G Model
ARTICLE IN PRESS
PEP 69455 1–6
Peptides xxx (2015) xxx–xxx
Contents lists available at ScienceDirect
Peptides journal homepage: www.elsevier.com/locate/peptides
Review
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Exploring the Alzheimer amyloid- peptide conformational ensemble: A review of molecular dynamics approaches
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Linh Tran, Tâp Ha-Duong ∗ BioCIS, UMR CNRS 8076, LabEx LERMIT, Faculty of Pharmacy, University Paris Sud, 5 Rue Jean-Baptiste Clément, 92296 Châtenay-Malabry, France
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7 22
a r t i c l e
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a b s t r a c t
8 9 10 11 12 13
Article history: Received 13 February 2015 Received in revised form 2 April 2015 Accepted 7 April 2015 Available online xxx
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Keywords: Alzheimer’s disease A peptide Conformational ensembles Molecular dynamics Enhanced sampling methods Coarse-grained protein models
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Contents
15 16 17 18 19 20
24 25 26 27 28 29 30 31 32
Alzheimer’s disease is one of the most common dementia among elderly worldwide. There is no therapeutic drugs until now to treat efectively this disease. One main reason is due to the poorly understood mechanism of A peptide aggregation, which plays a crucial role in the development of Alzheimer’s disease. It remains challenging to experimentally or theoretically characterize the secondary and tertiary structures of the A monomer because of its high flexibility and aggregation propensity, and its conformations that lead to the aggregation are not fully identified. In this review, we highlight various structural ensembles of A peptide revealed and characterized by computational approaches in order to find converging structures of A monomer. Understanding how A peptide forms transiently stable structures prior to aggregation will contribute to the design of new therapeutic molecules against the Alzheimer’s disease. © 2015 Elsevier Inc. All rights reserved.
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Simulations of various A peptide segments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Classical MD simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Enhanced sampling methods and simplified models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Simulations of full-length A peptide . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Classical MD simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Enhanced sampling methods and simplified models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conflict of interests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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Introduction
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Alzheimers disease (AD), first described by the german neuropathologist Alois Alzheimers in 1906 [1], is by far one of the most common forms of dementia among elderly, currently affects today more than 12 million people worldwide, consequently, the social economic burdens of AD continue to increase constantly [2]. Like many other human diseases, such as type II diabetes or Parkinson’s disease, AD is highly related with a common pathogenic progress, called amyloidogenesis [3–5], and recent advances in understanding the development of amyloid-related pathologies have helped
∗ Corresponding author. E-mail address: [email protected] (T. Ha-Duong).
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to solve the mystery on AD pathogenesis [6]. However, due to the lack of detailed understood mechanisms of the pathogenesis and despite the enormous efforts invested into AD research over the past century, there are still crucial questions that are not yet answered, in particular with respect to AD treatment [7]. An explanation for this may be found in the fact that, like many other amyloido-genic peptides, the preferential molecular target for AD drug development, the A peptide, is intrinsically disordered and rapidly self-aggregates [8–10]. The AD-related amyloid- peptide (A) has two common alloforms of 40 and 42 residues in length [11]. In solution at room temperature and physiological pH, it is in a dominating random coil secondary structure [12], but can undergo structural conversions between random coil and more structured conformations such as helices and -strands [13–15]. Within the “conformational
http://dx.doi.org/10.1016/j.peptides.2015.04.009 0196-9781/© 2015 Elsevier Inc. All rights reserved.
Please cite this article in press as: Tran L, Ha-Duong T. Exploring the Alzheimer amyloid- peptide conformational ensemble: A review of molecular dynamics approaches. Peptides (2015), http://dx.doi.org/10.1016/j.peptides.2015.04.009
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selection” paradigm for molecular recognition and selforganization [16–18], A peptides dynamically visit these transient structured conformations, and one of them being rich in -strands is “selected” to form oligomers through -strand/strand interactions [19]. As recently shown by Ma and Nussinov, this -rich structures “selection mechanism”, which seems to be common to many other amyloidogenic proteins, can explain the formation of cross-species or cross-sequences hetero-amyloids, such as those observed for prion proteins of different species or those discovered between A and islet amyloid polypeptides (IAPP) involved in the type II diabetes disease [20]. The A (1–42) peptide, which has the amino-acid sequence: DAEFR HDSGY EVHHQ KLVFF AEDVG SNKGA IIGLM VGGVV IA [21], is the alloform that is the most strongly linked to AD [22]. Because of its non-homogeneous conformation, it is diffcult to characterize its structure by X-ray or NMR approaches. Therefore, many computational approaches were in complementary used for studying the monomeric peptide conformational changes in order to identify its transient states. Among these theoretical studies, molecular dynamics (MD) simulations were extensively used because of its ability to characterize the biomolecules conformational dynamics. Effective efforts to exhaustively sample the A conformational space were also performed using enhanced techniques such as replica exchange molecular dynamics (REMD) or employing simplified coarse-grained models. We present here a review of these computational studies to attempt to draw a converging picture of the conformational ensemble of the A monomer in water.
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Simulations of various A peptide segments
59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84
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The A peptide visits various conformations which can be simulated by computational methods. However, the standard MD simulations in explicit solvent can hardly sample exhaustively the conformational ensemble of the full-length peptide, due to the high computational demands of this approach. Therefore, a lot of computational studies attempt to explore the structural role of short A segments in order to decrease the system size [23] (Table 1). The A peptide can be subdivided into four segments suspected to play different role during the oligomerization (Fig. 1) ([24]; [25,26]): the N-terminus (residues 1–16), the central hydrophobic core (CHC) (residues 17–21), the fibril-turn region (residues 22–29) and the C-terminus (residues 30–42).
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Classical MD simulations
87 88 89 90 91 92 93 94 95 96 97
100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118
Regarding the role of the N-terminus segment, a study from Takeda and Klimov was performed, using CHARMM simulations with SASA implicit solvent model, to examine the effect of deletion of residues 1–9 on A (1–40) peptides. They revealed that the deletion of the N-terminus appears to cause minor structural and energetic changes in monomeric peptide and that N-truncated A (10–40) peptide is adequate for studying A (1–40) aggregation [27]. MD simulations by Massi and coworkers on the A (10–35) fragment indicate that the CHC residues underwent considerably smaller structural uctuations than the rest of the peptide. They observed in this region significant turn structures imposed by the stable conformations of the LVFFA (residues 17–21) and VGSN (residues 24–27) along with their interactions [28]. In another work, Ma and Nussinov performed MD simulations to study the free-energy landscape of the A (25–35) segment. They found in that region stabilized ␣-helix and -strand, that can both contribute to the formation of oligomers [29]. Another theoretical studies suggested that the residues 23–28 could nucleate the
Fig. 1. Positions in the full-length A(1–42) peptide of regions with high propensity to form helices (top), -strands (middle) and turns (bottom). The orange, green, blue and magenta boxes respectively indicate the N-terminus, central hydrophobic core, fibril-turn region and C-terminus of A.
folding of A monomer and a bent in this region could be the rate-limiting step in A fibril formation [30]. This bent in the fibril-turn region (residues 22–29) seems to be favored by a salt bridge between residues ASP23 and LYS28. This electrostatic interaction was shown to form spontaneously in explicit water by Tarus et al. using all-atom MD simulations of A (10–35) monomer. They also highlighted that the 23–28 salt bridge provides a mild stabilization of the VGSN turn [31]. A complementary study by Reddy et al., using similar simulations, specified that the 23–28 salt bridge in the A (10–35) monomer remains hydrated and additionally emphasized a propensity for the residues 19–24 to form a ␣-helix [32].
Please cite this article in press as: Tran L, Ha-Duong T. Exploring the Alzheimer amyloid-ˇ peptide conformational ensemble: A review of molecular dynamics approaches. Peptides (2015), http://dx.doi.org/10.1016/j.peptides.2015.04.009
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Table 1 Theoretical studies on A segments conformations.
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Reference
Force field
Method
Segment
Observations
[28]
CHARMM
MD
A(10–35)
[29]
CHARMM
MD
A(25–35)
[37]
GROMOS + SPCE and HFIP
REMD
A(25–35)
[31] [39]
CHARMM OPLS
MD REMD
A(10–35) A(10–35)
[30]
OPEP
A(21–30)
[27]
CHARMM + SASA
MD REMD DMD ART REMD
The CHC residues have smaller structural fluctuations than the N- and C-termini Both ␣-helix and -strand can contribute to the formation of amyloid intermediates A(25–35) adopts a helical structure in apolar organic solvent and a collapsed coil or -hairpin in pure water D23-K28 salt bridge forms spontaneously The conformational ensemble is dominated by random coil and bend structures Region 21–30 could be the rate-limiting step in A fibrillization
[40]
AMBER
[38]
REMD + ion mobility mass spectrometry
A(10–40) A(1–40) A(29–42) A(30–42) A(31–42) A(39–42)
There is a small conversion of -strand structure into helix upon amino-terminal deletion C-terminal fragments adopt a metastable -structure and an extended -strand at position 39–42
OPEP
MD
A(16–35)
[33]
UNRES
A(1–28)
[34]
GROMOS
MD REMD T-REMD
[36]
AMBER
A(21–30)
Nguyen et al. (2013)
CHARMM
REMD + experimental techniques REMD
Minor population of -strands at position 20–21 and 29–30 The N-terminus has a propensity to form a -hairpin in monomeric state Most populated conformational states are random coil for both models A(21–30) can form a turn suspected to be the folding nucleus of full length A The population of -hairpins is increased by a factor of 4 in mutant A2V
OPLS
Enhanced sampling methods and simplified models Using the coarse-grained protein model UNRES, Rojas et al. studied the structural properties of N-terminus fragment of A (1–28) peptide. They demonstrated that the N-terminus half of the peptide has a propensity to form a -hairpin in the monomeric state, while its C-terminus half readily forms an ␣- helix, these features determine the mechanism of aggregation [33]. In another work, Cao et al. performed temperature replica exchange molecular dynamics (T-REMD) simulations on A (12–28) peptide, using two different force fields. They concluded that the most populated conformation states are random coil, with transient -hairpins located at residues 19–22 with GROMOS force field and at residue 17–20 with OPLS force field [34]. More recently, Nguyen et al. characterized the conformational ensembles of the fragment A (1–28) wild type and its mutant A2V, using atomistic REMD simulations. They reported that the population of -hairpins is increased by a factor of 4 in A2V-mutant, which may explain the latter enhanced aggregation kinetics with respect to A (1–40) wild type [35]. With the purpose to determine the biophysical characteristics of the folding nucleus, Roychaudhuri et al. studied the biologically relevant acetyl-A (21–30)-amide peptide using REMD simulations in combination with experimental techniques (limited proteolysis, thermal and urea denaturations, electron microscopy). They indicated that segment 21–30 forms a turn, that acts as a monomer folding nucleus [36]. Using also REMD simulations, Wei and Shea revealed that the A (25–35) segment adopts a helical structure in apolar organic solvent and presents a collapsed coil, or to a lesser extent, a -hairpin conformation in pure water [37]. Using the coarse-grained model OPEP, Chebaro et al. determined the structures and thermodynamics of the A (16–35) monomer and dimer. They found minor populations of -sheets at positions 20–21 and 29–30 and a ␣-helix spanning at the region 22–27. Moreover, it was shown that the C-terminus is poorer in -structures than the N-terminus [38]. Similarly, the conformational states
A(12–28)
A(1–28)
sampled by the A (10–35) peptide were probed by Baumketner et al., using all-atom REMD simulations in explicit solvent. They showed that the preferred conformational state is a random coil, with high propensity for -turns observed for the segments of residues 26–27, 13–15, 18–20 and 31–33. Bend motifs are also observed frequently at the same positions, with a highest propensity in the N-terminus. Moreover, it could be noted that the segment 12–16 has some non-vanishing propensities for helix formation [39]. Finally, Wu et al. conducted a detailed study, combining experiments and REMD simulations, on the forces governing the structuration of the A monomer at C-terminus. They found that the C-terminus fragments A (29–42), A (30–42), A (31–42) and A (39–42) preferentially adopt transient -structures. They concluded that the transient -strands or -hairpins in the C-terminus may entropically favor the peptide aggregation [40]. Overall, the findings from the studies of A segments contribute to our understanding on the conformational dynamics of the fulllength peptide. In general, every segment of the A peptide was assumed to be bioactive because of its propensity to form fibrils similarly to that of the full-length peptide [41]. The short sequence of peptide segments is more convenient for both experimental synthesis [42,43] and computational simulations ([37,29]). However, studies of A fragments lead to limited understanding of the full-length behavior, since different segments may form intramolecular interactions that could impact the overall structures. Therefore, many following studies started to characterize the structural conformations of full-length A peptide.
165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192
Simulations of full-length A peptide
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Classical MD simulations
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Early MD simulations of full-length A peptide attempted to figure out the conditions that favor the formation of transient
Please cite this article in press as: Tran L, Ha-Duong T. Exploring the Alzheimer amyloid- peptide conformational ensemble: A review of molecular dynamics approaches. Peptides (2015), http://dx.doi.org/10.1016/j.peptides.2015.04.009
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secondary structures. Studying A (1–40) unfolding in both aqueous and biomembrane environment, Xu et al. reported an all-atom MD simulation of helix-to-coil transitions. In aqueous solution, an ␣-helix to -sheet conformational change was emphasized for A (1–40) and an entire unfolding process from helix to coil was traced by their simulation. In the -rich structures observed as intermediates in the unfolding pathway, four glycines (GLY25, GLY29, GLY33 and GLY37) were highlighted as important to form -sheets in aqueous solution [44]. In a comparable study, Yang et al. used MD simulations to analyze the effects of four solvents (HFIP, TFE, H2O and DMSO) on the conformations of the A (1–42) peptide. Their findings shows that A (1–42) monomer adopts different conformations depending on the external solution conditions, particularly the C-terminus region which adopts a -sheet structure only in water and different conformations in other solvents [45]. In other all-atom MD simulations of A (1–42) in aqueous solvent at various temperatures and pH conditions, Flock et al. observed two ␣-helices at positions 8–25 and 28–39, but reported that at least one of these helices is not stable but rather rapidly converts into a random coil or a -strand. They also emphasized that a hydrophobic cluster is formed involving VAL18, PHE19, ALA21, and GLY25 residues [46]. Similarly, a recent study conducted by Olubiyi et al., using two different force fields, investigated different electrostatic conditions affecting the secondary structures of the two major alloforms A (1–40) and A (1–42). Their results revealed that the monomers are mainly unstructured in the N-terminus first 10 residues and that protonation of the three histidine residues in A (1–42) promotes the formation of -sheets via a reduction in electrostatic repulsion between the two terminal regions [47]. Regarding the influence of mutations on the peptide conformations, Triguero et al. performed all-atom MD simulations in explicit water to investigate the impact of substitutions of the MET35 C␥-methylene by sulfoxide (MET35(O)), sulfone (MET35(O2)) and norleucine (MET35(CH2)) upon A (1–42) secondary structures. Their simulations show that the hydrophobic region 29–35 is characterized by the formation of -sheets separated by stable turns in the MET35(O) mutant, exhibits a more helical conformation in the MET35(O2) one, and is partially unstructured in the wild type. They indicated that the oxidation of MET35 into MET35(O) and MET35(O2) influences its hydrophobic interactions with the neighboring residues ILE31, LYS34 and ILE41 [48]. Lin’s simulations, performed with AMBER forces field in TIP3P water, characterize the structural ensembles of full length A (1–42), A (1–40) and AE22K peptides on a submilisecond time scale. They observed that these monomers are largely unstructured, with a slight propensity to form short helical segments, the helix-forming tendency being strongest in the region 10–20. Some -hairpins were also found at positions 30–31 and 34–35, using ILE32 and GLY33 as a turn, and at residues 35–36 and 39–40, using GLY37 and GLY38 as a turn, these -strand structures being most populated in A (1–42) than in the two other peptides [49]. In another all-atom MD study in explicit solvent of both alloforms with their WT and D7 N sequences, Viet et al. reported that upon D7N mutation, residues ARG5 and MET35 have higher contents, while residues PHE4 and LYS16 have lower contents. Specifically, A (1–42)-WT is -rich at residues ARG5, LYS6, VAL18, PHE19, ASN27, LYS28, VAL40 and ILE41, while A (1–42)-D7 N is -rich at residues VAL12, HIS13, ILE31, ILE32, MET35 and GLY38 [50]. Overall, the various MD studies of full-length A monomer seem to confirm that there are specific characteristics for each segments of the peptide. For instance, the N-terminus (residues 1–10) is unstructured, the CHC region (residues 10–21) seems to have a significant propensity to form helical conformation, while the C-terminus (residues 30–42) preferentially adopts -sheet conformations. Moreover, residues 21–30 is considered to be the nucleus region for the oligomerization process. However, due to
the limitation of MD simulation times, it is not guaranteed that the conformational space of the intrinsically disordered A peptide is exhaustively sampled, and important transient structures may be unexplored [51]. Therefore, enhanced sampling methods are needed to provide new insights into the structural transitions of the full-length A peptide. Enhanced sampling methods and simplified models The highly parallel replica exchange molecular dynamics (REMD) is a suitable method for studying protein folding mechanisms, intermediate state structures and thermodynamic properties [52]. In their early work combining REMD simulations in implicit solvent and ion-mobility mass spectrometry, Baumketner et al. showed that the A (1–42) peptide does not adopt a unique fold. Its conformations are dominated by loops and turns and can show some helical structure in the C-terminus hydrophobic tail. From this structural study, they suggested a schematic representation of the A (1–42) conformations in the early stages of aggregation, in which five or six monomers assemble to form a paranucleus [26] with their hydrophobic C-terminus shielded from water by the more hydrophilic parts of the peptides [53]. To compare the conformational ensembles of the A (1–40) and A (1–42) monomers, Sgourakis et al. performed extensive REMD studies using different forces fields and water models. They found that the C-terminus of A (1–42) is more structured than that of A (1–40). The formation of a -hairpin at residues 31–34 and 38–41 reduces the C-terminus flexibility of the A (1–42) peptide and may be responsible for its higher propensity to form amyloids [54,55]. Moreover, the study from Rosenman et al. in the same group, using the OPLS forces field and the TIP3P water model, observed a high -population at region 16–23 [56]. In a similar comparison between A (1–40) and A (1–42) peptides, Yang et al. performed s-timescale REMD simulations and constructed their free-energy surfaces. As a result, the -structures of A (1–42) are confirmed to be more stable than that of A (1–40). Additionally, the contacts within the C-terminus of A (1–42) (residue 30–42) are more frequent than in A (1–40), as a result of turns formation [57]. Another study performing multi-reservoir replicas exchange (MRRE) ensemble sampling, using the AMBER forces field and the TIP4P water model, characterized the tertiary structures of A (1–42) and A (21–30) from the perspective of their classification as intrinsically disordered peptides. Their results highlighted a turn at residues 7–8 and at residues 34–35, an helix at residues 17–18 occuring with high frequency. They also showed that the region 16–21 is rich in -structures [58,59]. In the work from Xu et al., REMD trajectories generated with AMBER forces field, were deeply analyzed by combining dihedral principle component analysis (dPCA), potentials of mean force (PMF) calculations and Markov state models (MSM). Their study shows that A (1–42) predominantly adopts coil structure (60–80%), metastable -strands (10–20%) and negligible ␣-helices (
ARTICLE IN PRESS
PEP 69455 1–6
Peptides xxx (2015) xxx–xxx
Contents lists available at ScienceDirect
Peptides journal homepage: www.elsevier.com/locate/peptides
Review
1
Exploring the Alzheimer amyloid- peptide conformational ensemble: A review of molecular dynamics approaches
2
3
Q1
4
Linh Tran, Tâp Ha-Duong ∗ BioCIS, UMR CNRS 8076, LabEx LERMIT, Faculty of Pharmacy, University Paris Sud, 5 Rue Jean-Baptiste Clément, 92296 Châtenay-Malabry, France
5 6
7 22
a r t i c l e
i n f o
a b s t r a c t
8 9 10 11 12 13
Article history: Received 13 February 2015 Received in revised form 2 April 2015 Accepted 7 April 2015 Available online xxx
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Keywords: Alzheimer’s disease A peptide Conformational ensembles Molecular dynamics Enhanced sampling methods Coarse-grained protein models
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Contents
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24 25 26 27 28 29 30 31 32
Alzheimer’s disease is one of the most common dementia among elderly worldwide. There is no therapeutic drugs until now to treat efectively this disease. One main reason is due to the poorly understood mechanism of A peptide aggregation, which plays a crucial role in the development of Alzheimer’s disease. It remains challenging to experimentally or theoretically characterize the secondary and tertiary structures of the A monomer because of its high flexibility and aggregation propensity, and its conformations that lead to the aggregation are not fully identified. In this review, we highlight various structural ensembles of A peptide revealed and characterized by computational approaches in order to find converging structures of A monomer. Understanding how A peptide forms transiently stable structures prior to aggregation will contribute to the design of new therapeutic molecules against the Alzheimer’s disease. © 2015 Elsevier Inc. All rights reserved.
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Simulations of various A peptide segments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Classical MD simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Enhanced sampling methods and simplified models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Simulations of full-length A peptide . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Classical MD simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Enhanced sampling methods and simplified models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conflict of interests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
00 00 00 00 00 00 00 00 00 00
33
34
Introduction
Q2 35 36 37 38 39 40 41 42
Alzheimers disease (AD), first described by the german neuropathologist Alois Alzheimers in 1906 [1], is by far one of the most common forms of dementia among elderly, currently affects today more than 12 million people worldwide, consequently, the social economic burdens of AD continue to increase constantly [2]. Like many other human diseases, such as type II diabetes or Parkinson’s disease, AD is highly related with a common pathogenic progress, called amyloidogenesis [3–5], and recent advances in understanding the development of amyloid-related pathologies have helped
∗ Corresponding author. E-mail address: [email protected] (T. Ha-Duong).
43
to solve the mystery on AD pathogenesis [6]. However, due to the lack of detailed understood mechanisms of the pathogenesis and despite the enormous efforts invested into AD research over the past century, there are still crucial questions that are not yet answered, in particular with respect to AD treatment [7]. An explanation for this may be found in the fact that, like many other amyloido-genic peptides, the preferential molecular target for AD drug development, the A peptide, is intrinsically disordered and rapidly self-aggregates [8–10]. The AD-related amyloid- peptide (A) has two common alloforms of 40 and 42 residues in length [11]. In solution at room temperature and physiological pH, it is in a dominating random coil secondary structure [12], but can undergo structural conversions between random coil and more structured conformations such as helices and -strands [13–15]. Within the “conformational
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Please cite this article in press as: Tran L, Ha-Duong T. Exploring the Alzheimer amyloid- peptide conformational ensemble: A review of molecular dynamics approaches. Peptides (2015), http://dx.doi.org/10.1016/j.peptides.2015.04.009
44 45 46 47 48 49 50 51 52 53 54 55 56 57 58
G Model PEP 69455 1–6
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85
selection” paradigm for molecular recognition and selforganization [16–18], A peptides dynamically visit these transient structured conformations, and one of them being rich in -strands is “selected” to form oligomers through -strand/strand interactions [19]. As recently shown by Ma and Nussinov, this -rich structures “selection mechanism”, which seems to be common to many other amyloidogenic proteins, can explain the formation of cross-species or cross-sequences hetero-amyloids, such as those observed for prion proteins of different species or those discovered between A and islet amyloid polypeptides (IAPP) involved in the type II diabetes disease [20]. The A (1–42) peptide, which has the amino-acid sequence: DAEFR HDSGY EVHHQ KLVFF AEDVG SNKGA IIGLM VGGVV IA [21], is the alloform that is the most strongly linked to AD [22]. Because of its non-homogeneous conformation, it is diffcult to characterize its structure by X-ray or NMR approaches. Therefore, many computational approaches were in complementary used for studying the monomeric peptide conformational changes in order to identify its transient states. Among these theoretical studies, molecular dynamics (MD) simulations were extensively used because of its ability to characterize the biomolecules conformational dynamics. Effective efforts to exhaustively sample the A conformational space were also performed using enhanced techniques such as replica exchange molecular dynamics (REMD) or employing simplified coarse-grained models. We present here a review of these computational studies to attempt to draw a converging picture of the conformational ensemble of the A monomer in water.
86
Simulations of various A peptide segments
59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84
98
The A peptide visits various conformations which can be simulated by computational methods. However, the standard MD simulations in explicit solvent can hardly sample exhaustively the conformational ensemble of the full-length peptide, due to the high computational demands of this approach. Therefore, a lot of computational studies attempt to explore the structural role of short A segments in order to decrease the system size [23] (Table 1). The A peptide can be subdivided into four segments suspected to play different role during the oligomerization (Fig. 1) ([24]; [25,26]): the N-terminus (residues 1–16), the central hydrophobic core (CHC) (residues 17–21), the fibril-turn region (residues 22–29) and the C-terminus (residues 30–42).
99
Classical MD simulations
87 88 89 90 91 92 93 94 95 96 97
100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118
Regarding the role of the N-terminus segment, a study from Takeda and Klimov was performed, using CHARMM simulations with SASA implicit solvent model, to examine the effect of deletion of residues 1–9 on A (1–40) peptides. They revealed that the deletion of the N-terminus appears to cause minor structural and energetic changes in monomeric peptide and that N-truncated A (10–40) peptide is adequate for studying A (1–40) aggregation [27]. MD simulations by Massi and coworkers on the A (10–35) fragment indicate that the CHC residues underwent considerably smaller structural uctuations than the rest of the peptide. They observed in this region significant turn structures imposed by the stable conformations of the LVFFA (residues 17–21) and VGSN (residues 24–27) along with their interactions [28]. In another work, Ma and Nussinov performed MD simulations to study the free-energy landscape of the A (25–35) segment. They found in that region stabilized ␣-helix and -strand, that can both contribute to the formation of oligomers [29]. Another theoretical studies suggested that the residues 23–28 could nucleate the
Fig. 1. Positions in the full-length A(1–42) peptide of regions with high propensity to form helices (top), -strands (middle) and turns (bottom). The orange, green, blue and magenta boxes respectively indicate the N-terminus, central hydrophobic core, fibril-turn region and C-terminus of A.
folding of A monomer and a bent in this region could be the rate-limiting step in A fibril formation [30]. This bent in the fibril-turn region (residues 22–29) seems to be favored by a salt bridge between residues ASP23 and LYS28. This electrostatic interaction was shown to form spontaneously in explicit water by Tarus et al. using all-atom MD simulations of A (10–35) monomer. They also highlighted that the 23–28 salt bridge provides a mild stabilization of the VGSN turn [31]. A complementary study by Reddy et al., using similar simulations, specified that the 23–28 salt bridge in the A (10–35) monomer remains hydrated and additionally emphasized a propensity for the residues 19–24 to form a ␣-helix [32].
Please cite this article in press as: Tran L, Ha-Duong T. Exploring the Alzheimer amyloid-ˇ peptide conformational ensemble: A review of molecular dynamics approaches. Peptides (2015), http://dx.doi.org/10.1016/j.peptides.2015.04.009
119 120 121 122 123 124 125 126 127 128 129 130
G Model
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PEP 69455 1–6
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3
Table 1 Theoretical studies on A segments conformations.
131
132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164
Reference
Force field
Method
Segment
Observations
[28]
CHARMM
MD
A(10–35)
[29]
CHARMM
MD
A(25–35)
[37]
GROMOS + SPCE and HFIP
REMD
A(25–35)
[31] [39]
CHARMM OPLS
MD REMD
A(10–35) A(10–35)
[30]
OPEP
A(21–30)
[27]
CHARMM + SASA
MD REMD DMD ART REMD
The CHC residues have smaller structural fluctuations than the N- and C-termini Both ␣-helix and -strand can contribute to the formation of amyloid intermediates A(25–35) adopts a helical structure in apolar organic solvent and a collapsed coil or -hairpin in pure water D23-K28 salt bridge forms spontaneously The conformational ensemble is dominated by random coil and bend structures Region 21–30 could be the rate-limiting step in A fibrillization
[40]
AMBER
[38]
REMD + ion mobility mass spectrometry
A(10–40) A(1–40) A(29–42) A(30–42) A(31–42) A(39–42)
There is a small conversion of -strand structure into helix upon amino-terminal deletion C-terminal fragments adopt a metastable -structure and an extended -strand at position 39–42
OPEP
MD
A(16–35)
[33]
UNRES
A(1–28)
[34]
GROMOS
MD REMD T-REMD
[36]
AMBER
A(21–30)
Nguyen et al. (2013)
CHARMM
REMD + experimental techniques REMD
Minor population of -strands at position 20–21 and 29–30 The N-terminus has a propensity to form a -hairpin in monomeric state Most populated conformational states are random coil for both models A(21–30) can form a turn suspected to be the folding nucleus of full length A The population of -hairpins is increased by a factor of 4 in mutant A2V
OPLS
Enhanced sampling methods and simplified models Using the coarse-grained protein model UNRES, Rojas et al. studied the structural properties of N-terminus fragment of A (1–28) peptide. They demonstrated that the N-terminus half of the peptide has a propensity to form a -hairpin in the monomeric state, while its C-terminus half readily forms an ␣- helix, these features determine the mechanism of aggregation [33]. In another work, Cao et al. performed temperature replica exchange molecular dynamics (T-REMD) simulations on A (12–28) peptide, using two different force fields. They concluded that the most populated conformation states are random coil, with transient -hairpins located at residues 19–22 with GROMOS force field and at residue 17–20 with OPLS force field [34]. More recently, Nguyen et al. characterized the conformational ensembles of the fragment A (1–28) wild type and its mutant A2V, using atomistic REMD simulations. They reported that the population of -hairpins is increased by a factor of 4 in A2V-mutant, which may explain the latter enhanced aggregation kinetics with respect to A (1–40) wild type [35]. With the purpose to determine the biophysical characteristics of the folding nucleus, Roychaudhuri et al. studied the biologically relevant acetyl-A (21–30)-amide peptide using REMD simulations in combination with experimental techniques (limited proteolysis, thermal and urea denaturations, electron microscopy). They indicated that segment 21–30 forms a turn, that acts as a monomer folding nucleus [36]. Using also REMD simulations, Wei and Shea revealed that the A (25–35) segment adopts a helical structure in apolar organic solvent and presents a collapsed coil, or to a lesser extent, a -hairpin conformation in pure water [37]. Using the coarse-grained model OPEP, Chebaro et al. determined the structures and thermodynamics of the A (16–35) monomer and dimer. They found minor populations of -sheets at positions 20–21 and 29–30 and a ␣-helix spanning at the region 22–27. Moreover, it was shown that the C-terminus is poorer in -structures than the N-terminus [38]. Similarly, the conformational states
A(12–28)
A(1–28)
sampled by the A (10–35) peptide were probed by Baumketner et al., using all-atom REMD simulations in explicit solvent. They showed that the preferred conformational state is a random coil, with high propensity for -turns observed for the segments of residues 26–27, 13–15, 18–20 and 31–33. Bend motifs are also observed frequently at the same positions, with a highest propensity in the N-terminus. Moreover, it could be noted that the segment 12–16 has some non-vanishing propensities for helix formation [39]. Finally, Wu et al. conducted a detailed study, combining experiments and REMD simulations, on the forces governing the structuration of the A monomer at C-terminus. They found that the C-terminus fragments A (29–42), A (30–42), A (31–42) and A (39–42) preferentially adopt transient -structures. They concluded that the transient -strands or -hairpins in the C-terminus may entropically favor the peptide aggregation [40]. Overall, the findings from the studies of A segments contribute to our understanding on the conformational dynamics of the fulllength peptide. In general, every segment of the A peptide was assumed to be bioactive because of its propensity to form fibrils similarly to that of the full-length peptide [41]. The short sequence of peptide segments is more convenient for both experimental synthesis [42,43] and computational simulations ([37,29]). However, studies of A fragments lead to limited understanding of the full-length behavior, since different segments may form intramolecular interactions that could impact the overall structures. Therefore, many following studies started to characterize the structural conformations of full-length A peptide.
165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192
Simulations of full-length A peptide
193
Classical MD simulations
194
Early MD simulations of full-length A peptide attempted to figure out the conditions that favor the formation of transient
Please cite this article in press as: Tran L, Ha-Duong T. Exploring the Alzheimer amyloid- peptide conformational ensemble: A review of molecular dynamics approaches. Peptides (2015), http://dx.doi.org/10.1016/j.peptides.2015.04.009
195 196
G Model PEP 69455 1–6 4 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262
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secondary structures. Studying A (1–40) unfolding in both aqueous and biomembrane environment, Xu et al. reported an all-atom MD simulation of helix-to-coil transitions. In aqueous solution, an ␣-helix to -sheet conformational change was emphasized for A (1–40) and an entire unfolding process from helix to coil was traced by their simulation. In the -rich structures observed as intermediates in the unfolding pathway, four glycines (GLY25, GLY29, GLY33 and GLY37) were highlighted as important to form -sheets in aqueous solution [44]. In a comparable study, Yang et al. used MD simulations to analyze the effects of four solvents (HFIP, TFE, H2O and DMSO) on the conformations of the A (1–42) peptide. Their findings shows that A (1–42) monomer adopts different conformations depending on the external solution conditions, particularly the C-terminus region which adopts a -sheet structure only in water and different conformations in other solvents [45]. In other all-atom MD simulations of A (1–42) in aqueous solvent at various temperatures and pH conditions, Flock et al. observed two ␣-helices at positions 8–25 and 28–39, but reported that at least one of these helices is not stable but rather rapidly converts into a random coil or a -strand. They also emphasized that a hydrophobic cluster is formed involving VAL18, PHE19, ALA21, and GLY25 residues [46]. Similarly, a recent study conducted by Olubiyi et al., using two different force fields, investigated different electrostatic conditions affecting the secondary structures of the two major alloforms A (1–40) and A (1–42). Their results revealed that the monomers are mainly unstructured in the N-terminus first 10 residues and that protonation of the three histidine residues in A (1–42) promotes the formation of -sheets via a reduction in electrostatic repulsion between the two terminal regions [47]. Regarding the influence of mutations on the peptide conformations, Triguero et al. performed all-atom MD simulations in explicit water to investigate the impact of substitutions of the MET35 C␥-methylene by sulfoxide (MET35(O)), sulfone (MET35(O2)) and norleucine (MET35(CH2)) upon A (1–42) secondary structures. Their simulations show that the hydrophobic region 29–35 is characterized by the formation of -sheets separated by stable turns in the MET35(O) mutant, exhibits a more helical conformation in the MET35(O2) one, and is partially unstructured in the wild type. They indicated that the oxidation of MET35 into MET35(O) and MET35(O2) influences its hydrophobic interactions with the neighboring residues ILE31, LYS34 and ILE41 [48]. Lin’s simulations, performed with AMBER forces field in TIP3P water, characterize the structural ensembles of full length A (1–42), A (1–40) and AE22K peptides on a submilisecond time scale. They observed that these monomers are largely unstructured, with a slight propensity to form short helical segments, the helix-forming tendency being strongest in the region 10–20. Some -hairpins were also found at positions 30–31 and 34–35, using ILE32 and GLY33 as a turn, and at residues 35–36 and 39–40, using GLY37 and GLY38 as a turn, these -strand structures being most populated in A (1–42) than in the two other peptides [49]. In another all-atom MD study in explicit solvent of both alloforms with their WT and D7 N sequences, Viet et al. reported that upon D7N mutation, residues ARG5 and MET35 have higher contents, while residues PHE4 and LYS16 have lower contents. Specifically, A (1–42)-WT is -rich at residues ARG5, LYS6, VAL18, PHE19, ASN27, LYS28, VAL40 and ILE41, while A (1–42)-D7 N is -rich at residues VAL12, HIS13, ILE31, ILE32, MET35 and GLY38 [50]. Overall, the various MD studies of full-length A monomer seem to confirm that there are specific characteristics for each segments of the peptide. For instance, the N-terminus (residues 1–10) is unstructured, the CHC region (residues 10–21) seems to have a significant propensity to form helical conformation, while the C-terminus (residues 30–42) preferentially adopts -sheet conformations. Moreover, residues 21–30 is considered to be the nucleus region for the oligomerization process. However, due to
the limitation of MD simulation times, it is not guaranteed that the conformational space of the intrinsically disordered A peptide is exhaustively sampled, and important transient structures may be unexplored [51]. Therefore, enhanced sampling methods are needed to provide new insights into the structural transitions of the full-length A peptide. Enhanced sampling methods and simplified models The highly parallel replica exchange molecular dynamics (REMD) is a suitable method for studying protein folding mechanisms, intermediate state structures and thermodynamic properties [52]. In their early work combining REMD simulations in implicit solvent and ion-mobility mass spectrometry, Baumketner et al. showed that the A (1–42) peptide does not adopt a unique fold. Its conformations are dominated by loops and turns and can show some helical structure in the C-terminus hydrophobic tail. From this structural study, they suggested a schematic representation of the A (1–42) conformations in the early stages of aggregation, in which five or six monomers assemble to form a paranucleus [26] with their hydrophobic C-terminus shielded from water by the more hydrophilic parts of the peptides [53]. To compare the conformational ensembles of the A (1–40) and A (1–42) monomers, Sgourakis et al. performed extensive REMD studies using different forces fields and water models. They found that the C-terminus of A (1–42) is more structured than that of A (1–40). The formation of a -hairpin at residues 31–34 and 38–41 reduces the C-terminus flexibility of the A (1–42) peptide and may be responsible for its higher propensity to form amyloids [54,55]. Moreover, the study from Rosenman et al. in the same group, using the OPLS forces field and the TIP3P water model, observed a high -population at region 16–23 [56]. In a similar comparison between A (1–40) and A (1–42) peptides, Yang et al. performed s-timescale REMD simulations and constructed their free-energy surfaces. As a result, the -structures of A (1–42) are confirmed to be more stable than that of A (1–40). Additionally, the contacts within the C-terminus of A (1–42) (residue 30–42) are more frequent than in A (1–40), as a result of turns formation [57]. Another study performing multi-reservoir replicas exchange (MRRE) ensemble sampling, using the AMBER forces field and the TIP4P water model, characterized the tertiary structures of A (1–42) and A (21–30) from the perspective of their classification as intrinsically disordered peptides. Their results highlighted a turn at residues 7–8 and at residues 34–35, an helix at residues 17–18 occuring with high frequency. They also showed that the region 16–21 is rich in -structures [58,59]. In the work from Xu et al., REMD trajectories generated with AMBER forces field, were deeply analyzed by combining dihedral principle component analysis (dPCA), potentials of mean force (PMF) calculations and Markov state models (MSM). Their study shows that A (1–42) predominantly adopts coil structure (60–80%), metastable -strands (10–20%) and negligible ␣-helices (
