Computational Insights into Dynamics of Protein ... - ACS Publications

Nov 8, 2012 - An atomic-level understanding of these processes will advance our efforts to develop therapeutic strategies for several deadly diseases ...
0 downloads 0 Views 1MB Size
Download PDF
Perspective pubs.acs.org/JPCL

Computational Insights into Dynamics of Protein Aggregation and Enzyme−Substrate Interactions Mehmet Ozbil, Arghya Barman, Ram Prasad Bora, and Rajeev Prabhakar* Department of Chemistry, University of Miami, Coral Gables, Florida 33146, United States ABSTRACT: In this Perspective, the roles of protein dynamics have been discussed in the aggregation of amyloid beta (Aβ) peptides and formation of enzyme−substrate complexes of beta-secretase (BACE1) and insulin-degrading enzyme (IDE). The studies regarding the influence of individual amino acid residues and specific regions on the structures and oligomerization of early Aβ aggregates and computations of their translational and rotational diffusion coefficients and order parameters exhibited that even the short-time-scale molecular dynamics simulations can reproduce certain experimental parameters with reasonable accuracy. The simulations elucidating the enzyme−substrate interactions of BACE1 and IDE successfully showed that the chemical nature and length of the substrates influence the dynamics and plasticity of both the enzyme and substrate. An atomic-level understanding of these processes will advance our efforts to develop therapeutic strategies for several deadly diseases through the design of small molecules with antiaggregation properties and substrate-specific “designer” forms of enzymes.

P

well. On the other hand, elucidation of the roles of dynamics in the generation of enzyme−substrate complexes is critical to understanding the catalytic functioning of enzymes. The investigations concerning enzyme−substrate interactions (BACE1−amyloid precursor protein (APP) and IDE−Aβ) will advance this understanding. Furthermore, a comparison between the structures of the Aβ monomers in homogeneous aqueous solution and the heterogeneous enzymatic environment of IDE will allow us to comprehend the role of the environment on the dynamical fluctuations in the structures of these peptides. Peptide Aggregation. A great deal of genetic, animal modeling, and biochemical data indicate that 40−42 amino acid residues containing Aβ peptides (Aβ40 and Aβ42, respectively) are the major component of plaques found in the brains of patients suffering from AD.2 The brain of an AD patient has been observed to contain a continuous distribution of Aβ species from monomers up to large oligomers, with a major contribution coming from soluble early oligomers ranging from dimers to octamers.3 After the generation of the Aβ peptides through proteolytic activities of two proteases, BACE1 and gamma-secretase, these mostly α-helical peptides unfold to form β-sheet-rich aggregates.4,5 A large body of evidence suggests that the electronic nature of individual amino acid residues and specific regions of the Aβ peptides play important roles in the mechanism and rate of aggregation and morphology of aggregates.6,7 For instance, the oxidation of the Met35 residue to methionine sulfoxide (Met35(O)) and methionine sulfone (Met35(O2)) forms in the Aβ42 peptide

roteins are intrinsically dynamic macromolecules that undergo a wide range of internal motions such as folding−unfolding and conformational changes from one state to the other upon ligand binding. These motions can play critical roles in aggregation and functioning of proteins and span a large hierarchy of time scales, from picoseconds to milliseconds or even longer. There is a wealth of experimental and theoretical data available on various aspects of protein dynamics, but this Perspective primarily focuses on its roles in two fundamental processes, protein aggregation and the formation of enzyme−substrate complexes. In particular, the aggregation of amyloid beta (Aβ) peptide and conformational changes in beta-secretase (BACE1) that generates this peptide and insulin-degrading enzyme (IDE) that degrades it upon substrate binding have been discussed.

The studies regarding the aggregation of Aβ peptides discussed here will help to understand the function of dynamics in the oligomerization of other biomolecules as well. Aggregation is a basic property of polypeptide chains, and currently, more than 20 proteins are known to form amyloidlike fibrils. This process is associated with several lifethreatening diseases such as Alzheimer’s disease (AD), Parkinson’s disease, Huntington’s disease, mad cow disease, and type II diabetes.1 The studies regarding the aggregation of Aβ peptides discussed here will help to understand the function of dynamics in the oligomerization of other biomolecules as © 2012 American Chemical Society

Received: October 6, 2012 Accepted: November 8, 2012 Published: November 8, 2012 3460

dx.doi.org/10.1021/jz301597k | J. Phys. Chem. Lett. 2012, 3, 3460−3469

The Journal of Physical Chemistry Letters

Perspective

has been observed to attenuate the rate of aggregation.8,9 There are also several natural single mutants (e.g., Ala21 → Gly (Flemish), Glu22 → Gly (Arctic), Glu22 → Lys (Italian), Asp23 → Asn (Iowa), and Glu22 → Gln (Dutch)) that are known to aggregate with faster rates than the wild-type (WT) Aβ.10,11 In comparison to Aβ40, Aβ42 contains only two extra C-terminal hydrophobic residues (Ile41 and Ala42), but their mechanisms, rates of aggregation, and morphologies of oligomers are also significantly different. In addition, three specific regions of Aβ, the central hydrophobic core (CHC, L17-A21), the loop region (V23-N28), and the second hydrophobic region (SHR, G29-M35), have been reported to play critical roles in aggregation.12,13 Furthermore, the binding of metals ions (Cu2+, Zn2+, and Fe3+) to the Aβ peptides has been reported to influence the rate of aggregation.14 The structures of Aβ40 and Aβ42 aggregates have been determined by various experimental techniques such as electron microscopy, X-ray diffraction, electron paramagnetic resonance (EPR) spectroscopy, solid-state nuclear magnetic resonance (NMR) spectroscopy, and deep UV resonance Raman (DUVRR) spectroscopy.15−17 However, due to the fast rate of aggregation, a high-resolution structural determination of the early Aβ aggregates through experimental techniques is extremely difficult.18 Therefore, the structures of full-length early aggregates and mechanisms and controlling factors of the α-helix → β-sheet transformation and the subsequent aggregation remain unknown. In this aspect, molecular dynamics (MD) simulations can provide links between structure and dynamics by enabling the exploration of the conformational energy landscape accessible to molecules. These simulations can investigate dynamical transformations of these peptides and the correlation between the computed dynamics and the structural transformations reported in experimental studies. In the past few years, tremendous efforts have been made to address some of the aforementioned issues regarding Aβ aggregation through a variety of computational approaches such as replica exchange, coarse-grain, and classical MD simulations.19−28 The roles of the oxidation of Met35 (Met35(O)), the existence of the dipeptide (Ile41-Ala42) in WT-Aβ42, and their combinations on the secondary structures were investigated through classical MD simulations on the full-length WT-Aβ40, WT-Aβ42, Met35(O)-Aβ40, and Met35(O)-Aβ42 monomers in aqueous solution.29 The equilibrated structures derived from these simulations exhibited significant differences in the secondary structures that were well-supported by the measured NMR data.30 The overall structure of the Met35(O)-Aβ40 monomer was divided into three well-defined bend regions separated by coil segments, while WT-Aβ40 adopted the βhairpin like structure (Figure 1a and b). The oxidation of Met35 caused the loss of the Asp23-Lys28 salt-bridge observed in the WT-Aβ40 monomer.31 This bridge has been proposed to stabilize the loop region (V24-N28) and might be responsible for the β-hairpin-like conformation of WT-Aβ40.32 In comparison to WT-Aβ40, the N and C terminals lost close interactions with each other in the Met35(O)-Aβ40 monomer. These structural differences could be responsible for the measured slow rate of aggregation of the Met35(O)-Aβ40 peptide. On the other hand, the presence of the dipeptide (Ile41Ala42) in the WT-Aβ42 monomer induced drastic changes in its secondary structure. In particular, the CHC(17−21)containing Tyr10-Ala20 region in WT-Aβ42 was a stable

Figure 1. Secondary structures as a function of time and the snapshots of the MD simulations at every 10 ns interval: (a) Met35(O)-Aβ40, (b) WT-Aβ40, (c) WT-Aβ42, and (d) Met35(O)-Aβ42.

helix, while in WT-Aβ40, this region adopted a stable bend conformation (Figure 1b and c). In WT-Aβ40, the β-strands were located in a region comprised of Phe22-Ala30 residues, while in WT-Aβ42, they appeared in the Ala30-Val40 segment. In WT-Aβ42, the hairpin-like structure located in the loop region had a smaller curvature than WT-Aβ40, and the Asp233461

dx.doi.org/10.1021/jz301597k | J. Phys. Chem. Lett. 2012, 3, 3460−3469

The Journal of Physical Chemistry Letters

Perspective

Figure 2. Ribbon presentations of the representative conformers obtained from the MD simulations: (a) superposition between WT-Aβ42 and Met(O)-Aβ42, (b) superposition between WT-Aβ42 and Met35(O2)-Aβ42, and (c) superposition between WT-Aβ42 and Met35(CH2)-Aβ42.

residues and not as a result of interactions with N-terminus residues. In the sulfoxide (Met35(O)-Aβ42) form, the SASA value of 0.87 nm2 was almost twice that of the one (0.41 nm2) computed for WT-Aβ42. The root-mean-square deviation (RMSD) of 0.74 nm between these two peptides also supported the major alterations in this region. Even the CHC and loop regions that were located far from Met35(O) underwent significant changes. The former adopted a more open five-helix conformation, and the latter became wider. The oxidation of Met35 to the sulfone form (Met35(O2)) increased its dipole moment by 2.54 D and drastically decreased its hydrophobicity. Here, significant differences were observed in the E22-M35 region, which resides adjacent to the CHC and includes both loop (V24-N27) and SHR (G29-M35) segments (Figure 2b). Due to enhancement in the hydrophilic nature, the interactions between the Met35(O2 ) residue with the surrounding water molecules became more pronounced and, in comparison to WT-Aβ42, increased its SASA value by 0.83 nm2. The Met35 → Met35(CH2) substitution reduced the dipole moment of Met35 to zero and enhanced its hydrophobicity. This mutant was not found to introduce noticeable structural changes in the Met35(CH2)-Aβ42 peptide, and the overall structure of the peptide (including the CHC, loop, and SHR regions) remained very similar to WT-Aβ42 (Figure 2c). It has been observed experimentally that the WT-Aβ42 and Met35(CH2)-Aβ42 monomers yield similar oligomer size distributions.9 This observation appears to be in line with the results provided by MD simulations because monomers with similar secondary structures are likely to produce equivalent oligomerization patterns. These results indicated that the oxidation of Met35 to Met35(O) and Met35(O2) diminished this residue’s hydrophobicity and induced significant changes in the secondary structure. These alterations in conjunction with the increased solvation energy may hinder the intermolecular interactions between two monomers, which are required for oligomerization. The influence of a single residue on higher aggregates was also observed in a coarse-grain MD simulation study on Flemish (Ala21 → Gly), Arctic (Glu22 → Gly), and Dutch (Glu22 → Gln) mutants.34 It was found that, in comparison to the WT-Aβ40, the structural characteristics and

Lys28 salt-bridge was elusive. The WT-Aβ42 peptide aggregated substantially faster than WT-Aβ40. This difference in the aggregation rates could be associated with the opening of the loop, which was stabilized by the Asp23-Lys28 salt-bridge. Even the oxidized forms of Aβ40 and Aβ42 exhibited substantial structural differences (Figure 1a and d). In Met35(O)-Aβ42, the CHC region existed in a helical conformation, while in Met35(O)-Aβ40, this region was dominated by coil and bend conformations. In the former, the loop region was stabilized by the Asp23-Lys28 salt-bridge. The SHR segment in Met35(O)-Aβ42 largely formed the turn, while in Met35(O)-Aβ40, it adopted a stable bend conformation. These substantial structural differences were proposed to contribute to the difference in the aggregation rate of these two peptides. In order to examine possible electronic and steric effects of the Met35 on the morphology and size distribution of Aβ42 oligomers, Bitan et al. used four different Cγ-methylene chemical substitutions of Met35 (methionine sulfoxide (Met35(O)), methionine sulfone (Met35(O2)), norleucine (Met35(CH2)), and homoleucine (Met35(CHCH3)) and compared the results with the WT-Aβ40 and WT-Aβ42 peptides.9 It was observed that the oxidation of Met35 to Met35(O) and Met35(O2), in the WT-Aβ42 peptide, blocked the paranucleus formation and produced oligomers indistinguishable in size and morphology from those produced by WT-Aβ40. The chemical effects of these substitutions were investigated using classical MD simulations in aqueous solution on three full-length Aβ42 monomers (Met35(O), Met35(O2), and Met35(CH2)).33 Being a hydrophobic amino acid, Met35 (dipole moment = 1.76 D) should be protected from water solvent in the WTAβ42 peptide. As a result, the computed solvent-accessible surface area (SASA) value of this residue was only 0.42 nm2. Its oxidation to the Met35(O) form increased the dipole moment of the Cγ-substituted group by 1.96 D and made it more amenable to interactions with water molecules. Consequently, there were substantial, both short- and long-range, modifications in the structure of the Met35(O)-Aβ42 peptide (Figure 2a). For example, the SHR segment that includes this substitution adopted a more ordered helical-type structure because of the creation of close contacts between neighboring 3462

dx.doi.org/10.1021/jz301597k | J. Phys. Chem. Lett. 2012, 3, 3460−3469

The Journal of Physical Chemistry Letters

Perspective

Figure 3. Cartoon presentation of the most representative structures of the Aβ42 dimers obtained from MD simulations: (a) XP, (b) C−CAP, and (c) N−NP.

the overestimation due to the SPC water model into account, obtained for Aβ40 and Aβ42 were 0.085 and 0.071 ns−1, respectively. The value of DR for Aβ42 was also in agreement with the Debye−Stokes−Einstein’s theory that it is inversely proportional to the molecular weight of the protein. The computed effective τrot (2.30 ns) for Aβ40 at 291 K was in good agreement with the experimentally measured value of 2.45 ns.40 The S2 parameters were also found to be in line with the structural features observed in the previous theoretical studies at the CHC, the loop region, and the C-terminal domain.19,38 However, due to the short time scale of simulations, a slight discrepancy was observed in the N-terminal region of Aβ40 that could be caused by the interactions of the N-terminal with the CHC region. These results showed that even the short-term simulations can treat the motions of macromolecules with reasonable accuracy. The Aβ42 dimer has been reported to be the smallest neurotoxic species that adversely affects both memory and synaptic plasticity.41 The models of the Aβ42 dimer for MD simulations can be developed using the two different approaches, (1) top down and (2) bottom up.42−44 In the first (top down) approach, the starting structure of the dimer is abstracted from a structure of a high-order (n) oligomer by removing n − 2 monomers. However, in this case, the initial structures of both monomers are already in unfolded and βsheet-rich forms that do not resemble their native folded conformations. On the other hand, the second (bottom up) approach can precisely mimic the dimerization process starting from the native hairpin-like structures of the monomers. For the first time, the “bottom up” approach was applied to elucidate the mechanism of dimerization of the full-length Aβ42 peptides and structural properties of the three most stable dimers through all-atom 0.8 μs MD simulations in aqueous solution.45 On the basis of interactions between the distinct regions (CHC, loop, SHR, and N- and C-terminals) of the Aβ42 monomer, 10 different starting configurations were developed from their folded conformations. Among them, only six were found to form dimers, and on the basis of their electrostatic and hydrophobic binding energies, the three most

oligomer size of the critical nucleus drastically vary for these mutants The aggregation of the Aβ peptides is a complex dynamic event that depends on several factors, such as peptide concentration, pH, and the nature of the solvent.35 Like all macromolecules, these peptides in solution undergo translational, rotational, and internal motions. During the oligomerization from monomeric to higher-order states, the gradual increase in the hydrodynamic radius (rH) of the peptides should cause a decrease in the values of translational (DT) and rotational (DR) diffusion coefficients. Due to the association of DT and DR with sizes and shapes of interacting peptides, they can be used to elucidate the nature of aggregates (monomer, dimer, trimer, etc.) in the oligomerization and measured as a function of peptide concentration using the dynamic light scattering (DLS) and steady-state fluorescence anisotropy measurement techniques, respectively.36,37 These techniques can define the interaction parameters that determine the modifications in both coefficients with the variation in concentration from the infinite dilution (monomeric state). The oligomerization of Aβ is believed to be facilitated through the hydrophobic interactions between the CHC and SHR regions of monomers.38 The determination of the intramolecular order parameters (S2) can shed light on the mobility of the regions involved in the aggregation. A comparison between the S2 parameters of the CHC and SHR regions in the monomeric and oligomeric states of Aβ can help to elucidate the aggregation mechanisms. The rotational (τrot) and internal (τint) correlation times, DT, DR, and the S2 parameters of the full-length Aβ40 and Aβ42 monomers were computed from 150 ns simulations in an aqueous solution.39 The computed parameters of Aβ40 were compared with the available experimental values, but the corresponding parameters for Aβ42, not experimentally known, were predicted.40 The calculated DT values for Aβ40 and Aβ42 were 1.61 × 10−6 and 1.43 × 10−6 cm2/s, respectively, at 300 K and followed the correct trend defined through the Stokes− Einstein equation that DT should decrease with the increase in the molecular weight of the peptide. The DR values, by taking 3463

dx.doi.org/10.1021/jz301597k | J. Phys. Chem. Lett. 2012, 3, 3460−3469

The Journal of Physical Chemistry Letters

Perspective

stable (XP, C−CAP, and N−NP) dimers were analyzed in detail (Figure 3). The dimerization process was observed to be driven by hydrophobic interactions. The high content of the α-helical structure in all of the dimers was in agreement with its experimentally proposed role in the oligomerization.46,47 The generation of a zipper-like conformation in XP was also in line with its presence in the oligomers of several short amyloidogenic peptides.48 The calculated values of the DT and DR diffusion constants of 0.63 × 10−6 cm2/s and 0.035 ns−1, respectively, for this dimer were in accord with the corresponding values of the Aβ42 monomer. The creation of the salt-bridge between Glu22-Asp23 and Lys28 of different monomers in the XP and C−CAP dimers was supported by the recent NMR and theoretical results.20,49 In the C−CAP dimer, the formation of antiparallel (between Leu34-Val36 (M1) and Val39-Ile41 (M2)) and parallel (between Leu34-Gly37 (M1) and Leu34-Gly37 (M1)) β-sheets was supported by NMR data on Aβ42 oligomers.50 The generation of the N−NP dimer through the hydrophobic interactions between the CHC regions of both monomers was also in line with NMR data.51 The structures of the Aβ peptides are so complex that even with the current supercomputing power, the all-atom simulations cannot be extended to deal with a large number of interacting Aβ monomers involved in fibrillation. To address this problem, multiscale methodologies were developed in which the structures provided by all-atom simulations were utilized to devise mathematical models for cluster calculations to study the generation of higher aggregates.52,53 In these models, each monomer was described by an agent representation of its system-level characteristics. The aggregation process was then simulated by employing a variety of suitable interagent interaction rules in the multiagent population until maximal consistency with the known n-mer aggregation properties with experimental data was obtained.

are involved in the generation and degradation of the Aβ peptides, respectively. BACE1−Substrate Complexes. The Aβ peptides are generated through the hydrolytic cleavage of the Met671 and Asp672 peptide bond of the amyloid precursor protein (APP) by BACE1.55 The inhibition of this enzyme is widely acknowledged as a very promising therapeutic target for the treatment of AD.56 A large number of X-ray structures (∼170) in the apo and inhibitor-bound forms of BACE1 have been resolved. These structures reveal that the active site of the enzyme contains a catalytic Asp dyad (Asp32 and Asp228) covered over by an antiparallel hairpin-loop known as a flap (Figure 4). During the catalytic cycle, the flap must open to allow the entrance of the substrate into the active-site cleft and steer it toward the Asp dyad to attain a reactive conformation. In this conformation, the specific peptide bond of the substrate is hydrolytically cleaved. This type of gating mechanism has been reported to be utilized by almost all of the members of the aspartyl protease family. The similar flexibility-assisted mechanisms have also been suggested for a number of other enzymes57 such as dihydrofolate reductase,58 flavin reductase,59 and xylose isomerase.60 The flap of BACE1 is formed by an Nterminal, an 11-residue-long fragment (Val67-Glu77), Figure 4. The conserved Tyr71 residue of the flap forms hydrogen bonds with the APP substrate and adopts different rotameric orientations to facilitate the movement of the flap. Several specific regions of the enzyme such as the third strand (Lys107Gly117), N-terminus [insert-A (Gly158-Leu167)], and Cterminus [insert-B (Lys218-Asn221), insert-C (Ala251Pro258), insert-D (Trp270-Thr274), insert-F (Asp311Asp317)] facilitate the entry and binding of a substrate at the active site through their movements (Figure 4). These rearrangements are accompanied by structural alterations in the 10s loop and closing of the flap. A double mutant in the Nterminus region of APP (Lys670 → Asn and Met671 → Leu), known as the Swedish mutant (SW), has been reported to enhance the activity of BACE1 by 60-fold.61,62 This increase in the activity could be due to the differential substrate specificity of this enzyme. The interactions of the WT substrate (Glu-Val-Lys-Met-AspAla-Glu-Phe) and SW mutant (Glu-Val-Asn-Leu-Asp-Ala-GluPhe) with BACE1 were explored through all-atom, 20 ns MD simulations in an explicit TIP4P water model using the OPLSAA force field.63 These simulations were performed on the four different structures (apo BACE1, WT-BACE1, SW-BACE1, and an inhibitor (compound 11, PDB ID: 2qmg) bound BACE1).64 The starting models of the SW and WT substrates were built from the peptidic inhibitor OM99-2 (PDB ID: 1FKN) bound X-ray structure and placed inside of the apo form of BACE1 (PDB ID: 1w50) by superimposing these two structures.65,66 The most representative structure derived from the compound 11-BACE1 simulations accurately reproduced the positions of the flap and the 10s loop and the orientation and interaction of the inhibitor from the X-ray structure. The RMSD deviation of only 1.3 Å between the simulated and X-ray structures also validated the MD simulations. In the presence of the WT and SW substrates at the active site, the flap closed at around 3 ns, and two water molecules facilitated this process. In a site-directed mutagenesis study, electrostatic interactions between the Glu residue of the substrate and Arg307 of BACE1 were reported to enhance the catalytic efficiency of the enzyme.67 In the WT-BACE1 simulation, this interaction was lost, and the Glu residue interacted with the Lys residue of the

Elucidation of the roles of dynamics in the generation of enzyme−substrate complexes is critical to understanding the catalytic functioning of enzymes.

The site-specific structural data obtained from these studies will provide an atomic-level understanding of the aggregation process of Aβ and other biomolecules and could lead to the design of small molecules with antiaggregation properties for the treatment of various neurological disorders. Formation of Enzyme−Substrate Complexes. According to the induced fit model proposed for enzyme−substrate interactions,54 a substrate interacts with the side chains of the active site residues of an enzyme and forces them to change their positions into the optimal, precise conformation to host the substrate. Here, the dynamics of both the enzyme and substrate play a key role. It is worth mentioning that it is not possible to resolve a substrate-bound X-ray structure of a WT enzyme in its active form. In the absence of this structure, the atomistic-level understanding of residency of the substrate inside of the chamber and its interactions with the enzyme remains elusive. We have studied the role of protein dynamics in the formation of enzyme−substrate complexes for two critical enzymes, an aspartyl protease (BACE1) and a metalloprotease (IDE), that 3464

dx.doi.org/10.1021/jz301597k | J. Phys. Chem. Lett. 2012, 3, 3460−3469

The Journal of Physical Chemistry Letters

Perspective

Figure 4. The critical regions of BACE1 that participate in the substrate recognition and binding from the X-ray structure (PDB ID: 1FKN).

Aβ42, respectively, the X-ray structures consisted of only two discrete fragments of the substrates (Figure 5a). In the simulations, the X-ray structures of IDE (PDB ID: 2WK3 and PDB ID: 2G47) and NMR structures of the full-length Aβ40 (PDB ID: 1AML)71 and Aβ42 (PDB ID: 1IYT)72 were utilized to build the starting models for these simulations. In the absence of high-resolution structures of the Aβ monomers, the coordinates for the full-length Aβ40 and Aβ42 derived from MD simulations in aqueous solution were also used.29 During the simulations, both Aβ40 and Aβ42 were found to undergo significant structural changes inside of the cavity of the enzyme. In particular, Aβ40 formed a large number of intermolecular interactions in the Glu15-Phe19 and Ala21Lys28 regions (Figure 5b). In addition, N-terminal residues interacted with the exosite residues of IDE and the cleavage site (Phe19-Phe20) of Aβ40 located adjacent to the metal center of IDE associated with Ala140, Phe141, Arg824, and Tyr831 of the enzyme. The Asp1-Val12, Phe19-Glu22, and Ile32-Val40 regions of Aβ40 interacted with IDE through hydrogen bonds. The Phe4 and Tyr10 residues of the substrate formed π−π interactions with Tyr454 and Phe215 of IDE, respectively. It is noteworthy that the observed structural transformation of Aβ40 inside of the IDE led to the structure that was substantially different than that of in the aqueous environment. The α-helical character of the substrate vanished after only 6 ns of the simulation, and it formed the β-sheets in the Asp1-Glu3, Gln15Leu17, and Asn27-Gly29 regions. On the other hand, Aβ42 was found to be more flexible than Aβ40 inside of IDE throughout the simulation and formed a smaller number of hydrogen bonds with IDE (17−22) than Aβ40 did (25−30). Similar to Aβ40, its mostly helical structure underwent substantial changes (Figure 5c). However, in contrast to the three β-sheet regions formed by Aβ40, the Tyr10-Gln15 fragment of Aβ42 retained its α-helical character, and only Phe20-Ala21 formed a β-sheet. Furthermore, the Gln15-Leu17 and Asn27-Gly29 regions that formed antiparallel β-sheets in Aβ40 adopted a random coil structure in this peptide. Due to this plasticity of Aβ42 inside of IDE, the hydrogen bonding network between N-terminal residues of the

substrate. The loss of the Glu−Arg307 interaction was reported to cause the diminished activity of the enzyme for the WT substrate. A comparison between the three important interatomic distances (Cα(Thr72)−Cβ(Asp32), Cα(Thr72)− Cα(Thr329), and OG1(Thr72)−NH1(Arg235)) and the volume of the active site showed that, in comparison to the WT substrate, the flap was more closed and the active site was more constricted upon the binding of the SW substrate. The SW substrate was found to form about two times (8−10) more hydrogen bonds than the WT substrate, and the nature of hydrophobic interactions with the active site was also different for these two substrates. The electrostatic binding energy (−6.5 kcal/mol) for the SW substrate computed using APBS (adaptive Poisson−Boltzmann solver) was also 1.9 kcal/mol greater than the one for the WT substrate. The structures and positions of the inserts A, D, and F and the 10s loop of the enzyme were also substantially different upon the binding of these two substrates. All of these structural differences explicitly indicated that, in comparison to the WT substrate, BACE1 demonstrated greater affinity for the SW substrate and arranged it in a more bioactive conformation. IDE−Substrate Complexes. IDE is a Zn2+-containing metalloprotease that is known to cleave several biologically relevant peptides such as Aβ40, Aβ42, insulin, and amylin.68,69 The small-molecule activators and inhibitors of this enzyme have been proposed as potential drug targets for the treatment of AD and diabetes (both type I and II, respectively). A large triangular prism-shaped catalytic chamber of IDE can accommodate peptides smaller than 70 amino acid residues in length. Rather interestingly, the total volume of the catalytic chambers of this enzyme is almost half of the volume of its substrates. Here, the dynamics and plasticity of both the enzyme and substrate are critical for the formation of an active enzyme−substrate complex. The interactions between two fulllength substrates (Aβ40 and Aβ42) with IDE were explored through all-atom 20 ns classical MD simulations.70 On the basis of the experimental information, the inactive form of the enzyme in the X-ray structure was converted into the active form.69 Out of the 40 and 42 amino acid residues of Aβ40 and 3465

dx.doi.org/10.1021/jz301597k | J. Phys. Chem. Lett. 2012, 3, 3460−3469

The Journal of Physical Chemistry Letters

Perspective

Figure 5. (a) Starting structure of the IDE-Aβ40 complex used in the MD simulations, (b) snapshots of the secondary structure of Aβ40 inside of the IDE chamber at 5, 10, 15, and 20 ns, and (c) snapshots of the secondary structure of Aβ42 inside of the IDE chamber at 5, 10, 15, and 20 ns.

substrate and the exosite residues was disrupted. These simulations provided atomistic-level details of the roles of the active site and exosite residues to stabilize and position the substrate. In particular, Glu182 and Thr220 of IDE stabilize the metal center by forming hydrogen bonds with Zn2+-bound His112 and His108 ligands, and Ala40, Tyr831, and Arg824 were involved in positioning the scissile peptide bond adjacent to the metal center. It was observed that the hydrogen bonding in the IDE−Aβ42 complex was not as well-preserved as that in the IDE−Aβ40 complex, and Aβ42 remained largely in a disordered random coil formation inside of the enzyme. Evidently, the presence of the last two hydrophobic residues (Ile41-Ala42) enhanced the hydrophobicity of Aβ42 and caused it to fold in a very different manner than Aβ40 through

differences in intra- and intermolecular interactions. As observed in the Aβ40 case, the overall secondary structure of Aβ42 inside of IDE adopted different conformation than the one in the aqueous environment. The α-helical character of the peptide was substantially reduced, and the Leu34-Val36 and Val39-Ile41 fragments were converted into the β-sheet form in the aqueous solution. Here, only the Gln15-Val18 and Ala21Gly25 regions existed in the α-helical form, and the remaining fragments adopted random coil conformation. However, the Tyr10-Gln15 and Phe20-Ala21 regions formed α-helix and βsheet structures, respectively, and the rest of the peptide existed in random coil and loop structures inside of the cavity of IDE. These results showed that the length and the chemical nature of the substrate and the environment inside of the cavity of the 3466

dx.doi.org/10.1021/jz301597k | J. Phys. Chem. Lett. 2012, 3, 3460−3469

The Journal of Physical Chemistry Letters

■

enzyme can influence the dynamics and plasticity of both the enzyme and the substrate.

Perspective

AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Tel: 305-284-9372. Fax: 305-2844571.

Molecular dynamics (MD) simulations can provide links between structure and dynamics by enabling the exploration of the conformational energy landscape accessible to molecules.

Notes

The authors declare no competing financial interest. Biographies Mehmet Ozbil received his B.S. from Istanbul Technical University, Istanbul, Turkey in 2007. His research in the laboratory of Dr. Rajeev Prabhakar at the University of Miami, FL, U.S.A. involves the investigation of enzymatic and nonenzymatic reactions, structure− activity relationships of enzymes, and the aggregation process of biomolecules.

In both cases (BACE1 and IDE), the structures provided by the MD simulations reproduced several experimentally observed structural features and described the enzyme− substrate interactions and modifications in the structure of the enzyme. It is noteworthy that the structural and mechanistic information provided by computational approaches cannot be readily obtained through the experimental techniques that simply rely on deletion analysis or introduction of mutations at randomly chosen sites. This information will be useful in altering the substrate specificity of enzymes to develop their “designer” forms through site-directed mutagenesis experiments. Future Prospects. The focus of this Perspective has been to discuss the roles of dynamics in aggregation of the Aβ peptides and formation of enzyme−substrate complexes for BACE1 and IDE that are involved in the generation and clearance of this peptide, respectively. In the past decade, a variety of MD simulation approaches have been employed to study the roles of dynamics in aggregation and functioning of proteins. However, despite all of these efforts, there are still several unresolved issues. Regarding the protein folding and aggregation, the exact roles of individual amino acid residues, specific regions, and metal ions are still not exactly known, and there is a necessity to further develop this fundamental understanding. The computations of the diffusion constants (DT and DR) and the order parameters (S2) rely on the simulation conditions such as force field, temperature, time scale, and size of the box, but the optimum settings for their calculations are not clear.73,74 In the agent-based modeling, it remains unknown at what level interactions between peptides need to be included in their agents to predict the emerging cluster populations and their temporal dependence. Furthermore, the accuracy of the structural information concerning the enzyme−substrate interactions requires additional advancement. In this aspect, the application of other computational approaches such as meta, accelerated, dissipative particle, and polarizable dynamics simulations will be very useful.75−78 Moreover, the prediction of the free energy can be further improved by the application of the orthogonal space random walk (OSRW) method.79 Evidently, a successful realization of all of these goals will require a high accuracy level of MD simulations, which in turn depends on the nature of force field parameters. There have been instances in which different force fields have been found to provide altered results on the same system.45,80 Therefore, the quality of the force field parameters needs to be enhanced for nonmetallic, metallic, and solvent molecules. Although short-time-scale MD simulations were able to reproduce some of the experimentally observed parameters, fast and accurate methods will be needed in the future.

Arghya Barman received a M.S. in Biochemistry from the University of Calcutta, India in 2005. His graduate research in the laboratory of Dr. Rajeev Prabhakar at the University of Miami (U.S.A.) was focused on the mechanistic and structural aspects of proteases, peptidases, and amyloid beta peptides. Ram Prasad Bora received his M.S. in physical chemistry from Andhra University, Visakhapatnam, Andhra Pradesh, India. As a postdoctoral researcher (April 2008−March 2010) with Dr. Rajeev Prabhakar at the University of Miami, Miami, Florida, U.S.A., he studied enzymecatalyzed reactions, enzyme−substrate interactions, and the aggregation of biomolecules. Rajeev Prabhakar is an assistant professor at the University of Miami in the department of chemistry. His main research interests are applications of innovative theoretical and computational approaches to study mechanisms of chemical and biochemical reactions, the design of catalysts, computer-aided drug design, and chemistry associated with neurological disorders. He is the author of approximately 50 publications. http://umchemistry.cox.miami.edu/PrabhakarGroup/ index.html

■

ACKNOWLEDGMENTS Financial support from the James and Esther King Biomedical Research Program of the Florida State Health Department (DOH Grant Number 08KN-11) and the National Science Foundation (NSF Grant Number CHE 1152846) to R.P. is greatly acknowledged. Computational resources from the Center for Computational Science (CCS) at the University of Miami are greatly appreciated.

■

REFERENCES

(1) Sipe, J. D.; Cohen, A. S. Review: History of the Amyloid Fibril. J. Struct. Biol. 2000, 130, 88−98. (2) Hardy, J.; Selkoe, D. J. The Amyloid Hypothesis of Alzheimer’s Disease: Progress and Problems on the Road to Therapeutics. Science 2002, 297, 353−356. (3) McLean, C. A.; Cherny, R. A.; Fraser, F. W.; Fuller, S. J.; Smith, M. J.; Beyreuther, K.; Bush, A. I.; Masters, C. L. Soluble Pool of Abeta Amyloid as a Determinant of Severity of Neurodegeneration in Alzheimer’s Disease. Ann. Neurol. 1999, 46, 860−866. (4) Seubert, P.; Vigo-Pelfrey, C.; Esch, F. Isolation and Quantitation of Soluble Alzheimer’s β-Peptide from Biological Fluids. Nature 1992, 359, 325−327. (5) Haass, C.; Schlossmacher, M. G.; Hung, A. Y.; Vigo-Pelfrey, C.; Mellon, A.; Ostaszewski, B. L.; Lieberburg, I.; Koo, E. H.; Schenk, D.; Teplow, D. B.; et al. Amyloid Beta-Peptide is Produced by Cultured Cells During Normal Metabolism. Nature 1992, 359, 322−325. (6) Hamley, I. W. The Amyloid Beta Peptide: A Chemist’s Perspective. Role in Alzheimer’s and Fibrillization. Chem. Rev. 2012, 112, 5147−5192.

3467

dx.doi.org/10.1021/jz301597k | J. Phys. Chem. Lett. 2012, 3, 3460−3469

The Journal of Physical Chemistry Letters

Perspective

(7) Chiti, F.; Dobson, M. Protein Misfolding, Functional Amyloid, and Human Disease. Annu. Rev. Biochem. 2006, 75, 333−366. (8) Hou, L.; Kang, I.; Marchant, R. E.; Zagorski, M. G. Methionine 35 Oxidation Reduces Fibril Assembly of the Amyloid Aβ-(1−42) Peptide of Alzheimer’s Disease. J. Biol. Chem. 2002, 277, 40173− 40176. (9) Bitan, G.; Tarus, B.; Vollers, S. S.; Lashuel, H. A.; Condron, M. M.; Straub, J. E.; Teplow, D. B. A Molecular Switch in Amyloid Assembly: Met35 and Amyloid β-Protein Oligomerization. J. Am. Chem. Soc. 2003, 125, 15359−15365. (10) Levy, E.; Carman, M. D.; Fernandez-Madrid, I. J.; Power, M. D.; Lieberburg, I.; van Duinen, S. G.; Bots, G. T. A. M.; Luyendijk, W.; Frangione, B. Mutation of the Alzheimer’s Disease Amyloid Gene in Hereditary Cerebral Hemorrhage, Dutch-Type. Science 1990, 248, 1124−1126. (11) Walsh, D. M.; Hartley, D. M.; Condron, M. M.; Selkoe, D. J.; Teplow, D. B. In Vitro Studies of Amyloid Beta-protein fibril Assembly and Toxicity Provide Clues to the Aetiology of Flemish Variant (Ala692→Gly) Alzheimer’s Disease. Biochem. J. 2001, 355, 869−877. (12) Antzutkin, O. N.; Balbach, J. J.; Tycko, R. Site-Specific Identification of Non-ß-Strand Conformations in Alzheimer’s βAmyloid Fibrils by Solid-State NMR. Biophys. J. 2003, 84, 3326−3335. (13) Petkova, A. T.; Ishii, Y.; Balbach, J. J.; Antzutkin, N. O.; Leapman, R. D.; Delaglio, F.; Tycko, R. A Structural Model for Alzheimer’s β-Amyloid Fibrils Based on Experimental Constrains from Solid State NMR. Proc. Natl. Acad. Sci. U.S.A. 2002, 99, 16742−16747. (14) Hou, I.; Zagorski, M. G. NMR Reveals Anomalous Copper(II) Binding to Amyloid Aβ Peptide of Alzheimer’s Disease. J. Am. Chem. Soc. 2006, 128, 9260−9261. (15) Lührs, T.; Ritter, C.; Adrian, M.; Riek-Loher, D.; Bohrmann, B.; Döbeli, H.; Schubert, D.; Riek, R. 3D Structure of Alzheimer’s Amyloid-β(1−42) Fibrils. Proc. Natl. Acad. Sci. U.S.A. 2005, 102, 17342−17347. (16) Antzutkin, O. N.; Balbach, J. J.; Leapman, R. D.; Nancy, W. R.; Reed, J.; Tycko, R. Multiple Quantum Solid-State NMR Indicates a Parallel, not Antiparallel, Organization of β-sheets in Alzheimer’s βAmyloid Fibrils. Proc. Natl. Acad. Sci. U.S.A. 2000, 97, 13045−13050. (17) Popova, L. A.; Kodali, R.; Wetzel, R.; Lednev, I. K. Structural Variations in the Cross-β Core of Amyloid β Fibrils Revealed by Deep UV Resonance Raman Spectroscopy. J. Am. Chem. Soc. 2010, 132, 6324−6328. (18) Riek, R.; Guntert, P.; Dobeli, H.; Wuthrich, K. NMR Studies in Aqueous Solution Fail to Identify Significant Conformational Differences Between the Monomeric Forms of Two Alhzeimer Peptides with Widely Different Plaque-Competence, Ab(1−40)ox and Ab(1−42)ox. Eur. J. Biochem. 2001, 268, 5930−5936. (19) Massi, F.; Peng, J. W.; Lee, P. J.; Straub, J. E. Simulation Study of the Structure and Dynamics of the Alhzeimer’s Amyloid Peptide Congener in Solution. Biophys. J. 2001, 80, 31−44. (20) Buchete, N.-V.; Hummer, G. Structure and Dynamics of Parallel b-Sheets, Hydrophobic Core, and Loops in Alzheimer’s Aβ-Fibrils. Biophys. J. 2007, 92, 3032−3039. (21) Lakdawala, A. S.; Morgan, D. M.; Liotta, D. C.; Lynn, D. G.; Snyder, J. P. Dynamics and Fluidity of Amyloid Fibrils: A Model of Fibrous Protein Aggregates. J. Am. Chem. Soc. 2002, 124, 15150− 15151. (22) Buchete, N.-V.; Tycko, R.; Hummer, G. Molecular Dynamics Simulations of Alzheimer’s β-Amyloid Protofilaments. J. Mol. Biol. 2005, 353, 804−821. (23) Olubiyi, O. O.; Strodel, B. Structures of the Amyloid β-Peptides Aβ1−40 and Aβ1−42 as Influenced by pH and a D-Peptide. J. Phys. Chem. B 2012, 116, 3280−3291. (24) Mandal, P.; Eremina, N.; Barth, A. Formation of Two Different Types of Oligomers in the Early Phase of pH Induced Aggregation of the Alzheimer Aβ(12−28) Peptide. J. Phys. Chem. B 2012, 116, 12389−12397. (25) Chebaro, Y.; Jiang, P.; Zang, T.; Mu, Y.; Nguyen, P. H.; Mousseau, N.; Derreumaux, P. Structures of Aβ17−42 Trimers in

Isolation and with Five Small-Molecule Drugs Using a Hierarchical Computational Procedure. J. Phys. Chem. B 2012, 116, 8412−8422. (26) Hwang, W.; Zhang, S.; Kamm, R. D.; Karplus, M. Kinetic Control of Dimer Structure Formation in Amyloid Fibrillogenesis. Proc. Natl. Acad. Sci. U.S.A. 2004, 101, 12916−12921. (27) Nguyen, P. H.; Li, M. S.; Stock, G.; Straub, J. E.; Thirumalai, D. Monomer Adds to Preformed Structured Oligomers of Aβ-Peptides by a Two-Stage Dock−Lock Mechanism. Proc. Natl. Acad. Sci. U.S.A. 2007, 104, 111−116. (28) Ma, B. Y.; Nussinov, R. The Stability of Monomeric Intermediates Controls Amyloid Formation: Aβ 25−35 and Its N27Q Mutant. Biophys. J. 2006, 90, 3365−3374. (29) Triguero, L.; Singh, R.; Prabhakar, R. Comparative Molecular Dynamics Studies of Wild-Type and Oxidized Forms of Full Length Alzheimer’s Amyloid β-Peptides Aβ(1−40) and Aβ(1−42). J. Phys. Chem. B 2008, 112, 7123−7131. (30) Hou, L.; Shao, H.; Zhang, Y.; Li, H.; Menom, N. K.; Neuhaus, E. B.; Brewer, J. M.; Byeon, I-J. L.; Ray, D. G.; Viket, M. P.; et al. Solution NMR Studies of the Aβ(1−40) and Aβ(1−42) Peptides Establish that the Met35 Oxidation State Affects the Mechanism of Amyloid Formation. J. Am. Chem. Soc. 2004, 126, 1992−2005. (31) Baumketner, A.; Shea, J.-E. Folding Landscapes of the Alzheimer Amyloid-β(12−28) Peptide. J. Mol. Biol. 2006, 362, 567−579. (32) Pike, C. J.; Walencewicz-Wasserman, J. A.; Kosmoski, J.; Cribbs, H. D.; Glabe, C. G.; Cotman, C. W. Structure-Activity Analysis of βAmyloid Peptides: Contributions of the β(25−35) Region to Aggregation and Neurotoxicity. J. Neurochem. 1995, 64, 253−265. (33) Triguero, L.; Singh, R.; Prabhakar, R. A Molecular Dynamics Study to Investigate the Effect of Chemical Substitutions of Methionine 35 on the Secondary Structure of the Amyloid Beta (Aβ(1−42)) Monomer in Aqueous Solution. J. Phys. Chem. B 2008, 112, 2159−2167. (34) Fawzi, N. L.; Kohlstedt, K. L.; Okabe, Y.; Head-Gordon, T. Protofibril Assemblies of the Arctic, Dutch, and Flemish Mutants of the Alzheimer’s Aβ1−40 Peptide. Biophys. J. 2008, 94, 2007−2016. (35) Jarret, J. T.; Lansbury, P. T. Seeding “One Dimensional Crystallization” of Amyloid: A Pathogenic Mechanism of in Alzheimer’s Disease and Scrapie. Cell 1993, 73, 1055−1058. (36) Grigsby, J. J.; Blanch, H. W.; Prausnitz, J. M. Diffusivities of Lysozyme in Aqueous MgCl2 Solutions from Dynamic LightScattering Data: Effect of Protein and Salt Concentrations. J. Phys. Chem. B 2000, 104, 3645−3650. (37) Visser, N. V.; van Hoek, A.; Visser, A. J. W. G.; Frank, J.; Apell, H.; Clarke, R. J. Time-Resolved Fluorescence Investigations of the Interaction of the Voltage-Sensitive Probe RH421 with Lipid Membranes and Proteins. Biochemistry 1995, 34, 11777−11784. (38) Yang, M.; Teplow, D. B. Amyloid β-Protein Monomer Folding: Free-Energy Surfaces Reveal Allowform-Specific Differences. J. Mol. Biol. 2008, 384, 450−464. (39) Bora, R. P.; Prabhakar, R. Translational, Rotational and Internal Dynamics of Alzheimer Amyloid β-Peptides Aβ40 and Aβ42 from MD Simulations. J. Chem. Phys. 2009, 131, 1551031−15510311. (40) Danielsson, J.; Andersson, A.; Jarvet, J.; Graslund, A. 15N Relaxation Study of the Amyloid β-peptide: Structural Propensities and Persistence Length. Magn. Reson. Chem. 2006, 44, S114−S121. (41) Shankar, G. M.; Li, S.; Mehta, T. H.; Garcia-Munoz, A.; Shepardson, N. E.; Smith, I.; Brett, F. M.; Farrell, M. A.; Rowan, M. J.; Lemere, C. A.; et al. Amyloid−Protein Dimers Isolated Directly from Alzheimer’s Brains Impair Synaptic Plasticity and Memory. Nat. Med. 2008, 14, 837−842. (42) Gnanakaran, S.; Nussinov, R.; García, A. E. Atomic-Level Description of Amyloid β-Dimer Formation. J. Am. Chem. Soc. 2006, 128, 2158−2159. (43) Urbanc, B.; Cruz, L.; Ding, F.; Sammond, D.; Khare, S.; Buldyrev, S. V.; Stanley, H. E.; Dokholyan, N. V. Molecular Dynamics Simulation of Amyloid β Dimer Formation. Biophys. J. 2004, 87, 2310−2321. 3468

dx.doi.org/10.1021/jz301597k | J. Phys. Chem. Lett. 2012, 3, 3460−3469

The Journal of Physical Chemistry Letters

Perspective

(44) Zhao, L. N.; Chiu, S.-W.; Benoit, J.; Chew, L. Y.; Mu, Y. Amyloid β Peptides Aggregation in a Mixed Membrane Bilayer: A Molecular Dynamics Study. J. Phys. Chem. B 2011, 115, 12247−12256. (45) Zhu, X.; Bora, R.; Barman, A.; Singh, R.; Prabhakar, R. Dimerization of the Full-Length Alzheimer Amyloid β-Peptide (Aβ42) in Explicit Aqueous Solution: A Molecular Dynamics Study. J. Phys. Chem. B 2012, 116, 4405−4416. (46) Rojas, A. V.; Liwo, A.; Scheraga, H. A. A Study of the α-Helical Intermediate Preceding the Aggregation of the Amino-Terminal Fragment of the β Amyloid Peptide (Aβ1−28). J. Phys. Chem. B 2011, 115, 12978−12983. (47) Liu, G.; Prabhakar, A.; Aucoin, D.; Simon, M.; Sparks, S.; Robbins, K. J.; Sheen, A.; Petty, S. A.; Lazo, N. D. Mechanistic Studies of Peptide Self-Assembly: Transient α-Helices to Stable β-Sheets. J. Am. Chem. Soc. 2010, 132, 18223−18232. (48) Sawaya, M. R.; Sambashivan, S.; Nelson, R.; Ivanova, M. I.; Sievers, S. A.; Apostol, M. I.; Thompson, M. J.; Balbirnie, M.; Wiltzius, J. J. W.; McFarlane, H. T.; et al. Atomic Structures of Amyloid Cross[bgr] Spines Reveal Varied Steric Zippers. Nature 2007, 447, 453− 457. (49) Anand, P.; Nandel, F. S.; Hansmann, U. H. E. The Alzheimer βamyloid (Aβ1−39) Dimer in an Implicit Solvent. J. Chem. Phys. 2008, 129, 195102−195107. (50) Yu, L.; Edalji, R.; Harlan, J. E.; Holzman, T. F.; Lopez, A. P.; Labkovsky, B.; Hillen, H.; Barghorn, S.; Ebert, U.; Richardson, P. L.; et al. Structural Characterization of a Soluble Amyloid β-Peptide Oligomer. Biochemistry 2009, 48, 1870−1877. (51) Tjernberg, L. O.; Näslund, J.; Lindqvist, F.; Johansson, J.; Karlström, A. R.; Thyberg, J.; Terenius, L.; Nordstedt, C. Arrest of βAmyloid Fibril Formation by a Pentapeptide Ligand. J. Biol. Chem. 1996, 271, 8545−8548. (52) Zhao, Z.; Singh, R.; Barman, A.; Johnson, N. F.; Prabhakar, R. Modeling the Self-Assembly Dynamics of Macromolecular Protein Aggregates Underlying Neurodegenerative Disorders. Comput. Theor. Nanosci. 2008, 6, 1338−1351. (53) Fortuna, S.; Troisi, A. An Artificial Intelligence Approach for Modeling Molecular Self-Assembly: Agent-Based Simulations of Rigid Molecules. J. Phys. Chem. B 2009, 113, 9877−9885. (54) Koshland, J. D.E. Application of a Theory and Enzyme Specificity to the Protein Synthesis. Proc. Natl. Acad. Sci. U.S.A. 1958, 44, 98−104. (55) Vassar, R.; Bennett, B. D.; Babu-Khan, S.; Kahn, S.; Mendiaz, E. A.; Denis, P.; Teplow, D. B.; Ross, S.; Amarante, P.; Loeloff, R.; et al. β-Secretase Cleavage of Alzheimer’s Amyloid Precursor Protein by the Transmembrane Aspartic Protease BACE. Science 1999, 286, 735− 741. (56) Luo, Y.; Bolon, B.; Kahn, S.; Bennett, B. D.; Babu-Khan, S.; Denis, P.; Fan, W.; Kha, H.; Zhang, J.; Gong, Y.; et al. Mice Deficient in BACE1, the Alzheimer’s β-Secretase, Have Normal Phenotype and Abolished β-Amyloid Generation. Nat. Neurosci. 2001, 4, 231−232. (57) Garcia-Viloca, M.; Gao, J.; Karplus, M.; Truhlar, D. G. How Enzymes Work: Analysis by Modern Rate Theory and Computer Simulations. Science 2004, 303, 186−195. (58) Rod, T. H.; Radkiewicz, J. L.; Brooks, C. L. Correlated Motion and the Effect of Distal Mutations in Dihydrofolate Reductase. Proc. Natl. Acad. Sci. U.S.A. 2003, 100, 6980−6985. (59) Yang, H.; Luo, G.; Karnchanaphanurach, P.; Louie, T.-M.; Rech, I.; Cova, S.; Xun, L.; Xie, X. S. Protein Conformational Dynamics Probed by Single-Molecule Electron Transfer. Science 2003, 302, 262− 266. (60) Garcia-Viloca, M.; Alhambra, C.; Truhlar, D. G.; Gao, J. Quantum Dynamics of Hydride Transfer Catalyzed by Bimetallic Electrophilic Catalysis: Synchronous Motion of Mg2+ and H− in Xylose Isomerase. J. Am. Chem. Soc. 2002, 124, 7268−7269. (61) Citron, M.; Diehl, T. S.; Gordon, G.; Biere, A. L.; Seubert, P.; Selkoe, D. J. Evidence that the 42- and 40-Amino Acid Forms of Amyloid β Protein are Generated from the β-amyloid Precursor Protein by Different Protease Activities. Proc. Natl. Acad. Sci. U.S.A. 1996, 93, 13170−13175.

(62) Cai, X. D.; Golde, T. E.; Younkin, S. G. Release of Excess Amyloid Beta Protein from a Mutant Amyloid Beta Protein Precursor. Science 1993, 259, 514−516. (63) Barman, A.; Schurer, S; Prabhakar, R. Computational Modeling of Substrate Specificity and Catalysis of the β-Secretase (BACE1) Enzyme. Biochemistry 2011, 50, 4337−4349. (64) Iserloh, U.; Wu, Y.; Cumming, J. N.; Pan, J.; Wang, L. Y.; Stamford, A. W.; Kennedy, M. E.; Kuvelkar, R.; Chen, X.; Parker, E. M.; et al. Potent Pyrrolidine- and Piperidine-Based BACE-1 Inhibitors. Bioorg. Med. Chem. Lett. 2008, 18, 414−417. (65) Hong, L.; Koelsch, G.; Lin, X.; Wu, S.; Terzyan, S.; Ghosh, A. K.; Zhang, X. C.; Tang, J. Structure of the Protease Domain of Memapsin 2 (β-Secretase) Complexed with Inhibitor. Science 2000, 290, 150−153. (66) Patel, S.; Vuillard, L.; Cleasby, A.; Murray, C. W.; Yon, J. Apo and Inhibitor Complex Structures of BACE (β-secretase). J. Mol. Biol. 2004, 343, 407−416. (67) Turner, R. T.; Koelsch, G.; Hong, L.; Castenheira, P.; Ghosh, A.; Tang, J. Subsite Specificity of Memapsin 2 (β-Secretase): Implications for Inhibitor Design. Biochemistry 2001, 40, 10001− 10006. (68) Tseng, B. P.; Esler, W. P.; Clish, C. B.; Stimson, E. R.; Ghilardi, J. R.; Vinters, H. V.; Mantyh, P. W.; Lee, J. P.; Maggio, J. E. Deposition of Monomeric, Not Oligomeric, Aβ Mediates Growth of Alzheimer’s Disease Amyloid Plaques in Human Brain Preparations. Biochemistry 1999, 38, 10424−10431. (69) Shen, Y.; Joachimiak, A.; Rosner, M. R.; Tang, W. J. Structures of Human Insulin-Degrading Enzyme Reveal a New Substrate Recognition Mechanism. Nature 2006, 443, 870−874. (70) Bora, R. P.; Prabhakar, R. Elucidation of Interactions of Alzheimer Amyloid β-Peptides (Aβ40 and Aβ42) with Insulin Degrading Enzyme (IDE): A Molecular Dynamics Study. Biochemistry 2010, 49, 3947−3956. (71) Sticht, H.; Bayer, P.; Willbold, D.; Dames, S.; Hilbich, C.; Beyreuther, K.; Frank, R. W.; Rösch, P. Structure of Amyloid A4-(1− 40)-Peptide of Alzheimer’s Disease. Eur. J. Biochem. 1995, 233, 293− 298. (72) Crescenzi, O.; Tomaselli, S.; Guerrini, R.; Salvadori, S.; D̀ Ursi, A. M.; Temussi, A. P.; Picone, D. Solution Structure of the Alzheimer Amyloid Beta-Peptide (1−42) in an Apolar Microenvironment. Similarity with a Virus Fusion Domain. Eur. J. Biochem. 2002, 269, 5642−5648. (73) Wong, V.; Case, D. A. Evaluating Rotational Diffusion from Protein MD Simulations. J. Phys. Chem. B 2008, 112, 6013−6024. (74) Takemura, K.; Kitao, A. Effcts of Water Model and Simulation Box Size on Protein Diffusional Motions. J. Phys. Chem. B 2007, 111, 11870−11872. (75) Camilloni, C.; Schaal, D.; Schweimer, K.; Schwarzinger, S.; De Simone, A. Energy Landscape of the Prion Protein Helix 1 Probed by Metadynamics and NMR. Biophys. J. 2012, 102, 158−167. (76) Nasica-Labouze, J.; Meli, M.; Derreumaux, P.; Colombo, G.; Mousseau, N. A Multiscale Approach to Characterize the Early Aggregation Steps of the Amyloid-Forming Peptide GNNQQNY from the Yeast Prion Sup-35. PLoS Comput. Biol. 2011, 7, 1002051. (77) Vishnyakov, A.; Talaga, D. S.; Neimark, A. V. DPD Simulation of Protein Conformations: From α-Helices to β-Structures. J. Phys. Chem. Lett. 2012, 3, 3081−3087. (78) Kucukkal, T. G.; Stuart, S. J. Polarizable Molecular Dynamics Simulations of Aqueous Dipeptides. J. Phys. Chem. B 2012, 116, 8733− 8740. (79) Zheng, L.; Chen, M.; Yang, W. Random Walk in Orthogonal Space to Achieve Efficient Free-Energy Simulation of Complex Systems. Proc. Natl. Acad. Sci. U.S.A. 2008, 105, 20227−20232. (80) Sgourakis, N. G.; Merced-Serrano, M.; Boutsidis, C.; Drineas, P.; Du, Z.; Wang, C.; Garcia, A. E. Atomic-Level Characterization of the Ensemble of the Aβ(1−42) Monomer in Water Using Unbiased Molecular Dynamics Simulations and Spectral Algorithms. J. Mol. Biol. 2011, 405, 570−583.

3469

dx.doi.org/10.1021/jz301597k | J. Phys. Chem. Lett. 2012, 3, 3460−3469