Thermodynamic and kinetic stability of the Josephin Domain closed arrangement: evidences from replica exchange molecular dynamics
- Gianvito Grasso†1,
- Jack A. Tuszynski3,
- Umberto Morbiducci2,
- Ginevra Licandro1,
- Andrea Danani1 and
- Marco A. Deriu†1Email author
© The Author(s). 2017
Received: 15 September 2016
Accepted: 21 December 2016
Published: 19 January 2017
Molecular phenomena driving pathological aggregation in neurodegenerative diseases are not completely understood yet. Peculiar is the case of Spinocerebellar Ataxia 3 (SCA3) where the conformational properties of the AT-3 N-terminal region, also called Josephin Domain (JD), play a key role in the first step of aggregation, having the JD an amyloidogenic propensity itself. For this reason, unraveling the intimate relationship between JD structural features and aggregation tendency may lead to a step forward in understanding the pathology and rationally design a cure. In this connection, computational modeling has demonstrated to be helpful in exploring the protein molecular dynamics and mechanism of action.
Conformational dynamics of the JD is here finely investigated by replica exchange molecular dynamics simulations able to sample the microsecond time scale and to provide both a thermodynamic and kinetic description of the protein conformational changes. Accessible structural conformations of the JD have been identified in: open, intermediate and closed like arrangement. Data indicated the closed JD arrangement as the most likely protein arrangement. The protein transition from closed toward intermediate/open states was characterized by a rate constant higher than 700 ns. This result also explains the inability of classical molecular dynamics to explore transitions from closed to open JD configuration on a time scale of hundreds of nanoseconds.
This work provides the first kinetic estimation of the JD transition pathway from open-like to closed-like arrangement and vice-versa, indicating the closed-like arrangement as the most likely configuration for a JD in water environment. More widely, the importance of our results is also underscored considering that the ability to provide a kinetic description of the protein conformational changes is a scientific challenge for both experimental and theoretical approaches to date.
This article was reviewed by Oliviero Carugo, Bojan Zagrovic.
KeywordsAtaxin Replica exchange molecular dynamics Neurodegenerative Josephin Domain Protein plasticity Kinetics Thermodynamics
Protein conformational transition can be described as a complex search for a global energy minimum on a free energy surface, which depends on a huge number of molecular interactions and environmental factors [1–3]. In addition to their functional native states, globular proteins generally adopt intermediate conformational states corresponding to local minima on the free energy surface. Some of the energetically favorable alternatives may enable the exposure of hydrophobic protein domains, thus increasing the risk of both aberrant aggregation and related pathological transformations . This is the case of amyloidogenic proteins, where a direct correlation between thermodynamic stability and propensity to amyloid fibril formation has been convincingly demonstrated [4, 5]. As a consequence, the kinetic and thermodynamic estimation of the protein conformational changes represents a significant scientific challenge and an important contribution to the comprehension of the molecular basis of amyloidogenic aggregation.
Recently, the conformational stability of the Josephin Domain (JD) has gained considerable attention in the research community. The JD is the N-terminal region of the Ataxin-3 (AT3) protein which is responsible for the spinocerebellar Ataxia 3 (SCA3) , a polyglutamine (polyQ) disease also known as Machado Joseph Disease (MJD).
Although in polyQ diseases the expanded polyQ tract is considered the main cause for protein misfolding and aggregation [7–16], the JD structural features play a pivotal role in driving the aggregation propensities and toxicity of AT3 protein [17–24]. In this regard, experimental studies have demonstrated that the first step of AT3 fibrillogenesis is JD-mediated [22, 25–27].
Several models of the Josephin Domain of Ataxin 3, solved by NMR have become recently available [20, 28–30]; however, these structures strongly differ for the hairpin conformation (region α2-α3, residues Val31-Leu62). Indeed, whereas the 1YZB  and 2JRI  models show a hairpin region that protrudes out into solution, the “closed” 2AGA  and “half-closed” 2DOS  models are characterized by the hairpin packed against the protein globular structure . These JD models have been the subject of the several computational and experimental studies [31–33]. A recent investigation on JD conformational changes using both Classical MD and Metadynamics has estimated the whole free energy profile of the JD, demonstrating the closed conformation as the most representative for JD in water environment .
Computational modeling has been confirmed as a powerful tool to acquire indications and suggest hypothesis to be further tested by experiments [34–46]. For example, in one of our recent work , it was highlighted the propensity of the JD region α4 (and in particular Leu84-Trp87) to undergo high conformational changes as a consequence of the JD-JD binding. In a greater detail, α4 conformational changes, with consequent exposure of α4, was detected. Interestingly, both the α4 helical loss and its solvent exposure were observed only after the binding event. On the basis of our in silico results, we hypothesized a double step process involved in JD dimerization. Moreover, we suggested that the peptides sequence Lue84-Trp87 may be relevant for aberrant aggregation in a second step of the JD-JD binding, whereas the first step is mainly mediated by other residues such as Arg101. In this connection a recent experimental work  highlighted a transient local unfolding of α4, and consequent exposure of backbone amides to the solvent, able to trigger the AT-3 aggregation.
In the present work, additional evidence of the thermodynamic stability of the JD closed-like conformation is provided as a result of an extensive computational investigation concerning the JD conformational changes by Replica Exchange Molecular Dynamics. Moreover, a kinetic estimation of the conformational transition between the JD open and closed arrangements is reported here. The importance of the presented results is also underscored by the computational effort needed to provide kinetic description of the protein conformational changes, a scientific challenge for both experimental and theoretical approaches to date.
The 1YZB model [28, 33] was selected as starting structure for the present work. The 1YZB model was determined by NMR technique and deeply validated in literature [28, 33]. Moreover the 1YZB has been considered as starting structure in all previous computational investigations focused on the JD of At3 [20, 31, 32, 47, 48].
Replica exchange molecular dynamics
The 1YZB model was solvated in a dodecahedron box where the minimum distance between the protein and the edge of the box was 1 nm, resulting in a molecular system of about 40,000 interacting particles. The net charge of the system was neutralized at 0.15 M NaCl concentration. Energy minimization (1000 steps of Steepest Descent algorithm) and 50 ps of MD simulation with a Berendsen barostat  and a v-rescale thermostat  were performed to equilibrate the system at 310 K and 1 atm with time constants of τT = 0.1 ps and of τP = 0.2 ps, respectively. Replica Exchange Molecular Dynamics (REMD)  was carried out to explore the conformational ensembles of the JD. In detail, 128 replicas were simulated for temperatures ranging from 300 to 602 K in the NVT ensemble, as in previous works [52–54]. Temperatures were distributed according to an exponential spacing law, as suggested by previous studies [55, 56], keeping the overlap of the potential energy distributions constant across the temperature space (Section S1.1 of Additional file 1). The exchange attempt time interval was set to 2 ps. Each replica was simulated for 50 ns, obtaining a cumulative simulation time of 6.4 μs. AMBER99-ILDN force-field [57–59] and water TIP3P model  were chosen to describe the system topology. Electrostatic interactions were calculated at every step with the Particle-Mesh Ewald method with a short-range electrostatic interaction cut off of 1.2 nm. A cut-off of 1.2 nm was also applied to Lennard-Jones interactions. The v-rescale thermostat  was used for each replica to keep temperature constant with a time constant of τT = 0.1. The LINCS algorithm  approach allowed to apply a 2 fs time step integration strategy. GROMACS 4.6 package was used for all MD simulations and data analysis . AMBER99-ILDN force-field [57–59] and water TIP3P model  were chosen to describe the system topology. The Visual Molecular Dynamics (VMD)  package was used for the visual inspection of the simulated systems. Analysis of secondary structure (SS) dynamics was performed by applying the STRIDE software [64, 65]. GROMOS clustering approach  was applied to the Replica Exchange Molecular Dynamics trajectory at 310 K in order to get insight into the likelihood of JD conformational arrangements.
Then, the kinetics parameters E A u , E A f , A f , A u were obtained by numerically minimizing the functional Χ 2. A detailed description of the method and parameters is reported in a previous publication .
In the present work, the description of the JD conformational space was carried out by using two different indicators already known to be appropriate for describing the JD transition pathway, (1) the Radius of Gyration (RG) and (2) the distance between regions α3 (Asp57-Leu62) and α5 (Pro97-Arg101) [31, 32, 47, 48]. In this manner, an estimation of the forward and backward rate constants for JD conformational transition, together with the activation energies, was obtained at 310 K .
In agreement with a very recent in silico study , 2AGA, 2DOS and 2JRI models lie, in terms of employed descriptors, in regions regularly sampled by REMD, differently from what observed in case of 1YZB model. It is worth mentioning that these observations are in close agreement with both classical MD and metadynamics simulations . In this regard, it is also necessary to clarify that the findings of this study should not be intended as a quality check of 1YZB model, which has been already performed by applying appropriate methodologies . Nonetheless the findings of this study might suggest that the half-open structure is stabilized under specific conditions, e.g., the presence of an interacting protein. This hypothesis needs to be carefully evaluated by a dedicated computational exploration, by modeling the presence of ubiquitin or of specific environmental conditions (ion concentration, protonation state, temperature, etc.).
From the visual inspection of the JD arrangement corresponding to the mentioned conformational states (Fig. 2b), it can be observed that the centroid of the most populated cluster (obtained by using GROMOS  structure-based clustering approach) corresponds to the distribution peaks reported in Fig. 2a.
A detailed thermodynamic and kinetic description of protein conformational transition could markedly enrich the arsenal of knowledge which is necessary to a deep comprehension of the molecular basis of amyloidogenic aggregation. Prompted by evidences indicating an intimate interconnection between the JD plasticity and AT-3 aggregation propensity , this work focused on the application of computational molecular modeling to investigate JD conformational transition from a kinetic and thermodynamic point of view. In a greater detail, the JD conformational accessible states were thoroughly sampled by REMD simulations. Investigating JD conformational plasticity might be a key to deeply understand JD amyloidogenic properties and successfully design novel strategies targeting SCA3.
In this connection, several issues have been addressed by our work as listed in the following.
Kinetic estimation of Josephin Domain conformational transition
A kinetic description of the JD conformational changes is presented in this work, a novel aspect with respect to previous computational works focusing on JD conformational fluctuations [31, 32]. More specifically, kinetic data in terms of time constants, τ (Fig. 3), provide a further evidence that classical MD simulations may be unable to correctly sample the transition from JD closed to open conformations as also highlighted in previous studies [31, 47, 48]. Relevance of this kind of estimation is widely recognized, being protein conformational arrangements, in general, closely related to protein physiological function and a pathological behavior. This is also the case of the Josephin Domain. In this connection, kinetic analysis from our REMD simulations pointed out that, in absence of interacting proteins, the JD quickly (τI-O = 5.6 ± 0.7 ns) falls from an intermediate to a closed arrangement. This result has been confirmed also by a set of 100 independent unbiased MD (Additional file 1: S1.3). Instead the kinetics of the backward reaction is orders of magnitude slower (τO-I = 728.3 ± 170.9 ns).
Evidence for a three state pathway and thermodynamic stability of the Josephin Domain
Our data depict the JD conformational transition as a 3-state pathway. Cluster analysis on REMD trajectories at 310 K has highlighted that JD conformations fall in three main classes: a closed arrangement (C), an intermediate state (I) and an open-like structure (O) as shown in Figs. 2 and 3. The above-mentioned conformational states can be considered as free energy local minima separated by energy barriers. The quantification of those barriers allowed us to gain information on thermodynamic stability of accessible JD arrangements. In a greater detail, data in Fig. 3 clearly show how thermal fluctuations might in principle provide the sufficient energy for driving an open-to-closed transition, moving through the intermediate state. Conversely, the reverse pathway is an unlikely event which necessitates an energy supply of an order of magnitude higher than thermal energy (310 K). It is opinion of the authors that our data do not exclude the existence of an open JD conformation which might be more likely under specific environmental conditions such as different pH value (which may cause protonation/deprotonation of specific protein residues). Within this framework, a recent work suggested  that structural variations of the JD arrangement may be due to the different experimental conditions , i.e., 298 K, pH 6.4 (2AGA ) or pH 6.5 (1YZB ) or 303 K, pH 7.0 (2DOS ). Moreover, the presence of JD functional partners, such as Ubiquitin protein, may change the JD conformational space and the related free energy landscape, leading to a higher stability of the JD open conformation. Our statement is supported by recent experimental works , showing that the interaction of JD with its physiological partner Ubiquitin strongly influences JD accessible conformations and aggregation propensity. In conclusion, information coming from our work, considering only the JD, together with further studies evaluating the influence of ubiquitin or other JDs on the JD conformational arrangements will greatly help in better understanding molecular reasons behind physiological function and pathological behavior. Nevertheless, the open, closed and intermediate models obtained in the present study may be considered as starting point for the above mentioned further computational investigations.
Further evidences of the Josephin Domain closed arrangement stability
Here, REMD has been employed to explore accessible conformations of the JD of Ataxin 3. The obtained data strengthen the hypothesis of the closed-like arrangement as the most stable JD structure in water, as already suggested by previous investigations [31, 47]. It is interesting to notice that REMD simulations does not need any preliminary information regarding the molecular transition which were instead required to determine the collective variable in our previous work employing metadynamics driven by essential coordinates . However, thermodynamics and kinetic analyses from REMD provided information on free energy barriers and time constants without depicting the overall free energy landscape shown in our previous work . Taken together, the above mentioned computational evidences provide a complete and decisive picture of the conformational behavior characterizing the single JD of Ataxin 3 in water environment.
Further studies will involve the presence of interacting proteins, molecules, surfaces, or will consider specific point mutations. A change of the conformational space accessible to the JD [47, 48] and of the related free energy landscape is expected.
The present study draws the attention on both thermodynamics and kinetic stability of the JD closed arrangement, which has been estimated to represent roughly 100% of the JD folded fraction (Fig. 3) at 310 K. The ability to provide a kinetic and thermodynamic description of the JD conformational changes is hoped to trigger further valuable advances in Ataxia research. For example, the approach here employed might be used to clarify the influence of small molecules, natural binders, and environmental factors, on the JD structural conformation. On the other hand, ambient conditions that have already shown to affect JD aggregation dynamics and kinetics (i.e., variation in pH or temperature, point mutations [26, 47], interaction with binders [21, 29, 68], interaction with surfaces ) might be simulated to verify their potential effect on JD structure. Given the already known intimate relationship between the JD structural plasticity and aggregation propensity , the identification of specific JD amyloidogenic conformations might open new routes for the design of novel rational drugs able to drive the JD thermodynamic and kinetic stability toward specific non-amyloidogenic conformations.
In conclusion, data presented in this study can be considered as a first important step showing a working methodology to be extended, in the future, to evaluate how physiological partners or designed compounds may influence the JD conformational arrangments from the thermodynamic and kinetic point of view. This information might be useful for a better understanding of the molecular reasons behind the JD aggregation propensity or for developing novel aggregation inhibitors.
Reviewer’s report 1: Oliviero Carugo, University of Vienna, Austria
1. The manuscript submitted by Marco A. Deriu described a MD study of the Josephin domain of ataxin 3. With a computationally demanding Replica Exchange Molecular Dynamics, Deriu and colleagues were able to propose a mechanism of the open-close pathway and to estimate the thermodynamics and kinetics parameters of the path. Although interesting, this manuscript has a major problem. The open conformation of the 1YZB file of the Protein Data Bank was not observed amongst the computational models. Although the authors mention it in the manuscript, this is not enough. It is mandatory to examine in detail this discrepancy. It is possible that the NMR experimental structure is (partially) inaccurate. It is possible that the MD is (partially) inaccurate. It is possible that the physico-chemical conditions are different in the NMR experiment and in the simulation. Unless this point is completely clarified, I think that it is unnecessary to further review this manuscript.
As a second point, the reviewer said that “It is mandatory to examine in detail this discrepancy. It is possible that the NMR experimental structure is (partially) inaccurate. It is possible that the MD is (partially) inaccurate. It is possible that the physico-chemical conditions are different in the NMR experiment and in the simulation.”
For what concern the possible inaccuracy of the NMR experimental structures we would like to specify that our work is not oriented to demonstrate the “quality” of the 1YZB model, which was already evaluated in several high quality published experimental works [28, 33] in literature. On the other side, our MD simulations were performed following the state-of-the-art concerning atomistic Replica Exchange Molecular Dynamics protocols, which have widely demonstrated to be useful in describing the protein folding mechanism from the thermodynamic and kinetic point of view [39, 40, 55, 56, 69, 70].
However, the authors would like to better explain why 1YZB arrangement (open-like JD) is less sampled than the closed like JD. More than a limit of the work, this evidence represents in our opinion one of the most important results of the present work. It is opinion of the authors that our data do not exclude the existence of an open JD conformation which might be more likely under specific environmental conditions such as different pH value (which may cause protonation/deprotonation of specific protein residues). Within this framework, a recent work suggested  that structural variations of the JD arrangement may be due to the different experimental conditions , i.e., 298 K, pH 6.4 (2AGA ) or pH 6.5 (1YZB ) or 303 K, pH 7.0 (2DOS ).
More importantly, the presence of JD functional partners, such as Ubiquitin protein, may change the JD conformational space and the related free energy landscape, leading to a higher stability of the JD open conformation. Our assumption is supported by recent experimental work , showing that the interaction of JD with its physiological partner Ubiquitin strongly influences JD accessible conformations and aggregation propensity. We might expect a more sampled 1YZB in presence of ubiquitin. This investigation is a very demanding computational task and will be the subject of future research.
The authors would consider the above presented novel results as part of a further work more oriented in investigating the relationship between JD conformation and JD interaction with protein “partners”. However, we are more than willing to insert this part into the manuscript if it is so desired by the reviewer.
Several sentences have been added in the revised version of the manuscript (Discussion section), as requested by the reviewer:
“It is opinion of the authors that our data do not exclude the existence of an open JD conformation which might be more likely under specific environmental conditions such as different pH value (which may cause protonation/deprotonation of specific protein residues). Within this framework, a recent work suggested  that structural variations of the JD arrangement may be due to the different experimental conditions , i.e., 298 K, pH 6.4 (2AGA ) or pH 6.5 (1YZB ) or 303 K, pH 7.0 (2DOS ). Moreover, the presence of JD functional partners, such as Ubiquitin protein, may change the JD conformational space and the related free energy landscape, leading to a higher stability of the JD open conformation. Our statement is supported by recent experimental works , showing that the interaction of JD with its physiological partner Ubiquitin strongly influences JD accessible conformations and aggregation propensity. In conclusion, information coming from our work, considering only the JD, together with further studies evaluating the influence of ubiquitin or other JDs on the JD conformational arrangements will greatly help in better understanding molecular reasons behind physiological function and pathological behavior.”
Reviewer’s report 2: Bojan Zagrovic, Mediterranean Institute for Life Sciences, Croatia
1. The authors use replica exchange molecular dynamics simulations to study to open-to-close conformational transition of the Josephin Domain (JD) of Ataxin 3. While technically solid and biologically relevant, the paper still suffers from multiple deficiencies, which should be addressed prior to publication. A major criticism concerns the presentation of the simulated trajectories and the associated rates as relating to “folding” of the JD. Namely, what the authors examine is just the very few last steps in the complete folding mechanism of the JD, the transition from the open to closed arrangement, and not nearly anything related to the folding process of the molecule starting from the unfolded state. The text should be accurately rephrased, starting with the title, to better reflect this fact.
Author’s response: We thank the reviewer for highlighting this point. The authors agree that the protein conformational transition described in the present work from open to closed JD and viceversa, is not “ related to the folding process of the molecule starting from the unfolded state ”. For this reason, the authors propose to modify the title as follows:
“Thermodynamic and Kinetic Stability of the Josephin Domain Closed Arrangement: Evidences from Replica Exchange Molecular Dynamics”
Following the suggestions of the Reviewer, we have also accurately rephrased the misleading sentences in the revised version of the manuscript, when appropriate.
2. The main descriptors of conformational transition used in the analysis are radius of gyration and distance between regions alpha3 and alpha5. As one of the main challenges in the accurate determination of rates from folding simulations is the proper choice of definition of the folded and other states, the authors should analyze their trajectories from the perspective of other relevant order parameters, such as RMSD or number of native contacts.
3. Also, the authors should provide a detailed sensitivity analysis concerning the dependence of the obtained rates on the exact definition of the relevant macrostates.
Author’s response: The authors have analyzed the “ dependence of the obtained rates on the exact definition of the relevant macrostates” following the procedure reported in the reference literature [67, 73]. In detail, the error value corresponding to each thermodynamic and kinetic quantity estimated in the manuscript is calculated by varying the cutoff for identifying the protein conformational states (open, intermediate or closed, respectively). The previously mentioned approach has already demonstrated to be appropriate in literature , considering that the largest error is mainly related to the definition of the cutoff value . In detail, we performed our kinetic analysis by varying of 0.02 nm the Radius of Gyration threshold to discern between open/intermediate, and intermediate/closed. Figure 3 of the manuscript has been modified accordingly.
4. The Methods sections should be expanded such as to include relevant technical details (type of thermostat and barostat, settings for the two, ion concentration, details of PME calculations etc.)
Author’s response: The authors have added several sentences in the “Materials and Methods” section to include relevant technical details as requested by the reviewer:
“The 1YZB model was solvated in a dodecahedron box where the minimum distance between the protein and the edge of the box was 1 nm, resulting in a molecular system of about 40,000 interacting particles. The net charge of the system was neutralized at 0.15 M NaCl concentration. Energy minimization (1000 steps of Steepest Descent algorithm) and 50 ps of MD simulation with a Berendsen barostat  and a v-rescale thermostat  were performed to equilibrate the system at 310 K and 1 atm with time constants of τT = 0.1 ps and of τP = 0.2 ps, respectively.”
“Each replica was simulated for 50 ns, obtaining a cumulative simulation time of 6.4 μs. AMBER99-ILDN force-field [57–59] and water TIP3P model  were chosen to describe the system topology. Electrostatic interactions were calculated at every step with the Particle-Mesh Ewald method with a short-range electrostatic interaction cut off of 1.2 nm. A cut-off of 1.2 nm was also applied to Lennard-Jones interactions. The v-rescale thermostat  was used for each replica to keep temperature constant with a time constant of τT = 0.1. The LINCS algorithm  approach allowed to apply a 2 fs time step integration strategy. GROMACS 4.6 package was used for all MD simulations and data analysis .”
5. It is not clear how the energy barrier (p.7, l31) is evaluated. Is this a potential energy barrier or a free energy barrier?
Author’s response: We thank the reviewer for highlighting this point. The energy barriers reported in (p.7, L31) are free energy values of activation that contain also information about the entropic contribution. The previously mentioned values are computed following the procedure reported in the “Thermodynamic and kinetic estimation” of the Materials and Methods section of our paper, and implemented in the g_kinetics tool of GROMACS package. In order to better clarify this point, the authors have added few sentences in the results section as well as in the Caption of Fig. 3 :
“The kinetic and thermodynamic results shown in Fig. 3 are computed following the procedure reported in the Materials and Methods section of our paper, and implemented in the g_kinetics tool of GROMACS package. It is worth mentioning that the energy barrier reported in Fig. 3 represents free energy values of activation, containing also information about the entropic contribution. As a result, the closed JD arrangement represents the most energetically favorable configuration. In detail, transition between closed and intermediate states is characterized by a deep energy barrier (EC-I = 35.8 ± 0.6 kJ/mol), in close agreement with the recently obtained computational results .”
Caption Fig. 3:
“Visual inspection of the JD conformational arrangements corresponding to closed (C), intermediate (I) and open (O) state together with the estimated forward and backward rate constants for folding, together with activation energy values at 310 K. The kinetic and thermodynamic results shown in Fig. 3 are computed following the procedure reported in the Materials and Methods section of our paper, and implemented in the g_kinetics tool of GROMACS package. It is worth mentioning that the energy barrier reported in Fig. 3 represents free energy values of activation, containing also information about the entropic contribution. Errors estimate is also presented in brackets. Error analysis was performed by varying the cutoff distance used to define the protein folded state (open, intermediate or closed), as in previous works . As pointed out Rhee and coworkers , the largest error is mainly related to the definition of what is folded . In detail, a kinetic analysis was carried out by varying of 0.02 nm the Radius of Gyration threshold to discern between open/intermediate, and intermediate/closed.”
6. The melting curve shown in Additional file 1: Figure S3 suggests that the protein remains well-folded (i.e. in a closed conformation) well over 400 K, and that its melting temperature is likely well over 500 or 600 K, which is clearly a physical impossibility. The authors should comment on this excessively high stability and link it with the potential methodological deficiencies.
7. The written text, while as a whole acceptable, should still undergo a round of proofreading for grammatical and stylistic errors. Altogether, the article should provide more mechanistic details about the folding mechanism of JD.
Author’s response: The text has been checked and corrected to the best of our ability, as suggested by the reviewer. The authors believe that the previous corrections and added comments will provide to the manuscript a more mechanistic view of the JD opening/closing dynamics.
Replica exchange molecular dynamics
Radius of gyration
This work was supported by a grant from the Swiss National Supercomputing Centre (CSCS) under project ID S530.
Availability of data and materials
MAD, and GG conceived the research. MAD, and GG did the molecular dynamics simulations and data processing. MAD, GG, GL, and UM, analyzed the data. MAD, GG, JAT, UM, and AD wrote the paper and critically commented to the manuscript. All authors read and approved the final manuscript.
Gianvito Grasso has received a Master Degree in Biomedical Engineering at Politecnico di Torino in 2015. His research activities are mainly focused on computational techniques applied to protein folding, protein-protein interactions and protein aggregation leading to neurodegenerative diseases. He is a PhD student of Computational Science at University of Italian Switzerland (USI) and works in the computational biophysics group at IDSIA, the Dalle Molle Institute for Artificial Intelligence affiliated to USI and SUPSI (Switzerland). He is author of 5 publications International peer-reviewed Journals.
Jack A. Tuszynski, Ph.D
Prof. Tuszynski is Allard Chair and Professor in Experimental Oncology in the Department of Oncology at the University of Alberta’s Cross Cancer Institute and a Professor in the Department of Physics. Prof. Jack Tuszynski heads a multi-disciplinary team creating “designer drugs” for cancer chemotherapy and neurodegenerative diseases by using computational biophysics methods. He has published almost 400 peer-reviewed papers and 10 books; delivered over 400 scientific talks (including 150+ invited talks) on five continents. His research has been supported by numerous research grants from Canadian, US and European funding agencies.
Umberto Morbiducci, Ph.D
Prof. Umberto Morbiducci has a PhD degree in Mechanical Engineering at the Università Politecnica delle Marche, Italy. His research activity is in the fields of the cardiovascular fluid mechanics, artificial organs and implantable devices for the cardiovascular system, computational multiscale/multiphysics biomechanics, molecular dynamics applied to cytoskeleton filaments, fibril mechanics in neurodegenerative disease and protein folding dynamics, bio-transport phenomena, modelling of glucose metabolism, biosignal processing and data analysis. At present, he is Associate Professor at the Politecnico di Torino, where he leads the Cardiovascular Bioengineering Group. He is author of more than 270 publications.
Ginevra Licandro is a molecular and cellular biologist who worked at academic institutes, pharmaceutical company (Helsinn Healthcare SA) and Institute of Pathology (Locarno, CH). Her experimental and computational research mainly focus on inflammation, metabolism, cancer, protein-protein interactions, drug discovery and bioinformatics. She works as researcher in the computational biophysics group at IDSIA, Dalle Molle Institute for Artificial Intelligence affiliated to USI and SUPSI (Switzerland). She is lecturer of “Fundamental of Biology for Engineers” at Master of Science in Engineering, SUPSI. She is author of 5 publications in International peer-reviewed Journals.
Andrea Danani, Ph.D
Prof. Andrea Danani, is the head of the computational biophysics group at IDSIA, the Dalle Molle Institute for Artificial Intelligence affiliated to USI and SUPSI (Switzerland). His scientific activity was first centered on the modellization of many-electron systems and their phase transitions and later on the description of surface diffusion of adatoms in metals using analytical models and several numerical simulation methods like Cluster Variation Method, MonteCarlo and Molecular dynamics. His group works on multiscale modelling applied to several systems mainly at the molecular level: polymer nanocomposites, biological systems in interaction with drugs and DNA/RNA with related structure-based virtual screening, macromolecules for drug delivery, protein/protein interaction for neurodegenerative problems. Author of more than 100 publications among papers in International peer-reviewed Journals, book chapters and proceedings.
Marco A. Deriu, PhD
Marco A. Deriu has received the European Doctorate in Biomedical Engineering in 2009 at Politecnico di Torino. His research is focused on computational modelling applied to cancer and neurodegenerative diseases, investigation of drug mechanism of action, drug discovery & optimization, drug delivery systems, protein folding. He is lecturer of “Multiscale Modelliing in Biomechanics” at Politecnico di Torino (MS course) and he works as research fellow in the computational biophysics group at IDSIA, the Dalle Molle Institute for Artificial Intelligence affiliated to USI and SUPSI (Switzerland). Author of more than 80 publications among papers in International peer-reviewed Journals, book chapters and proceedings.
The authors declare that they have no competing interests.
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