Volume 17 Supplement 4
A novel molecular dynamics approach to evaluate the effect of phosphorylation on multimeric protein interface: the αB-Crystallin case study
© Chiappori et al. 2016
Published: 2 March 2016
Phosphorylation is one of the most important post-translational modifications (PTM) employed by cells to regulate several cellular processes. Studying the effects of phosphorylations on protein structures allows to investigate the modulation mechanisms of several proteins including chaperones, like the small HSPs, which display different multimeric structures according to the phosphorylation of a few serine residues. In this context, the proposed study is aimed at finding a method to correlate different PTM patterns (in particular phosphorylations at the monomers interface of multimeric complexes) with the dynamic behaviour of the complex, using physicochemical parameters derived from molecular dynamics simulations in the timescale of nanoseconds.
We have developed a methodology relying on computing nine physicochemical parameters, derived from the analysis of short MD simulations, and combined with N identifiers that characterize the PTMs of the analysed protein. The nine general parameters were validated on three proteins, with known post-translational modified conformation and unmodified conformation. Then, we applied this approach to the case study of αB-Crystallin, a chaperone which multimeric state (up to 40 units) is supposed to be controlled by phosphorylation of Ser45 and Ser59. Phosphorylation of serines at the dimer interface induces the release of hexamers, the active state of αB-Crystallin. 30 ns of MD simulation were obtained for each possible combination of dimer phosphorylation state and average values of structural, dynamic, energetic and functional features were calculated on the equilibrated portion of the trajectories. Principal Component Analysis was applied to the parameters and the first five Principal Components, which summed up to 84 % of the total variance, were finally considered.
The validation of this approach on multimeric proteins, which structures were known both modified and unmodified, allowed us to propose a new approach that can be used to predict the impact of PTM patterns in multi-modified proteins using data collected from short molecular dynamics simulations. Analysis on the αB-Crystallin case study clusters together all-P dimers with all-P hexamers and no-P dimer with no-P hexamer and results suggest a great influence of Ser59 phosphorylation on chain B.
KeywordsPost-translational modification Phosphorylation Molecular dynamics PCA Clustering αB-Crystallin Chaperone Small HSP
Post-translational modifications (PTMs) regulate cell life. Several types of PTMs have been observed in proteins, alone or combined in order to regulate their activity and function. In particular, PTMs govern tertiary and quaternary protein structures . One of the most extensively PTMs used by cells is phosphorylation. It regulates several cellular processes, among which are differentiation, growth, metabolism, apoptosis, cellular transport and signal transduction .
Phosphorylation consists in the esterification of a residue possessing a hydroxyl with phosphoric acid, a small and negatively charged group. This PTM can regulate a protein function by affecting its conformation and aggregation capabilities. Frequently, intrinsically disordered protein regions undergo to a structure rearrangement subsequently to a phosphorylation . These structural rearrangements allow protein multimerization (small HSPs [4–6]), protein ordering (Microtubule Associated Protein Tau ), and protein-protein interactions (p47 ). Moreover, phosphorylation can affect the enzyme activity endorsing the ligand binding in the active site (Thymidylate synthase) [9, 10]. Frequently, the modification of multiple phosphorylation sites of a protein constitutes more than an on/off mechanism, since the level of phosphorylation can induce threshold related events. This mechanism is widely employed in eukaryotic regulatory proteins, like G protein-coupled  or tyrosine-kinase receptors , where the number of phosphorylated serine/threonine and tyrosine regulates the signal transduction. The availability of multiple phosphorylation sites in proteins provides a precise tool for dynamic regulation of the downstream processes. Different phosphorylation profiles of a single protein might be linked to different functions .
Considering multi-PTMs involved in the multimerization level, the aim of this study is to develop a methodology to classify PTM patterns to predict their impact on the protein, by using data collected from short molecular dynamics (MD) simulations. In particular, MD simulations were extensively used to study conformational changes induced by Ser/Thr phosphorylations (tau peptide ; Na+/K+ - ATPase (NKA) , myelin basic protein (MBP) , and ADP ribosylation factor nucleotide site opener (ARNO) ). Nonetheless, simulations of large multimers in different phosphorylated conformations can take huge computational effort and impracticable analysis time. Therefore, we developed an approach to estimate the behaviour of structures by extracting physicochemical parameters from MD trajectories in the nanosecond timescale. Using this approach, structures resulting from different PTM patterns can be classified depending on their behaviour. According to our validation, this approach is reliable in discriminating the evolution of the system, caused by PTMs, relying on the variation of key indicators of the structure conformation and stability. Moreover, this approach has been applied to the different phosphorylation patterns of the αB-Crystallin in a 24meric state in order to identify the corresponding behaviour, starting from MD of dimeric structures.
Results and discussion
Input for computation
Average value calculated on the eq trajectory
Average value calculated on the eq trajectory
Average value calculated on the eq trajectory
Average value calculated on the eq trajectory
Calculated on an average conformation
Calculated on an average conformation
Average value calculated on the eq trajectory
Calculated on FEL
Calculated on FEL
The structural parameters evaluated on the equilibrated portion of the whole trajectories include: (1) Total SAS, (2) Hydrophobic SAS, (3) Buried SAS and (4) inter-chain hydrogen bonds number. SAS and the related hydrophobic SAS were evaluated according to Eisenhaber et al. , while Buried SAS was calculated as (Monomer 1 Total SAS + Monomer 2 Total SAS) - Complex Total SAS. Among all the hydrogen bonds established during the trajectory, only those involving residues of different chains were considered. (5) Gap_Index and (6) volume of the interface region are calculated on average conformations obtained from MD simulations. The first was described by Jones et al. , in order to better understand the protein-protein recognition mechanism, calculated on crystal structure of bound and unbound dimers, see Methods for details. Enclosed volume was calculated with the gap-sphere method described in . Total SAS, Gap_Index and hydrogen bonds were previously described as parameters characterizing the interaction observed between proteins in the light of their biological function .
The 9 general parameters used by this approach were validated analysing three proteins for which both wild-type (wt) and post-translational conformations were experimentally available in the PDB. In particular, the validation of our approach has been performed on two X-ray structures of the Serine/threonine-protein kinase (TBK1) (PDB id: 4EUU/4EUT) , two NMR conformations of the CT10-Regulated Kinase isoform II (CRKII) (PDB id: 2DVJ/2EYZ) , and two crystal structures of the Glycogen Phosphorylase (GP) (PDB id: 8GPB/7GPB) .
TBK1 is a homodimer in its inactive form, and undergoes to a large structural rearrangement after phosphorylation of Ser172 in order to obtain the active conformation. Similarly to the αB-Crystallin the phosphorylation triggers a structural change that detaches regions of the 2 chains previously connected.
CRKII is a splicing isoform of the oncoprotein CRK. It is composed by three Src Homology (SH) domains, ordered according the SH2-SH3-SH3 pattern. Phosphorylation localizes in a linker region between the two SH3 domains, and induces the binding of the SH2 domain of CRKII itself, disrupting its biological activity.
GP exists as an inactive homotetramer and as an active homodimer. This protein was selected as validation benchmark given that the homodimer dimerizes when Ser14 is coupled with sulphate ions. Therefore, this is a negative example with respect to TBK1, CRKII, because the PTM induce the multimerization, resulting in a more compact structure without detachment of side chains. Our expectation is that wt GP will cluster together with the PTM conformations of TBK1 and CRKII (and finally with the phosphorylated αB-Crystallin), while the PTM conformation of GP will cluster with wt TBK1 and CRKII (and finally with wt αB-Crystallin). With this example we want to verify if our method is able to capture the global effect on the proteins conformation, connected with the structural rearrangement caused by PTM, instead of the local effects of the PTM (i.e. changes in the local charge, changes in the local shape).
αB-Crystallin case study
The αB-Crystallin was chosen as case study, in order to clarify the role of phosphorylation on the multimerization state and on the achievement of the active conformation. Dimers and hexamers (Fig. 1), and the related phosphorylated forms, were obtained from the deposited 24-meric structure 2YGD, as described in the Methods section.
All complexes, 16 dimers and 2 hexamers, were simulated by MD for 30 ns, in order to obtain equilibrated conformations of unknown structures and a sufficient conformational sampling for the analysis. We defined four peculiar parameters according to the phosphorylation states of Ser45 and Ser59 of both chains A and B: in detail, value 1 was assigned to phospho-serine and 0 to the serine. Moreover, minima which frequency was 0.7 times the FEL absolute minimum were considered for the analysis. The parameters achieved for the different αB-Crystallin conformations underwent to a statistical analysis both to identify phosphorylation effects (parameters mainly correlated with the phosphorylatable residues) and to link phosphorylation patterns to measurable characteristics evaluated on protein structure.
We preliminary analysed the applicability of the approach to αB-Crystallin by mapping some of its conformers on the first two PC achieved during the parameter validations. In particular, all-phosphorylated and wt forms of the αB-Crystallin dimer II and hexamer were considered in the frame of the clusters achieved during validation. As expected, both the phosphorylated forms fall near the cluster centroid of the detached structures analysed in the validation examples, while wt conformers fall near the cluster centroid of the aggregated complexes. This preliminary example is a proof that the proposed approach is robust also for the analysis of our case study.
Principal Component Analysis
In order to study the key phosphorylations for αB-Crystallin activation, a PCA was applied considering all the 13 parameters, 9 general (Table 1) and 4 peculiar for the considered phosphorylations. The first 5 principal components were analysed, as they summed up to 84 % of the total variation and each of them had standard deviation > 1. Several methods for clustering the structures were tested on PCA data, in order to define which patterns behaved similarly, but the final choice was to use k-means because of the suitable possibility of integrating this algorithm with PCA (which in fact is known to be a relaxation of the k-means clustering ).
Loading matrix interpretation
Experimental evidences supporting our results
Looking at the results of the αB-Crystallin case study, we can infer a great influence of Ser59 phosphorylation on chain B for the regulation of the protein multimerization. The key role of phosphorylation for the αB-Crystallin structure was widely demonstrated [16, 19, 20], as also the connection between phosphorylation level and chaperone activity . In particular, phosphorylation of Ser59 has a role in ischemic stress, in the expression and regulation of cytoprotective proteins, like Bcl2 , and in the ubiquitin–proteasome system . There are evidences that Ser59 phosphorylated αB-Crystallin accumulates in brains of patients with neurodegenerative disease, like Alexander and Alzheimer disease . Moreover, inhibiting Ser59 phosphorylation leads to cell death . These results support our claim that Ser59 is a key residue for determining the multimeric conformation of αB-Crystallin.
PTMs, and in particular phosphorylations, have a great importance in the regulation of protein activity. Using molecular dynamics simulations to study their effects is a natural approach to the problem. Nonetheless, simulations of large multimers in different phosphorylated conformations can take huge computational effort (since very long simulations are necessary to reach an equilibrated conformation) and impracticable analysis time. Therefore, we developed an approach to estimate the behaviour of structures by extracting physicochemical parameters from MD trajectories in the nanosecond timescale. We validated our approach on two examples of structures known both in unmodified and post-translational modified states, achieving very good results in terms of predictions of the behaviour of the systems. In particular, the most interesting patterns which are able to describe the behaviour of the system from the very beginning of the simulation are the variation of the Total SAS, that is the geometrical structure of the protein, and the information about the stability (frequency and number of minima). Moreover, we applied our approach to different subunits of the 24meric structure of αB-Crystallin, showing that the parameters obtained from analysis of the asymmetric unit interface may predict the behaviour of the whole multimeric state.
The 24meric structure of αB-Crystallin (PDB id: 2YGD) has been obtained from PDB . The monomer displays 2 conformations inside the oligomers, one bent and one straight. They assemble in dimer I, that can exist alone, and also in a different dimeric structure, named dimer II, that does not exist alone, but includes the inter-dimeric interface. Dimer I interface is composed by antiparallel β-strands of the α-Crystallin Domain (ACD), while in dimer II the interface is between the C-terminal Domain (CTD), where both the serines localize. We focused the analysis only on dimer II, as both phosphorylation sites are far from the interface in dimer I. We obtained the smaller units from the 24mer structure: dimer II and the hexamer. Using Chimera , we achieved all the 24 = 16 combinations of the two possible phosphorylation residues (Ser45 and Ser59) for dimer II. Moreover, two hexamers were prepared for simulation: one with no phosphorylations and one with all the serines phosphorylated. Globally we modelled 18 structures, 16 dimers and 2 hexamers. In order to collect data for both the non-phosphorylated (non-P-) and the phosphorylated (P-) hexamer, we used the average of the values calculated for the structures composed by assembling dimer II monomers, since that interfaces can be both phosphorylated or not.
All structures have been solvated and neutralized with Na+ ions, then their free energy has been minimized using the Steepest Descent algorithm until the maximum force was smaller than 500 kJ(mol-1 nm-1). Then, a simulation of 30 ps in NVT environment was performed at 300 K, followed by 100 ps of simulation in NPT environment performed at 300 K and 1.0 bar.
Molecular dynamics simulation and analysis
MD simulations of 30 ns at 300 K were obtained with Gromacs 4.5 , employing the amber99sbP force field, which includes an energy model for phospho-serines. All bonds were constrained using LINCS algorithm , and periodic boundary conditions were applied in all directions. Long-range electrostatic forces were treated using the PME method.
The representative conformation is the central structure of the first cluster obtained by clustering conformations sampled in the equilibrated portion of the trajectories, using the Gromacs tool, g_cluster on Ca atoms the gromos method  and applying a cut-off distance of 0.3 nm.
Equilibrated portion of the trajectories was evaluated based on RMSD plot. Representative conformations were evaluated using QMEAN  and Verify_3D  server. Ramachandran plots of the achieved structures were also analysed. Based on these evaluations, representative conformations quality is comparable to the 24-meric PDB structure (data not shown).
Energy evaluation, Hydrogen bonds analysis, Solvent Accessible Surface and RMSF were obtained using different tools from the Gromacs Suite, while data from the PCA of the trajectories was used for evaluating protein stability and metastable structures. Exploiting the Chimera plugin SurfNet, using 0.8 Å as grid interval and 5 Å as distance cut-off values, we obtained the volume of the interface region and its surface, and the ratio of volume on surface returns the Gap_Index (= Gap Volume (Å3)/ interface ASA (Å2)) .
R (http://www.r-project.org/) was used to obtain the Principal Component Analysis of the selected parameters, to cluster similar structures and to obtain the correlation matrix for the parameters and its heat map.
minimum absolute value
CT10-Regulated Kinase isoform II
Free Energy Landscape
Heat Shock Proteins
Principal Component Analysis
Solvent Accessible Surface
The Italian Ministry of Education and Research has supported the work through the Flagship InterOmics (PB05) and HIRMA (RBAP11YS7K) projects, and the European MIMOmics (305280) project.
Publication charges for this article have been funded by the Flagship InterOmics project (PB05).
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- Seo J, Lee KJ. Post-translational modifications and their biological functions: proteomic analysis and systematic approaches. J Biochem Mol Biol. 2004;37:35–44.View ArticlePubMedGoogle Scholar
- Ahmad MF, Raman B, Ramakrishna T, Rao CM. Effect of Phosphorylation on αB-crystallin: Differences in Stability, Subunit Exchange and Chaperone Activity of Homo and Mixed Oligomers of αB-Crystallin and its Phosphorylation-mimicking Mutant. J Mol Biol. 2008;375:1040–51.View ArticlePubMedGoogle Scholar
- Nishi H, Shaytan A, Panchenko AR. Physicochemical mechanisms of protein regulation by phosphorylation. Front Genet. 2014;5:270.View ArticlePubMedPubMed CentralGoogle Scholar
- Jovcevski B, Kelly MA, Rote AP, Berg T, Gastall HY, Benesch JLP, et al. Phosphomimics Destabilize Hsp27 Oligomeric Assemblies and Enhance Chaperone Activity. Chem Biol. 2015;22:186–95.View ArticlePubMedGoogle Scholar
- Delbecq SP, Klevit RE. One size doesn’t fit all: the oligomeric states of αB crystallin. FEBS letters. 2013;587:1073–80.View ArticlePubMedGoogle Scholar
- Aquilina JA, Shrestha S, Morris AM, Ecroyd H. Structural and Functional Aspects of Hetero-oligomers formed by the Small Heat Shock Proteins B-Crystallin and HSP27. J Biol Chem. 2013;288:13602–9.View ArticlePubMedPubMed CentralGoogle Scholar
- Lyons AJ, Gandhi NS, Mancera RL. Molecular dynamics simulation of the phosphorylation-induced conformational changes of a tau peptide fragment. Proteins. 2014;82:1907–23.View ArticlePubMedGoogle Scholar
- Meijles DN, Fan LM, Howlin BJ, Li JM. Molecular Insights of p47phox Phosphorylation Dynamics in the Regulation of NADPH Oxidase Activation and Superoxide Production. J Biol Chem. 2014;289:22759–70.View ArticlePubMedPubMed CentralGoogle Scholar
- Jarmuła A, Fraczyk T, Cieplak P, Rode W. Mechanism of influence of phosphorylation on serine 124 on a decrease of catalytic activity of human thymidylate synthase. Bioorg Med Chem. 2010;18:3361–70.View ArticlePubMedPubMed CentralGoogle Scholar
- Krebs EG, Beavo JA. Phosphorylation-Dephosphorylation of Enzymes. Ann Rev Biochem. 1979;48:923–59.View ArticlePubMedGoogle Scholar
- Tyanova S, Cox J, Olsen J, Mann M, Frishman D. Phosphorylation Variation during the Cell Cycle Scales with Structural Propensities of Proteins. PLoS Comput Biol. 2013;9:e1002842.View ArticlePubMedPubMed CentralGoogle Scholar
- Rizzuto R, Marchi S, Bonora M, Aguiari P, Bononi A, De Stefani D, et al. Ca(2+) transfer from the ER to mitochondria: when, how and why. Biochim Biophys Acta. 2009;1787:1342-51
- Varedi KSM, Ventura AC, Merajver SD, Lin XN. Multisite Phosphorylation Provides an Effective and Flexible Mechanism for Switch-Like Protein Degradation. PLoS ONE. 2010;5:e14029.View ArticleGoogle Scholar
- Kato K, Inaguma Y, Ito H, Iida K, Iwamoto I, Kamei K, et al. Ser-59 is the major phosphorylation site in aB-crystallin accumulated in the brains of patients with Alexander's disease. J Neurochem. 2001;76:730–6.View ArticlePubMedGoogle Scholar
- Arrigo AP, Gibert B. HspB1, HspB5 and HspB4 in Human Cancers: Potent Oncogenic Role of Some of Their Client Proteins. Cancers (Basel). 2014;6:333–65.View ArticleGoogle Scholar
- Ecroyd H, Meehan S, Horwitz J, Aquilina JA, Benesch JLP, Robinson CV, et al. Mimicking phosphorylation of αB-crystallin affects its chaperone activity. Biochem J. 2007;401:129–41.View ArticlePubMedGoogle Scholar
- Carver JA, Rekas A, Thorn DC, Wilson MR. Small Heat-shock Proteins and Clusterin: Intra- and Extracellular Molecular Chaperones with a Common Mechanism of Action and Function? IUBMB Life. 2003;55:661–8.View ArticlePubMedGoogle Scholar
- Aquilina JA, Benesch JL, Bateman OA, Slingsby C, Robinson CV. Polydispersity of a mammalian chaperone: mass spectrometry reveals the population of oligomers in alphaB-crystallin. Proc Natl Acad Sci U S A. 2003;100:10611–6.View ArticlePubMedPubMed CentralGoogle Scholar
- Peschek J, Braun N, Rohrberg J, Back KC, Kriehuber T, Kastenmüller A, et al. Regulated structural transitions unleash the chaperone activity of αB-crystallin. Proc Natl Acad Sci U S A. 2013;110:E3780–9.View ArticlePubMedPubMed CentralGoogle Scholar
- Aquilina JA, Benesch JL, Ding LL, Yaron O, Horwitz J, Robinson CV. Phosphorylation of alphaB-crystallin alters chaperone function through loss of dimeric substructure. J Biol Chem. 2004;279:28675–80.View ArticlePubMedGoogle Scholar
- Li R, Reiser G. Phosphorylation of Ser45 and Ser59 of αB-crystallin and p38/extracellular regulated kinase activity determine αB-crystallin-mediated protection of rat brain astrocytes from C2-ceramide- and staurosporine-induced cell death. J Neurochem. 2011;118:354–64.View ArticlePubMedGoogle Scholar
- Launay N, Tarze A, Vicart P, Lilienbaum A. Serine 59 Phosphorylation of αB-Crystallin Down-regulates Its Anti-apoptotic Function by Binding and Sequestering Bcl-2 in Breast Cancer Cells. J Biol Chem. 2010;285:37324–32.View ArticlePubMedPubMed CentralGoogle Scholar
- Boelens WC. Cell biological roles of αB-crystallin. Prog Bioph Mol Biol. 2014;115:3–10.View ArticleGoogle Scholar
- den Engelsman J, van de Schootbrugge C, Yong J, Pruijn GJ, Boelens WC. Pseudophosphorylated αB-crystallin is a nuclear chaperone imported into the nucleus with help of the SMN complex. PLoS One. 2013;8:e73489.View ArticleGoogle Scholar
- Braun N, Zacharias M, Peschek J, Kastenmüllera A, Zouc J, Hanzlika M, et al. Multiple molecular architectures of the eye lens chaperone αB-crystallin elucidated by a triple hybrid approach. Proc Natl Acad Sci U S A. 2011;108:20491–6.View ArticlePubMedPubMed CentralGoogle Scholar
- Poulsen H, Nissen P, Mouritsen OG, Khandelia H. Protein Kinase A (PKA) Phosphorylation of Na+/K+-ATPase Opens Intracellular C-terminal Water Pathway Leading to Third Na+-binding site in Molecular Dynamics Simulations. J Biol Chem. 2012;287:15959–65.View ArticlePubMedPubMed CentralGoogle Scholar
- Vassall KA, Bessonov K, De Avila M, Polverini E, Harauz G. The Effects of Threonine Phosphorylation on the Stability and Dynamics of the Central Molecular Switch Region of 18.5-kDa Myelin Basic Protein. PLoS ONE. 2013;8:e68175.View ArticlePubMedPubMed CentralGoogle Scholar
- Srinivasaraghavan K, Nacro K, Grüber G, Verma CH. Effect of Ser392 Phosphorylation on the Structure and Dynamics of the Polybasic Domain of ADP Ribosylation Factor Nucleotide Site Opener Protein: A Molecular Simulation Study. Biochemistry. 2013;52:7339–49.View ArticlePubMedGoogle Scholar
- Eisenhaber F, Lijnzaad P, Argos P, Sander C, Scharf M. The double cubic lattice method: Efficient approaches to numerical integration of surface area and volume and to dot surface contouring of molecular assemblies. J Comput Chem. 1995;16:273–84.View ArticleGoogle Scholar
- Jones S, Thornton JM. Principles of protein-protein interactions. Proc Natl Acad Sci U S A. 1996;93:13–20.View ArticlePubMedPubMed CentralGoogle Scholar
- Laskowski RA. SURFNET: a program for visualizing molecular surfaces, cavities, and intermolecular interactions. J Mol Graph. 1995;13:323–30.View ArticlePubMedGoogle Scholar
- Papaleo E, Mereghetti P, Fantucci P, Grandori R, De Gioia L. Free-energy landscape, principal component analysis, and structural clustering to identify representative conformations from molecular dynamics simulations: The myoglobin case. J Mol Graph Model. 2009;27:889–99.View ArticlePubMedGoogle Scholar
- Chiappori F, Merelli I, Milanesi L, Rovida E. Exploring the role of the phospholipid ligand in endothelial protein C receptor: A molecular dynamics study. Proteins. 2010;78:2679–90.PubMedGoogle Scholar
- Ma X, Helgason E, Phung QT, Quan CL, Iyer RS, Lee MW, et al. Molecular basis of Tank-binding kinase 1 activation by transautophosphorylation. Proc Natl Acad Sci U S A. 2012;109:9378–83.View ArticlePubMedPubMed CentralGoogle Scholar
- Kobashigawa Y, Sakai M, Naito M, Yokochi M, Kumeta H, Makino Y, et al. Structural basis for the transforming activity of human cancer-related signaling adaptor protein CRK. Nat Struct Mol Biol. 2007;14(6):503–10.View ArticlePubMedGoogle Scholar
- Barford D, Hu SH, Johnson LN. Structural mechanism for glycogen phosphorylase control by phosphorylation and AMP. J Mol Biol. 1991;218:233–60.View ArticlePubMedGoogle Scholar
- Cohen M, Elder S, Musco C, Musco C, Persu M. Dimensionality reduction for k-means clustering and low rank approximation. arXiv preprint arXiv. 2014;1410.6801
- Thornell E, Aquilina A. Regulation of αA- and αB-crystallins via phosphorylation in cellular homeostasis. Cell Mol Life Sci. ArXiv e-prints. 2015;72(21):4127–37.View ArticleGoogle Scholar
- Launay N, Tarze A, Vicart P, Lilienbaum A. Serine 59 phosphorylation of αB-Crystallin down-regulates its anti-apop- totic function by binding and sequestering Bcl-2 in breast cancer cells. J Biol Chem. 2010;285:37324–32.View ArticlePubMedPubMed CentralGoogle Scholar
- Kato K, Ito H, Kamei A, Iwamoto II Y. Innervation- dependent phosphorylation and accumulation of aB-crystallin and Hsp27 as insoluble complexes in disused muscle. FASEB J. 2002;16:1432–4.PubMedGoogle Scholar
- Kato K, Inaguma Y, Ito H, Iida K, Iwamoto I, Kamei A, et al. Ser-59 is the major phosphoryla- tion site of αB-Crystallin accumulated in the brains of patients with Alexander’s disease. J Neurochem. 2001;76:730–6.View ArticlePubMedGoogle Scholar
- Hoover HE, Thuerauf DJ, Martindale JJ, Glembotski CC. αB-crystallin Gene Induction and Phosphorylation by MKK6-activated p38: a potential role for αb-crystallin as a target of the p38 branch of the cardiac stress response. J Biol Chem. 2000;275:23825–33.View ArticlePubMedGoogle Scholar
- Pettersen EF, Goddard TD, Huang CC, Couch GS, Greenblatt DM, Meng EC, et al. UCSF Chimera--a visualization system for exploratory research and analysis. J Comput Chem. 2004;25:1605–12.View ArticlePubMedGoogle Scholar
- Pronk S, Páll S, Schulz R, Larsson P, Bjelkmar P, Apostolov R, et al. GROMACS 4.5: a high-throughput and highly parallel open source molecular simulation toolkit. Bioinformatics. 2013;29:845–54.View ArticlePubMedPubMed CentralGoogle Scholar
- Hess B, Bekker H, Berendsen HJC, Fraaije JGEM. LINCS: A linear constraint solver for molecular simulations. J Comput Chem. 1997;18:1463–72.View ArticleGoogle Scholar
- Daura X, Gademann K, Jaun B, Seebach D, van Gunsteren WF, Mark AE. Peptide folding: When simulation meets experiment. Angew Chem Int Ed Engl. 1999;38:236–40.View ArticleGoogle Scholar
- Benkert P, Künzli M, Schwede T. QMEAN Server for Protein Model Quality Estimation. Nucleic Acids Res. 2009;1:W510–4.View ArticleGoogle Scholar
- Bowie JU, Lüthy R, Eisenberg D. A method to identify protein sequences that fold into a known three-dimensional structure. Science. 1991;253:164–70.View ArticlePubMedGoogle Scholar