Alanine Scanning Effects on the Biochemical and Biophysical

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Alanine Scanning Effects on the Biochemical and Biophysical Properties of Intrinsically Disordered Proteins: A Case Study of the Histidine to Alanine Mutations in Amyloid-#42 Orkid Coskuner, and Vladimir N. Uversky J. Chem. Inf. Model., Just Accepted Manuscript • DOI: 10.1021/acs.jcim.8b00926 • Publication Date (Web): 29 Jan 2019 Downloaded from http://pubs.acs.org on January 30, 2019

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Journal of Chemical Information and Modeling

Alanine Scanning Effects on the Biochemical and Biophysical Properties of Intrinsically Disordered Proteins: A Case Study of the Histidine to Alanine Mutations in Amyloid-β42 Orkid Coskuner-Weber1* and Vladimir N. Uversky2,3* 1Turkish-German

University, Molecular Biotechnology, Sahinkaya Caddesi, No. 86, Beykoz, Istanbul 34820

Turkey; 2Department of Molecular Medicine and USF Health Byrd Alzheimer's Research Institute, Morsani College of Medicine, University of South Florida, Tampa, Florida 33612, USA; 3Institute for Biological Instrumentation, Russian Academy of Sciences, 142290 Pushchino, Moscow region, Russia Emails: [email protected] and [email protected]

Abstract Alanine scanning is a tool in molecular biology that is commonly used to evaluate the contribution of a specific amino acid residue to the stability and function of a protein. Additionally, this tool is also used to understand whether the side chain of a specific amino acid residue plays a role in the protein´s bioactivity. Furthermore, computational alanine scanning methods are utilized to predict the thermodynamic properties of proteins. These studies are utilized with the assumption that the biochemical and biophysical properties of a protein do not change with alanine scanning. Our study was dedicated to analyze the effect of alanine scanning on the biochemical and biophysical properties of intrinsically disordered proteins. To this end, we studied the impact of widely used histidine to alanine mutations in amyloid-β (Aβ). We found that the secondary and tertiary contacts, salt bridge formations and thermodynamic properties, as well as disorder propensities and aggregation predisposition of Aβ are impacted by the single and triple point histidine to alanine mutations. Experimental and computational studies employing the alanine scanning technique for mutating histidine to alanine in the analysis of intrinsically disordered proteins have to consider these effects. Keywords: Alanine scanning, disordered proteins, mutations. 1 ACS Paragon Plus Environment

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Introduction Alanine scanning has been widely used in studying the ligand and receptor interactions of intrinsically disordered proteins without considering how these artificial mutations may affect the biophysical and biochemical properties of query proteins (see, for example refs.1-15) For instance, histidine to alanine mutations have been commonly utilized in the ligand and receptor interaction studies of amyloid-β42 (Aβ42).5-15 The Aβ42 peptide has three histidine (His) residues (His6, His13, and His14) in its primary structure, all located in the Nterminal half of this peptide (Scheme 1). While the mechanism of Aβ toxicity is poorly understood, it has previously been demonstrated that modifications of three His residues (His6, His13, and His14) of Aβ are able to modulate toxicity of this protein.7,8 Furthermore, ligand and receptor interaction studies of Aβ42 found that His residues bind to metal ligands, such as zinc, copper, cadmium and iron ions.6-27 These studies usually involve alanine scanning (mutation of His to Ala) for detailed investigations of the binding mechanisms between the ligand and receptor.6-27 However, Aβ42 is a rather short intrinsically disordered polypeptide that lacks stable two and three-dimensional (3D) structures and that exists as a highly dynamic conformational ensemble. Therefore, it is likely that these mutation(s) can impact the biochemical and biophysical properties of Aβ42, change the conformational ensemble, and thereby affect its structural and thermodynamic properties. We hypothesized that any mutation can cause enhancement or inhibition of Aβ42 toxicity. This is because we are dealing with the dynamic conformational ensembles, not stable structures, and these conformational ensembles are expected to be extremely sensitive to both changes in the environment and in the protein sequence. This hypothesis is in line with a model where an intrinsically disordered protein is considered as a ball located, in unstable equilibrium, at the top of a hill. This ball has limitless possibilities for movement and would make very different trajectories and would end up in very different new equilibria, depending on how it is disturbed and pushed away from the top of the hill.84 This is in a strict contrast to an ordered protein, which is typically depicted as a ball in the stable equilibrium at the bottom of the energy well that has some resilience to minor changes and can return to its original state after some time.84 Despite the ability of (at least some of) synthetic 2 ACS Paragon Plus Environment

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mutations to affect the dynamic conformational ensemble of an intrinsically disordered protein, the structureand conformational dynamics-modulating capabilities of the alanine scanning have not been investigated as of yet. Therefore, the caution should be used while designing and interpreting experimental and computational studies, where alanine scanning is used to modulate Aβ42 kinetics and thermodynamics, when the impacts of such mutations on Aβ42 are unknown at the atomic level with dynamics. In general, Aβ is a disordered peptide consisting of 38-43 residues, which is produced by the sequential proteolytic cleavage of the amyloid-β precursor protein (APP) by β- and γ-secretase enzymes.31-34 The whole spectrum of biological functions and associated molecular mechanisms of Aβ are poorly understood. However, Aβ is at the center of Alzheimer’s disease (AD) and cerebral amyloid angiopathy pathologies.31-36 The hallmark of AD is the presence of parenchymal plaques, which have Aβ aggregated peptides as major building blocks. Monomeric and oligomeric Aβ forms were defined as ‘toxic’ to neuronal cell cultures.35,36 The misfolding of Aβ, which leads to distorted conformational ensembles, has been directly associated with monomeric and oligomeric Aβ toxicities.31-36 Parkin and co-workers investigated the role of His residues in the β-secretase processing of the Aβ precursor protein in AD.7,8 Specifically, they studied the roles of His residues within the extracellular E1 copper binding and Aβ domains of APP. To this end, they used alanine scanning and mutated His to Ala and showed that many of these mutations had no impact on α- and β-secretase processing. However, they also reported that the His14Ala mutation down regulated β-secretase. Barnham and co-workers studied single His to Ala mutations in Aβ and ascertained how these alanine substitutions affected peptide aggregation, metal binding, redox chemistry, and cell-membrane interactions.37 They reported that His6Ala and His13Ala peptides were able to induce significant cell toxicity in primary cortical cell cultures at levels similar to the wild type Aβ42. However, they could not detect the same trend for His14Ala point mutant.37 This lack of toxicity was then correlated with the inability of the His14Ala point mutant to bind to cell membranes. Their data showed that the imidazole ring of His14 might modulate the interactions between Aβ and cell membranes. Fraser and coworkers studied zinc ion binding sites of Aβ through alanine scanning (systematic His to Ala mutations).38 3 ACS Paragon Plus Environment


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Feaga et al. investigated the affinity of Cu(I) for the copper binding domain of Aβ utilizing alanine scanning (His → Ala) mutations and reported that the dissociation constants are in the femtomolar range for both the wild-type and His → Ala mutants.12 Furthermore, Syme and Viles investigated the coordination mechanism of zinc and cadmium by Aβ through His → Ala mutations using NMR and CD measurements.11 The validation of His binding sites was accomplished through His to Ala mutations. Aβ42: Asp1 Ala2 Glu3 Phe4 Arg5 His6 Asp7 Ser8 Gly9 Tyr10 Glu11 Val12 His13 His14 Gln15 Lys16 Leu17 Val18 Phe19 Phe20 Ala21 Glu22 Asp23 Val24 Gly25 Ser26 Asn27 Lys28 Gly29 Ala30 Ile31 Ile32 Gly33 Leu34 Met35 Val36 Gly37 Gly38 Val39 Val40 Ile41 Ala42. Scheme 1. Amino acid residue sequence of Aβ42. The His residues are shown in bold. Despite these and other studies, the exact roles of His6Ala, His13Ala and His14Ala point mutations in the structural and thermodynamic properties of Aβ42 remain to be investigated in detail. Also, it is not clear how His6Ala/His13Ala/His14Ala triple point mutation might impact the biochemical and biophysical properties of Aβ42. Specifically, how does alanine scanning impact the secondary and tertiary contacts and the thermodynamic properties of Aβ42 in solution? Do these single and triple point mutations, which are widely used in experimental and computational studies of Aβ, impact the disorder and aggregation propensities of this important peptide? In this study, we used extensive sets of replica exchange molecular dynamics (REMD) simulations coupled with thermodynamic calculations to answer these questions. Namely, four different sets of REMD simulations were conducted along with harmonic and quasi-harmonic thermodynamic calculations for gaining insights into the entropic contribution. The secondary and tertiary contacts including salt bridge formations of His6Ala, His13Ala, His14Ala point mutants and those of the His6Ala/His13Ala/His14Ala triple mutant Aβ42 were calculated in aqueous environments. The disorder propensities and aggregation predispositions of the His6Ala, His13Ala, and His14Ala point mutants and of the triple mutant His6Ala/His13Ala/His14Ala were studied using a set of computational methods. Obtained results were compared to the wild-type Aβ42 peptide and part of the wild-type Aβ42 data that was presented by Coskuner and co-workers in the Journal of Chemical Physics, 2011, 135(20):205101. 4 ACS Paragon Plus Environment

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Methods Separate four sets of REMD simulations of the His6Ala, His13Ala, His14Ala point mutant and the His6Ala/His13Ala/His14Ala triple Aβ42 mutant were performed in an aqueous medium. The NMR structure, PDB ID: 1Z0Q, of the wild-type Aβ42 was used as an initial structure,40 and the alanine scanning-based mutant structures were prepared by single point and triple point mutations of the His residues to Ala. The Amber FF99SB force field parameters were utilized for the solutes.41 Most recently, we and our collaborators validated the AMBER FF99SB and CHARMM27 parameters through calculating the root mean square values between simulated and experimental Cα, Cβ and Hα chemical shift values.29,

45, 46

Additional four sets of REMD

simulations for gaining insights into force-field parameter-dependent toxic β-sheet structure formation were utilized using the CHARMM36 parameters for the protein and the modified TIP5P model for water (Supplementary Information section).39 The AMBER10 software package was used for the REMD simulations.47 For implicit solvent simulations, the Onufriev-Bashford-Case implicit model was used to represent the aqueous solution environment around the disordered peptide for avoiding confined aqueous volume effects and specific heat errors on the simulated solute structures.15, 36, 48-50 Particle mesh Ewald method with a cut off value of 25 Ǻ was used for treating long-range interactions.51-53 Langevin dynamics was used to control the temperature with a collision frequency of 2 ps-1.51-53 Different replicas with temperatures distributed exponentially between 280 K and 400 K were used in each simulation.15,36,49,50 These were validated in our earlier studies.15-17, 28-30, 36, 46, 49, 50, 67-69 The integration time step was set to 2 fs and trajectories were saved every 500 steps. Initial structures were equilibrated for 200 ps at each replica. Exchange between replicas was attempted every 5 ps, which yielded a total simulation time of 14.4 µs. Exchange probability was set to 0.74 for both the wild-type and His to Ala mutants of Aβ42. The convergence of all simulations occurred at 60 ns as shown by time dependent average secondary structure abundances and NMR chemical shift values. This is in accord with previous studies including our own.42, 43, 46, 49, 50 Structural and thermodynamic calculations were conducted after convergence through the replica closest to physiological temperature (310 K). Following our recent studies, thermodynamic properties were calculated using the molecular mechanics/generalized Born 5 ACS Paragon Plus Environment


surface area (MM/GBSA) method.15,

17, 28-30, 36, 46, 49, 50, 67-69, 54-56

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This method computes the conformational

Gibbs free energy (G) value using the potential energy (EMM) values, the solvation free energy (Gsol), and the entropy (S) at specific temperatures (T) based on equation 1:54-58

G  E MM  G sol  TS

(1)

Gsol embraces electrostatic and nonpolar contributions using the generalized Born and molecular surface area methods.58 The entropic contributions were calculated using the harmonic (normal mode analysis) and quasiharmonic (Schlitter) methods.59-64 Following our previous studies, intra-molecular interactions exist when the centers of mass of two residues are within 9.0 Å of each other.15, 17, 28-30, 36, 46, 49, 50, 67-69 A salt bridge occurs when two hydrogen bonded atoms possess opposite formal charges. A hydrogen bond occurs when the distance between a donor hydrogen atom and the acceptor atom is ≤ 2.5 Å and when the hydrogen bond angle is > 113°.71.65 For calculating the prominence of secondary structure elements per residue with dynamics, we used the DSSP algorithm.66 Effects of His6Ala, His13Ala, and His14Ala point mutations as well as His6Ala/His13Ala/His14Ala triple mutation on the intrinsic disorder propensity of human Aβ42 were evaluated by several common disorder predictors, such as PONDR®FIT,70 PONDR®VLXT,71 PONDR®VSL2,72 and IUPred.73 Here, scores above 0.5 are considered to correspond to the disordered residues/regions. PONDR® VSL2B is one of the more accurate stand-alone disorder predictors,74-76 PONDR® VLXT is known to have high sensitivity to local sequence peculiarities and can be used for identifying disorder-based interaction sites,77 whereas a metapredictor PONDR-FIT is moderately more accurate than each of the component predictors,70 PONDR®VLXT,77 PONDR® VSL2,74 PONDR®VL3,77 FoldIndex,78 IUPred,73 and TopIDP.79 IUPred was designed to recognize IDPRs from the amino acid sequence alone based on the estimated pairwise energy content, where it was hypothesized globular proteins are composed of amino acids which have the potential to form a large number of favorable interactions, whereas IDPs/IDPRs do not have unique 3D structure because their amino acid composition does not allow sufficient favorable interactions to form.72,80 We also analyzed mean disorder propensity calculated by averaging disorder profiles of individual predictors. Use of consensus for evaluation of 6 ACS Paragon Plus Environment

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intrinsic disorder is motivated by empirical observations that this approach usually increases the predictive performance compared to the use of a single predictor.76,81-83 Effects of individual single histidine to alanine substitutions and triple mutation on the aggregation propensity of human Aβ42 are studied by several common computational tools for prediction of protein/peptide aggregation propensity, such as Ziggregator,85-88 CamSol,89 Aggrescan,90,91 and FoldAmyloid.92 Each of these techniques uses specific attributes for finding regions with enhanced aggregation propensity in a query protein. For example, FoldAmyloid is based on premises that the most amyloidogenic regions in a protein are those with high expected probability of the formation of backbone-backbone hydrogen bonds and high expected packing density.92 Aggrescan finds, in a query protein, short specific regions (so-called aggregation hot spots) that modulate protein aggregation using an aggregation-propensity scale that was derived for natural amino acids from the in vivo experiments.90 Zyggregator predicts the propensity of a query protein for formation of amyloidlike fibrils. To this end, it calculates a position-specific aggregation score for each residue in a sequence that takes into account hydrophobicity, secondary structure propensity, charge, and the presence of hydrophobic patterns.88 CamSol builds the intrinsic solubility profile of a query protein using a linear combination of hydrophobicity, the electrostatic charge (at neutral pH), the α-helix propensity, and the β-strand propensity, which are smoothed over a window of seven residues to account for the effect of the neighboring residues.89 Although strategically CamSol is similar to Zyggregator, this method utilizes a different set of parameters aiming at removal of the bias toward predicting amyloid-like aggregation.89 Also, in comparison with the outputs of Zyggregator, the CamSol sequence-based solubility profiles use the converted sign at the y-axis to mark soluble and insoluble regions with positive and negative CamSol values, respectively.89

Results and Discussion Figures S1A and S1B in the Supplementary Information section depict the time courses of the average α-helix abundance and average potential energy (from the 310 K replica). These findings support results of earlier studies., which demonstrated that 60 ns of simulation time are required to reach a convergence for 7 ACS Paragon Plus Environment


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Aβ42.36, 42, 43, 49, 50 and references therein Based on these results, convergence is reached after 60 ns of the simulation time. Converged configurations were utilized in the thermodynamic and structural property calculations. Figures S2A and S2B (Supplementary Information section) show the calculated Cα and Hα chemical shift values (from the 280 K replica) and the comparison to experimental data obtained from Dr. Michael Zagorski (CWRU) at 280 K. These results illustrate the physical relevance of our theoretical studies and represent that the converged configurations produce results, which are in the excellent agreement with experiments. Correlation between the calculated (δsim.) and experimental (δexp.) values for the chemical shifts possesses Pearson correlation coefficient values of 0.98 and 0.93 for the Cα and Hα chemical shifts, respectively. Table 1 lists the calculated conformational enthalpy, conformational entropy, and conformational Gibbs free energy values for the wild-type Aβ42, His6Ala, His13Ala, His14Ala point mutants, and for the His6Ala/His13Ala/His14Ala triple point mutant Aβ42 in an aqueous environment using the harmonic entropy method (normal mode analysis). These results show that the His6Ala, His13Ala, and His14Ala mutations destabilize the structures of the wild-type Aβ42 by values ranging between 31.5 and 49.6 kJ mol-1. However, free energy values – using the normal mode analysis – are close to one another, when we consider statistical deviation values. The His6Ala/His13Ala/His14Ala triple point mutation impacts the thermodynamic properties of the wild-type Aβ42 and destabilizes its structures by 85.4 kJ mol-1 based on the conformational Gibbs free energy values (Table 1). The entropy values – using the normal mode analysis – of His6Ala, His13Ala, and His14Ala mutants are close to each other, whereas the His6Ala/His13Ala/His14Ala possesses a larger conformational entropy in comparison with the structures of the wild-type Aβ42 by about 50 kJ mol-1 (Table 1). Enthalpic contribution in the Gibbs free energy values is larger than the entropic contribution in all disordered peptides. However, the enthalpy differences are unremarkably small and entropic differences dominate the structural stability variations (Table 1). In parallel to harmonic calculations, we also conducted quasi-harmonic calculations utilizing the Schlitter method to gain more insights into the conformational entropy and its effects on the conformational 8 ACS Paragon Plus Environment

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Gibbs free energy values. Table 2 lists the calculated enthalpy, quasi-harmonic entropy, and quasi-harmonic conformational Gibbs free energy values. Based on these studies, entropy, but not enthalpy, dominates the free energy values. These analyses reveal that the His6Ala, His13Ala, and His14Ala mutants are more stable than the wild-type Aβ42 due to entropic effects by 297.4, 187.9 and 536.2 kJ mol-1, respectively. The His6Ala/His13Ala/His14Ala mutant is more stable than the His6Ala, His13Ala, and His14Ala mutants (Table 2) by 364.1, 473.6 and 125.3 kJ mol-1, respectively. The His6Ala/His13Ala/His14Ala mutant is by 661.5 kJ mol-1 more stable than the wild-type Aβ42. We find that the His6Ala/His13Ala/His14Ala mutations stabilize the structures of the wild-type Aβ42 due to entropic factors (Table 2). This information could not be gained using the normal mode analysis for entropy. These differences are attributed to entropic factors, which result from secondary and tertiary structural property differences. These thermodynamic findings indicate that His to Ala single or triple point mutations have a significant impact on the thermodynamic properties of Aβ42. Such large differences in the thermodynamic properties are expected to affect the biochemical and biophysical properties of the disordered peptide. Our thermodynamic calculations on the wild-type Aβ42 are in excellent agreement with previous theoretical studies.36 Less toxic Aβ42 species are expected due to increased stability via His to Ala mutations. We should note here that the consideration of the statistical deviation values yields quasi-harmonic conformational Gibbs free energy values for His6Ala and His13Ala that are close to each other even though a clear difference exists with the wild-type Aβ42, indicating that these two mutants might possess similar biophysical properties. To gain insights into the impact of His6Ala, His13Ala, and His14Ala point mutations, and the His6Ala/His13Ala/His14Ala triple point mutation on the structural properties of Aβ42, we studied the secondary structure propensities including the abundance of secondary structure elements per residue with dynamics. Figure 1A illustrates the calculated secondary structure properties along with residual abundances for the His6Ala, His13Ala, His14Ala, and His6Ala/His13Ala/His14Ala mutants of Aβ42. The secondary structures for the wild-type Aβ42 were reported in ref.36 Figure 1B presents the differences in secondary structure elements and their abundances between the mutated and wild-type Aβ42 peptides in an aqueous medium. Based on the 9 ACS Paragon Plus Environment


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results shown in Figures 1A and 1B, single point mutations His6Ala, His13Ala, and His14Ala and the triple His6Ala/His13Ala/His14Ala mutation have strong impacts on the secondary structure properties of Aβ42. The N-terminal region (Asp1-Lys16) adopts more abundantly the α-helical structure upon His6Ala, His13Ala, and His14Ala point substitutions. This abundance of helical structure is even larger in the structures of Aβ42 with the triple His6Ala/His13Ala/His14Ala mutation, especially at the Tyr10-Ala14 region in comparison with the wildtype Aβ42. The central hydrophobic core (CHC) region (Leu17-Ala21) adopts less prominent (up to 17%) αhelix in the structures of His6Ala, His13Ala, and His6Ala/His13Ala/His14Ala mutants in comparison with the same region in the structures of the wild-type Aβ42 peptide. Surprisingly, a reversed trend is obtained for the His14Ala mutant Aβ42 (see Figures 1A and 1B). These can be attributed to the intra-molecular peptidic interactions (see below). The decapeptide region Ala21-Ala30 forms more abundant α-helix conformation in the structures of the His6Ala mutant, parts of His14Ala and His6Ala/His13Ala/His14Ala mutants of Aβ42 in comparison with the same region of the wild-type Aβ42 peptide. Interestingly, structures of the His13Ala mutant possess less stable α-helix in the Ala21-Ala30 decapeptide region in comparison with Aβ42. The C-terminal region possesses more prominent α-helix in the structures of the His6Ala mutant at Glu22-Lys28, which also shows distinct tertiary structure formations (see below). The same trend is detected for the His6Ala/His13Ala/His14Ala mutant but with smaller abundances. On the other hand, His13Ala adopts less prominent α-helical structures in its C-terminal region in comparison with the same region of the wild-type Aβ42 peptide (up to 8%). The abundance of 310-helix in the structures of Aβ42 increases in the N-terminal region (except the Asp7Glu11 region) upon the His6Ala, His13Ala, and His14Ala point mutations and the His6Ala/His13Ala/His14Ala triple mutation (Figures 3A and 3B). His14Ala mutant adopts more prominent 310-helix in the CHC region of Aβ42 (up to 11%). Contrarily, the 310-helix abundance decreases upon His13Ala mutation in the Aβ42 structures (up to 9%). The Ala21-Ala30 decapeptide region forms less stable 310-helix upon His6Ala, His13Ala, His14Ala and His6Ala/His13Ala/His14Ala mutations (Figures 1A and 1B) except at Asn27-Ala30. Parts of the Cterminal region adopt slightly less prominent 310-helix upon single and triple point mutations. However, 10 ACS Paragon Plus Environment

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residues

Ile31

and

Ile32

form

more

abundant

310-helical

conformation

upon

His14Ala

and

His6Ala/His13Ala/His14Ala mutations in the aqueous medium. The β-sheet structure formation in Aβ42 is directly linked to the increased propensity of this peptide to self-assembly and the accelerated reactivity and aggregation. Specifically, it has been proposed that residues forming β-sheet structure are involved in the self-assembly reactions of Aβ42. Figures 1A and 1B show that βsheet structure formation in the mid-domain and C-terminal regions of the Aβ42 peptide is greatly influenced by alanine scanning. Specifically, His13Ala mutant adopts significantly more stable β-sheet conformation at Lys16-Phe20 (CHC region), Ala30-Ile32, and in the C-terminus region (up to 44%) in comparison with the wild-type Aβ42. Similar trend is obtained for the His6Ala, but with smaller abundances than those reported for the His13Ala point mutant (Figures 1A and 1B). Namely, residues Glu11-His13, Val18-Asp23 including the CHC region, Gly25-Ile31, Val39, and Val40 in the structures of Aβ42 adopt more prominent β-sheet conformation upon His6Ala point mutation (up to 19%). Such large differences in the β-sheet structure formation could not be detected for the His14Ala point mutation except for the residues located in the Cterminal region of this mutated peptide (up to 8%). A similar trend is detected for the triple His6Ala/His13Ala/His14Ala Aβ42 mutant (Figures 1A and 1B). Regions adopting β-sheet structures are affected by His to Ala mutations, indicating that the residues reactive toward self-assembly change with the single and triple point mutations of Aβ42. The formation of the turn conformation is affected in the N-terminal, mid-domain, and C-terminal regions of Aβ42 upon His to Ala single point and triple mutations in the aqueous solution. Specifically, residues Gly9, His/Ala13, and Lys16 in the N-terminal region of Aβ42 adopt significantly more abundant turn conformation upon His6Ala, His13Ala, His14Ala, and His6Ala/His13Ala/His14Ala mutations (up to 28%). In contrast, residues Asp7, Ser8, Val12 adopt less abundant turn conformation in the N-terminal region of Aβ42 upon His by Ala single and triple point mutations (Figures 1A and 1B). The CHC region adopts slightly less prominent turn conformation upon His to Ala single and triple point mutations. Residues Ala21-Ser26 in the N11 ACS Paragon Plus Environment


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terminal region of the Ala21-Ala30 decapeptide possess less abundant turn conformation in the structures of Aβ42 upon His to Ala single and triple point mutations (up to 16%), whereas the C-terminal residues Asn27Ala30 of the Ala21-Ala30 decapeptide region are characterized by an opposite trend and adopt more abundant turn conformation upon His by Ala mutations except residues Gly29 and Ala30 for the His13Ala mutation (Figures 1A and 1B). The His6Ala/His13Ala/His14Ala mutant adopts significantly more prominent turn conformation at Ile31-Gly33 in the C-terminal region in comparison to the wild-type Aβ42. Finally, the Cterminus of Aβ42 has slightly less stable turn conformation upon His to Ala single and triple point mutations (Figures 1A and 1B). These results clearly show that His6Ala, His13Ala, and His14Ala point mutations impact the secondary structural properties of Aβ42. This effect is more prominent in the structures of the His6Ala/His13Ala/His14Ala triple mutant in comparison to the structures of Aβ42. The secondary structure properties of the wild-type Aβ42 were studied before and results are not discussed in this study (see ref.36 for the detailed description of the secondary structure properties of the wild-type Aβ42). The secondary structure properties of the wild-type Aβ42 obtained in our study are in excellent agreement with experiments and previous theoretical studies. The tertiary contacts were calculated for gaining insights into the intra-molecular peptidic interactions and for better understanding of how they impact the Aβ42 structure upon His to Ala mutations. Figures 2A-2D depict the calculated tertiary structures for the His6Ala, His13Ala, His14Ala, and His6Ala/His13Ala/His14Ala mutants of Aβ42, respectively. The tertiary structure calculations for the wild-type Aβ42 can be found in ref.36 Figure 2A shows the calculated intra-molecular peptidic interactions for the His6Ala point Aβ42 mutant. The interactions within the N-terminal regions through His residues are weakened due to the His6Ala mutation. However, we see starker interactions within the N-terminal region in general upon His6Ala mutation. The CHC region interacts with residues Ser8-Tyr10 more prominently upon His6Ala mutation, whereas the CHC region interactions with the N-terminus disappear. Furthermore, the CHC region interactions with the C-terminal region are slightly weaker and less abundant upon His6Ala mutation. The Ala21-Ala30 decapeptide region interacts less prominently with the N- and C-terminal regions in the structures of the His6Ala mutant. The 12 ACS Paragon Plus Environment

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prominent CHC region interactions with residues Asn27-Gly33 are weaker upon His6Ala mutation. The interactions between the C- and N-terminal regions are slightly more prominent in the structures of the His6Ala mutant in comparison to the wild-type Aβ42. A significantly different trend is obtained for the structures of the His13Ala mutant (Figure 2B). We notice slightly more abundant intra-molecular interactions within the Nterminal region in Aβ42 upon His13Ala mutation. The CHC region interactions with the parts of the mid-domain and C-terminal regions are starker in the structures of the His13Ala mutant Aβ42. Specifically, we notice more prominent interactions between residues His13-Lys28 and Val24-Ile41. N-terminal interactions with the middomain and C-terminal regions are either weaker or diminish upon His13Ala mutation. (Figure 2B). These intra-molecular peptidic interactions show a different course upon His14Ala mutation as presented in Figure 2C. Namely, the mid-domain interactions with the N- or C-terminal regions are reduced in abundances upon His14Ala mutation. The interactions within the N-terminal region are greater in prominence and the CHC region interactions with the N- and C-terminal regions disappear in the structures of the His14Ala mutant in comparison to the structures of the wild type Aβ42 (Figure 2C). In parallel, we notice more stable intra-molecular interactions within the N-terminal region of the His6Ala/His13Ala/His14Ala triple mutant (Figure 2D) in comparison to those of the wild-type Aβ42. The mid-domain interactions, such as the CHC region and N-terminal interactions as well as Val24-Ala30 with the N-terminal region disappear upon His6Ala/His13Ala/His14Ala triple mutation in the structures of Aβ42. On the other hand, CHC region interactions with the C-terminal region are more widely distributed and are more abundant due to the His6Ala/His13Ala/His14Ala triple mutation. These results, along with those obtained for the secondary structure properties and quasi-harmonic thermodynamic calculations, indicate that more order is introduced upon alanine scanning (His to Ala mutations) in the structures of Aβ42. More ordered or less disordered mutant structures are expected to be less reactive toward other species including self-assembly and reactions with metals. Experiments that study the complexation of Aβ42 with metals, such as zinc and copper ions, reported slower self-assembly kinetics and these were attributed only to the metal ion effects. However, His to Ala mutations, which are widely used in the studies of Aβ with 13 ACS Paragon Plus Environment


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metals, impact the chemical and physical properties of Aβ and introduce more order and more stability in the structures of Aβ42. These might be the additional reasons for slower aggregation kinetics in the self-assembly of metal ion-coordinated Aβ42. The salt bridge formations between Lys28 and Glu22 or Asp23 are calculated using the distribution of the distance between the Cγ and Nζ atoms of Lys28 and Glu22 or Asp23, respectively. Lys28 and Glu22 or Asp23 salt bridge formations are associated with turn structure formation in the Ala21-Ala30 decapeptide region. Figures 3A and 3B show the probability distribution of the distance between Lys28 and Glu22 or Asp23 for gaining insights into the impacts of alanine scanning (His6Ala, His13Ala, His14Ala point mutants and for the His6Ala/His13Ala/His14Ala triple mutant) on Aβ42 peptides in the aqueous medium. The His14Ala point mutant possesses the highest peak (2.6 %) for the Lys28-Glu22 salt bridge formation at a distance of 4.0 Å (Figure 3A). His6Ala mutant and His6Ala/His13Ala/His14Ala triple mutant have reduced (up to 73%) peak heights at a distance of 4.0 Å (Figure 3A). On the other hand, His13Ala and His6Ala/His13Ala/His14Ala have the highest peaks for the Lys28-Asp23 salt bridge formation at a distance of 4.0 Å (Figure 3B). The peak is shifted to 3.3 Å in the case of His13Ala mutant. The Lys28-Glu22 peak height at 4.0 Å is higher than Lys28Asp23 peak height for His13Ala and His6Ala/His13Ala/His14Ala mutants. An opposite trend is observed for the wild-type Aβ42 and for the His6Ala and His14Ala point mutants (Figures 3A and 3B). These trends might be linked to the more prominent turn structure formation at Lys28 in the structures of His13Ala point mutant and His6Ala/His13Ala/His14Ala triple mutant in comparison to the wild-type Aβ42 (see above). Long-range interactions between Lys28 and Glu22 or Asp23 are more prominent in the structures of the His6Ala point mutant Aβ42 peptide (Figures 3A and 3B). In addition to Lys28 and Glu22 or Asp23 salt bridge formations, we also calculated the stability of additional salt bridges, which are formed in the structures of the His6Ala, His13Ala, His14Ala, and His6Ala/His13Ala/His14Ala Aβ42 mutants. Table 3 presents the stability of various salt bridges. Obtained results were then compared to salt bridges formed in the structures of the wild-type Aβ42 peptide.36 Based on these analyses, the salt bridge abundance between Arg5 and Glu22 decreases upon His to Ala mutations from 26.4% (wild-type) to 2.4% (His6Ala), 8.8% (His13Ala), 6.5% (His14Ala), and 0.2% 14 ACS Paragon Plus Environment

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(His6Ala/His13Ala/His14Ala). The same trend is noted for the salt bridge between Lys16 and Asp23. On the other hand, the salt bridge between Arg5 and Glu11 increases from 14.1% (wild-type) to 39.5% (His6Ala), 43.6% (His13Ala), 34.8% (His14Ala) and 32.3% (His6Ala/His13Ala/His14Ala). A similar trend is detected for the Lys28-Ala42 salt bridge. To shed more light on the correlation of His to Ala mutations in human Aβ42 with the propensity of this protein for intrinsic disorder, we analyzed the His6Ala, His13Ala, His14Ala point mutants and the His6Ala/His13Ala/His14Ala triple mutant Aβ42 peptides by several commonly used disorder predictors (see Methods section). Results of this multi-parametric computational analysis of intrinsic disorder predisposition in the wild-type Aβ42 (Figure 4A), its point mutations His6Ala (Figure 4B), His13Ala (Figure 4C), and His14Ala (Figure 4D) and His6Ala/His13Ala/His14Ala triple mutation (Figure 4E) are shown together with the representation of the dependence of the per-residue mean disorder propensity calculated for various His to Ala mutants versus the per-residue mean disorder propensity calculated for the wild type Aβ42 peptide (Figure 4F). Data shown in Figure 4 clearly illustrates that all alanine scanning-related mutations considered in this study cause some increase in the local order propensity (since all mutants were characterized by the decreased intrinsic disorder propensity), with the largest effect being induced by the triple mutation. Curiously, Figure 4F shows that the effects of mutations on disorder propensity is systematically extended beyond the actual mutation site, with the disorder propensity being decreased for almost the entire N-terminal half of this peptide. These observations support an important notion that not only the triple His6Ala/His13Ala/His14Ala mutation but also single His to Ala point mutations (His6Ala, His13Ala, and His14Ala) might have detectable effects on the biochemical and biophysical properties of the disordered Aβ42 peptide. Finally, we analyzed the effect of His to Ala mutations on the aggregation propensity of the human Aβ42 protein using several common tools developed to find aggregation-prone regions in a query protein. Aggregation/solubility profiles generated by four common predictors are summarized in Figure 5, where results produced by Zyggregator, CamSol, Aggrescan, and FoldAmyloid, are shown in plots A, B, C and, respectively. 15 ACS Paragon Plus Environment


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Figure 5 shows that the His to Ala mutations are expected to affect local aggregation propensity and solubility of the Aβ42 protein. Curiously, results of these four tools do not agree with each other likely due to the different considerations given to the potential effects of these mutations on protein aggregation propensities. In fact, according to the CamSol, the local solubility of the Aβ42 protein is not affected by the mutations (see Figure 5B). On the other hand, the Zyggregator suggests that the local aggregation propensity is slightly weakened by the mutations (see Figure 5A). Similarly, according to the FoldAmyloid, local aggregation propensity is decreased by the His6Ala, His13Ala, His14Ala point mutations and more profoundly by the His6Ala/His13Ala/His14Ala triple mutation that produces the strongest effect on the aggregation propensity decrease, showing some additivity in the region around residues 13 and 14 (see Figure 5D). Contrarily, the Aggrescan suggested that the local aggregation propensity should increase due to mutations (see Figure 5C). Despite the disagreement between the results from different aggregation predictors, this analysis clearly indicates that aggregation propensity of the human Aβ42 protein is expected to be changed by the alanine scanning-related mutations. Therefore, based on the results of our analyses, we conclude that alanine scanning affects the structural and conformational aspects of the human Aβ42 protein analyzed in this study, modifies the intrinsic disorder propensity of this protein, and modulates its aggregation predisposition. Results of our computational analysis of the effects of the mutations on aggregation propensity of human Aβ42 protein is supported by the results of several experimental studies. In fact, it was shown that the His to Gln substitutions, made individually or en masse and leading to the elimination of the some or all His imidazole cations, caused significant retardation of the Aβ40 fibrillogenesis, with the largest effects being observed for the His13Gln mutation.93 In another study, the fibrillation rates of His13Ala and His14Ala peptides were found to be similar to that for the wild-type Aβ42, whereas His6Ala mutant fibrillated approximately two times faster than the wild-type protein.94 It was also shown that the capability of the Aβ peptides to form cation channels in lipid bilayers was successfully inhibited by replacing His13 and His14 to alanine or lysine.95 In other words, similar to the outputs of our analyses, experimental studies showed that the mutations of histidine residues might have different effects on the aggregation propensity of the Aβ protein, 16 ACS Paragon Plus Environment

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with the outcomes being highly sensitive to the peculiarities of experimental conditions used in the aggregation studies.

Conclusions Separate sets of REMD simulations and thermodynamic calculations were performed to study the role of His6Ala, His13Ala, and His14Ala point mutations, as well as the triple His6Ala/His13Ala/His14Ala mutation in the structural and thermodynamic properties of Aβ42. The secondary structure properties and thermodynamic properties were investigated using various methods. Overall, our results show that the structural and thermodynamic properties of the Aβ42 peptide in an aqueous medium are significantly altered upon alanine scanning based on single and triple point His to Ala mutations. Different thermodynamic methods yield different thermodynamic trends; the usage of harmonic methods cannot provide detailed insights into the thermodynamic properties of intrinsically disordered proteins. Using a quasi-harmonic method, we find that the His6Ala, His13Ala and His14Ala point mutations stabilize the structures of Aβ42 due to entropic effects. This trend is even larger with the His6Ala/His13Ala/His14Ala triple mutation, and the Aβ42 structure is even more stable upon triple point mutation due to entropic effects. His to Ala single point and triple mutations impact the secondary structure propensities of Aβ42. The N-terminal region of Aβ42 adopts more prominent α-helix upon His6Ala, His13Ala, His14Ala, and His6Ala/His13Ala/His14Ala mutations. The His14Ala point mutation shows deviations in trends in comparison to His6Ala and His13Ala point mutations. For instance, the CHC region of Aβ42 adopts α-helical structure less abundantly upon His6Ala, His13Ala, and His6Ala/His13Ala/His14Ala mutations. However, an opposite trend is obtained in the structures of the His14Ala point Aβ42 mutant. These differences are associated with varying intra-peptidic interactions (see above). His14Ala point mutant Aβ42 also adopts more abundant 310-helix in the CHC region. Furthermore, β-sheet formation is especially affected by the His6Ala and His13Ala point mutations. Such large deviations could not be detected for the His14Ala point and His6Ala/His13Ala/His14Ala triple mutant Aβ42 peptides. The CHC region interactions with the C-terminal region are slightly weaker and less prominent upon His6Ala mutation. The Ala21-Ala30 decapeptide region 17 ACS Paragon Plus Environment


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interacts less abundantly with the N- and C-terminal regions upon His6Ala point mutation. The N- and Cterminals interact more prominently upon His6Ala mutation. A significantly different trend is obtained for the His13Ala mutant. N-terminal interactions with the mid-domain and C-terminal regions are either weaker or disappear upon His13Ala point mutation. Mid-domain interactions with the N- or C-terminal regions are weaker upon His14Ala mutation. CHC region interactions with the N- or C-terminal regions disappear upon His14Ala mutation. The same trend between the CHC region and N-terminal region is obtained in the structures of the His6Ala/His13Ala/His14Ala triple mutant Aβ42. However, CHC interactions with the C-terminal region of Aβ42 are widely distributed and are more prominent upon His6Ala/His13Ala/His14Ala triple mutation. The His14Ala point mutant has the highest Lys28-Glu22 salt bridge peak at 4.0 Å. His6Ala and His6Ala/His13Ala/His14Ala mutants have reduced peak heights. Despite, His13Ala and His6Ala/His13Ala/His14Ala have the highest Lys28-Asp23 salt bridge peak at 4.0 Å. This peak is shifted to 3.3 Å in the case of the Aβ42 His13Ala point mutant. Overall, our results including quasi-harmonic thermodynamic calculations, secondary and tertiary structure analyses reveal that more order is introduced in the structures of Aβ42 upon single and triple point His to Ala mutations. More ordered disordered Aβ42 species are expected to be less reactive towards reactions including self-assembly. Our aggregation propensity analyses reveal that FoldAmyloid and Zyggregator algorithms yield predictions in agreement with our thermodynamic and structural property calculations from REMD simulations and show that His to Ala mutations decrease the aggregation tendency of Aβ42. Our disorder propensity calculations support our thermodynamic and structural property analyses and illustrate that more order is introduced in the structures of Aβ42 upon His to Ala single and triple point mutations. All in all, this study shows that alanine scanning – on the basis of four different single and triple point His to Ala mutations impacts the theoretically predicted biochemical and biophysical properties of the disordered Aβ42 peptide in an aqueous solution medium at the atomic level with dynamics. However, we should mention here that this study reflects the impacts of His6Ala, His13Ala, His14Ala and His6Ala/His13Ala/His14Ala mutations in human Aβ42 and that additional detailed studies are needed to draw a general conclusion on the usefulness and limitations of alanine scanning in disordered protein investigations. Currently, we are working on developing a 18 ACS Paragon Plus Environment

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new tool for predicting the contribution of a specific residue to the stability and function of a disordered protein via capturing the residual secondary structure inter-conversion stabilities.

Acknowledgement O.C. thanks Michael G. Zagorski (CWRU) for the experimental data and helpful discussions.

Supporting Information Convergence figures for the wild-type Aβ42, His6Ala, His13Ala, His14Ala single point mutants and His6Ala/His13Ala/His14Ala triple point mutant Aβ42. Correlations between theoretical and experimental chemical shift values. Calculated β-sheet structure along with abundances for the His6Ala, His13Ala, His14Ala single point mutants and His6Ala/His13Ala/His14Ala triple point mutant Aβ42 using the CHARMM36 and modified TIP5P models.

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Table Captions: Table 1. Thermodynamic properties of the wild-type, His6Ala, His13Ala, His14Ala single point and His6Ala/His13Ala/His14Ala triple point mutant Aβ42 peptides using a harmonic entropy method. The calculated potential energy (E), conformational enthalpy (H), conformational harmonic entropy (SNMA) and conformational harmonic Gibbs free energy (GNMA) for the simulated full-length wild-type and His6Ala, His13Ala, His14Ala single point mutants and His6Ala/His13Ala/His14Ala triple point mutant Aβ42 structures in an aqueous solution medium. Table 2. Thermodynamic properties of the wild-type, His6Ala, His13Ala, His14Ala single point and His6Ala/His13Ala/His14Ala triple point mutant Aβ42 peptides using a quasi-harmonic entropy method. The calculated conformational enthalpy (H), quasi-harmonic conformational entropy (SQH), quasi-harmonic conformational Gibbs free energy (GQH) and radius of gyration (Rg) for the simulated full-length wild-type and His6Ala, His13Ala, His14Ala single point mutants and His6Ala/His13Ala/His14Ala triple point mutant Aβ42 structures in an aqueous solution medium. Table 3. Salt bridges in the structures of the wild-type, His6Ala, His13Ala, His14Ala single point mutants and His6Ala/His13Ala/His14Ala triple point mutant Aβ42 peptides. R(Cγ-Nζ) is the distance between carboxylate C atom (Cγ) and the side-chain or N-terminal N atom (Nζ) of the residues involved in the formed salt bridges. The values presented are the abundances of the R(Cγ-Nζ) being less than or equal to the specified distance in the table for the converged structures of the wild-type. His6Ala, His13Ala, His14Ala single point mutants and His6Ala/His13Ala/His14Ala triple point mutant Aβ42.



Page 28 of 43

Figure Captions Figure 1. Secondary Structure Properties. (A) Calculated secondary structures along with abundances for the His6Ala (black), His13Ala (red), His14Ala (green) single point mutants and His6Ala/His13Ala/His14Ala (blue) triple point mutant Aβ42. (B) The difference in secondary structure abundances between the mutants and wild-type Aβ42: His6Ala single point mutant and wild-type Aβ42 (black), His13Ala single point mutant and wild-type Aβ42 (red), His14Ala single point mutant and wild-type Aβ42 (green), His6Ala/His13Ala/His14Ala triple point mutant and wild-type Aβ42 in aqueous solution. Figure 2. Tertiary structures of the His6Ala, His13Ala, His14Ala single point mutants and His6Ala/His13Ala/His14Ala triple point mutant Aβ42. The calculated intra-molecular peptidic interactions for

the

(A)

His6Ala,

(B)

His13Ala,

(C)

His14Ala

single

point

mutants

and

for

the

(D)

His6Ala/His13Ala/His14Ala triple point mutant Aβ42 in an aqueous medium at 310 K. The color scale corresponds to the probability (P) of the distance between the centers of mass between two residues being ≤9 Å from each other. Figure 3. Probability distribution for Lys28 and Glu22 or Asp23 salt bridge formations. Calculated probability distribution of the distance between Cγ of the (A) Glu22 (B) or Asp23 residues and Nζ atom of the Lys28 residue in the structures of the His6Ala (black), His13Ala (red), His14Ala (green) single point mutants and in His6Ala/His13Ala/His14Ala (blue) triple point mutant Aβ42. Figure 4. Analysis of the effects of the His6Ala, His13Ala, His14Ala single point mutations and the His6Ala/His13Ala/His14Ala triple point mutation on the intrinsic disorder propensity of human Aβ42 evaluated by several common disorder predictors. The calculated disorder profiles are shown for the wildtype protein (A), as well as for the His6Ala (B), His13Ala (C), and His14Ala single point mutants (D), and for the His6Ala/His13Ala/His14Ala triple point Aβ42 mutant (E). Intrinsic disorder propensity was analyzed using PONDR® FIT,70 PONDR® VLXT,71 PONDR® VSL2,72 and IUPred.73 Mean disorder propensity was also calculated by averaging disorder profiles of individual predictors. In all these plots, scores above 0.5 are 28 ACS Paragon Plus Environment

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considered to correspond to the disordered residues/regions. Light pink shadow around the PONDR® FIT curves show the distribution of errors in the evaluation of the intrinsic disorder propensity. Plot F shows the per-residue mean disorder propensities calculated for various His to Ala mutants versus the per-residue mean disorder propensity calculated for the wild type Aβ42 peptide. Figure 5. Analysis of the effects of the His6Ala, His13Ala, His14Ala single point mutations and the His6Ala/His13Ala/His14Ala triple point mutation on the aggregation propensity of human Aβ42 evaluated by several computational tools. The per-residue aggregation profiles generated by Zyggregator (A), Aggrescan (C), and FoldAmyloid (D) are compared to each other and to the CamSol-based solubility profile (B). Note, in the plots generated by Zyggregator, Aggrescan, and FoldAmyloid the negative scores correspond to soluble regions, whereas insoluble regions are characterized by the positive values. On the other hand, CamSol uses the converted sign at the Y-axis to mark soluble and insoluble regions with positive and negative CamSol values, respectively.



Page 30 of 43

Tables: Table 1.

-T

(kJ mol-1)

(kJ mol-1)

(kJ mol-1)

(kJ mol-1)

(kJ mol-1)

WT Aβ42

-188.0 (±72.3)

-2424.9 (±76.0)

-2612.9 (±25.4)

-2196.4 (±7.4)

-4809.3 (±20.8)

His6Ala Aβ42

-268.4 (±160.6)

-2329.4

-2597.8 (±51.8)

-2180.0 (±10.5)

-4777.8 (±42.1)

(±116.8) His13Ala Aβ42

-166.1 (±91.9)

-2415.0 (±62.5)

-2581.1 (±44.1)

-2185.6 (±8.0)

-4766.7 (±37.8)

His14Ala Aβ42

-198.9 (±139.8)

-2375.6 (±96.9)

-2574.5 (±59.0)

-2185.2 (±11.3)

-4759.7 (±50.7)

His6Ala/His13A la/His14Ala Aβ42

-251.4 (±131.9)

-2330.0 (±90.9)

-2581.4 (±58.6)

-2142.5 (±11.9)

-4723.9 (±49.5)

Table 2.

-T

(kJ mol-1)

(kJ mol-1)

(kJ mol-1)

WT Aβ42

-2612.9 (±25.4)

-5125.2 (±57.1)

-7738.1 (±41.3)

His6Ala Aβ42

-2597.8 (±51.8)

-5437.7 (±49.9)

-8035.5 (±50.9)

His13Ala Aβ42

-2581.1 (±44.1)

-5344.9 (±58.2)

-7926.0 (±51.2)

His14Ala Aβ42

-2574.5 (±59.0)

-5699.8 (±56.2)

-8274.3 (±57.6)

His6Ala/His13A la/His14Ala

-2581.4 (±58.6)

-5818.2 (±61.1)

-8399.6 (±59.9)


Page 31 of 43 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Aβ42

Table 3.

Salt Bridge Abundance (R(C-N),%) Donor

Acceptor

WT

H6A

H13A

H14A

H6A/H13A/H14A

≤4 Å

≤5 Å

≤6 Å

≤4 Å

≤5 Å

≤6 Å

≤4 Å

≤5 Å

≤6 Å

≤4 Å

≤5 Å

≤6 Å

≤4 Å

≤5 Å

≤6 Å

Arg5

Glu3

57.7

59.2

59.6

62.9

66.8

67.4

28.2

31.9

33.0

44.4

48.0

49.1

45.0

48.5

49.8

Arg5

26.4

26.7

26.7

2.4

2.5

2.5

8.8

9.0

9.0

6.5

6.7

6.8

0.2

0.2

0.2

Arg5

Glu22 Ala42 (-COO)

20.2

21.0

21.3

30.6

31.7

31.9

11.7

13.5

13.7

20.7

21.3

21.3

20.0

20.9

21.3

Arg5

Glu11

14.1

15.9

16.7

39.5

42.0

42.5

43.6

48.9

50.6

34.8

38.5

39.7

32.2

37.8

39.1

Arg5

Asp1

13.9

14.8

15.0

17.7

19.5

19.8

37.6

40.4

41.1

34.4

37.6

38.4

24.2

25.8

26.1

Arg5

Asp23

8.2

8.4

8.4

0.0

0.1

0.5

4.0

5.0

5.2

3.8

3.8

3.9

16.8

16.9

16.9

3.3

6.1

7.8

3.7

6.9

9.1

7.8

14.9

17.4

3.5

5.8

6.8

1.0

1.5

1.8

6.2

9.9

11.1

1.3

2.0

2.3

1.8

2.8

3.0

Lys28

7.3

11.6

13.2

Lys16

Glu22 Ala42 (-COO)

5.6

8.1

9.4

Lys28

Asp23

3.9

6.2

7.1

1.7

2.6

3.1

7.3

8.8

9.7

4.9

7.0

8.6

8.2

11.6

13.1

6.3

7.1

3.6

5.5

6.1

10.5

16.6

19.6

5.2

9.3

10.4

Lys16

Glu11

3.8

6.7

8.0

3.8

Lys16 Asp1 (NH3+)

Asp7

3.8

8.3

11.1

10.1

12.8

13.6

1.6

2.7

3.4

2.0

3.1

3.9

2.4

3.7

4.1

Glu3

2.8

4.5

5.3

2.0

2.9

3.2

4.0

5.4

6.0

2.4

3.4

4.1

3.8

5.5

6.0

Lys16

1.9

3.6

4.1

1.0

1.5

1.7

0.1

0.1

0.2

0.3

0.6

0.7

0.5

0.9

1.0

Lys28

Asp23 Ala42 (-COO)

1.8

2.6

2.9

3.6

5.3

5.7

6.3

7.2

7.6

1.6

2.6

3.0

4.7

5.9

6.3

Lys16

Glu3

0.9

1.6

1.8

0.6

1.0

1.3

1.0

1.7

1.9

1.7

2.6

3.1

1.8

2.6

2.7

Arg5

Asp7

0.7

0.9

1.2

2.5

2.8

3.3

8.0

9.1

12.6

2.4

2.9

4.0

8.2

8.5

8.7

0.6

0.7

0.6

1.0

1.2

0.9

1.8

2.2

0.3

0.5

0.6

Lys16

Asp1

0.7

0.9

1.0

0.3

Lys16

Glu22

0.4

0.6

0.7

2.1

3.3

3.5

0.4

0.7

0.8

0.2

0.3

0.3

1.1

1.8

2.2

Lys28

Asp7

0.2

0.2

0.3

2.9

4.3

4.6

4.1

6.0

6.1

0.4

0.7

0.8

2.6

4.1

5.1

Lys28 Asp1 (NH3+) Asp1 (NH3+)

Glu11

0.1

0.2

0.3

0.2

0.3

0.4

1.0

1.5

1.7

0.5

0.8

0.9

2.2

3.4

3.5

Glu11

0.1

0.2

0.2

0.1

0.1

0.1

0.2

0.7

0.8

1.6

3.8

4.4

0.1

0.1

0.2

Asp7

0.0

0.2

0.2

0.1

0.1

0.2

0.3

0.7

0.8

0.2

0.4

0.5

0.9

1.9

2.4

Lys28

Asp1

0.0

0.0

0.0

0.1

0.1

0.1

0.2

0.4

0.5

0.0

0.0

0.0

0.2

0.3

0.4

Lys28 Asp1 (NH3+) Asp1 (NH3+) Asp1 (NH3+)

Glu3

0.0

0.0

0.0

0.3

0.5

0.5

0.2

0.4

0.5

0.2

0.3

0.4

0.6

1.0

1.4

Glu22

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.1

0.2

0.2

0.0

0.0

0.0

Asp23 Ala42 (-COO)

0.0

0.0

0.0

0.0

0.0

0.0

0.1

0.1

0.1

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.1

0.3

0.4

0.1

0.3

0.3

0.1

0.2

0.3

0.2

0.4

0.5



Page 32 of 43

Figure 1.

(A)


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Page 34 of 43

Figure 2.

(A)


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(B)



Page 36 of 43

(C)


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(D)



Page 38 of 43

Figure 3.

(A)


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(B)



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Figure 4.

B 1.0 PONDR VLXT PONDR FIT PONDR VSL2 IUPRed_short Mean

0.8

Disorder propensity

Disorder propensity

A 1.0

0.6 0.4 0.2

0

10

20

30

0.8 0.6 0.4 0.2

10

Disorder propensity

0.6 0.4 0.2 0.0

20

30

40

Residue number

D 1.0

PONDR VLXT PONDR FIT PONDR VSL2 IUPRed_short Mean

0.8

0

40

Residue number

C 1.0 Disorder propensity


0.0

0.0


0.8 0.6 0.4 0.2 0.0

0

10

20

30

40

Residue number

E 1.0

10

0.6 0.4 0.2

20

30

40

Residue number

F 1.0


0.8

0

Mean disorder mutant

Disorder propensity

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


His6Ala His13Ala His14Ala His6Ala/His13Ala/His14/Ala

0.8 0.6

N

C

0.4 0.2 0.0

0.0 0

10

20

30

40

0.0

Residue number

0.2

0.4

0.6

0.8

1.0

Mean disorder wild type



Figure 5.

C 1.5

2.0

A wt A His6Ala A His13Ala A His14Ala A triple

Zyggregator score

1.5


1.0

Aggrescan score

A

1.0 0.5 0.0

Aggrescan

0.5 0.0 -0.5

Zyggregator

-0.5

-1.0 0

10

20

30

40

Residue number

B

0

CamSol

-1 A wt A His6Ala A His13Ala A His14Ala A triple

-2

0

10

20

30


24

FoldAmyloid score

0

10

40

Residue number

D

1

CamSol score

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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FoldAmyloid

22

20

18 20

30

40

0

Residue number

10

20

30

40

Residue number


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TOC Figure


Alanine Scanning Effects on the Biochemical and Biophysical

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