pH-Induced Misfolding Mechanism of Prion Protein: Insights from

Subscriber access provided by ALBRIGHT COLLEGE

Article

The pH-induced misfolding mechanism of prion protein: insights from microsecond accelerated molecular dynamics simulations Shuangyan Zhou, Danfeng Shi, Xuewei Liu, Xiaojun Yao, Lin-Tai Da, and Huanxiang Liu ACS Chem. Neurosci., Just Accepted Manuscript • DOI: 10.1021/ acschemneuro.8b00582 • Publication Date (Web): 09 May 2019 Downloaded from http://pubs.acs.org on May 10, 2019

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

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

ACS Chemical Neuroscience

The pH-induced Misfolding Mechanism of Prion Protein: Insights from Microsecond Accelerated Molecular Dynamics Simulations Shuangyan Zhouac, Danfeng Shib, Xuewei Liub, Xiaojun Yaobd, Lin-Tai Dae*, Huanxiang Liua* aSchool

bState

of Pharmacy, Lanzhou University, Lanzhou 730000, China

Key Laboratory of Applied Organic Chemistry and Department of Chemistry, Lanzhou

University, Lanzhou 730000, China cChongqing

Key Laboratory on Big Data for Bio Intelligence, Chongqing university of posts and

telecommunications, Chongqing 400065, China dState

Key Laboratory of Quality Research in Chinese Medicine, Macau Institute for Applied

Research in Medicine and Health, Macau University of Science and Technology, Taipa, Macau, China e

Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Center for Systems

Biomedicine, Shanghai JiaoTong University, Shanghai 200240, China.

* Corresponding author Tel.: +86-931-891-2578 Fax: +86-931-891-2582 E-mail address: [email protected], [email protected].

1

ACS Paragon Plus Environment

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

Abstract The conformational transition of prion protein (PrP) from a native form PrPC to a pathological isoform PrPSc is the main cause of a number of prion diseases in human and animals. Thus, understanding the molecular basis of conformational transition of PrP will be valuable for unveiling the etiology of PrP-related diseases. Here, to explore the potential misfolding mechanism of the PrP under the acidic condition, which is known to promote PrP misfolding and trigger its aggregation, the conventional and accelerated molecular dynamics (MD) simulations combined with Markov state model (MSM) analysis were performed. The conventional MD simulations reveal that at an acidic pH, the globular domain of PrP is partially unfolded, particularly for the α2 Cterminus. Structural analysis of the key macrostates obtained by MSM indicates that the α2 C-terminus and the β2-α2 loop may serve as important sites for the pH-induced PrP misfolding. Meanwhile, the α1 may also participate in the pH-induced structural conversion by moving away from the α2-α3 subdomain. Notably, dynamical network analysis of the key metastable states indicates that the protonated H187 weakens the interactions between the α2 C-terminus, α1-β2 loop, and α2-α3 loop, leading these domains especially the α2 C-terminus become unstable and begin to misfold. Therefore, the α2 C-terminus plays a key role in the PrP misfolding process and serves as a potential site drug targeting. Overall, our findings can deepen the understanding of the pathogenesis related to PrP and provide useful guidance for the future drug discovery. Key words: prion, misfolding, low pH, molecular dynamics simulation, Markov state model. 2


Page 2 of 46

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


Introduction Prion diseases are infectious neurodegenerative diseases that include a range of diseases in human and animals, such as Creutzfeld-Jakob disease (CJD), fatal familial insomnia (FFI) and bovine spongiform encephalopathy (BSE) etc1. According to the “protein-only” hypothesis, the key event involved in the prion disease is the conformational transition from the normal α-helix-rich cellular prion protein (PrPC) into the infectious and pathogenic β-sheet-rich scrapie prion protein (PrPSc)2, 3. Importantly, the infectious PrPSc can further act as a seed to guide the structural conversion of PrPC to PrPSc. Given the significant role of the PrPSc formation in prion pathogenesis, it is thus important to unravel the underlying mechanism and structural dynamics of the PrPC→PrPSc transition process. The three-dimensional structure of the human PrPC (huPrP) was firstly reported by Wuthrich’s group with the method of solution NMR4. Thereafter, many other huPrP structures have been obtained by X-ray diffraction or solution NMR, etc5-7. These structures reveal that the huPrP is composed of a disordered N-terminus (residue 23124) and a globular C-terminal domain (residues 125-231) with three α-helices (α1:144154, α2:173-194, α3:200-228) and two native β-sheets (β1:128-131, β2:161-164). Nevertheless, except for the increased β-sheet content8, we knew very few for the structure of PrPSc. Presently, there is still lacking of the high-resolution threedimensional structure of full-length PrPSc, which makes a great challenge for us to elucidate the mechanism PrPC→PrPSc conformational transition. Former studies have indicated the environmental perturbation have profound 3



influences on the dynamics of PrPC9-11, such as pH and temperature. Among these environmental perturbations, reducing the pH is one of the remarkable factors which can destabilize PrPC and facilitate its structural conversion into pathogenic PrPSc10, 12, 13.

More importantly, the oligomers of PrP formed at acidic pH are shown to be

cytotoxic14. Thus, exploring the pH-induced PrP misfolding is significant to unravel the potential mechanism of PrPC→PrPSc transition. To date, a number of experiments have been conducted to elucidate the pH-induced PrP misfolding mechanism by the solution NMR15, hydrogen-deuterium exchange mass spectrometry (HDX-MS) measurements10, 16,

far-UV CD spectra17, or other experiments methods. For example, using HDX-MS

technique, Moulick et al proposed that a partially unfolded intermediate of PrP was the monomeric precursor that directly initiated the misfolding at low pH18. Using similar strategy, Singh et al found that the increased structural flexibilities in the α1 and α1-β2 loop regions played a critical role in the early acid-induced misfolding of PrP10. They also found that rational stabilization of the α2 C-terminus can prevent the misfolding and oligomerization of the PrP even in the acidic environment16, indicating the α2 Cterminus domain has a profound influences on the PrP amyloid formation. In contrast to the Singh’s findings, Honda et al, by combing NMR and far-UV CD, reported that the β1-α1-β2 segment was preferentially unfolded at acidic environment while the α2α3 region remained relatively stable17. Taken together, the above experimental studies provide deep mechanistic insights into the structural features for the PrP misfolding process. However, an atomistic-level understanding of the PrPC to PrPSc conversion is still hard to be probed by experimental techniques due to limited spatiotemporal 4


Page 4 of 46

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


resolutions. Fortunately, molecular dynamics (MD) simulations has been a valuable tool to investigate the dynamics of biological systems with respect to the simulation time at an atomistic resolution, e.g., for the PrP systems19-22. Moreover, MD simulation can readily evaluate the environmental effects, i.e., pH values, temperature, and residue mutations on the structural dynamics of the biomolecules. Recently, many computational studies have been performed to unravel the PrPC structural conversion. For example, by employing the MD simulations under neutral and low pH conditions, Van Der Kamp et al found that the loss of the salt-bridge between Arg156 and Glu196 drives the α1 domain away from the α2-α3 core, causing the hydrophobic core more exposed to the solvent environments23. On the other hand, the extension of the native β-sheets with disordered N-terminus and the structural rearrangement of residues in α2α3 loop also play a significant role in the PrP misfolding23, 24. Interestingly, similar to the former experiments18, Singh et al, using Metadynamics simulations, identified that the partially unfolded isoforms of PrP were the main global minima states on the conformational free energy landscape25. Notably, these states do not involve increased β-sheet content, which suggests the β-rich conformations are possibly the off-pathway intermediates during PrP misfolding, in contrast to the previous MD studies of Daggett et al23, 26, 27.

Recently, Garrec et al have also performed microsecond simulations of

mouse PrP at acidic pH. They found the electrostatic repulsion between the protonated H187 and the guanidinium group of R136 greatly contributed to the PrP misfolding, leading to the unraveling of α2 alone or the unraveling of α2 with simultaneous 5



elongation of native β-sheet28. Despite of the above computational efforts, the simulation times in most of these works are limited to tens to hundred nanoseconds23, 24, 26, 27, which are still too short to reveal the biologically relevant structural changes of the PrP system. Moreover, it is also worth to note that even under strongly pathogenic condition (i.e., acidic pH) the transition barriers between PrPC and PrPSc remains high and the transition process is shown to be ~microseconds to milliseconds29, 30, thus, much longer MD simulation is required. Besides, previous studies have also highlighted the importance of the interactions between the N-terminal domain and the C-terminal domain in PrP misfolding31, 32, and the amyloid fibrils from the N-terminal fragment are shown to be infectious33. However, most of the former studies were only focused on the structural change of the globular C-terminal domain but ignoring the role of the disordered Nterminus21, 28. Hence, investigating the influence of the disordered N-terminus on the globular C-terminus is also important. In this work, taken the simulation timescale and the influence of the disordered Nterminus into consideration, we have performed microsecond MD simulations on the huPrP conformation consisting of both part of the disordered N-terminal and the Cterminal globular domains (residue 90-231) to investigate the misfolding mechanisms of the PrPC → PrPSc transition induced under low pH condition. The accelerated molecular dynamic (AMD) simulations, an enhanced-sampling method, is applied to improve the structural sampling in the conformational space34. In addition, to identify the key conformational states during the PrP misfolding, a Markov state model (MSM) 6


Page 6 of 46

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


based on AMD simulations on the microsecond timescale was constructed. MSM, as a kinetic network model of many discrete conformational states, can predict relatively long timescale dynamics of proteins from many discrete

MD trajectories35,

36.

Importantly, the conformation states in a MSM are typically defined based on kinetic criteria rather than geometric criteria37. Therefore, it is possible to accurately reflect the underlying free energy landscapes. Taken together, in this work, by combing long timescale MD simulations and MSM, we are intending to unravel the potential PrP misfolding mechanism, which may provide a valuable understanding of the pathogenesis related to prion diseases.

Results and Discussion Acidic environment induces the structural conversion of PrPC To investigate the structural features of PrPC in both neutral and acidic environments, we firstly performed the conventional MD simulations at the neutral and acidic condition, respectively, starting from a PrPC conformation obtained from Protein Data Bank (PDB ID:2LSB7, model 2) consisting of disordered N-terminus (residue 90124) and globular C-terminus (residue 125-231). For each system, three parallel runs were performed. For the system at low pH, we adopted all the histidines (label in Figure 1) as double protonated states23 while at neutral pH, these histidines are monoprotonated. We first monitored the structural stability of the PrPC at different pH by calculating the RMSD of Cα atoms of the globular domain (residues 128 to 228). As shown in Figure 2, each system reaches to a relative equilibration in the last 100 ns. 7



Notably, it can be seen that the acidic system has an increased RMSD compared to the system at neutral pH from run1 and run2, indicating that acidic condition can indeed influence the structural stability of PrPC. Consistently, the calculations of root-meansquare fluctuations (RMSFs) for all the Cα atoms (residue 90-231) in run1 and run2 also indicate an increased flexibility of the PrP structure at low pH. In particular, the β1-α1 loop, β2-α2 loop, α2 C-terminus, α2-α3 loop regions show significantly enhanced structural flexibilities. In addition, the secondary structure analysis and the representative structures extracted from the last snapshots of run1 and run2 further suggests that the abovementioned motifs at low pH change their secondary structures significantly, especially for the α2 C-terminus (shown in Figure 3 and Figure S1) which is converted into disordered turn structures. These results indicate the instability of α2 C-terminus, which is also consistent with previous experimental results obtained by HDX-MS method16. As for run3, although the RMSD fluctuation of acidic system is smaller than that of neutral system during the last 150 ns with the helix structure in the α2 C-terminus unaffected, anti-parallel β-sheet structure is found to be formed between the β2-α2 loop and the disordered N-terminus (see Figure S1 and Figure 3), suggesting the potential role of β2-α2 loop and the disordered N-terminus in the PrP misfolding process. To further exam how the introduced perturbations at acidic pH affect the global structures of the PrPC, we then constructed the free energy landscapes (FELs) for the neutral and acidic systems based on the last 100 ns trajectories by projecting the conformations from MD onto two reaction coordinates: the Cα RMSD and Rg of the 8


Page 8 of 46

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


globular region. Only the FELs of run1 and run2 with similar tendency were constructed. Expectedly, as shown in Figure 4, the FELs in run1 and run2 resemble well with two energy basins separated from each other, leaving energy basin of neutral system at the top left corner and the acidic one at the lower right corner. The result of FELs suggest that the conformational space is altered significantly upon the change of pH values. Interestingly, the PrP conformations at low pH in run1 and run2 tend to adopt more compact forms than that at neutral pH, as reflected from the relatively smaller Rg values (see the magenta FELs in Figure 4). This result also fit well with the previously reported studies that human PrP is likely to form more compact states upon misfolding than the native structure 25, 31. Overall, all the above analysis indicates that the acidic condition can significantly alter the structure of PrP and promote PrP misfolding.

Both the α2 C-terminus and β2-α2 loop are critical for PrP misfolding To further investigate the molecular mechanism underlying the pH-induced PrP misfolding process, we performed additional microsecond AMD simulations on huPrP90-231 attempting to enhance the sampling in the conformational space. Considering the influence of the initial structure, 6 different PrP conformations were extracted from run1 at acidic pH and then used as the input structures for subsequence 1 μs AMD simulations (a total of six 1-μs AMD simulations). Firstly, to monitor the structural change, we performed secondary structural analysis for each AMD trajectory. As can be seen from Figure 5, the secondary structure change of PrP for all AMD 9



trajectories shares some common features despite the different input structures. For example, as we observed in the above acidic conventional MD simulation, the helices of α2 C-terminus are disrupted significantly in all AMD trajectories, especially for AMD1, AMD4, AMD5 and AMD6, further verifying the instability of α2 C-terminus in the acidic environment. Similarly, Bjorndahl et al have also reported that the N- and C-termini of α2 are the first regions to experience conformational conversion upon a reduction in pH38 by using NMR spectroscopy and other biophysical techniques. Except for the large structural change of the α2 C-terminus, we also observed that α1 in AMD1 and AMD6 is partially unfolded, indicating that α1 may also play an important role in the pH-induced PrP misfolding, since previous studies have shown that α1 can act as a gate-keeper39,

40

by preventing α2-α3 subdomain from becoming hydrated and it is

tending to unravel under the RNA-induced PrP misfolding41, 42. Meanwhile, as we observed in run3, we also find that in the trajectories AMD2 and AMD4, typical βstructures are newly formed at β2-α2 loop, which was not observed in the works of Kamp/Daggett23, 27. This finding further supports that β2-α2 loop can also serve as an important site for PrP misfolding since single residue substitution of this region in mouse PrPSc can reduce or prevent prion conversion in vitro43, 44. To further exam the structural differences among six AMD trajectories, we projected all the sampled conformations onto the FELs as illustrated in Figure 4. Here, the unweighted FELs were presented, since the overall shape of the FELs can be maintained with the introduced bias and the profiles in the absence of reweighting can also provide an estimate of free energy differences45. As shown in Figure 6, two main 10


Page 10 of 46

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


energy basins are observed for the complete AMD dataset, centering at the RMSD values ~3.3 Å and ~4.0 Å, respectively. Further examinations of each AMD simulations roughly separate the complete dataset into two groups: one consists of AMD1, AMD3 and AMD6 that exhibit the main energy basin locating at RMSD value ~4.0 Å and Rg value < 14.5 Å, and another group consists of AMD2, AMD4 and AMD5 with the main energy basin centered at RMSD values ~3.5 Å or < 3.5 Å and Rg value > 14.5 Å. We speculate that the conformations within each of the above two groups may share similar structural features, thus leading to a fast inter-state transitions. Next, to obtain the key intermediate states involved in the pH-induced PrP misfolding process, we constructed one Markov state model (MSM) based on all the conformations from the AMD trajectories. The detailed construction and validation of MSM were described in Text S1 in the supporting information. The MSM was constructed by using the time-structure independent components analysis (tICA) algorithm to reduce the dimension of conformational space. Comparing the other dimension reduction methods (i.e., principle component analysis or PCA), tICA maximizes the autocorrelation of transformed coordinates46 which is shown to find a maximally slow subspace underlying the molecular dynamics data. Finally, we projected all the conformations onto the top two tICAs that represent the directions along which the conformational changes take place the slowest. The results show that the projected conformations exhibit two main energy basins (Figure 7a) which is consistent with the FEL of the complete AMD dataset shown in Figure 6. Meanwhile, many local potential wells are also found in the tICA projection, indicating that there 11



are many intermediates during the PrP misfolding. Moreover, the decomposition of whole tICA projection into individual AMD simulation maps the AMD1, AMD3 and AMD6 onto the left basin in Figure 7a, and AMD2, AMD4 and AMD5 onto the right basin (Figure 7b). This observation is in line with the above analysis shown in Figure 6. In conclusion, our analysis suggests that two main metastable states exist in the AMD simulation. Then, to identify the key metastable states involved in the PrP misfolding process, we constructed the MSM by firstly clustering the MD conformations into 200 microstates followed by lumping these microstates into 20 macrostates using PCCA+ algorithm47. The details of the MSM construction are described in the Method S1. Interestingly, among the 20 macrostates identified by MSM, two macrostates, termed as S18 and S19, are the top two most populated states with equilibrium population of 51.5% and 39.6%, respectively. In contrast, the equilibrium population of other macrostates is less than 1%. This result is consistent well with the above tICA projection in which two major intermediate states are presented in Figure 7. To obtain the detailed structural information, we then extracted 5000 conformations for each of these two macrostates, respectively, and performed the secondary structure analysis for each residue. The representative structures indicate the globular domain of both S18 and S19 are partially misfolded as shown in Figure 8. Notably, the contents of βstructure in some regions of the N-terminus, including residues 95-97, 107-109, 112114 and 121-125, significantly increased, indicating their higher tendencies and critical roles in the misfolding of prion. It is worth to note that most of the above residues 12


Page 12 of 46

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


belong to the PrP106-126 segment, which is well consistent with previous experimental observations that this segment is critical for fibril formation48.

Meanwhile, the β-sheet

contents of two protonated His96 and His111 in the N-terminus are also high, indicating that these two residues may play an important role in the fibril formation of PrP since previous studies have suggested that Cu2+ binding to His96 and His111 can promote the β-sheet formation in PrP91-11549. For S18, the extracted conformations are mainly from trajectory AMD1 with the helices of α2 C-terminus (residue 186 to 194) partially unfolding into disordered coil and turn structures (Figure 8b and 8c). In particular, within S18, there is also a tendency of α2 C-terminus and α2-α3 loop (residue 185 to 199) to form anti-parallel β-sheet structures (Figure S4), though the probability is low. These information suggest that the α2 C-terminus plays a key role in the α→β transition of PrP, as also supported by previous experimental work by Dima et al50. In comparison, for S19, the extracted conformations are mainly from the trajectory AMD4, and the most obvious conformational change is the newly formed parallel βstrands between residues 112-114 in the disordered N-terminus and residues 169-170 in the β2-α2 loop (Figure 8d and Figure 8e) with the probabilities around 90%. Relative to the initial structure of the β2-α2 loop (Figure S5a), native hydrogen bonds (H-bonds) R164:NH1-S170:OG and P165:O -E168:N and E168:O-S170:N are disrupted in S19 and a new H-bond (D167:N-E168:O) is formed (Figure S5b). The disrupted native Hbonds increase the structural flexibility of β2-α2 loop, leading to the sidechain of Y169 move towards to the other side of the loop (Figure S5b and Figure S5c). Meanwhile, the structural rearrangement of Y169 allows the β2-α2 loop to form three backbone H13



bonds (as shown in Figure S5b) with the disordered N-terminus and thus form β-sheet structure. We speculate that Y169 may play an important role in structural transition of β2-α2 loop. Moreover, Kurt et al has previously reported that the conserved Y169 in the β2-α2 loop can promote efficient prion conversion51. Caldarulo et al has also reported that the pathogenic mutation Y169A can reduce the energy barriers for the conformational conversion of β2-α2 loop by performing Metadynamics simulations19. In addition to the above backbone H-bonds, one H-bond between the sidechain of E168 and the protonated H111 at disordered N-terminus is also formed (Figure S5b), indicating that H111 likely adopts a double protonation state (positively charged) in order to form more favorable interactions with the negatively charged residues in β2α2 loop. These observations indicate that the β2-α2 loop serves as a misfolding site for the PrPC conformational transition and the disordered N-terminus also participates in the PrP misfolding. Taken together, the two misfolded states obtained in our work confirm that both α2 C-terminus and β2-α2 loop are critical for PrP misfolding. Consistently, former experiments and MD studies have also demonstrated the significant role of the α2 C-terminus and the β2-α2 loop in the misfolding process of PrPC 16, 21, 52, 53.

Protonation of H187 drives the early PrP misfolding Our above obtained results indicating that the macrostates S18 and S19 are important metastable states during the pH-induced PrP misfolding. Therefore, to decipher how the acidic condition can induce the PrP misfolding, we performed 14


Page 14 of 46

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


dynamical network analysis by using the Networkview plugin54 in VMD55 for macrostates S18 and S19 as well as the native state extracted from run1 at neutral pH for comparisons. The dynamical network analysis derived from MD simulations is often used for the allosteric analysis of protein56, 57. Generally, in networks, the Cα atom of each residue is represented as a “node”, and the “edges” are used to connect pairs of “nodes” if the corresponding residues were in contact. Therefore, dynamical network is also a way to monitor the residue interactions of a protein. In the present work, the only difference of the PrP conformation at neutral and acidic pH is the protonation state of six histidines, including H96, H111, H140, H155, H177 and H187. We thus focused on the interaction network changes of these involved His and their surrounding residues upon their protonations. The results of the dynamics network analysis are shown in Figure S6 to Figure S8 with the detailed node connections of each His displayed and the connected residues of each His in different states were shown in Table S1 in the supplementary material as well. From Figure S6 to Figure S8 and Table S1, it can be seen that the node connection of H187 is changed most in S18 and S19 compared with the native state at neutral pH, followed by H96 and H111 in the disordered N-terminus. For H96 and H111, the large difference of node connections can be explained by the large flexibility of the disordered N-terminus. As such, we speculate that the disrupted interactions surrounding H187 may play an important role in the conformational transition of PrP at the acidic environment. It is worth noting that the H187R mutation is widely known to be associated with Gerstmann-Sträjssler-Scheinker (GSS)58. Moreover, previous studies have shown that 15



the mutation of H187 to an Arg imposes a permanently positively residue, which dramatically affects the folding of PrPC and increases its propensity to oligomerize59. Notably, the protonation of H187 also introduces one positively charge, resembling to effects of Arg. To depict the disturbed node connections of H187 more clearly, we displayed the detailed node connections of H187 in each state. As shown in Figure 9, it clearly shows that H187 at neutral pH has the most node connections, including the residues T183, I184, K185, Q186, etc in the α2 C-terminus, F198 in α2-α3 loop, and R156, Y157, P158 in the α1, α1-β2 loop. In comparison, for S18 and S19 where the residue H187 is double protonated, the node connections with R156, Y157, P158 in the α1, α1-β2 loop and F198 in the α2-α3 loop disappeared, suggesting that the protonation of H187 can completely interrupt its interactions with these regions. In addition, the node connection of H187 within the α2 C-terminus in both S18 and S19 are disrupted significantly, especially in S18, only the connections with K185 and V189 are kept. Overall, the double protonation of H187 largely disrupts its interactions with the α2 C-terminus, α2α3 loop and α1, α1-β2 loop. Meanwhile, we also superposed representative structures of S18 and S19 into the native PrP structure to exam the structural dynamic of α1. From Figure S9, it is clear that α1 in S18 is moved away from the α2-α3 subdomain while it remains well in S19. Thus, the α1 may also contribute to the structural conversion of S18. In S19, except for the structural change induced by protonated H187, the protonated H111 also contribute a lot to the conformational change of S19 in the β2-α2 loop. As shown in Table S1, compared to the native state and S18, H111 in S19 has the 16


Page 16 of 46

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


most node connections, including residues E168 and Y169 in the β2-α2 loop. This observation fits well with the formed β-sheets between β2-α2 loop and disordered Nterminus, which also indicates that the protonation of His at disordered N-terminus increases its interaction with globular C-terminus as well as enhances the misfolding tendency of PrP. The above results indicate that the protonation of H187 disrupts the interactions among the α2 C-terminus, α2-α3 loop and α1, α1-β2 loop, but what is the reason for these disrupted interactions? As displayed in Figure 10a, H187 is positioned at α2 Cterminus and is buried and interacts with residues P158, F198 and M206 in the initial structure, forming a stable hydrophobic core. However, the double protonation of H187 can potentially increase its hydrophilicity and makes it easier to expose to the solvents. Considering this, we thus calculated the solvent accessible surface area (SASA) of H187. In addition, the SASA of F198 is also calculated since it is the hydrophobic core residue in the α2-α3 loop and interacts with residue H187 in the neutral state. Moreover, the mutation of F198 can cause prion diseases58. As expected, H187 and F198 at neutral pH have the smallest SASAs as shown in Figure 10b. In comparison, the SASAs of H187 and F198 in S18 and S19 significantly increase, suggesting a higher solventexposed tendency of H187 and F198 (Figure 10c). Indeed, as displayed in Figure 10c, the location of H187 relative to P158, F198 and M206 changed a lot in both S18 and S19, and the interactions of H187 with P158 and M206 were mostly lost. Meanwhile, the hydrophobic core residue F198 in S18 and S19 also moves away from the hydrophobic core. These observations indicate the protonation of H187 makes the 17



region from α2 C-terminus to α2-α3 loop easier to expose to the solvents and become more flexibility, which explains well with the disrupted node connections of H187 with the residues in the α2 C-terminus and with F198 in the α2-α3 loop in Figure 9. Taken together, we conclude that the α2 C-terminus is a more important site for the pHinduced PrP misfolding and the protonated H187 in acidic environment drives the structural transition of PrPC at acidic pH. Although the β2-α2 loop is also an important misfolding site of PrP, the structural rearrangement of β2-α2 loop is mainly observed in S19, while the conformational change of α2 C-terminus is observed both in S18 and S19. Thus, relatively speaking, the α2 C-terminus is easier to unfold than β2-α2 loop.

Conclusion In this work, we intended to explore the potential pH-induced misfolding mechanism of PrP by combing microsecond AMD simulations and MSM. MD results show that the acidic environment indeed disrupts the native structure of PrP. From the constructed MSM based on the AMD simulations of PrP at acidic condition, we identified two important misfolded states S18 and S19 with the highest populations. By analyzing the structural features of these two macrostates, we find that both α2 Cterminus and β2-α2 loop can serve as important sites for the pH-induced PrP misfolding. The dynamical networks analysis with and without the protonation of His upon the change of pH indicates that the protonation of H187 drives the structural conversion in acidic environment by increasing structural flexibility of α2 C-terminus and thus reducing the interactions among the α2 C-terminus, the α1-β2 loop and the α2-α3 loop. 18


Page 18 of 46

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


Except for the α2 C-terminus and β2-α2 loop, the α1 is also found to contribute to the misfolding of S18 as reflected by the movement of α1 away from α2-α3 subdomain. Moreover, as indicated by S19, the disordered N-terminus can certainly interact with the globular domain and participate in the misfolding of PrP. As a whole, the obtained misfolding mechanism will be valuable to deepen our understanding of pathogenesis of prion diseases and can provide useful guidance for the future drug discovery. For example, a possible strategy to find small molecules inhibiting the conformational conversion of PrPC may be suggested by stabilizing the flexible α2 C-terminus based on our results.

Computational Methods Preparation of starting structures for MD simulations The fragment PrP90-231, without the N-terminal residue 23-89 included, was adopted to investigate the pH-induced PrP misfolding mechanism. Although the fragment 23-89 is also part of the disordered N-terminus and a lot of prion biology, e. g. copper binding, takes place at this region, this region may not serve as the key regulator for PrPSc formation since the fragment ~90-230 is reported to be the pathogenic and infectious core of PrPSc 60 and is also the main region responsible for the proteinase K-resistant property of PrPSc 61.The initial structure of human PrP90-231 (denoted as huPrP90-231) was from the Protein Data Bank (PDB ID: 2LSB), which is obtained by solution NMR method7. To reduce the initial influence of the disordered N-terminus, model 2 with the extended N-terminus away from the globular C-terminus 19



was chosen as the start structure and its representative structure was shown in Figure 1. As displayed, the structure contains the disordered N-terminus (displayed in silver) and the globular C-terminus (displayed in purple). All the histidines (His), including H96, H111, H140, H155, H177 and H187, were displayed. For simulation at neutral condition, all the His residues were kept neutral with the Nε mono-protonated except for His-140, which was mono-protonated on Nδ24. For simulation at acidic condition, we kept only all the His residues double protonated on both Nδ and Nε (all titratable side chains were charged) to imitate the acidic environment as done by Kamp et al 23, 24. We did not utilize constant pH simulations since among all His, H187 is the only buried residue with the smallest calculated pKa value (~4.72± 0.11)62, while other His are all located at the surface of PrPC, meaning that they can be all titrated once H187 is protonated. In addition to the titratable side chains of His, the calculated pKa of Asp and Glu acids are ~3 and ~462. Therefore, it is reasonable to expect these residues to be deprotonated when keep only all His protonated. More importantly, the reported lowest pH value of endosomes is about 4.363, corresponding approximately to the pH for the titration of H187. As thus, the approximation for fixing the protonation state of all His to imitate acidic pH may not cause significant bias for relevant states.

Conventional MD simulations of huPrP90-231 at neutral and acidic condition To study the effects of pH on the overall structure of native PrPC, the conventional MD simulations were performed firstly for huPrP90-231 at the neutral and acidic 20


Page 20 of 46

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


condition, respectively. Three parallel runs for each system were performed to confirm the reliability of obtained data. All MD simulations were performed using the Amber 14 package64 with the Amber ff99SB force field65. The starting structure was then solvated into a cubic periodic box using the TIP3P water model66 and the box edges were set at least 10 Å around protein. To keep the electroneutrality of systems, one Clion was added to the neutral system and seven Cl- ions were added to the acidic system. After that, 2500 steps of steepest decent minimization followed by 2500 steps of conjugate gradient minimization were performed for each system to eliminate unnatural collision. The systems were then warmed up from 0 to 310 K in the NVT ensemble by keeping the protein constrained and temperature was controlled by the Langevin thermostat. Subsequently, 1 ns equilibration MD simulations were carried out in the NPT ensemble and followed by 500 ns production MD simulations. A 2 fs time step was used to integrate the equations of motion. During the simulation, SHAKE algorithm67 was utilized to constrain the hydrogen-involved bonds and the particle mesh Ewald method68 was used for the calculation of electrostatic contributions to the non-bonded interactions with non-bonded cutoff distance set to 10 Å.

AMD simulation of huPrP90-231 at acidic condition As we discussed above, the energy barriers of PrP misfolding between states remain high and the transition process remains slow even under pathogenic conditions. Therefore, in order to ensure the adequate sampling in the conformational space of PrP misfolding at low pH, the AMD simulation

34, 69

were performed. In this method, the

21



Page 22 of 46

potential energy surface is altered by adding a bias potential to the true potential such that the rates from potential wells are enhanced with the shape of potential landscape unaffected34. The relationship between the modified potential V*(r), true potential V(r) and bias potential ∆V(r) can be described as:

{

V(r), V(r) ≥ E, V ∗ (r) = V(r) + ∆V(r), V(r) < 𝐸.

(1)

E represents for the reference energy. That is, when the true potential V(r) is below a certain E, a nonnegative boost potential ∆V(r) will be added to V(r). The choice of ∆V(r) is given by: (E ― V(r))2 ∆V(r) = α + (E ― V(r))

(2)

Where α is the acceleration factor. Generally, the potential energy surface is flattened as the decreases of α. Despite the introduced bias potential, one can also recover the original potential landscape by reweighting of each configuration sampled on the modified potential energy70. However, it is worth noting that the boost potential weakens the contribution of potential energy to the free energy change, which enhances the contribution of conformational entropy. Therefore, states may tend to be distributed in high entropy regions in AMD simulation71. To reduce the influence of initial conformations, diverse structures were used as input structure for AMD simulations. The initial structures were extracted from cluster analysis of trajectory run1 at acidic condition. The self-organizing maps(SOM) algorithm72, which is based on pairwise similarity (measured by RMSD), was used to generate clusters. Only the representative structures of the six most populated clusters were used as the initial structures to perform AMD simulations. All His residues were 22


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


kept double protonated to imitate the acidic environment. Dual-boost AMD simulation was used in this work, which the bias potential is applied to both the total boost potential and the dihedral boost. Before AMD simulation, 5 ns conventional equilibration MD simulations was performed firstly to determine the parameters of dual-boost AMD simulations (Edihed, αdihed; Etotal, αtotal), the parameters calculated for globular proteins were as follow69, 70: Edihed = Vdihedavg +3.5Nres

αdihed =

Etotal = Vtotalavg +0.175Natoms

3.5Nres 5

αtotal = 0.175Natoms

(3) (4)

where Nres is number of protein residues, Natoms is the total atom number and Vdihed_avg and Vtotal_avg are the average dihedral and total potential energies calculated from 5 ns conventional MD simulations, respectively. Later, 1 μs AMD simulation was conducted for each input structure and six trajectories (6 μs in total) were obtained. Based on these obtained trajectories, we then investigated the pH-induced PrP misfolding mechanism deeply.

Corresponding Author *Mailing address: School of Pharmacy, Lanzhou University, Lanzhou 730000, China. E-mail: [email protected] Author Contributions H. L. and X. Y. conceived the project. H. L and S. Z designed the experiments. S. Z., D. S. and X. L. carried out the research and analysis of data. S. Z., H. L. and L. D. wrote the paper. 23



Funding This work is supported by the National Nature Science Foundation of China (Grant No. 21675070) and the Fundamental Research Funds for the Central Universities (Grant No. lzujbky-2017-k24). Notes The authors declare no competing financial interest. Supporting information Text: Construction of MSM; Validation of MSM; Detailed steps for MSM construction and validation. Figure & Table: Secondary structure analysis of conventional trajectories; Implied relaxation timescale plot for MSM; Chapman-Kolmogorov tests for MSM; β-sheet structure formed at the α2 C-terminus; Structural conversion of β2-α2 loop; Dynamical network analysis for native state, S18 and S19, respectively; Superposed structures of S18 and S19 relative to native PrP; Node connections of residue His;

References 1. Collinge, J. (2001) Prion diseases of humans and animals: their causes and molecular basis, Annu. Rev. Neurosci. 24, 519-550. 2. Soto, C., Estrada, L., and Castilla, J. (2006) Amyloids, prions and the inherent infectious nature of misfolded protein aggregates, Trends Biochem. Sci. 31, 150-155. 3. Prusiner, S. B. (1998) Prions, Proce. Natl. Acad. Sci. U.S.A. 95, 13363-13383. 4. Zahn, R., Liu, A., Lührs, T., Riek, R., von Schroetter, C., García, F. L., Billeter, M., 24


Page 24 of 46

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


Calzolai, L., Wider, G., and Wüthrich, K. (2000) NMR solution structure of the human prion protein, Proce. Natl. Acad. Sci. U.S.A. 97, 145-150. 5. Knaus, K. J., Morillas, M., Swietnicki, W., Malone, M., Surewicz, W. K., and Yee, V. C. (2001) Crystal structure of the human prion protein reveals a mechanism for oligomerization, Nat. Struct. Mol. Biol. 8, 770-774. 6. Lee, S., Antony, L., Hartmann, R., Knaus, K. J., Surewicz, K., Surewicz, W. K., and Yee, V. C. (2010) Conformational diversity in prion protein variants influences intermolecular β‐sheet formation, EMBO J. 29, 251-262. 7. Biljan, I., Giachin, G., Ilc, G., Zhukov, I., Plavec, J., and Legname, G. (2012) Structural basis for the protective effect of the human prion protein carrying the dominant-negative E219K polymorphism, Biochem. J. 446, 243-251. 8. Pan, K.-M., Baldwin, M., Nguyen, J., Gasset, M., Serban, A., Groth, D., Mehlhorn, I., Huang, Z., Fletterick, R. J., and Cohen, F. E. (1993) Conversion of alpha-helices into beta-sheets features in the formation of the scrapie prion proteins, Proce. Natl. Acad. Sci. U.S.A. 90, 10962-10966. 9. Baskakov, I. V., Legname, G., Baldwin, M. A., Prusiner, S. B., and Cohen, F. E. (2002) Pathway complexity of prion protein assembly into amyloid, J. Biol. Chem. 277, 21140-21148. 10. Singh, J., and Udgaonkar, J. B. (2016) Unraveling the molecular mechanism of pHinduced misfolding and oligomerization of the prion protein, J. Mol. Biol. 428, 13451355. 11. Hart, T., Hosszu, L. L., Trevitt, C. R., Jackson, G. S., Waltho, J. P., Collinge, J., 25



and Clarke, A. R. (2009) Folding kinetics of the human prion protein probed by temperature jump, Proce. Natl. Acad. Sci. U.S.A. 106, 5651-5656. 12. Swietnicki, W., Morillas, M., Chen, S. G., Pierluigi Gambetti, A., and Surewicz, W. K. (2000) Aggregation and Fibrillization of the Recombinant Human Prion Protein huPrP90−231, Biochemistry 39, 424-431. 13. Srivastava, K. R., and Lapidus, L. J. (2017) Prion protein dynamics before aggregation, Proce. Natl. Acad. Sci. U.S.A. 114, 3572-3577. Sanghera, N., Wall, M., Vénien-Bryan, C., and Pinheiro, T. J. (2008) Globular and prefibrillar prion aggregates are toxic to neuronal cells and perturb their electrophysiology, BBA-Proteins and Proteom. 1784, 873-881. 15. Sengupta, I., Bhate, S. H., Das, R., and Udgaonkar, J. B. (2017) Salt-Mediated Oligomerization of the Mouse Prion Protein Monitored by Real-Time NMR, J. MolM Biol. 429, 1852-1872. 16. Singh, J., Kumar, H., Sabareesan, A. T., and Udgaonkar, J. B. (2014) Rational stabilization of helix 2 of the prion protein prevents its misfolding and oligomerization, J. Am. Chem. Soc. 136, 16704-16707. 17. Honda, R. P., Yamaguchi, K.-i., and Kuwata, K. (2014) Acid-induced molten globule state of a prion protein crucial role of strand 1-helix 1-strand 2 segment, J. Biol. Chem. 289, 30355-30363. 18. Moulick, R., and Udgaonkar, J. B. (2017) Identification and structural characterization of the precursor conformation of the prion protein which directly initiates misfolding and oligomerization, J. Mol. Biol. 429, 886-899. 26


Page 26 of 46

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


19. Caldarulo, E., Barducci, A., Wüthrich, K., and Parrinello, M. (2017) Prion protein β2-α2 loop conformational landscape, Proc. Natl. Acad. Sci. U.S.A. 114, 9617-9622. 20. Paz, S. A., Vandeneijnden, E., and Abrams, C. F. (2017) Polymorphism at 129 dictates metastable conformations of the human prion protein N-terminal β-sheet, Chem. Sci.8, 1225-1232. 21. Chakroun, N., Fornili, A., Prigent, S., Kleinjung, J., Dreiss, C., Rezaei, H., and Fraternali, F. (2013) Decrypting Prion Protein Conversion into a β-RichConformer by Molecular Dynamics, J. Chem. Theory Comput. 9, 2455-2465. 22. Guo, J., Ning, L., Ren, H., Liu, H., and Yao, X. (2012) Influence of the pathogenic mutations T188K/R/A on the structural stability and misfolding of human prion protein: insight from molecular dynamics simulations, Biochim Biophys Acta 1820, 116-123. 23. Kamp, M. W. V. D., and Daggett, V. (2010) Influence of pH on the Human Prion Protein: Insights into the Early Steps of Misfolding, Biophys. J. 99, 2289-2298. 24. Chen, W., Mw, V. D. K., and Daggett, V. (2014) Structural and dynamic properties of the human prion protein, Biophys. J. 106, 1152-1163. 25. Singh, R. K., Chamachi, N. G., Chakrabarty, S., and Mukherjee, A. (2017) Mechanism of Unfolding of Human Prion Protein, J. Phys. Chem. B 121, 550-564. 26. Demarco, M. L., Daggett, V., and Fersht, A. (2004) From Conversion to Aggregation: Protofibril Formation of the Prion Protein, Proc. Natl. Acad. Sci. U.S.A. 101, 2293-2298. 27. DeMarco M L, D. V. (2007) Molecular Mechanism for Low pH Triggered Misfolding of the Human Prion Protein, Biochemistry 46, 3045-3054. 27



28. Garrec, J., Tavernelli, I., and Rothlisberger, U. (2013) Two Misfolding Routes for the Prion Protein around pH 4.5, Plos Comput. Biol. 9, e1003057-e1003057. 29. Moulick, R., Goluguri, R. R., and Udgaonkar, J. B. (2017) Role of Folding Intermediates in Initiating Aggregation of the Prion Protein, FASEB J 31, 763.766763.766. 30. Apetri, A. C., Maki, K., Roder, H., and Surewicz, W. K. (2006) Early intermediate in human prion protein folding as evidenced by ultrarapid mixing experiments, J. Am. Chem. Soc. 128, 11673-11678. 31. Chamachi, N. G., and Chakrabarty, S. (2017) Temperature Induced Misfolding in Prion Protein: Evidences of Multiple Partially Disordered States Stabilized by Nonnative Hydrogen Bonds, Biochemistry 56, 833-844. 32. Moulick, Roumita, Udgaonkar, Jayant, and nbsp. (2014) Thermodynamic Characterization of the Unfolding of the Prion Protein, Biophys. J. 106, 410-420. 33. Choi, J. K., Cali, I., Surewicz, K., Kong, Q., Gambetti, P., and Surewicz, W. K. (2016) Amyloid fibrils from the N-terminal prion protein fragment are infectious, Proc. Natl. Acad. Sci. U.S.A. 113, 13851-13856. 34. Hamelberg, D., Mongan, J., and McCammon, J. A. (2004) Accelerated molecular dynamics: a promising and efficient simulation method for biomolecules, J. Chem. Phys. 120, 11919-11929. 35. Bowman, G. R., Huang, X., and Pande, V. S. (2009) Using generalized ensemble simulations and Markov state models to identify conformational states, Methods 49, 197-201. 28


Page 28 of 46

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


36. Noé, F., and Fischer, S. (2008) Transition networks for modeling the kinetics of conformational change in macromolecules, Curr. Opin. Struct. Biol. 18, 154-162. 37. Bowman, G. R., Pande, V. S., and Noé, F. (2013) An introduction to Markov state models and their application to long timescale molecular simulation, Vol. 797, Springer Science & Business Media. 38. Bjorndahl, T. C., Zhou, G. P., Liu, X., Perezpineiro, R., Semenchenko, V., Saleem, F., Acharya, S., Bujold, A., Sobsey, C. A., and Wishart, D. S. (2011) Detailed biophysical characterization of the acid-induced PrP(c) to PrP(β) conversion process, Biochemistry 50, 1162-1173. 39. Sabareesan, A., and Udgaonkar, J. B. (2016) Pathogenic mutations within the disordered palindromic region of the prion protein induce structure therein and accelerate the formation of misfolded oligomers, J. Mol. Biol. 428, 3935-3947. 40. De Simone, A., Zagari, A., and Derreumaux, P. (2007) Structural and hydration properties of the partially unfolded states of the prion protein, Biophys. J. 93, 12841292. 41. Alred, E. J., Lodangco, I., Gallaher, J., and Hansmann, U. H. (2018) Mutations alter RNA-mediated conversion of human prions, ACS omega 3, 3936-3944. 42. Alred, E. J., Nguyen, M., Martin, M., and Hansmann, U. H. (2017) Molecular dynamics simulations of early steps in RNA‐mediated conversion of prions, Protein Sci. 26, 1524-1534. 43. Geoghegan, J. C., Miller, M. B., Kwak, A. H., Harris, B. T., and Supattapone, S. (2009) Trans-dominant inhibition of prion propagation in vitro is not mediated by an 29



accessory cofactor, PLoS Pathog. 5, e1000535. 44. Perrier, V., Kaneko, K., Safar, J., Vergara, J., Tremblay, P., DeArmond, S. J., Cohen, F. E., Prusiner, S. B., and Wallace, A. C. (2002) Dominant-negative inhibition of prion replication in transgenic mice, Proc. Natl. Acad. Sci. U. S. A. 99, 13079-13084. 45. Miao, Y., Nichols, S. E., and McCammon, J. A. (2014) Free energy landscape of G-protein coupled receptors, explored by accelerated molecular dynamics, Phys. Chem. Chem. Phys. 16, 6398-6406. 46. Pérez-Hernández, G., Paul, F., Giorgino, T., De Fabritiis, G., and Noé, F. (2013) Identification of slow molecular order parameters for Markov model construction, J. Chem. Phys. 139, 07B604_601. 47. Röblitz, S., and Weber, M. (2013) Fuzzy spectral clustering by PCCA+: application to Markov state models and data classification, Adv. Data Anal. Classif. 7, 147-179. 48. Walsh, P., Simonetti, K., and Sharpe, S. (2009) Core structure of amyloid fibrils formed by residues 106–126 of the human prion protein, Structure 17, 417-426. 49. Jones, C. E., Abdelraheim, S. R., Brown, D. R., and Viles, J. H. (2004) Preferential Cu2+ coordination by His96 and His111 induces β-sheet formation in the unstructured amyloidogenic region of the prion protein, J. Biol. Chem. 279, 32018-32027. 50. Dima, R., and Thirumalai, D. (2002) Exploring the propensities of helices in PrP C to form β sheet using NMR structures and sequence alignments, Biophys. J. 83, 12681280. 51. Kurt, T. D., Jiang, L., Bett, C., Eisenberg, D., and Sigurdson, C. J. (2014) A proposed mechanism for the promotion of prion conversion involving a strictly 30


Page 30 of 46

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


conserved tyrosine residue in the β2-α2 loop of PrPC, J. Biol. Chem. 289, 10660-10667. 52. Giachin, G., Biljan, I., Ilc, G., Plavec, J., and Legname, G. (2013) Probing early misfolding events in prion protein mutants by NMR spectroscopy, Molecules 18, 94519476. 53. Meli, M., Gasset, M., and Colombo, G. (2011) Dynamic diagnosis of familial prion diseases supports the β2-α2 loop as a universal interference target, PloS one 6, e19093. 54. Eargle, J., and Luthey-Schulten, Z. (2012) NetworkView: 3D display and analysis of protein· RNA interaction networks, Bioinformatics 28, 3000-3001. 55. Humphrey, W., Dalke, A., and Schulten, K. (1996) VMD: visual molecular dynamics, J. Mol. Graph. 14, 33-38. 56. Sethi, A., Eargle, J., Black, A. A., and Luthey-Schulten, Z. (2009) Dynamical networks in tRNA: protein complexes, Proc. Natl. Acad. Sci. U.S.A. 106, 6620-6625. 57. VanWart, A. T., Eargle, J., Luthey-Schulten, Z., and Amaro, R. E. (2012) Exploring residue component contributions to dynamical network models of allostery, J. Chem. Theory Comput. 8, 2949-2961. 58. Takada, L. T., Kim, M. O., Cleveland, R. W., Wong, K., Forner, S. A., Gala, I. I., Fong, J. C., and Geschwind, M. D. (2017) Genetic prion disease: Experience of a rapidly progressive dementia center in the United States and a review of the literature, Am. J. Med. Genet. B 174, 36-69. 59. Hosszu, L. L., Tattum, M. H., Jones, S., Trevitt, C. R., Wells, M. A., Waltho, J. P., Collinge, J., Jackson, G. S., and Clarke, A. R. (2010) The H187R mutation of the human prion protein induces conversion of recombinant prion protein to the PrPSc-like form, 31



Biochemistry 49, 8729-8738. 60. Legname, G., Baskakov, I. V., Nguyen, H.-O. B., Riesner, D., Cohen, F. E., DeArmond, S. J., and Prusiner, S. B. (2004) Synthetic mammalian prions, Science 305, 673-676. 61. Zou, W.-Q., Capellari, S., Parchi, P., Sy, M.-S., Gambetti, P., and Chen, S. G. (2003) Identification of novel proteinase K-resistant C-terminal fragments of PrP in Creutzfeldt-Jakob disease, J. Biol. Chem. 305, 40429-40436. 62. Campos, S. R., Machuqueiro, M., and Baptista, A. M. (2010) Constant-pH molecular dynamics simulations reveal a β-rich form of the human prion protein, The J. Phys. Chem. B 114, 12692-12700. 63. Lee, R. J., Wang, S., and Low, P. S. (1996) Measurement of endosome pH following folate receptor-mediated endocytosis, BBA-Mol. Cell Res. 1312, 237-242. 64. Case, D. A., Babin, V., Berryman, J. T., Betz, R. M., Cai, Q., Cerutti, D. S., Chealtham, T. E., Darden, T. A., Duke, R. E., Gohlke, H., Goetz, A. W., Gusarov, S., Homeyer, N., Janowski, P., Kaus, J., Kolossváry, I., ovalenko, A., Lee, T. S., LeGrand, S., Luchko, T., Luo, R., Madej, B., Merz, K. M., Paesani, F., Roe, D. R., Roitberg, A., Sagui, C., Salomon-Ferrer, R., Seabra, G., Simmerling, C. L., Smith, W., Swails, J., Wang, J., Wolf, R. M., Wu, X., Kollman, P. A., (2014) AMBER 2014, University of California, San Francisco. 65. Hornak, V., Abel, R., Okur, A., Strockbine, B., Roitberg, A., and Simmerling, C. (2006) Comparison of multiple Amber force fields and development of improved protein backbone parameters, Proteins 65, 712-725. 32


Page 32 of 46

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


66. Jorgensen, W. L., Chandrasekhar, J., Madura, J. D., Impey, R. W., and Klein, M. L. (1983) Comparison of simple potential functions for simulating liquid water, J. Chem. Phys. 79, 926-935. 67. Ryckaert, J.-P., Ciccotti, G., and Berendsen, H. J. (1977) Numerical integration of the cartesian equations of motion of a system with constraints: molecular dynamics of n-alkanes, J. Comput. Phys. 23, 327-341. 68. Darden, T., York, D., and Pedersen, L. (1993) Particle mesh Ewald: An N⋅ log (N) method for Ewald sums in large systems, J. Chem. Phys. 98, 10089-10092. 69. Pierce, L. C., Salomon-Ferrer, R., Augusto F. de Oliveira, C., McCammon, J. A., and Walker, R. C. (2012) Routine access to millisecond time scale events with accelerated molecular dynamics, J. Chem. Theory Comput. 8, 2997-3002. 70. Miao, Y., Sinko, W., Pierce, L., Bucher, D., Walker, R. C., and McCammon, J. A. (2014) Improved reweighting of accelerated molecular dynamics simulations for free energy calculation, J. Chem. Theory Comput. 10, 2677-2689. 71. Lan, P., Tan, M., Zhang, Y., Niu, S., Chen, J., Shi, S., Qiu, S., Wang, X., Peng, X., and Cai, G. (2018) Structural insight into precursor tRNA processing by yeast ribonuclease P, Science 362, eaat6678. 72. Vesanto, J., and Alhoniemi, E. (2000) Clustering of the self-organizing map, IEEE Trans. Neural Netw. 11, 586-600.

33



Figure captions Figure 1 The initial simulation structure of huPrP90-231 (PDB ID: 2LSB, model 2), consisting of both disordered N-terminus (silver) and globular C-terminus (purple). The detailed secondary structures are shown as cartoon in below with their corresponding residue number. All six histidines, which are mono-protonated (~pH7) or double protonated (~pH5) in our work, are also displayed in the structure. Figure 2 The monitoring of structural features of PrP90-231 calculated from each parallel run, including the RMSDs of Cα atoms relative to the globular domain from residue 128 to 228 of initial structure, and the RMSFs of Cα atoms as a function of residue number. Figure 3. The superposed structures of last snapshot of each run relative to the initial PrP structure. The gray structures represent the initial structure, the ice blue structures represent the last snapshot of runs in neutral system and the magenta structures 34


Page 34 of 46

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


represent the last snapshot of runs in acidic system. Figure 4 Free energy landscapes (FELs) of PrP at neutral pH (blue), and at acidic pH (magenta). The representative structures superposed to the initial globular domain of PrP (shown in purple) are also displayed for each energy basin. Figure 5 Graphical representation of secondary structure analysis for each AMD trajectory. Figure 6 Free energy landscapes (FELs) for all sampled conformations (all AMD trajectories) and for each AMD trajectory. Figure 7 Free energy landscapes as a function of the first two independent components based on the tICA dimension reduction of all AMD trajectories. Figure 8 The important misfolded states S18 and S19 in MSM. (a) the representative structure of S18; (b-c) the probabilities of each residue to adopt different secondary structures in S18; (d) the representative structure of S19; (e-f) the probabilities of each residue to adopt different secondary structures in S19. Figure 9 The node connections of H187 in different states. The “nodes” represent for the Cα atoms residues and the “edges” are used to connect pairs of “nodes” if the corresponding residues were in contact. Conventional MD trajectory of the last 50 ns at neutral condition was used to perform dynamical network analysis at pH7. Figure 10 Representative structural characterization of typical macrostates. (a) The relative location of residues around H187 in the native structure. (b) SASA calculations of residue H187 and hydrophobic core residue F198 in different states. (c) Structural changes of H187 and F198 in different states. 35



Figure 1

36


Page 36 of 46

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


Figure 2

37



Figure 3

38


Page 38 of 46

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


Figure 4

39



Figure 5

40


Page 40 of 46

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


Figure 6

41



Figure 7

42


Page 42 of 46

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


Figure 8

43



Figure 9

44


Page 44 of 46

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


Figure 10

45



For Table of Contents Use Only

The pH-induced Misfolding Mechanism of Prion Protein: Insights from Microsecond Accelerated Molecular Dynamics Simulations Shuangyan Zhou, Danfeng Shi, Xuewei Liu, Xiaojun Yao, Lin-Tai Da, Huanxiang Liu

46


Page 46 of 46

pH-Induced Misfolding Mechanism of Prion Protein: Insights from

Recommend Documents