 mM stock solutions in ultrapure water and U3.5 G1L and G1L-R7A were stored at -20°C, prior to use. Samples for the Thioflavin T (ThT) assays were prepared by diluting the stock solutions with ultrapure water. The peptides were studied at 100 μM, 50 μM and 10 μM. ThT (SigmaAldrich, USA) was diluted in DMSO to obtain a 1 mM stock solution that was stored at -20°C and protected from light. The final concentration of ThT in the assay was 20 μM. Samples containing peptide stock, ThT stock and water were vortexed before the transfer of 50 μL into each sample well. Black polystyrene 384 well microplates with flat, clear bottom and nonbinding coating (Greiner Bio-One, Germany) were used. The ThT fluorescence was recorded using a CLARIOstar plate reader (BMG Labtech, Germany) with excitation and emission wavelength set to 430/15 nm and 480/20 nm, respectively. The experiments were performed at 25°C and all samples were tested at least in triplicate. The microplate was agitated for ten seconds before each measurement cycle (six minutes) using double orbital shaking (200 rpm). First, all samples were prepared in ultrapure water. After 1 hour, highly concentrated (10x) PBS buffer was injected online to obtain final concentrations of 20 mM phosphate and 100 mM NaCl. After the injection was completed, the microplate was closed using ThinSealTM adhesive sealing films (Astral Scientific, Australia). The online buffer injection enables to follow the rapid aggregation of the peptide variants. b) Peptide aggregation with lipids. U3.5 wt peptide was studied at 50 μM. It was first solved in DMSO (20 μL DMSO per 1 mL final solution volume) and then diluted with PBS buffer with 0, 5, 50 or 450 μM lipid present. The fluorescence instrument and ThT stock solution were used as described above in a). The samples were vortexed before transfer of 150 μL into each sample well. Black polystyrene 96 well microplates with solid, clear bottom and non-binding coating (Greiner Bio-One, Germany) were used and closed using ThinSealTM adhesive sealing films (Astral Scientific, Australia). Experiments were performed at 37°C with excitation and emission wavelength set to 440/10 nm and 480/10 nm, respectively. The microplate was agitated for 40 seconds before each measurement cycle (five minutes) using double orbital shaking (300 rpm).


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c) Seeding experiments. The peptides U3.5 wt and U3.5 R7A were studied at 100 μM (prepared from 200 μM stock solutions) with 0, 5 or 25% seeds present. The seed solution was prepared by incubating 100 μM peptide in PBS buffer at 37°C overnight. It was either directly used, or the buffer was removed by centrifugation of the seed solution, removal of the supernatant and subsequent resuspension in pure water. This procedure was repeated twice to ensure the buffer was removed. The fibrillar seeds were added to the sample either at the start or together with the buffer after one hour. The ThT assays were run using the parameters as described in a) for measurements at 25°C or b) for measurements at 37°C. d) Data analysis. Raw data were assessed and, except for obvious outliers, were averaged. Data were plotted in OriginPro 8G (OriginLab Corporation, USA). The fluorescence intensity data for the peptides after PBS buffer injection (at 1 hour) were (0-1) normalized to better understand the kinetics and mechanisms of peptide aggregation. To normalize, the ThT fluorescence data between 1.3-24 hours were used for peptides with a measurable lag time to correct for the small increase in ThT fluorescence intensity upon buffer addition, whereas the data between 0.9-24 hours were used for all peptides that rapidly aggregated to consider the ThT fluorescence intensity value at the start without formed amyloid peptide fibrils. To test different mechanisms for amyloid peptide aggregation, the AmyloFit online program was used.49 For this, data after PBS buffer injection were normalized (0-1) and fitted to existing peptide aggregation models. Aggregation rates at halfmaximum of the total fluorescence were determined as first derivative at this time point. Circular dichroism (CD) spectroscopy. Stock solutions of U3.5 wt and U3.5 R7A were prepared at 200 μM in ultrapure water. Aliquots for each CD experiment were stored at -20°C. For the other U3.5 peptide variants, the 1 mM stock solutions from the ThT fluorescence assay were used. The peptides were studied at 100 μM if not stated otherwise. Far-UV CD spectra (260-190 nm in water, 260-195 nm after buffer addition) were recorded using a J-815 CD spectropolarimeter (Jasco, USA) with 1 mm path length quartz cuvettes (21/10/Q/1/CD, Starna Scientific, UK) at standard sensitivity (DIT: 1 second, band width: 1 nm, data pitch: 0.5 nm, continuous, scanning speed: 50 nm/min, 5 scans). The peptide samples were first measured in water at 25°C and then at 37°C. Highly concentrated PBS buffer (10x) was injected to obtain final concentrations of 20 mM phosphate and 100 mM NaCl. Samples were mixed before each measurement by inverting the cuvettes multiple times for 30 seconds if not stated different. The buffer contribution was subtracted for each experiment. Experiments were repeated at least in duplicate. CD experiments in the presence of 0-50 % (v/v) 2,2,2trifluorethanol (TFE) were recorded in water (260-180 nm) and after buffer addition (260-195 nm) at 37°C. For studies with lipids, U3.5 wt samples at 50 μM were first dissolved in a 1:1 (v/v) mixture of acetonitrile (≥99.9%, Merck, Germany) and water (40 μL per 1 mL final solution volume). PBS buffer and 450 μM lipid were added. Raw data were assessed and


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representative data were chosen. Data were plotted in OriginPro 8G (OriginLab Corporation, USA). The Mean Residue Molar Ellipticity (MRE) was determined from the measured ellipticity values using the formula shown below (with Θ𝜆 measured ellipticity, 𝑐 peptide concentration, 𝑙 cuvette path length and 𝑛 number of residues, i.e. number of peptide bonds = 16).

Θ𝑚𝑜𝑙𝑎𝑟, 𝜆 =

Θ𝜆[mdeg]106 𝑐 [μM] 𝑙 [mm] 𝑛

Several approaches for the quantitative analysis and determination of peptide secondary components of the U3.5 peptide variants in water, after addition of buffer, and after one and five days of incubation were applied. The BeStSel approach and web server (http://bestsel.elte.hu) predicts protein secondary structures based on eight secondary structure elements.50,51 The CAPITO approaches and web server (http://capito.nmr.fli-leibniz.de) estimate the protein secondary structure either by using basis spectra (helical, β-strand and irregular) or matching-based methods (similarity based on lowest area difference to protein in reference dataset).52 The DichroWeb web server (http://dichroweb.cryst.bbk.ac.uk) is an interface to multiple algorithms that estimate protein secondary structures from CD data.53–55 The K2D algorithm uses a neural network for its secondary structure prediction.56 The CDSSTR algorithm (modified from VARSLC) uses the variable selection method and singular value decomposition (SVD) algorithm to assign secondary structure.57–59 The CONTIN-LL algorithm (average of all matching solutions used) is based on a locally linearized model in selecting basis set proteins from the reference database.60,61 The SELCON3 algorithm is a self-consistent method that uses the singular value decomposition algorithm.62,63 CDSSTR, CONTIN-LL and SELCON3 need reference datasets of proteins for their calculations.59 We used reference sets 4 and 7 for our calculations. A goodness-of-fit parameter, the normalized root mean square deviation (NRMSD), is stated for all methods.64

RESULTS AND DISCUSSION Uperin 3.5 peptide variants. The kinetics of amyloid peptide aggregation was studied using U3.5 as a model peptide system. Variants of U3.5 wt were used to understand the role of charge, hydrophobicity and resulting secondary structure for the aggregation kinetics. The peptide positions 1, 4 and 7 were modified by replacement with more aliphatic amino acids (G1L) or removal of charges (D4A, R7A); thus making the peptide variants more hydrophobic. An overview of all U3.5 variants investigated is presented in Table 1. Thioflavin T (ThT) fluorescence assay. To follow the aggregation kinetics of all U3.5 variants, we used a ThT fluorescence assay.65,66 ThT exhibits an enhanced fluorescence quantum yield upon intercalation into β-sheet rich amyloid


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peptide fibrils.67 Previous studies from our group revealed a faster and larger extent of aggregation for the more hydrophobic U3.5 R7A and U3.5 G1L-R7A variants while the U3.5 wt and U3.5 G1L did not undergo significant aggregation within 90 minutes under the studied conditions.46 The formation of fibrillar amyloid structures was confirmed for both U3.5 wt and U3.5 R7A, with the significantly faster aggregating U3.5 R7A showing a higher yield as shown using TEM.46 Here, we additionally investigated the U3.5 D4A variants (U3.5 D4A, U3.5 G1L-D4A, and U3.5 G1L-D4A-R7A) and we extended our studies for all U3.5 variants over longer time scales and with varying peptide concentrations. Table 1. Overview of U3.5 variants studied with primary sequence and net charge at pH 7 (partial charges in brackets). U3.5 variant

Primary sequence

Charge, pH 7

wt

GVGDLIRKAVSVIKNIV-NH2

+3 (+4,-1)

G1L

LVGDLIRKAVSVIKNIV-NH2

+3 (+4,-1)

D4A

GVGALIRKAVSVIKNIV-NH2

+4 (+4)

R7A

GVGDLIAKAVSVIKNIV-NH2

+2 (+3,-1)

G1L-D4A

LVGALIRKAVSVIKNIV-NH2

+4 (+4)

G1L-R7A

LVGDLIAKAVSVIKNIV-NH2

+2 (+3,-1)

G1L-D4A-R7A

LVGALIAKAVSVIKNIV-NH2

+3 (+3)

The ThT fluorescence intensity profiles over time at a peptide concentration of 100 μM (see Figure 2) show that all U3.5 variants required the addition of buffer to initiate significant amyloid peptide aggregation. Experiments were started with the U3.5 peptides in water and buffer was added after one hour. This approach was used to monitor the rapid aggregation of the U3.5 variants. Within the first hour in ultrapure water, no significant ThT fluorescence was observed, except for U3.5 R7A and U3.5 G1L-R7A that already displayed some fluorescence (see SI Figure S1 for a detailed presentation of the ThT fluorescence data during the first 54 minutes). The increase in ionic strength upon buffer addition shields charges on the peptide and thus initiated peptide aggregation as shown in previous studies68–71 In accord with previous studies,46,72 peptide variants with R7A modification exhibited a higher maximum fluorescence and faster rate of aggregation than U3.5 wt or U3.5 G1L. U3.5 R7A aggregated at a rate 10 times faster than U3.5 wt with a higher fibril yield, i.e. double the maximum fluorescence (see SI Table S2 for details on aggregation rates). The U3.5 D4A


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and U3.5 G1L-D4A variants had a medium aggregation rate, i.e. 2-5 times faster than that of U3.5 wt. Peptide aggregation into mature fibrils was dependent on the peptide net charge, although other factors also contributed. A higher aggregation rate was observed for the more hydrophobic and less charged U3.5 variants (see Figure 2 and SI Table S2).20,73,74 The peptides with the highest positive charge (+4), U3.5 D4A and U3.5 G1L-D4A gave the slowest aggregation rate at half maximum fluorescence (k = 0.05 h-1 and k = 0.02 h-1, respectively), whereas U3.5 R7A and U3.5 G1L-R7A with a net charge of +2 aggregated the fastest (k = 1.08 h-1 and k = 2.10 h-1, respectively). Electrostatic charge repulsion interactions between the peptide monomers seem to account for a slower aggregation rate for U3.5 D4A (+4) compared to U3.5 R7A (+2).68,75 U3.5 wt had a very distinct lag time of around three hours following buffer addition until fibril formation commenced. U3.5 G1L aggregation was extremely small, although above zero. All peptide variants reached their maximum aggregation plateau in less than 24 hours.

Figure 2. ThT fluorescence curves of the aggregation of U3.5 variants at 100 μM for 24 hours 25°C. The initial fluorescence in water (until 1 hour) for all U3.5 variants was very low (U3.5 R7A and U3.5 G1L-R7A already exhibited some fluorescence). After 1 hour, PBS buffer was injected which resulted in an immediate response for all peptide variants except U3.5 wt which exhibited a lag time of around 3 hours following buffer addition (final concentrations of 20 mM phosphate and 100 mM NaCl). The data represent the fluorescence intensities with the highest fluorescence observed for U3.5 G1L-R7A set to 1. The peptides U3.5 R7A, U3.5 G1L-R7A and U3.5 G1L-D4A-R7A had the greatest aggregation rates (marked with dashed ellipsoid); U3.5 D4A and U3.5 G1L-D4A also presented similar aggregation rates. (Note that the symbols are used to distinguish the data sets. Data were recorded every six minutes).

Aggregation mechanism. The distinct aggregation kinetics observed for U3.5 wt compared with D4A or R7A modified U3.5 variants encouraged us to probe the peptides at varying concentrations (10, 50 and 100 μM) and we fitted kinetic models to our data using the AmyloFit tool.49 At 10 μM, no significant aggregation was observed, except for U3.5 G1L-D4A


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which presented a lag phase with t1/2 of 10 hours (see SI Figure S2). The kinetic profiles of U3.5 wt and U3.5 R7A variant in Figure 3, normalized to 0-1, illustrate the difference in amyloid fibril formation. Only U3.5 wt gave a sigmoidal ThT profile at 50 and 100 μM, whereas the other U3.5 peptide variants showed a concave or exponential ThT fluorescence curve due to the absence of a lag phase. An overview of all U3.5 peptide variants is summarized in the SI (Figure S2).

Figure 3. Normalized ThT fluorescence data for U3.5 wt (a) and U3.5 R7A (b) at 100 μM (black) and 50 μM (blue) following buffer addition (final concentrations of 20 mM phosphate and 100 mM NaCl) 25°C. The U3.5 wt peptide formed fibrils largely following a fragmentation and secondary nucleation dominated mechanism, whereas the nucleation step seemed to be bypassed for the U3.5 R7A variant.5 (c,d) Experiments with 5% (grey) or 25% (green) fibril seeds added together with the buffer after one hour 37°C. The added seeds accelerated the aggregation of U3.5 wt (c).

Amyloid fibril formation is based on classical nucleation-polymerization theory.76 The lag phase is determined by the rate-limiting step. It can be dominated by primary nucleation (formation of aggregates from monomers), or secondary processes,77 such as secondary nucleation (existing fibrils catalyze the formation of aggregates from monomers) or fibril fragmentation (existing fibrils separate in parts and grow further).22,78,79 As a result of fitting different models to our data,78–81 we identified a fragmentation and secondary nucleation dominated mechanism for U3.5 wt (see SI Figure S2 h). The lag time for peptide aggregation for U3.5 wt decreased with increasing concentration (see Figure 3). In contrast, the U3.5 peptide variants with R7A or D4A modification rapidly aggregated upon buffer addition, no lag phase was detected at 50 and 100 μM. Such an exponential behavior of fibril formation is characteristic for conditions in which the nucleation step is bypassed.5 This could be achieved by the presence of pre-formed critically-sized fibril nuclei.82 The U3.5 variants likely followed the same mechanism as U3.5 wt, but were seeded with fibril nuclei that formed in water prior to buffer addition


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due to their higher aggregation propensity.46 Consistent with this, the ThT fluorescence intensity data indicated the formation of some β-sheet rich structures in water prior to the addition of buffer (see SI Figure S1). To verify our hypothesis of a fragmentation and secondary nucleation dominated mechanism for U3.5 wt, we performed seed experiments77 in which preformed fibril aggregates were added, together with buffer after one hour, to the monomer solutions (see Figure 3 c,d). The presence of fibril nuclei (seeds) accelerated the aggregation of U3.5 wt, confirming a fragmentation and secondary nucleation dominated mechanism. The aggregation kinetics were also studied by addition of seeds together with the monomers in water. In this case, seeds were present from the seed stock solutions, which contained buffer, and thus the buffer initiated the aggregation process (see Figure S3 a,b). To prevent peptide aggregation due to the buffer presence with the seeds, these were centrifuged and resuspended in ultrapure water, prior to their addition to the sample. In these cases, amyloid peptide aggregation was not accelerated by the presence of the seeds (see Figure S3 c,d). Taken together, these data suggest that mature fibrils are not the seeds of aggregation but that intermediate oligomers are acting as nuclei for secondary nucleation processes. These oligomers must be soluble as they were only present in the original seed stock solution, containing buffer. A further observation was that U3.5 wt aggregated with a shorter lag time at higher temperature (see Figure 3 c) or if the sample was prepared from an older frozen or higher concentrated stock solution (see Figure S 3e), indicating that aggregation nuclei were formed. Circular dichroism (CD) spectroscopy. To better understand the difference in kinetics of amyloid peptide aggregation between the U3.5 variants, CD spectroscopy was used. This technique enables the monitoring of peptide secondary structural changes over time due to their characteristic spectra.83,84 All U3.5 variants were studied at 37°C at a peptide concentration of 111 μM in water and at 100 μM following addition of PBS buffer and incubation for one and five or seven days to follow the changes in secondary structure (see Figure 4).


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Figure 4. CD spectra of U3.5 variants at 37°C at 111 μM (in water) and at 100 μM (following buffer addition). Plots (a) to (g) show the CD spectra of the U3.5 variants in water (dashed black), after adding of PBS buffer (final concentrations of 20 mM phosphate and 100 mM NaCl) (black) and after incubation for one day (blue) and several days (green) to follow the changes in secondary structure over time. In all CD spectra, the sample was shaken before the measurements after one and five days. Plot (h) shows the effect of agitation on the CD spectrum of U3.5 R7A; it initiates the formation of insoluble aggregates. (i) Overview of quantitative secondary structure estimation for the U3.5 variants in water (DICROWEB53–55 CDSSTR algorithm57–59 with reference data set 7). Note: A comprehensive overview of all results can be found in the SI (Tables S3-S10) Characteristic CD spectra for α-helix: minima at 222 nm and 208 nm, maximum at 193 nm; β-sheet: minimum at 218 nm, maximum at 195 nm, and ‘random coil’: minimum at 198 nm.83,85,86

The U3.5 variants were predominantly unstructured in water, as expected for linear antimicrobial peptides, such as U3.5, in solution. 87,88 U3.5 R7A and U3.5 G1L-R7A had a larger α-helical content, most likely due to the higher hydrophobicity and thus higher aggregation propensity (see ThT assay in Figure 2). Remarkably, the kinetics of secondary structure changes


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upon addition of buffer related to the kinetics observed in the ThT fluorescence assay (G1L-R7A, R7A > G1L-D4A-R7A, G1LD4A, D4A > G1L > WT). While the U3.5 variants R7A, G1L-R7A and G1L-D4A-R7A adopted a higher α-helical content in water or upon buffer addition compared to all other U3.5 variants, they also rapidly aggregated. To detail the secondary structural changes of all U3.5 variants in water, upon buffer addition and after incubation over one and several days, we applied secondary structure prediction algorithms (BeStSel,50,51 CAPITO,52 DichroWeb53–55: K2D,56 CDSSTR,57–59 CONTIN-LL60,61 and SELCON362,63). The quantitative analysis of the CD spectra is, however, challenging as we study small peptides (17 amino acids long) here and available algorithms and reference data sets have been optimized for larger proteins. Furthermore, deconvolution of CD spectra of peptide solutions with high β-sheet content is not trivial.50,51 The estimated secondary structures of the U3.5 variants in water are summarized in Figure 4 i. A comprehensive overview of all results can be found in the SI (Tables S3-S10). Although these models are not infallible, they provide a quantitative insight into the secondary structure composition to assist in analyzing the CD data. The U3.5 R7A and U3.5 G1L-R7A variants already formed structures with some α-helical and β-sheet content in water as they have been further advanced along the aggregation pathway due to their higher propensity to form a folded secondary structure (see SI Figure S4 for a correlation of aggregation rates and structural content). We also observed that older (repeatedly) frozen samples or higher concentrated peptide solutions had higher α-helical content in water or upon buffer addition compared to fresh samples (see e.g. SI Figure S3 f) that also resulted in faster aggregation kinetics. This supports the hypothesis that an α-helical conformation is an intermediate along the pathway of amyloid peptide aggregation.32–34,89 Upon addition of buffer, the U3.5 variants can be grouped in an analogous way to the ThT fluorescence assay. U3.5 wt did not significantly alter its ‘random coil’ secondary structure upon buffer addition. After one day, it transformed to β-sheet rich structures (albeit with some α-helical content) that remained stable for five days (see Figure 4 a). The U3.5 G1L variant did also not change its ‘random coil’ secondary structure greatly upon buffer addition. However, after one day, the peptide was predominately α-helical and to some extent β-sheet rich (see Figure 4 b). This secondary structure remained relatively stable for five days. The U3.5 D4A, G1L-D4A and G1L-D4A-R7A variants transitioned from ‘random coil’ to α-helical dominated structures upon buffer addition (see Figure 4 c,e,g). Over the course of five days, the α-helical content decreased and the β-sheet content increased to varying extents. Finally, the U3.5 R7A and G1L-R7A variants immediately transformed to β-sheet rich structures upon buffer addition. Over time, the β-sheet content decreased as insoluble fibrils were precipitated (visually confirmed). The formation of β-sheet rich structures for U3.5 R7A and its precipitation into fibrils was monitored with intermittent shaking (see Figure 4h). When the CD cuvette was not shaken, the secondary structure


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remained unchanged over time, at least one hour. By contrast, after shaking, the peptide precipitated immediately, i.e. independent of time. Thus, it can be concluded that agitation of the sample solution initiates the formation of insoluble peptide aggregates, as also observed for many other amyloid fibril forming species.77 Role of α-helical conformation. We used 2,2,2-trifluorethanol (TFE), an organic solvent that enhances helical secondary structure content, to detail the role of the α-helical peptide conformation in U3.5 aggregation using CD spectroscopy (see Fig. 5 a,b).86,90 The U3.5 wt, which started to aggregate after a lag time of a few hours, and U3.5 R7A, as a representative of the rapidly aggregating U3.5 variants, were both unstructured in 0-8 % (v/v) TFE and α-helical in 14-50 % (v/v) TFE. Between 10 and 13 % (v/v) TFE, both U3.5 variants formed intermediate structures suggesting coexistence of ‘random coil’ as well as α-helical conformations, and possibly a partially α-helical peptide intermediate. A phase transition diagram (Fig. 5 c) was prepared by plotting the mean residue ellipticity at 198 nm against its value at 222 nm, each characteristic for ‘random coil’ and α-helical structures. The intersecting lines indicate the region with intermediate secondary structures. In a previous study, we showed that the presence of TFE changes the aggregation kinetics by stabilizing the α-helical structure for U3.5 wt.47 In the presence of 10 % (v/v) TFE, peptide aggregation was accelerated, whereas peptide aggregation was inhibited with 50 % (v/v) TFE present.47 Here, we confirmed these observations by CD spectroscopy (see Figure 5 d) and studied the secondary structure over time to further verify the importance of an α-helical conformation for amyloid fibril formation. U3.5 wt in the presence of 8 % (v/v) TFE in water gave rise to a CD spectrum with α-helical content similar to that of U3.5 R7A or U3.5 G1L-R7A in pure water. Upon buffer addition, it rapidly transformed to α-helical and β-sheet-rich structures that aggregated further. In the presence of 20 % (v/v) TFE, U3.5 wt was already aggregated. A higher TFE content, such as 40 % (v/v) in this study, stabilized the U3.5 wt peptide in an α-helical conformation so that it could not aggregate (quantitative secondary structure estimates in the SI, Tables S11-S13).


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Figure 5. The effect of 0-50 % (v/v) TFE on 100 μM U3.5 wt (a) and R7A (b) was studied and a transition diagram (c) prepared by plotting the mean residue ellipticity at 198 nm against its value at 222 nm. The isodichroic point at which both structures coexist was at 201 nm. 86,90 (d) U3.5 wt was studied in solutions containing 8, 20 or 40 % (v/v) TFE over time to follow peptide aggregation kinetics. (e) Overview of quantitative secondary structure estimation for the U3.5 variants in TFE mixtures (DICROWEB53–55 CDSSTR algorithm57–59 with reference data set 7). Note: A comprehensive overview of all results can be found in the SI (Tables S11S13).

With these results in mind, we conclude that the stability of the α-helical state of a peptide in its respective environment determines its aggregation kinetics. If the α-helical state of a peptide is not favored, then it does not aggregate or it aggregates only very slowly. If the α-helical state is too stable, then the peptide remains in this state, i.e. it is trapped, and cannot aggregate. In between these extremes, α-helical peptide intermediates form that can then transition to β-sheet rich fibrils. Since biological membranes stabilize the α-helical conformation of peptides,41 it could explain why aggregation inhibitors, such as polyphenols, are less efficient in preventing amyloid aggregation when the peptide is in contact with lipid membrane interfaces compared to bulk conditions.91 The influence of the phospholipids DMPC and DMPG, as well as cholesterol, on the aggregation kinetics of U3.5 wt was studied using ThT fluorescence assays and CD spectroscopy (see Figure 6). If the lipid was present in excess, such as in ‘crowded’ physiological environments (peptide:lipid 1:9), U3.5 wt aggregation was inhibited by DMPG, retarded by DMPC and not influenced by cholesterol (Figure 6 a). The membrane mimetic mixtures DMPC:cholesterol (4:1, v/v) and DMPC:DMPG (4:1, v/v)92 had similar effects as those observed with pure


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DMPC or DMPG, respectively. CD spectra of the U3.5 wt peptide in buffer and containing cholesterol were similar (Figure 6 b), both rich in α-helical and β-sheet content. After 15 hours, the α-helical content decreased and β-sheet content increased, as the peptide aggregated. The presence of DMPC led to a higher initial α-helical content and the peptide was β-sheet rich after 15 hours. DMPG strongly stabilized the α-helical conformation of the peptide that inhibited peptide aggregation (quantitative secondary structure estimates in the SI, Table S14). As observed for varying concentrations of TFE, we also varied the amount of lipid introduced (Figure 6 c,d). Higher peptide-to-lipid ratios (peptide:lipid 10:1 or 1:1) led to an acceleration of the aggregation kinetics, consistent with an intermediate stabilization of the α-helical conformation of U3.5 wt.

Figure 6. The aggregation of 50 µM U3.5 wt in buffer (black) and in the presence of the lipids DMPC (blue), cholesterol (brown), DMPG (red) and the membrane mimetic mixtures DMPC-cholesterol (4:1, v/v) (green) and DMPC-DMPG (4:1, v/v) (orange) was studied. The peptide-lipid ratio was varied (1:9, 1:1, 10:1). Studies were performed at 37°C. U3.5 wt aggregation was followed using (a, c, d) ThT fluorescence assays and (b) CD spectroscopy. The phospholipids DMPC and DMPG, especially DMPG, stabilized the α-helical conformation of U3.5 wt and inhibited aggregation. (It needs to be noted that, unlike in Figure 4 a, the U3.5 wt was prepared at a higher stock concentration using organic solvent, which was diluted with buffer.)


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We applied peptide sequence-based algorithms to predict the secondary structure of the U3.5 variants (see SI Table S15 for summary of models).93–102 These models compare with existing structures in databases that are often measured in an environment that stabilizes the secondary structure.103 A helical secondary structure with unstructured termini was predicted for all U3.5 variants, consistent with our CD spectra in TFE in Figure 5 a,b for U3.5 wt and R7A. Assuming an αhelical structure is a general intermediate for all U3.5 variants, we visualized the sequences for U3.5 wt and U3.5 G1L-D4A-R7A (containing all modifications) as a helical wheel (see Figure 7).104 In these helical wheels, the peptide residues are located around the helical axis from a top-down perspective. It is evident that U3.5 has an amphipathic structure with hydrophilic and hydrophobic side, typical for antimicrobial peptides.88 In support of this, an NMR study concluded that uperin 3.6, a closely related peptide to U3.5 wt, adopted an amphipathic, α-helical structure in 50 % (v/v) TFE.105 The U3.5 G1L modification slightly extends the hydrophobic area, whereas the D4A and R7A modifications decrease the polarity of the hydrophilic side leading to an overall smaller hydrophobic moment of the helix, a typical measure of the amphiphilicity (see arrows in Figure 7). These changes would impact the probability for the formation of an α-helix and its propensities to transition into β-sheet rich structures.46

Figure 7. U3.5 wt (left) and U3.5 G1L-D4A-R7A (right) are represented as helical wheels.104 Assuming an α-helical conformation, the view is along the helical axis. The arrow indicates the vector of the hydrophobic moment. The U3.5 peptide presents an amphipathic structure with nonpolar (yellow, grey), polar uncharged (purple, pink) and charged (red, blue) residues. The modified sequence positions 1, 4 and 7 are highlighted in green.

Discussion and model. Amphipathic peptides have previously been shown to exhibit unstable secondary structures that are highly dependent on the physicochemical properties of the environment.106 This study shows that the environment (water, buffer, TFE mixtures or phospholipids) (de)stabilizes specific secondary structures. The buffer addition initiated fast aggregation of the U3.5 R7A variants and medium-fast aggregation of the U3.5 D4A variants (see Figure 2), consistent with the secondary structure transitions of the respective peptides (see Figure 4).


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Our study reinforces the importance of an α-helical conformation in amyloid fibril formation, although our results do not discern whether the α-helical conformation is an on-pathway intermediate to fibrils or an off-pathway species that forms concurrently with β-sheet structures. Kim et al.33 studied the connection between the secondary structure and aggregation propensity of so-called chameleon peptides, i.e. peptides that can adopt α-helical and β-sheet structures. Surprisingly, peptides with helical flanking residues aggregated faster than the ones with β-sheet flanking residues.33 Likewise, in our study, U3.5 R7A and U3.5 G1L-R7A exhibited some α-helical structural content in water (see Figure 4 d,f) and rapidly aggregated. In contrast, all other U3.5 variants (see Figure 4) were predominantly ‘random coil’ in water and did not show any aggregation prior to buffer addition (see SI Figure 1). Helical peptide conformations might facilitate the formation of critically sized fibril nuclei that then transform into larger β-sheet rich structures.34,38,89,107 The terminal region of a peptide in other studies adopted an α-helical conformation during the lag phase of aggregation.37,108 Intermolecular helix-helix interactions supported the formation of nuclei that then initiated conformational changes in other regions of the peptide.37 Anderson et al. suggested α-synuclein with its N-terminus in a partially α-helical conformation as an on-pathway intermediate to fibrils.90 This model proposes that regions within U3.5 would adopt an α-helical conformation that facilitate the nucleation of U3.5 monomers. Subsequently, unstructured regions of U3.5 transition into β-sheet rich fibril aggregates. The driving force for a peptide to undergo a change from an α-helical to an energetically unfavoured β-hairpin conformation was simulated as result of an increase in entropy.109 A ‘random coil’ intermediate from the α-helical to β-hairpin conformation was also observed.109 Interestingly, a predominately α-helical intermediate was not sampled by U3.5 wt (see Figure 4 a). Thus, its α-helical state was transiently present and rapidly transitioned into β-sheets. In the presence of secondary structure stabilizing TFE or the phospholipid DMPG, α-helical intermediates were indeed detected for U3.5 wt (see Figures 5 a,d and 6 b). The stability of the α-helical conformation determined the aggregation kinetics (see model in Figure 8). If the α-helical conformation was unstable, the peptide aggregated very slowly (e.g. U3.5 wt in water and buffer). Likewise, if the α-helical conformation of the peptide was highly stabilized, peptide fibril formation was not observed (e.g. U3.5 wt in 40 % (v/v) TFE or with an excess of DMPG). In between these extremes, U3.5 variants rapidly aggregated (e.g. U3.5 R7A or U3.5 G1L-R7A).


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Figure 8. Model for the transition of unstructured peptides into β-sheet rich fibrils during amyloid peptide fibril aggregation via an α-helical conformation. If the α-helical conformation is unstable, the peptide does not aggregate or aggregates very slowly. Likewise, if the α-helical conformation is highly stabilized and thus energetically trapped in its conformation, it will remain in this state and also not aggregate. In between these extremes, the peptide rapidly forms β-sheets. (Uperin 3.5 wt structures were predicted25–29 and visualized in VMD.30)

The distinct peptide aggregation pathways observed for U3.5 wt and the D4A or R7A containing U3.5 variants were related to the kinetics of the conformational transitions observed. The physiological environment of the peptides determined the secondary structure and thus the peptide fibril aggregation kinetics. The α-helical conformation was confirmed as an intermediate in the aggregation of U3.5.

CONCLUSION The U3.5 peptides were used to exemplify the role of secondary structure in the aggregation of amyloidogenic peptides. The aggregation mechanism and kinetics were determined by the peptide’s secondary structure in the respective physiological environments. The existence of an α-helical intermediate on or off the pathway to β-sheet-rich fibrils was endorsed. The major impact of a single point mutation in the peptide on its secondary structure and thus the aggregation kinetics provides an opportunity for tailoring aggregation propensities during peptide design. Further, peptides can be (de)stabilized in their secondary structure by varying the solvent properties or ionic strength. In a cellular context, changes in the cell’s physiological environment could cause alterations in peptide secondary structure and thus initiate amyloid aggregation and related neurodegenerative diseases.

ASSOCIATED CONTENT


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Supporting Information. Peptide purity information, secondary structure prediction models, ThT fluorescence data in water, ThT fluorescence data for U3.5 variants at 100, 50 and 10 μM, Kinetic analysis of ThT fluorescence assay, Additional ThT fluorescence data of seed experiments, Summary of quantitative secondary structure estimations, Correlation between U3.5 fibril formation rates and ’random coil’ content, supporting references. This material is available free of charge via the Internet at http://pubs.acs.org.

AUTHOR INFORMATION Corresponding Author *E-mail: [email protected]

Present Addresses #

(N.A.P.) Department of Microbiology, Monash University, Clayton, Victoria 3800, Australia.

Notes The authors declare no competing financial interest.

Author contributions The study was designed by TJ, BA, JAC, YH and LLM. Experiments were performed and analyzed by TJ, TJAD and NPG. Peptides were synthesized by NAP and MAH. The manuscript was written by TJ and advanced by all authors.

ACKNOWLEDGMENTS LLM would like to thank Professor John Bowie, University of Adelaide, for his insights and many hours of discussions. This work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Science Foundation, project number 189853844, SFB-TRR 102, B1). TJ thanks the Friedrich-Ebert-Stiftung for a PhD fellowship, and the Australian Government, Department of Education and Training, and Scope Global for the support through a 2018 Endeavour Research Fellowship. JC’s work was supported by a grant (1068087) from the National Health and Medical Research Council of Australia. YH acknowledges the support by the Australian Research Council (DE170100058).

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