ARTICLE
Journal of Cellular Biochemistry 117:1439–1445 (2016)
Molecular Dynamics Studies on D835N Mutation in FLT3—Its Impact on FLT3 Protein Structure Rayapadi G. Swetha, Sudha Ramaiah, and Anand Anbarasu* Medical and Biological Computing Laboratory, School of Biosciences and Technology, VIT University, Vellore 632014, India
ABSTRACT Mutations in Fetal Liver Tyrosine Kinase 3 (FLT3) genes are implicated in the constitutive activation and development of Acute Myeloid Leukaemia (AML). They are involved in signalling pathway of autonomous proliferation and block differentiation in leukaemia cells. FLT3 is considered as a promising target for the therapeutic intervention of AML. There are a few missense mutations associated with FLT3 that are found in AML patients. The D835N mutation is the most frequently observed and the aspartic acid in this position acts as a key residue for the receptor activation. The present study aims to understand the structural effect of D835N mutation in FLT3. We carried out the molecular dynamics (MD) simulation for a period of 120 ns at 300 K. Root-mean square deviation, root-mean square fluctuations, surface accessibility, radius of gyration, hydrogen bond, eigenvector projection analysis, trace of covariance matrix, and density analysis revealed the instability of mutant (D835N) protein. Our study provides new insights on the conformational changes in the mutant (D835N) structure of FLT3 protein. Our observations will be useful for researchers exploring AML and for the development of FLT3 inhibitors. J. Cell. Biochem. 117: 1439–1445, 2016. © 2015 Wiley Periodicals, Inc.
KEY WORDS:
A
FLT3; MUTATIONS; STABILITY; MOLECULAR DYNAMICS; FLEXIBILITY
ML represents a heterogeneous group of haematopoietic progenitor cell disorders. It is characterized by malfunction in differentiation of haematopoiesis through gaining of multiple gene mutations and chromosomal rearrangements. This results in clonal population of non-functional cells named neoplastic cells or blasts [Stone et al., 2004; Estey and D€ ohner, 2006; Shipley and Butera, 2009; Rubnitz et al., 2010; Mathews, 2013]. The clinical symptoms of AML are varied and nonspecific but the leukaemic infiltration of bone marrow is common which results in cytopenia. AML patients present signs and symptoms of haemorrhage, fatigue, and infections due to decrease in red blood cells and platelets. The leukaemic infiltration of different tissues including spleen, liver, lymph nodes, skin, gingival, and central nervous system can develop a range of other symptoms [Lowenberg et al., 1999]. The management of AML requires an integrative teamwork approach because the treatment intensity which is needed to decrease the disease-associated morbidity may in fact lead to increased mortality on account of toxicity [Bonilla and Ribeiro, 2014]. The incidence of AML increases with age and can occur at any age with 15% of cases in individuals
below 40 years of age [Dores et al., 2012; Grove and Vassiliou, 2014]. In 2010, the National Center for Health Statistics reported that roughly 12,330 individuals were diagnosed to have AML in United States. Among them, 8,950 succumbed to the disease [Jemal et al., 2010; O0 Donnell et al., 2011]. Many studies suggest that AML is a consequence of two classes of mutation. The class I mutation provides a proliferative signal to haematopoietic progenitors and also activates mutation in tyrosine kinase (TK) genes namely FLT3, BCR-ABL, KIT, and ETV6-PDGFRB. The class II mutation impairs haematopoietic differentiation, as well as point mutation or rearrangements in PML-PARA and core-binding factor genes [Care et al., 2003; Kiyoi and Naoe, 2006]. FLT3 plays a significant role in the pathogenesis of AML. The human FLT3 gene is located on chromosome 13q12 and organized in 24 exons [Blume-Jensen and Hunter, 2001; Reilly, 2002; Schenone et al., 2008]. Studies have proved that FLT3 gene exists in two forms: (1) a membrane-bound protein of approximately 158–160 kd and glycosylated in extracellular domain at N-linked glycosylation sites; (2) a non-membrane-bound protein of around 130–143 kd which is
Abbreviations: AML, acute myeloid leukaemia; DOPE, discrete optimized protein energy; FLT3, fetal liver tyrosine kinase 3; H, hydrogen; Hsp90, heat shock protein 90; MD, molecular dynamics; PCA, principal components analysis; PDB, protein data bank; RMSD, root-mean squared deviation; RMSF, Root-Mean-Square Fluctuation; Rg, radius of gyration; SASA, solvent accessibility surface area; TK, tyrosine kinase. Grant sponsor: Indian Council of Medical Research (ICMR); Grant numbers: 2014-0099, 2014-23910. *Correspondence to: Dr. Anand Anbarasu, Professor, Medical and Biological Computing Laboratory, School of Biosciences and Technology, VIT University, Vellore 632014, Tamil Nadu, India. E-mail: [email protected] Manuscript Received: 22 July 2015; Manuscript Accepted: 10 November 2015 Accepted manuscript online in Wiley Online Library (wileyonlinelibrary.com): 13 November 2015 DOI 10.1002/jcb.25434 © 2015 Wiley Periodicals, Inc.
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unglycosylated [Lyman, 1995; Abu-Duhier et al., 2001; Kiyoi and Naoe, 2006]. The internal tandem duplications in juxta-membrane domain and point mutations in TK domain are the two types of FLT3 mutations seen in AML patients [Gilliland, 2003; Levis and Small, 2003; Parcells et al., 2006; Schenone et al., 2008]. These mutations are associated with the poor prognosis in AML patients. The most common point mutation occurs at 835th residue (D835N) within the activation loop of FLT3. This aspartic acid is noted to be a key regulatory residue of TK receptor and it is highly conserved [Morley et al., 1999; Fenski et al., 2000]. In a notable percentage of AML patients, this mutation (D835N) increases receptor signalling and causes tumorigenesis [Kiyoi and Naoe, 2006]. The most deleterious mutations may cause protein disfunctionalization by de-stabilizing the protein. [Lander, 1996; Chasman and Adams, 2001; Tokuriki and Tawfik, 2009]. Even though there are many experimental studies that describes the effect of point mutation at D835N residue [Yamamoto et al., 2001; Kiyoi and Naoe, 2006; Weisberg et al., 2010], none of the studies illustrate the structural effect of this mutation. We assume that the experimental structure of FLT3 protein is also not available in public databases. Thus, the main objective of our study is to recognize the structural consequences and functional effects of D835N mutation on three-dimensional structure of FLT3 protein using in-silico approaches. The outcome of our study may help researchers in developing the potential FLT3 inhibitors. We performed MD simulation of wild-type and D835N mutant of FLT3 and our observations indicate that the D835N results in structural changes in FLT3 protein which may probably lead to AML.
MATERIALS AND METHODS DATASET COLLECTION AND PROTEIN STRUCTURE MODELING The amino acid sequence of FLT3 protein (UniProt ID: P36888) was retrieved from UniProt database [Magrane and Consortium, 2011]. The MODELLER program [Eswar et al., 2006] was used for modeling the FLT3 protein. We performed a BLAST search with the target sequence [Altschul et al., 1990] against Protein Data Bank (PDB) [Berman et al., 2002] for template selection. The program generates five different models after the alignment of target with template. Out of these five models, a model with lowest discrete optimized protein energy (DOPE) score and highest GA341 score was considered as reliable model. The Swiss PDB viewer package was used to generate mutant structure from modelled FLT3 protein [Schwede et al., 2003]. Further, PROSA [Sippl, 1993; Wiederstein and Sippl, 2007] and PROCHECK [Laskowski et al., 1993] was employed to validate the quality of both wild-type and mutant (D835N) FLT3 structures. MD SIMULATIONS The simulation of wild-type and mutant FLT3 structures were carried out using the GROMACS v4.5.5 package [Pronk et al., 2013] with Gromos96 53a6 force field [Oostenbrink et al., 2004; Kumar et al., 2014a]. The proteins were simulated in a water filled dodecahedron box with the distance of 1.0 nm between the protein and box. The proteins were positively charged at physiological pH and hence the charges of system were neutralized by adding counter ions, either Naþ or cl with the help of genion tool. Then the solvated, electro
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neutral system was relaxed through a process called energy minization (EM) to remove all short contacts. Following EM, the equilibration was conducted in two phases namely temperature and pressure. The proteins were position restrained and once the equilibration was completed, the production MD was performed for 120 ns at 300 K. The bond length was constrained using LINCS algorithm [Hess, 2008; Kumar et al., 2014b]. The simulation trajectories were analyzed using the various tools available in the GROMACS package. The root-mean squared deviation (RMSD), root-mean-square fluctuation (RMSF), radius of gyration (Rg), solvent accessibility surface area (SASA), density plot, and the number of hydrogen (H) bond formation were analyzed by r_rms, r_rmsf, g_gyrate, g_sas, g_density, and g_hond tool, respectively. The program DSSP was used to calculate the secondary structure of proteins [Kabsch and Sander, 1983; Joosten et al., 2011]. The evolution of the secondary structural content along the trajectory was calculated by time-averaging the number of residues in each secondary structure of protein with do_dssp as an interface. The essential dynamics was performed for all the trajectories according to principal components analysis (PCA). A set of eigenvectors and eigenvalues was identified by diagonalizing the matrix by g_covar and g_anaeig tools. We used PERL scripts for some calculations and the plots were generated using XMGRACE tool (Turner, 2005).
RESULTS PREDICTION OF THREE-DIMENSIONAL STRUCTURE OF FLT3 PROTEIN The experimental three-dimensional structure of FLT3 protein is not available in PDB which prompted us to construct three-dimensional model of FLT3 protein from its sequence by homology based modeling. We used MODELLER program to predict the structure of FLT3 protein. We did a BLAST search using the target sequence against PDB and a hit (PDB ID: 1RJB_A, Resolution: 2.1 Å ) is selected as the template. The program generates five different models and the model with DOPE Score 62674.22 and GA341 score 0.96 is selected as a best model. The Swiss PDB viewer is used to generate mutant structure by changing aspartic acid to asparagine in 835 position of modelled FLT3 protein. Both the wild-type and mutant model of FLT3 protein is further validated individually by PROCHECK and PROSA, which exhibit good stereo chemical properties. The structure of wild-type and mutant FLT3 proteins show 96.5% and 97.1% of residues in most favored and allowed regions, respectively, and their corresponding Z-score is 2.70 and 3.91. Hence, these structures are found to be suitable for further MD analysis. MD SIMULATION The structural consequences or impact of a mutation are well understood by performing the MD simulation of wild-type and mutant (D835N) FLT3 protein. The MD simulation generates different plots to analyze the fluctuations and conformational changes in wild-type and mutant (D835N) FLT3 protein. Preliminary analysis of trajectory. The level of compaction of protein along the trajectory is analyzed by calculating Rg of protein using g_gyrate weighted by atomic mass. The Rg plot for Ca atoms of protein with time at 300 K is shown in Figure 1A. It is observed that
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Fig 1. Plot of Rg (A) and SASA (B) of Ca atoms of wild-type and mutant FLT3 proteinat 300 K.
Rg of both wild-type and mutant protein constantly decreases from 3.22 nm (start of MD) to 2.95 nm (end of MD) and 3.16 nm (start of MD) to 2.87 nm (end of MD) respectively. After 22,000 ps, they show only the slight fluctuations. It is noted that there is a sudden increase of Rg at 15,000 ps to 3.05 nm in wild-type protein. The buried hydrophobic residues are exposed to water environment upon protein unfolding. This is investigated by measuring SASA of protein and for each group of hydrophilic (Asn, Gln, Gly, His, Pro, Ser, and Thr) and hydrophobic residues (Ala, Cys, Ile, Leu, Met, Phe, Trp, Tyr, and Val). SASA plot (Fig. 1B) suggests that both wild-type and mutant structure show similar type of deviation throughout the simulation. In-depth analysis shows the SASA value of 347 nm2 (wildtype protein) and 335 nm2 (D835N) for the initial structures. During simulation, these values gradually drops down and the equilibrium is achieved 241 nm2 (wild-type protein) and 234 nm2 (D835N) at the end of 120 ns of simulation. The Figure 1B shows the mutant has higher SASA from 3,100 ps to 38,000 ps than the wild-type protein. The mutant exhibit low SASA value of 225 nm2 compared to the wild-type protein at 100,100 ps. Table I shows the time-averaged values of Rg and SASA with standard deviations. The secondary structural content (a-helix þ b-bridge þ b-strand þ turn þ bend) of wild-type and mutant protein before and after simulation is given in Figure 2A and B, respectively. Prior to simulation, the wild-type and mutant protein has total of 606 and 601 secondary structural elements, respectively. The significant increase in the elements is noticed after 120 ns of simulation. The wild-type and mutant protein has total of 663 and 634 elements, respectively. RMSD and fluctuation analysis. A standard way to measure the stability of protein is RMSD of all heavy atoms with respect to its TABLE I. Time-Average and Standard Deviation of RMSD, H-Bond, Rg, and SASA
Wild-typea RMSD H-bond Rg SASA Hydrophobic Hydrophilic a
Mutanta
0.88 0.10 668 29.92 2.85 0.04
0.89 0.08 641 29.96 2.93 0.07
249.94 13.69 182.48 9.87
250.02 15.73 191.57 9.89
Average standard deviation.
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structure. The RMSD of Ca atoms is illustrated in Figure 3A and the inspection of RMSD plot indicates that the constant fluctuation can be seen throughout the simulation. The wild-type protein structure shows the constant rise in RMSD value till 16,500 ps and then minimum deviation pattern seen till the end of simulation. At 16,500 ps, the RMSD value is 0.99 nm. Observations on the mutant structure exhibits steady increase in RMSD value till 9,500 ps where the value is 0.89 nm. After 51,000 ps of simulation, it is noted that the mutant structure has high RMSD value when compared to wild-type structure. The time-averaged value of RMSD with standard deviation is depicted in Table I. The fluctuations of residues are analyzed by calculating RMSF of the backbone atoms given in Figure 3B. As expected, the flexibility is higher in mutant and the highest fluctuations are seen at these amino acids positions: Val36 of 1.61 nm, Trp495 of 0.49 nm, Tyr597 of 0.64 nm, Gly636 of 0.51 nm, Asn722 of 0.69 nm, His761 of 0.93 nm, and Ser993 of 0.97 nm. By analyzing the inset graph of Figure 3B, TK domain 2 of FLT3 protein [Ishiko et al., 2005] shows significant increase in RMSF value in mutant than in the wild-type protein structure. H bonding. The H bond plays a vital role in protein folding, stability, and functioning. We calculated intermolecular H bond with respect to time for wild-type and mutant protein to understand their stability. On average, the mutant structure has less number of H bond formations during simulation as compared to wild-type protein (Fig. 4). The time-averaged value of H bond and standard deviation is shown in Table I. PCA and density maps. The PCA is helpful in analyzing the motions of flexible regions and detection of ill-equilibrated regions in proteins. The range of eigenvalues designates the fluctuation intensity and dynamic action of protein. It is primarily restricted within the foremost two eigenvectors. The mutant protein structure occupies slightly larger region of phase space specifically in PC2 plane than wild-type protein and it is well shown in Figure 5. The trace of diagonalized covariance matrix of Ca atoms fluctuations further reveal the overall fluctuation of two proteins and their values are found to be 175.141 nm2 for wild-type structure and 284.705 nm2 for mutant structure. Additionally, we investigated the density of system as a function of a specified box vector. The average density value of wild-type
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Fig 2.
The number of residues in different secondary structures of wild-type and mutant FLT3 proteins (A) prior to simulation, (B) after simulation.
Fig 3.
The Ca RMSD (A) and backbone RMSF (B) plots of wild-life and mutant proteins at 300 K. The inset graph in B shows the residue position from 780 to 920.
Fig 4. The plot of number of protein-solvent intermolecular H bond in wildtype and mutant FLT3 proteins at 300 K.
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Fig 5. The projection of motion of wild-type and mutant in FLT3 proteins in phase space along the first two principle eigenvectors at 300 K.
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Fig 6. The protein density plot of wild-type (A) and mutant (B) FLT3 proteins.
structure is 32.6 nm3 and the mutant structure is 31.2 nm3 (Fig. 6).
DISCUSSION The point mutations at D835 in the FLT3 play an important role in the pathogenesis of AML and can be found in approximately 25–30% of patients with AML. FLT3 mutants represent a promising molecular target for FLT3 inhibitors due to their essential pro-proliferative and anti-apoptotic role in AML cells. In many AML patients, it is observed that aspartic acid in 835th position is mutated to asparagine and resulted in conformational changes of FLT3 protein [Yamamoto et al., 2001; Kiyoi and Naoe, 2006; Weisberg et al., 2010]. The aspartic acid carries a hydrophilic acidic group with strong negative charge and binds to positivelycharged molecules and ions whereas asparagine is the amide of aspartic acid. This amide group does not carry a formal charge under any biologically relevant pH conditions. The mutation of aspartic acid with asparagine residue is one of the factors related to the molecular basis of aging and the carboxyl group in aspartic acid is being converted into amide group of asparagine and makes it polar in nature. To understand the structural behavior of this mutation in 835th position, we carried MD simulations for wild-type and mutant (D835N) FLT3 protein structures. The MD simulations help in investigating the physical basis of the structure and function of biological macromolecules [Karplus and McCammon, 2002; Vinay Kumar et al., 2014]. The MD simulation (120 ns each) on wild-type and mutant (D835N) FLT3 protein structures provide valuable clue to understand the structural consequences and stability of the most prioritized mutation (D835N) in FLT3. From our results, it is evident that the stability loss is observed in mutant (D835N) structure as provided from the plots including Rg, SASA, RMSD, and RMSF generated by MD simulations. The averaged Rg value of mutant is 2.93, which is found to be greater than the value of wild-type (Table I). Thus, increase in Rg indicates
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expansion of mutant structure probably due to the loosening of the structural network. In correlation with the amino acid composition, the mutant protein has higher accessible surface area of both hydrophobic and hydrophilic residues compared to the wild-type protein (Table I). Interestingly, it is noticed that the secondary structural elements are slightly higher in wild-type than mutant structure (Fig. 2) for both initial and simulated structures. These findings suggest that the mutation (D835N) induces the conformational changes in wild-type protein and provide insights towards the loss of stability in mutant FLT3 structure. In RMSD plot (Fig. 3A), it is observed that mutant (D835N) structure showed maximum deviation whereas the wild-type structure showed minimal deviation. The RMSF measures the average fluctuation of the atoms over the whole trajectory. Figure 3B represents the higher fluctuations of the mutant structure and exhibits large flexibility compared to wild-type structure till the end of simulation. Thus, both RMSD and RMSF results supports our findings from Rg and SASA plot. Furthermore, intermolecular H bond is calculated for both wild-type and mutant (D835N) structures during the simulation time and notable differences are observed by comparing these structures. The mutant structure has showed comparatively less number of intermolecular H bond formations resulting in greater flexibility. The wild-type protein has high number of intermolecular H bond formation which makes the structure more rigid. Together the analysis of RMSF and intermolecular H bond formation indicate that mutation leads to a more flexible conformation with less number of hydrogen bonds. Additionally, to support our MD simulations result, we have determined extensive collective motions of wild-type and mutant structures using essential dynamics. These motions in phase space are observed in projection of trajectories at 300 K onto first two principal components (PC1 and PC2). The clusters are well defined in wild-type than mutant structure and also the mutant occupies slightly larger space in PC2 (Fig. 5). The result obtained from PCA justifies the idea of higher flexibility of mutant than wild-type at
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300 K. The increased flexibility of mutant is confirmed by the trace of diagonalized covariance matrix values as the mutant has higher value than wild-type structure. It is also supported by the atomic density where the mutant has less atom density than wild-type protein (Fig. 6). Previous study suggests that, survival advantage of leukemic cells driven by mutant FLT3 is to a large extent because of its activation of three main intracellular signaling pathways, PI3K/PTEN/Akt/mTOR, Jak/STAT, and RAS/Raf/MEK/ ERK. AML cells expressing mutant FLT3 are killed by inhibition of mutant FLT3 chaperone, heat shock protein 90 (Hsp90) [Weisberg et al., 2010]. Hsp90 is required for the stability and function of a number of conditionally activated and expressed signaling proteins as well as multiple mutated signaling proteins, which promote cancer cell growth or survival or both [Neckers and Ivy, 2003]. The D835N mutation in FLT3 is concerned with the constitutive activation of kinases which results in improved receptor signaling. Our results indicate that, the D835N mutation results in loss of stability in FLT3 protein. Thus, this mutation resulting in an altered protein structure might affect the protein function and signal activation.
CONCLUSION On the whole, our study provides valuable insights on the structural consequences of D835N mutation in FLT3 that are evident from MD simulation results for both wild-type and mutant (D835N) protein. The mutant structure exhibits the loss of stability as observed in Rg, SASA, and the overall secondary structural content. It is further supported by RMSD and RMSF. As a result of D835N mutation, the structure becomes more flexible as indicated from H bond, PCA, and density analysis. Our results will be an important starting point for researchers to develop promising FLT3 inhibitors and to perform future experimental studies on pathological mutations and its structural consequences on FLT3.
ACKNOWLEDGMENTS SR and AA gratefully acknowledge the Indian Council of Medical Research (ICMR), Government of India Agency for the research Grant (IRIS ID: 2014-0099). RGS thanks ICMR for the Senior Research Fellowship (IRIS ID: 2014-23910). The authors would also like to thank the management of VIT University for providing necessary facilities to carry out this research project.
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Journal of Cellular Biochemistry 117:1439–1445 (2016)
Molecular Dynamics Studies on D835N Mutation in FLT3—Its Impact on FLT3 Protein Structure Rayapadi G. Swetha, Sudha Ramaiah, and Anand Anbarasu* Medical and Biological Computing Laboratory, School of Biosciences and Technology, VIT University, Vellore 632014, India
ABSTRACT Mutations in Fetal Liver Tyrosine Kinase 3 (FLT3) genes are implicated in the constitutive activation and development of Acute Myeloid Leukaemia (AML). They are involved in signalling pathway of autonomous proliferation and block differentiation in leukaemia cells. FLT3 is considered as a promising target for the therapeutic intervention of AML. There are a few missense mutations associated with FLT3 that are found in AML patients. The D835N mutation is the most frequently observed and the aspartic acid in this position acts as a key residue for the receptor activation. The present study aims to understand the structural effect of D835N mutation in FLT3. We carried out the molecular dynamics (MD) simulation for a period of 120 ns at 300 K. Root-mean square deviation, root-mean square fluctuations, surface accessibility, radius of gyration, hydrogen bond, eigenvector projection analysis, trace of covariance matrix, and density analysis revealed the instability of mutant (D835N) protein. Our study provides new insights on the conformational changes in the mutant (D835N) structure of FLT3 protein. Our observations will be useful for researchers exploring AML and for the development of FLT3 inhibitors. J. Cell. Biochem. 117: 1439–1445, 2016. © 2015 Wiley Periodicals, Inc.
KEY WORDS:
A
FLT3; MUTATIONS; STABILITY; MOLECULAR DYNAMICS; FLEXIBILITY
ML represents a heterogeneous group of haematopoietic progenitor cell disorders. It is characterized by malfunction in differentiation of haematopoiesis through gaining of multiple gene mutations and chromosomal rearrangements. This results in clonal population of non-functional cells named neoplastic cells or blasts [Stone et al., 2004; Estey and D€ ohner, 2006; Shipley and Butera, 2009; Rubnitz et al., 2010; Mathews, 2013]. The clinical symptoms of AML are varied and nonspecific but the leukaemic infiltration of bone marrow is common which results in cytopenia. AML patients present signs and symptoms of haemorrhage, fatigue, and infections due to decrease in red blood cells and platelets. The leukaemic infiltration of different tissues including spleen, liver, lymph nodes, skin, gingival, and central nervous system can develop a range of other symptoms [Lowenberg et al., 1999]. The management of AML requires an integrative teamwork approach because the treatment intensity which is needed to decrease the disease-associated morbidity may in fact lead to increased mortality on account of toxicity [Bonilla and Ribeiro, 2014]. The incidence of AML increases with age and can occur at any age with 15% of cases in individuals
below 40 years of age [Dores et al., 2012; Grove and Vassiliou, 2014]. In 2010, the National Center for Health Statistics reported that roughly 12,330 individuals were diagnosed to have AML in United States. Among them, 8,950 succumbed to the disease [Jemal et al., 2010; O0 Donnell et al., 2011]. Many studies suggest that AML is a consequence of two classes of mutation. The class I mutation provides a proliferative signal to haematopoietic progenitors and also activates mutation in tyrosine kinase (TK) genes namely FLT3, BCR-ABL, KIT, and ETV6-PDGFRB. The class II mutation impairs haematopoietic differentiation, as well as point mutation or rearrangements in PML-PARA and core-binding factor genes [Care et al., 2003; Kiyoi and Naoe, 2006]. FLT3 plays a significant role in the pathogenesis of AML. The human FLT3 gene is located on chromosome 13q12 and organized in 24 exons [Blume-Jensen and Hunter, 2001; Reilly, 2002; Schenone et al., 2008]. Studies have proved that FLT3 gene exists in two forms: (1) a membrane-bound protein of approximately 158–160 kd and glycosylated in extracellular domain at N-linked glycosylation sites; (2) a non-membrane-bound protein of around 130–143 kd which is
Abbreviations: AML, acute myeloid leukaemia; DOPE, discrete optimized protein energy; FLT3, fetal liver tyrosine kinase 3; H, hydrogen; Hsp90, heat shock protein 90; MD, molecular dynamics; PCA, principal components analysis; PDB, protein data bank; RMSD, root-mean squared deviation; RMSF, Root-Mean-Square Fluctuation; Rg, radius of gyration; SASA, solvent accessibility surface area; TK, tyrosine kinase. Grant sponsor: Indian Council of Medical Research (ICMR); Grant numbers: 2014-0099, 2014-23910. *Correspondence to: Dr. Anand Anbarasu, Professor, Medical and Biological Computing Laboratory, School of Biosciences and Technology, VIT University, Vellore 632014, Tamil Nadu, India. E-mail: [email protected] Manuscript Received: 22 July 2015; Manuscript Accepted: 10 November 2015 Accepted manuscript online in Wiley Online Library (wileyonlinelibrary.com): 13 November 2015 DOI 10.1002/jcb.25434 © 2015 Wiley Periodicals, Inc.
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unglycosylated [Lyman, 1995; Abu-Duhier et al., 2001; Kiyoi and Naoe, 2006]. The internal tandem duplications in juxta-membrane domain and point mutations in TK domain are the two types of FLT3 mutations seen in AML patients [Gilliland, 2003; Levis and Small, 2003; Parcells et al., 2006; Schenone et al., 2008]. These mutations are associated with the poor prognosis in AML patients. The most common point mutation occurs at 835th residue (D835N) within the activation loop of FLT3. This aspartic acid is noted to be a key regulatory residue of TK receptor and it is highly conserved [Morley et al., 1999; Fenski et al., 2000]. In a notable percentage of AML patients, this mutation (D835N) increases receptor signalling and causes tumorigenesis [Kiyoi and Naoe, 2006]. The most deleterious mutations may cause protein disfunctionalization by de-stabilizing the protein. [Lander, 1996; Chasman and Adams, 2001; Tokuriki and Tawfik, 2009]. Even though there are many experimental studies that describes the effect of point mutation at D835N residue [Yamamoto et al., 2001; Kiyoi and Naoe, 2006; Weisberg et al., 2010], none of the studies illustrate the structural effect of this mutation. We assume that the experimental structure of FLT3 protein is also not available in public databases. Thus, the main objective of our study is to recognize the structural consequences and functional effects of D835N mutation on three-dimensional structure of FLT3 protein using in-silico approaches. The outcome of our study may help researchers in developing the potential FLT3 inhibitors. We performed MD simulation of wild-type and D835N mutant of FLT3 and our observations indicate that the D835N results in structural changes in FLT3 protein which may probably lead to AML.
MATERIALS AND METHODS DATASET COLLECTION AND PROTEIN STRUCTURE MODELING The amino acid sequence of FLT3 protein (UniProt ID: P36888) was retrieved from UniProt database [Magrane and Consortium, 2011]. The MODELLER program [Eswar et al., 2006] was used for modeling the FLT3 protein. We performed a BLAST search with the target sequence [Altschul et al., 1990] against Protein Data Bank (PDB) [Berman et al., 2002] for template selection. The program generates five different models after the alignment of target with template. Out of these five models, a model with lowest discrete optimized protein energy (DOPE) score and highest GA341 score was considered as reliable model. The Swiss PDB viewer package was used to generate mutant structure from modelled FLT3 protein [Schwede et al., 2003]. Further, PROSA [Sippl, 1993; Wiederstein and Sippl, 2007] and PROCHECK [Laskowski et al., 1993] was employed to validate the quality of both wild-type and mutant (D835N) FLT3 structures. MD SIMULATIONS The simulation of wild-type and mutant FLT3 structures were carried out using the GROMACS v4.5.5 package [Pronk et al., 2013] with Gromos96 53a6 force field [Oostenbrink et al., 2004; Kumar et al., 2014a]. The proteins were simulated in a water filled dodecahedron box with the distance of 1.0 nm between the protein and box. The proteins were positively charged at physiological pH and hence the charges of system were neutralized by adding counter ions, either Naþ or cl with the help of genion tool. Then the solvated, electro
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neutral system was relaxed through a process called energy minization (EM) to remove all short contacts. Following EM, the equilibration was conducted in two phases namely temperature and pressure. The proteins were position restrained and once the equilibration was completed, the production MD was performed for 120 ns at 300 K. The bond length was constrained using LINCS algorithm [Hess, 2008; Kumar et al., 2014b]. The simulation trajectories were analyzed using the various tools available in the GROMACS package. The root-mean squared deviation (RMSD), root-mean-square fluctuation (RMSF), radius of gyration (Rg), solvent accessibility surface area (SASA), density plot, and the number of hydrogen (H) bond formation were analyzed by r_rms, r_rmsf, g_gyrate, g_sas, g_density, and g_hond tool, respectively. The program DSSP was used to calculate the secondary structure of proteins [Kabsch and Sander, 1983; Joosten et al., 2011]. The evolution of the secondary structural content along the trajectory was calculated by time-averaging the number of residues in each secondary structure of protein with do_dssp as an interface. The essential dynamics was performed for all the trajectories according to principal components analysis (PCA). A set of eigenvectors and eigenvalues was identified by diagonalizing the matrix by g_covar and g_anaeig tools. We used PERL scripts for some calculations and the plots were generated using XMGRACE tool (Turner, 2005).
RESULTS PREDICTION OF THREE-DIMENSIONAL STRUCTURE OF FLT3 PROTEIN The experimental three-dimensional structure of FLT3 protein is not available in PDB which prompted us to construct three-dimensional model of FLT3 protein from its sequence by homology based modeling. We used MODELLER program to predict the structure of FLT3 protein. We did a BLAST search using the target sequence against PDB and a hit (PDB ID: 1RJB_A, Resolution: 2.1 Å ) is selected as the template. The program generates five different models and the model with DOPE Score 62674.22 and GA341 score 0.96 is selected as a best model. The Swiss PDB viewer is used to generate mutant structure by changing aspartic acid to asparagine in 835 position of modelled FLT3 protein. Both the wild-type and mutant model of FLT3 protein is further validated individually by PROCHECK and PROSA, which exhibit good stereo chemical properties. The structure of wild-type and mutant FLT3 proteins show 96.5% and 97.1% of residues in most favored and allowed regions, respectively, and their corresponding Z-score is 2.70 and 3.91. Hence, these structures are found to be suitable for further MD analysis. MD SIMULATION The structural consequences or impact of a mutation are well understood by performing the MD simulation of wild-type and mutant (D835N) FLT3 protein. The MD simulation generates different plots to analyze the fluctuations and conformational changes in wild-type and mutant (D835N) FLT3 protein. Preliminary analysis of trajectory. The level of compaction of protein along the trajectory is analyzed by calculating Rg of protein using g_gyrate weighted by atomic mass. The Rg plot for Ca atoms of protein with time at 300 K is shown in Figure 1A. It is observed that
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Fig 1. Plot of Rg (A) and SASA (B) of Ca atoms of wild-type and mutant FLT3 proteinat 300 K.
Rg of both wild-type and mutant protein constantly decreases from 3.22 nm (start of MD) to 2.95 nm (end of MD) and 3.16 nm (start of MD) to 2.87 nm (end of MD) respectively. After 22,000 ps, they show only the slight fluctuations. It is noted that there is a sudden increase of Rg at 15,000 ps to 3.05 nm in wild-type protein. The buried hydrophobic residues are exposed to water environment upon protein unfolding. This is investigated by measuring SASA of protein and for each group of hydrophilic (Asn, Gln, Gly, His, Pro, Ser, and Thr) and hydrophobic residues (Ala, Cys, Ile, Leu, Met, Phe, Trp, Tyr, and Val). SASA plot (Fig. 1B) suggests that both wild-type and mutant structure show similar type of deviation throughout the simulation. In-depth analysis shows the SASA value of 347 nm2 (wildtype protein) and 335 nm2 (D835N) for the initial structures. During simulation, these values gradually drops down and the equilibrium is achieved 241 nm2 (wild-type protein) and 234 nm2 (D835N) at the end of 120 ns of simulation. The Figure 1B shows the mutant has higher SASA from 3,100 ps to 38,000 ps than the wild-type protein. The mutant exhibit low SASA value of 225 nm2 compared to the wild-type protein at 100,100 ps. Table I shows the time-averaged values of Rg and SASA with standard deviations. The secondary structural content (a-helix þ b-bridge þ b-strand þ turn þ bend) of wild-type and mutant protein before and after simulation is given in Figure 2A and B, respectively. Prior to simulation, the wild-type and mutant protein has total of 606 and 601 secondary structural elements, respectively. The significant increase in the elements is noticed after 120 ns of simulation. The wild-type and mutant protein has total of 663 and 634 elements, respectively. RMSD and fluctuation analysis. A standard way to measure the stability of protein is RMSD of all heavy atoms with respect to its TABLE I. Time-Average and Standard Deviation of RMSD, H-Bond, Rg, and SASA
Wild-typea RMSD H-bond Rg SASA Hydrophobic Hydrophilic a
Mutanta
0.88 0.10 668 29.92 2.85 0.04
0.89 0.08 641 29.96 2.93 0.07
249.94 13.69 182.48 9.87
250.02 15.73 191.57 9.89
Average standard deviation.
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structure. The RMSD of Ca atoms is illustrated in Figure 3A and the inspection of RMSD plot indicates that the constant fluctuation can be seen throughout the simulation. The wild-type protein structure shows the constant rise in RMSD value till 16,500 ps and then minimum deviation pattern seen till the end of simulation. At 16,500 ps, the RMSD value is 0.99 nm. Observations on the mutant structure exhibits steady increase in RMSD value till 9,500 ps where the value is 0.89 nm. After 51,000 ps of simulation, it is noted that the mutant structure has high RMSD value when compared to wild-type structure. The time-averaged value of RMSD with standard deviation is depicted in Table I. The fluctuations of residues are analyzed by calculating RMSF of the backbone atoms given in Figure 3B. As expected, the flexibility is higher in mutant and the highest fluctuations are seen at these amino acids positions: Val36 of 1.61 nm, Trp495 of 0.49 nm, Tyr597 of 0.64 nm, Gly636 of 0.51 nm, Asn722 of 0.69 nm, His761 of 0.93 nm, and Ser993 of 0.97 nm. By analyzing the inset graph of Figure 3B, TK domain 2 of FLT3 protein [Ishiko et al., 2005] shows significant increase in RMSF value in mutant than in the wild-type protein structure. H bonding. The H bond plays a vital role in protein folding, stability, and functioning. We calculated intermolecular H bond with respect to time for wild-type and mutant protein to understand their stability. On average, the mutant structure has less number of H bond formations during simulation as compared to wild-type protein (Fig. 4). The time-averaged value of H bond and standard deviation is shown in Table I. PCA and density maps. The PCA is helpful in analyzing the motions of flexible regions and detection of ill-equilibrated regions in proteins. The range of eigenvalues designates the fluctuation intensity and dynamic action of protein. It is primarily restricted within the foremost two eigenvectors. The mutant protein structure occupies slightly larger region of phase space specifically in PC2 plane than wild-type protein and it is well shown in Figure 5. The trace of diagonalized covariance matrix of Ca atoms fluctuations further reveal the overall fluctuation of two proteins and their values are found to be 175.141 nm2 for wild-type structure and 284.705 nm2 for mutant structure. Additionally, we investigated the density of system as a function of a specified box vector. The average density value of wild-type
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Fig 2.
The number of residues in different secondary structures of wild-type and mutant FLT3 proteins (A) prior to simulation, (B) after simulation.
Fig 3.
The Ca RMSD (A) and backbone RMSF (B) plots of wild-life and mutant proteins at 300 K. The inset graph in B shows the residue position from 780 to 920.
Fig 4. The plot of number of protein-solvent intermolecular H bond in wildtype and mutant FLT3 proteins at 300 K.
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Fig 5. The projection of motion of wild-type and mutant in FLT3 proteins in phase space along the first two principle eigenvectors at 300 K.
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Fig 6. The protein density plot of wild-type (A) and mutant (B) FLT3 proteins.
structure is 32.6 nm3 and the mutant structure is 31.2 nm3 (Fig. 6).
DISCUSSION The point mutations at D835 in the FLT3 play an important role in the pathogenesis of AML and can be found in approximately 25–30% of patients with AML. FLT3 mutants represent a promising molecular target for FLT3 inhibitors due to their essential pro-proliferative and anti-apoptotic role in AML cells. In many AML patients, it is observed that aspartic acid in 835th position is mutated to asparagine and resulted in conformational changes of FLT3 protein [Yamamoto et al., 2001; Kiyoi and Naoe, 2006; Weisberg et al., 2010]. The aspartic acid carries a hydrophilic acidic group with strong negative charge and binds to positivelycharged molecules and ions whereas asparagine is the amide of aspartic acid. This amide group does not carry a formal charge under any biologically relevant pH conditions. The mutation of aspartic acid with asparagine residue is one of the factors related to the molecular basis of aging and the carboxyl group in aspartic acid is being converted into amide group of asparagine and makes it polar in nature. To understand the structural behavior of this mutation in 835th position, we carried MD simulations for wild-type and mutant (D835N) FLT3 protein structures. The MD simulations help in investigating the physical basis of the structure and function of biological macromolecules [Karplus and McCammon, 2002; Vinay Kumar et al., 2014]. The MD simulation (120 ns each) on wild-type and mutant (D835N) FLT3 protein structures provide valuable clue to understand the structural consequences and stability of the most prioritized mutation (D835N) in FLT3. From our results, it is evident that the stability loss is observed in mutant (D835N) structure as provided from the plots including Rg, SASA, RMSD, and RMSF generated by MD simulations. The averaged Rg value of mutant is 2.93, which is found to be greater than the value of wild-type (Table I). Thus, increase in Rg indicates
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expansion of mutant structure probably due to the loosening of the structural network. In correlation with the amino acid composition, the mutant protein has higher accessible surface area of both hydrophobic and hydrophilic residues compared to the wild-type protein (Table I). Interestingly, it is noticed that the secondary structural elements are slightly higher in wild-type than mutant structure (Fig. 2) for both initial and simulated structures. These findings suggest that the mutation (D835N) induces the conformational changes in wild-type protein and provide insights towards the loss of stability in mutant FLT3 structure. In RMSD plot (Fig. 3A), it is observed that mutant (D835N) structure showed maximum deviation whereas the wild-type structure showed minimal deviation. The RMSF measures the average fluctuation of the atoms over the whole trajectory. Figure 3B represents the higher fluctuations of the mutant structure and exhibits large flexibility compared to wild-type structure till the end of simulation. Thus, both RMSD and RMSF results supports our findings from Rg and SASA plot. Furthermore, intermolecular H bond is calculated for both wild-type and mutant (D835N) structures during the simulation time and notable differences are observed by comparing these structures. The mutant structure has showed comparatively less number of intermolecular H bond formations resulting in greater flexibility. The wild-type protein has high number of intermolecular H bond formation which makes the structure more rigid. Together the analysis of RMSF and intermolecular H bond formation indicate that mutation leads to a more flexible conformation with less number of hydrogen bonds. Additionally, to support our MD simulations result, we have determined extensive collective motions of wild-type and mutant structures using essential dynamics. These motions in phase space are observed in projection of trajectories at 300 K onto first two principal components (PC1 and PC2). The clusters are well defined in wild-type than mutant structure and also the mutant occupies slightly larger space in PC2 (Fig. 5). The result obtained from PCA justifies the idea of higher flexibility of mutant than wild-type at
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300 K. The increased flexibility of mutant is confirmed by the trace of diagonalized covariance matrix values as the mutant has higher value than wild-type structure. It is also supported by the atomic density where the mutant has less atom density than wild-type protein (Fig. 6). Previous study suggests that, survival advantage of leukemic cells driven by mutant FLT3 is to a large extent because of its activation of three main intracellular signaling pathways, PI3K/PTEN/Akt/mTOR, Jak/STAT, and RAS/Raf/MEK/ ERK. AML cells expressing mutant FLT3 are killed by inhibition of mutant FLT3 chaperone, heat shock protein 90 (Hsp90) [Weisberg et al., 2010]. Hsp90 is required for the stability and function of a number of conditionally activated and expressed signaling proteins as well as multiple mutated signaling proteins, which promote cancer cell growth or survival or both [Neckers and Ivy, 2003]. The D835N mutation in FLT3 is concerned with the constitutive activation of kinases which results in improved receptor signaling. Our results indicate that, the D835N mutation results in loss of stability in FLT3 protein. Thus, this mutation resulting in an altered protein structure might affect the protein function and signal activation.
CONCLUSION On the whole, our study provides valuable insights on the structural consequences of D835N mutation in FLT3 that are evident from MD simulation results for both wild-type and mutant (D835N) protein. The mutant structure exhibits the loss of stability as observed in Rg, SASA, and the overall secondary structural content. It is further supported by RMSD and RMSF. As a result of D835N mutation, the structure becomes more flexible as indicated from H bond, PCA, and density analysis. Our results will be an important starting point for researchers to develop promising FLT3 inhibitors and to perform future experimental studies on pathological mutations and its structural consequences on FLT3.
ACKNOWLEDGMENTS SR and AA gratefully acknowledge the Indian Council of Medical Research (ICMR), Government of India Agency for the research Grant (IRIS ID: 2014-0099). RGS thanks ICMR for the Senior Research Fellowship (IRIS ID: 2014-23910). The authors would also like to thank the management of VIT University for providing necessary facilities to carry out this research project.
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