Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 129 (2014) 345–351
Contents lists available at ScienceDirect
Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy journal homepage: www.elsevier.com/locate/saa
Quantum-mechanical DFT calculation supported Raman spectroscopic study of some amino acids in bovine insulin Bidisha Tah a, Prabir Pal a, Sourav Roy b, Debodyuti Dutta c, Sabyashachi Mishra c, Manash Ghosh a, G.B. Talapatra a,⇑ a b c
Department of Spectroscopy, Indian Association for the Cultivation of Science, Jadavpur, Kolkata 700032, India Department of Physics and Meteorology, Indian Institute of Technology, Kharagpur, West Bengal 721302, India Department of Chemistry, Indian Institute of Technology, Kharagpur, West Bengal 721302, India
h i g h l i g h t s
g r a p h i c a l a b s t r a c t
Raman spectra of amino acids by DFT
method have been calculated. Experimental Raman spectra of
insulin has been done. The simulated Raman spectrum of
insulin is obtained from amino acid spectrum.
a r t i c l e
i n f o
Article history: Received 29 November 2013 Received in revised form 12 March 2014 Accepted 20 March 2014 Available online 31 March 2014 Keywords: Insulin Raman spectroscopy Density functional theory Protein Amino acid
a b s t r a c t In this article Quantum mechanical (QM) calculations by Density Functional Theory (DFT) have been performed of all amino acids present in bovine insulin. Simulated Raman spectra of those amino acids are compared with their experimental spectra and the major bands are assigned. The results are in good agreement with experiment. We have also verified the DFT results with Quantum mechanical molecular mechanics (QM/MM) results for some amino acids. QM/MM results are very similar with the DFT results. Although the theoretical calculation of individual amino acids are feasible, but the calculated Raman spectrum of whole protein molecule is difficult or even quite impossible task, since it relies on lengthy and costly quantum-chemical computation. However, we have tried to simulate the Raman spectrum of whole protein by adding the proportionate contribution of the Raman spectra of each amino acid present in this protein. In DFT calculations, only the contributions of disulphide bonds between cysteines are included but the contribution of the peptide and hydrogen bonds have not been considered. We have recorded the Raman spectra of bovine insulin using micro-Raman set up. The experimental spectrum is found to be very similar with the resultant simulated Raman spectrum with some exceptions. Ó 2014 Elsevier B.V. All rights reserved.
Introduction
⇑ Corresponding author. Tel.: +91 33 24734971; fax: +91 33 24732805. E-mail address: [email protected] (G.B. Talapatra). http://dx.doi.org/10.1016/j.saa.2014.03.044 1386-1425/Ó 2014 Elsevier B.V. All rights reserved.
In recent years laser Raman spectroscopy is widely used as a protein, peptides and amino acids sensing tool. It provides important structural information on conformational changes of protein
346
B. Tah et al. / Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 129 (2014) 345–351
[1–5]. Changes between native and denatured insulin in the solid form as well as the spectral features of proinsulin and insulin fibrils were studied previously [3]. Computation biology [6] and molecular biology [7] is very interesting field for modern research. Our group is actively involved in studying Raman and SERS of many important bio molecules and carried out theoretical calculations using DFT and ab initio methods to get the vibrational frequencies and their detail structural information [8–11]. Many other researchers are also involved in this type of project [12–14]. The building blocks of a protein are the amino acids, having common backbone consisting of an amine group at one terminus and a carboxyl group at the other. They are different in their side chains, which give amino acids their chemical properties. These groups branch from the a-carbon separating the two terminal groups. To the best of our knowledge, the simulated Raman spectrum of whole protein molecule by DFT method is very rare or it is quite impossible till date for larger sized protein like insulin [15]. Insulin, an essential protein to control the glucose level into the blood stream, contains 51 amino acids in one monomer unit, thus the calculations quickly become very costly in terms of computational time and computer resources. There are twenty amino acids in nature. The primary structure of proteins consists of linear and unbranched polypeptides of different amino acids. The sequence and combination of amino acids are different for different protein. It is to be noted that in insulin, the individual amino acids are in their L-stereo isomeric form. In this paper, we have carried out QM calculation by DFT of each individual amino acid present in insulin and compared with their experimental Raman spectra. Major bands are also assigned. Moreover, to get the simulated Raman spectra of insulin, the proportionate contribution of Raman spectra of each amino acid was algebraically added, without considering the peptide and hydrogen bonds. Here we have also neglected some effects: (i) interactions between the neighbouring molecules, (ii) intermolecular H-bonding between amino acids, (iii) inhomogeneous excitation of amino acids. Homogeneous excitation of all amino acids may not be possible by using laser light. The surface lying amino acids may excite more than the inner amino acids. Even with these restrictions, the simulated results are in good agreement with experiment. We have also performed QM/MM calculations and verified DFT results of some individual amino acid in insulin.
Experimental section and theoretical calculations Chemical and procedure The bovine pancreas insulin was purchased from Sigma Aldrich. The insulin powder was placed on a quartz slide. Then the Raman spectra were collected using micro-Raman set up.
Instrumentation A Raman triple spectrometer (Model-T64000) fitted with holographic gratings of 1800 grooves/mm and a back illuminated CCD (Synapse) were used for Raman spectra recording. It was equipped with open stage Olympus microscope with 10, 50, and 100 objectives. The samples were placed under the microscope on a glass slide. It was excited with 514.5 nm radiation from a Spectra Physics Ar+ ion laser (Model-Stabilite 2017) source with 6 mW power at the sample. The integration time was 10 s and the number of accumulation was 5 to get better signal to noise ratio. Measurements were performed over the spectral range to cover the wave number 400–1800 cm 1 with a resolution of about 2 cm 1.
Theoretical calculations Insulin is a globular protein consisting of chain A and chain B. Schematic diagram is shown in Fig. 1. Fig. 1(a) shows the structure of bovine insulin monomer taken from PDB 2A3G. Fig. 1(b) displays that chain A contains 21 and chain B contains 30 amino acid residues. The both chains are connected by two disulfide inter-chain bridges between A7Cys and B7Cys and between A20Cys and B19Cys, while a third intra-chain bridge binds A6Cys and A11Cys of the chain A. [16,17] The A chain have two segments of a-helix (A1Gly-A8Ala) and (A13Leu-A16Leu), whereas, B chain has a central segment of a-helix (B9Ser-B19Cys) [15]. Fig. 1(c) shows the population of different amino acids present in bovine insulin molecule. Its most interesting characteristics are the ability to form different structures, including dimers, tetramers, and hexamers. Insulin is very large molecule and the hydrogen bonding effected only in the amide region and COOH region. The peptide bond between these two regions attached one amino acid with another. The theoretical calculations were carried out using Gaussian 09 code operated in the windows operating system [18,19]. Optimization of the molecular structures of different amino acids and the calculations of the vibrational frequencies for the optimized structures were done by DFT. Since Raman intensities are known to be sensitive to diffuse augmentation of the basis set [20–22]. The B3LYP hybrid density functional [23] and 6311++ G (d, p) [24] basis set were used in the DFT calculations. Cartesian displacement and calculated vibrational modes of different amino acids have been displayed using Gauss View-05 software. Many researchers performed theoretical calculations by using this basis set, since the scaled vibrational frequencies have seen to coincide with the experimentally observed values with acceptable deviations for it [25]. The theoretically estimated vibrational frequencies of all the different probable forms of the molecule as obtained from the B3LYP/6-311++ G (d, p) level of calculations were presented using appropriate scaling factor. Additionally, the calculated vibrational frequencies have been clarified by means of the potential energy distribution (PED) analysis of all the fundamental vibration modes by using VEDA 4 program [26–28]. Hybrid quantum-mechanics and molecular-mechanics (QM/ MM) method [29] was employed to account for the effects of the neighbouring residues on the Raman spectra of different aminoacids calculated from the DFT method. In QM/MM method, the region of interest in large macromolecules is treated with QM methods while including the influence of its surrounding environment at the MM level [30,31]. To the crystal structure of the protein (chain A and B of the insulin monomer), hydrogen atoms were added and the system was subjected to a short minimization employing CHARMM22 force field [32] and NAMD program [33]. The resulting structure was subjected to QM/MM optimizations and the Raman vibrational spectra were computed at the QM/ MM optimized structure. Since the experimental Raman spectrum of insulin is dominated by the contributions arising from Phe, Tyr, and Cys side chains (see Table 4), three QM/MM calculations were performed keeping one of the above amino acid side chains in the QM region and the rest of the system in the MM region. The bond between Ca and Cb of the corresponding amino acid served as the boundary between QM and MM regions. The valency of the terminal QM atoms were fulfilled by using Hydrogen as link atoms [34] and the interaction between the QM and MM regions were calculated using the so-called electronic embedding scheme [35] The QM region was treated with DFT-B3LYP functional and 6-311++G (d, p) basis set, while the MM region was treated with the AMBER force field [36]. The ONIOM method which employs a subtractive QM/MM scheme to evaluate the properties of the multi-level
B. Tah et al. / Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 129 (2014) 345–351
347
Fig. 1. (a) Structure of bovine insulin monomer (PDB 2A3G) (b) the amino acid sequence with inter- and intra-chain disulfide bridges and the a-helix segments of insulin (c) population of different amino acids present in bovine insulin monomer.
Table 1 Band assignment for simulated spectra of Phenylalanine (Fig. 2). Simulation (cm DFT
1
)
487 576 627 713 864 961 1001 1033 1074 1203 1266 1355 1468 1597 1618
Simulation (cm QM/MM
1
)
Experiment (cm
1
) [25]
495
Ring def. {bend. CCC ( 11), twist. HCCC ( 10), out CCC (55)} Ring breath. {bend. CCC (41)} Ring def. {bend. CCC (80)} COO wag. (32), ring breath {CCC str. (12), CH wag. (11)} NH2 wag* (18), CH wag. of ring (10) HNC bend. (16) Sym. ring C–C str. (43) Ring def. {CC str. (35), bend. HCC (21)} CH wag. ( 15) Ring breath. {CC str. (32)} CH wag. (31) Ring CH rock. (37) CH2 sciss. (74) Asym. ring C–C str. ( 64) In phase C2–C3 and C5–C6 str. (62)
632 722 800 1005 1014
1001 1034
1220 1264 1334 1486 1608 1629
1212
Proposed band assignment (PED%)
1602
Stretching: str.; deformation: def.; scissors: sciss.; rocking: rock.; twisting: twist.; wagging: wag.; bending: bend.; asymmetric: assy.; symmetric: sym.; breathing: breath. Negative sign in PED indicates the anti clock wise or out of phase motion. * Band is only present in QM simulation.
system is used in the present work [37,38]. All QM/MM calculations were carried out using Gaussian 09 program. Results and discussion After generating the Raman spectra of each amino acid, the individual scale factors have evaluated by comparing with experimental data from previous literature [25]. To illustrate briefly, discussion on only three main amino acid residues (Phenylalanine, Tyrosine, and Cystine) are presented in the main manuscript. The simulated Raman spectra of remaining amino acid residues and their band assignments are shown in SI (Figs. S1–S13 and Tables S1–S13). Phenylalanine (Phe) Phenylalanine (Phe) is one of the important amino acids present in insulin molecule. It has some characteristics band. Fig. 2 shows the simulated Raman spectra of L-Phe in the range 400–1700 cm 1. In order to obtain a considerably better agreement with the experimental data, scaling factor has to be used [39–41]. The
observed disagreement between the theory and experiment could be a consequence of the anharmonicity and of the general tendency of the quantum chemical methods to overestimate the force constants at the exact equilibrium geometry [42]. Nevertheless, after applying the respective scaling factors on the ab initio and DFT normal mode calculations, as one can see from Table 1, the theoretical calculations reproduce the experimental data well and allow us to assign the vibrational modes. Here we use the experimental results published in previous literature [25] to calculate the scale factor. The value of the scale factor in DFT calculation is 0.98 for Phe. The vibrational modes due to the benzene ring of Phe, appear at 1618, 1355, 1203, 1033, 1001 cm 1 from the QM calculations, which are very similar to those obtained from QM/ MM calculations as well as the experimental results [25]. The Cartesian displacements and normal modes of some selected vibrations of Phe residue are shown in Fig. 3. Tyrosine (Tyr) Tyrosine (Tyr) is also an important amino acid in insulin. Due to the absence of tryptophan, Tyrosine dominates the florescence
348
B. Tah et al. / Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 129 (2014) 345–351 Table 2 Band assignment for simulated spectra of Tyrosine (Fig. 4). Simulation (cm DFT
1
)
481 642 699 773 837 859 987 1064 1160 1199 1253 1278 1314 1336 1458 1621
Simulation (cm QM/MM
1
)
1
Experiment (cm
) [25]
Proposed band assignment (PED%)
496 637
C–C str. (36), ring def. Ring def. {bend. CCC ( 66), bend. CCO ( 10)} Ring def. {twist. CCCC (30), twist. HCCC (11)} Ring breath., {str. CC ( 12)}, COO wag.* ( 12) Ring breath. {str. CC ( 39)}, NH2 wag. (12), CH2 wag. (12) Ring CH wag. (15) CH2 rock. (34) HNC wag. (14) CH and OH of ring wag. (49) C–C str. (48), ring CH bend. (12) CO in ring str. ( 66), CH of ring wag. (17) CH wag. ( 30) Bend. HCC (38) CH wag. (47), ring CH bend. (13) CH2 sciss. (80) In phase C2–C3 and C5–C6 str. (77)
802 847 843 989 [43] 1172 1199 1267 [44]
1258
1326 1343 1479 1614
1613
Stretching: str.; deformation: def.; scissors: sciss.; rocking: rock.; twisting: twist.; wagging: wag.; bending: bend.; asymmetric: assy.; symmetric: sym.; breathing: breath. Negative sign in PED indicates the anti clock wise or out of phase motion. * Band is only present in QM simulation.
Table 3 Band assignment for simulated spectra of Cystine (Fig. 6). Simulation (cm DFT
1
)
Simulation (cm QM/MM
481 526 604 661 693 750 802 880 966 1098 1133 1205 1251 1341 1417 1450 1468 1643
1
)
Experiment (cm
501
1
) [25]
Proposed band assignment (PED%)
499 [44]
S–S str. (83) CCO bend. (34) C–S str. (53) C–S str. (21) COO- def. ( 13) C–C str. (16), COO sciss. (21) NH2 wag. (45) C–C str. (34) C–C str. (48), CH2 rock. ( 12) C–N str. (46) NH2 wag (12) NH2 twist. ( 10), CH2 wag. (37) CH wag. (48) CH wag. ( 19), CH2 wag. (37) CH wag. (48) CH sciss. (41), NH2 rock. (29) CH2 wag. (63) CH2 sciss. (82) NH2 sciss. (67)
671 720
836 1066 1091
1067 1140
1371 1429 1476
Stretching: str.; deformation: def.; scissors: sciss.; rocking: rock.; twisting: twist.; wagging: wag.; bending: bend.; asymmetric: assy.; symmetric: sym.; breathing: breath. Negative sign in PED indicates the anti clock wise or out of phase motion.
Table 4 Band assignments for the insulin spectra. The comparison between the experimental and theoretical result have been shown here with reference (Fig. 8). Our experiment (cm
1
)
Other experiment (cm
1
) [3]
Simulation (cm QM/MM
512 643
515 644
495 637
829 852 899 962 1003 1032 1123 1174 1205 1270 1336 1448 1607 1659 1336
832 854 900 963 1004 1032 1128 1177 1212 1270 1344 1450 1607 1662 1344
802 843
1014 1045
1
)
Simulation (cm DFT 481 641 718 837 859
1220
1001 1034 1116 1175 1198
1613
1335 1456 1619 1335
1
)
Proposed band assignment S–S str. Tyr COO wag. Tyr Tyr C–C str. C–C str. Phe Phe ring def. C–N str. Tyr Tyr and Phe Amide III (a helical) C–H def. CH2 def. Phe and Tyr Amide I (a helical) C–H def.
B. Tah et al. / Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 129 (2014) 345–351
Fig. 2. The simulated Raman spectrum of Phenylalanine.
behaviour of insulin. Moreover, in Raman spectra of insulin, some peaks are the characteristic peaks of Tyr. Fig. 4 shows the simulated Raman spectra of L-Tyr in the range 400–1700 cm 1. After comparing the simulated data with the experimental data [25], we have calculated the scale factor nearly equal to 0.98 for DFT calculation. In QM calculation of Tyr, the peaks around 837, 1199, 1621 cm 1 [25] originates from benzene ring vibrations which are comparable with the QM/MM results. From this observation, we can conclude that the benzene ring vibrations of Phe and Tyr residues show peaks at different position. The reason of this dissimilarity is due to difference in their side chains. In DFT calculation the peak at 773 cm 1 is due to mixture of amine, carboxylate and CH vibrations; whereas, 987 cm 1 band is originated due to the rocking vibration of CH2. The peaks at 1278, 1336 cm 1 is arising from CH vibration. The Table 2 shows the band assignment of simulated Raman spectra of Tyr and the corresponding peaks of experimental data [25,43,44]. The Cartesian displacements and normal modes of some selected vibrations of Tyr residue are shown in Fig. 5.
Cystine (Cys) In insulin, Cysteine is the only amino acid, which contains sulphur atom. There are three-disulphide bonds between Cys molecules (Fig. 1(a)). Fig. 6 shows the simulated Raman spectra of two Cys, which are connected by an S–S bond. We do not use scale factor as the experimental [25] and simulated data are comparable. In case of Raman spectra of Cys by DFT calculation, the peak 481 cm 1 is due to the vibration of S–S bond, which appears at 495 cm 1 in the QM/MM calculation. Due to S–S vibration, the peak in QM/MM calculation is originated around 495 cm 1.
349
Fig. 4. The simulated Raman spectrum of Tyrosine.
In the experimental spectra of insulin, this bond appeared around 512 cm 1. Whereas, the peaks at 661, 693 and 880 cm 1 are due to C–S stretching, COO deformation and C–C stretching respectively [25]. The band at 1098 cm 1 originated for C–N stretching vibration [22]. The peaks around 1133 and 1450 cm 1 are due to NH2 and CH2 wagging respectively. Table 3 shows the band assignment of simulated Raman spectra of Cys and the corresponding peaks of experimental data [25]. The Cartesian displacements and normal modes of some selected vibrations of Cys residue are shown in Fig. 7. Insulin Fig. 8 shows the experimental (Curve-a) and simulated (Curveb) Raman spectra of bovine insulin. They are very much similar in nature. The vibrational frequencies in cm l with assignments as well as a comparison between the experimental and theoretical results are given in Table 4. It is to be noted that our experimental spectrum is similar with the result of others [3]. Careful observation of Fig. 8 shows interesting spectral similarity between experimental and calculation along with two dissimilar regions indicated by shaded areas. We assume that the spectral changes observed here are mainly due to neglect of interactions between the neighbouring molecules and the contribution of intermolecular H-bonding between amino acids. The first spectral change is in the region (700–800 cm 1) where a peak at 718 cm 1 appeared in the QM calculated result due to the wagging of COO group, which did not appear in our experimental study. Amino acids are molecules containing an amine group (NH2), a carboxylic acid group (COOH), a hydrogen atom (H) and one of the twenty different organic groups. In protein, one amino acid is connected with other amino acid by a peptide bond when the carboxyl
Fig. 3. Cartesian displacements and normal modes of some selected vibrations of Phenylalanine residue. The numbers in the parentheses referred to the experimental value of the assigned band.
350
B. Tah et al. / Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 129 (2014) 345–351
Fig. 5. Cartesian displacements and normal modes of some selected vibrations of Tyrosine residue. The numbers in the parentheses referred to the experimental value of the assigned band.
Fig. 6. The simulated Raman spectrum of Cystine.
Fig. 7. Cartesian displacements and normal modes of some selected vibrations of Cystine residue. The numbers in the parentheses referred to the experimental value of the assigned band.
group (COOH) of one amino acid reacts with the amino group (NH2) of the other amino acid, causing the release of a molecule
Fig. 8. show the experimental (Curve-a) and QM calculated Raman spectra (Curveb) of bovine insulin.
of water (H2O). In calculation, we have not considered the effect of peptide bonds between different amino acids. Therefore, it is expected that the peak for COO wagging vibration in the experimental spectra should not appear. The second dissimilarity arises nearly 1659 cm 1 recorded in experiment, is the signature of protein’s secondary structure, assigned as amide-I (a helical) [3]. This band has been attributed to have contribution from secondary structure of insulin and several H-bonding in a-helix region that have not been considered in calculated results. Thus, in calculation, only the summation of the proportionate contribution of amino acids could not give the exact Raman spectra of insulin molecule. However, except these dissimilarities, the simulated and experimental spectra of insulin are very much similar. The observed peak 512 cm 1 in experimental spectra of insulin is due to S–S (disulfide bond between two Cys molecules) stretching vibration. The QM simulated spectrum shows a peak 481 cm 1 (for QM/MM 495 cm 1) after considering the S–S bond between Cys molecules. This shifting in simulated spectrum may be responsible for not considering the inter-chain effect between chain A and chain B, connected each other by S–S bond. The peak around 643, 829, 852and 1174 cm 1 is due to the vibration of Tyr residue [3,45]. The corresponding vibration can be observed in spectra obtained from QM calculation of Tyr (Fig. 4) and the peaks are 641, 837, 859 and 1175 cm 1. In experimental data, the bands around 1003 and 1032 cm 1 are originated from the vibrations of Phe [25]. In the QM simulated spectra of Phe these peaks are at 1001 and 1033 cm 1 (Fig. 2). Whereas the peaks around 1205, 1609 cm 1 in experimentally obtained Raman
B. Tah et al. / Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 129 (2014) 345–351
spectra, is originated due the mixture vibration of Phe and Tyr [3]. The subsequent peaks in QM simulated spectra (Figs. 2 and 4) are observed at 1198 and 1619 cm 1 respectively. In experimental spectra, the C–N vibration mode is responsible for the peak at 1123 cm 1 [3]. The corresponding peak for QM calculated spectra is at 1116 cm 1. In the experimental data the band around 1336 and 1448 cm 1 are originated due to the CH and CH2 deformation respectively. The vibrational frequencies obtained from QM/MM calculations are in good agreement with those obtained from DFT calculations and the experimental values. Conclusion All spectra obtained by QM and QM/MM calculation were compared with experimental Raman spectra. Both results were in good agreement with the experimental observation. All major bands in spectra are assigned. These amino acids are the different components of insulin molecule. By adding the proportionate contribution of simulated Raman spectra of each amino acid we get a resultant simulated spectrum. This is very similar to insulin Raman spectra with some exceptions. All the exceptions are categorically discussed. All these spectra can be used to identify an amino acid. This report gives an important database of Raman spectra of amino acids which can be useful in bio-chemical field of application. Acknowledgments The authors acknowledge Joydeep Chowdhury, Department of Physics, Sammilani Mahavidyalaya, Baghajatin Station, E.M. Bypass, Kolkata-700075, India for his valuable suggestions. The authors also thank DST, Government of India (Project No-SR/S2/ CMP-0079/2010(G)) for partial financial support. Thanks also go to the authority of the IACS for providing central instrumental facilities of micro-Raman system. Appendix A. Supplementary material Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.saa.2014.03.044. References [1] N.C. Maiti, M.M. Apetri, M.G. Zagorski, P.R. Carey, V.E. Anderson, J. Am. Chem. Soc. 126 (2004) 2399–2408. [2] C. Ortiz, D. Zhang, A.E. Ribbe, Y. Xie, D. Ben-Amotz, Biophys. Chem. 128 (2007) 150–155. [3] N.T. Yu, C.S. Liu, D.C. O’shea, J. Mol. Biol. 70 (1972) 117–132. [4] M. Mahato, P. Pal, B. Tah, M. Ghosh, G.B. Talapatra, J. Colloids Surf. B 88 (2011) 141–149. [5] V.P. Drachev, M.D. Thoreson, E.N. Khaliullin, V. Jo Davisson, V.M. Shalaev, J. Phys. Chem. B 108 (2004) 18046–18052. [6] M. Levitt, A. Warshel, Nature 253 (1975) 694–698.
351
[7] M. Levitt, Nature 224 (1969) 759–763. [8] J. Chowdhury, J. Sarkar, R. De, M. Ghosh, G.B. Talapatra, Chem. Phys. 330 (2006) 172–183. [9] J. Chowdhury, J. Sarkar, T. Tanaka, G.B. Talapatra, J. Phys. Chem. C 112 (2008) 227–239. [10] S. Chandra, J. Chowdhury, M. Ghosh, G.B. Talapatra, J. Phys. Chem. A 116 (2012) 10934–10947. [11] S. Chandra, J. Chowdhury, M. Ghosh, G.B. Talapatra, J. Colloids Inter. Sci. 399 (2013) 33–45. [12] S. Yamamoto, J. Kaminsky, P. Bour, Anal. Chem. 84 (2012) 2440–2451. [13] M. Xie, Y. Qi, Y. Hu, J. Phys. Chem. A 115 (2011) 3060–3067. [14] M. Xie, G. Zhu, Y. Hu, H. Gu, J. Chem. Phys. C 115 (2011) 20596–20602. [15] Y. Qi, Y. Hu, M. Xie, D. Xing, H. Gu, J. Raman Spectrosc. 42 (2011) 1287–1293. [16] J.L. Whittingham, D.J. Scott, K. Chance, A. Wilson, J. Finch, J. Brange, G.G. Dodson, J. Mol. Biol. 318 (2002) 479–490. [17] S. Mangialardo, F. Piccirilli, A. Perucchi, P. Dore, P. Postorino, J. Raman Spectrosc. 43 (2012) 692–700. [18] A. Atac, M. Karabacak, C. Karaca, E. Kose, Spectrochim. Acta Part A 85 (2012) 145–154. [19] M.J. Frisch, G.W. Trucks, H.B. Schlegel, G.E. Scuseria, M.A. Robb, J.R. Cheeseman, G. Scalmani, V. Barone, B. Mennucci, G.A. Petersson, H. Nakatsuji, M. Caricato, X. Li, H.P. Hratchian, A.F. Izmaylov, J. Bloino, G. Zheng, J.L. Sonnenberg, M. Hada, M. Ehara, K. Toyota, R. Fukuda, J. Hasegawa, M. Ishida, T. Nakajima, Y. Honda, O. Kitao, H. Nakai, T. Vreven, J.A. Montgomery Jr., J.E. Peralta, F. Ogliaro, M. Bearpark, J.J. Heyd, E. Brothers, K.N. Kudin, V.N. Staroverov, R. Kobayashi, J. Normand, K. Raghavachari, A. Rendell, J.C. Burant, S.S. Iyengar, J. Tomasi, M. Cossi, N. Rega, J.M. Millam, M. Klene, J.E. Knox, J.B. Cross, V. Bakken, C. Adamo, J. Jaramillo, R. Gomperts, R.E. Stratmann, O. Yazyev, A.J. Austin, R. Cammi, C. Pomelli, J.W. Ochterski, R.L. Martin, K. Morokuma, V.G. Zakrzewski, G.A. Voth, P. Salvador, J.J. Dannenberg, S. Dapprich, A.D. Daniels, O. Farkas, J.B. Foresman, J.V. Ortiz, J. Cioslowski, D.J. Fox, GAUSSIAN 09, Revision A.1, Gaussian Inc., Wallingford, CT, 2009. [20] J.R. Cheeseman, M.J. Frisch, J. Chem. Theory Comput. 7 (2011) 3323–3334. [21] D. Rappoport, F. Furche, J. Chem. Phys. 126 (2007) 1–6. [22] M.D. Halls, H.B. Schlegel, J. Chem. Phys. 111 (1999) 8819. [23] J.P. Perdew, Y. Wang, Phys. Rev. B 45 (1992) 13244–13249. [24] G. Shakila, S. Periandy, J. Atom. Mol. Opt. Phys. 201 (2011) 1–10. [25] S. Stewart, P.M. Fredericks, Spectrochim. Acta Part A 55 (1999) 1641–1660. [26] M.H. Jamróz, Jan Cz. Dobrowolski, Robert Brzozowski, J. Mol. Struct. 787 (2006) 172–183. [27] M.H. Jamróz, Spectrochim. Acta A 114 (2013) 220–230. [28] Ç. Çırak, Y. Sert, F. Ucun, Spectrochim. Acta A 92 (2012) 406–414. [29] Warshel, M. Levitt, J. Mol. Biol. 103 (1976) 227–249. [30] D. Bakowies, W. Thiel, J. Phys. Chem. 110 (1996) 10580–10594. [31] H. Lin, D.G. Truhlar, Theor. Chem. Acc. 117 (2007) 185–199. [32] J.P. Foster, F. Weinhold, J. Phys. Chem. B 102 (1998) 3586–3616. [33] J.C. Phillips, R. Braun, W. Wang, J. Gumbart, E. Tajkhorshid, E. Villa, C. Chipot, R.D. Skeel, L. Kale, K. Schulten, J. Comput. Chem. 26 (2005) 1781–1802. [34] N. Reuter, A. Dejaegere, B. Maigret, M. Karplus, J. Phys. Chem. A 104 (2000) 1720–1735. [35] J. Kstner, S. Thiel, H. M Senn, P. Sherwood, W. Thiel, J. Chem. Theory Comput. 3 (2007) 1064–1072. [36] P.K. Weiner, P.A. Kollman, J. Comput. Chem. 2 (1981) 287–303. [37] S. Dapprich, I. Komaromi, K. Byun, K. Morokuma, M. Frisch, J. Mol. Struct. (Theochem) 461 (1999) 1–21. [38] T. Vreven, K.S. Byun, I. Komaromi Jr., S. Dapprich, J.A. Montgomery, K. Morokuma, M.J. Frisch, J. Chem. Theory Comput. 2 (2006) 815–826. [39] A.P. Scott, L. Radom, J. Phys. Chem. 100 (1996) 16502–16513. [40] M.W. Wang, Chem. Phys. Lett. 256 (1996) 391–399. [41] J. Sarkar, J. Chowdhury, P. Pal, G.B. Talapatra, Vib. Spec. 41 (2006) 90–96. [42] G. Rauhut, P. Pulay, J. Phys. Chem. 99 (1995) 3093–3100. [43] L.I. Grace, R. Cohen, T.M. Dunn, D.M. Lubman, M.S. de Vries, J. Mol. Spec. 215 (2002) 204–219. [44] G. Zhu, X. Zhu, Q. Fan, X. Wan, Spectrochim. Acta Part A 78 (2011) 1187–1195. [45] W. Qian, W. Zhao, S. Krimm, J. Mol. Struct. 250 (1991) 89–102.
Contents lists available at ScienceDirect
Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy journal homepage: www.elsevier.com/locate/saa
Quantum-mechanical DFT calculation supported Raman spectroscopic study of some amino acids in bovine insulin Bidisha Tah a, Prabir Pal a, Sourav Roy b, Debodyuti Dutta c, Sabyashachi Mishra c, Manash Ghosh a, G.B. Talapatra a,⇑ a b c
Department of Spectroscopy, Indian Association for the Cultivation of Science, Jadavpur, Kolkata 700032, India Department of Physics and Meteorology, Indian Institute of Technology, Kharagpur, West Bengal 721302, India Department of Chemistry, Indian Institute of Technology, Kharagpur, West Bengal 721302, India
h i g h l i g h t s
g r a p h i c a l a b s t r a c t
Raman spectra of amino acids by DFT
method have been calculated. Experimental Raman spectra of
insulin has been done. The simulated Raman spectrum of
insulin is obtained from amino acid spectrum.
a r t i c l e
i n f o
Article history: Received 29 November 2013 Received in revised form 12 March 2014 Accepted 20 March 2014 Available online 31 March 2014 Keywords: Insulin Raman spectroscopy Density functional theory Protein Amino acid
a b s t r a c t In this article Quantum mechanical (QM) calculations by Density Functional Theory (DFT) have been performed of all amino acids present in bovine insulin. Simulated Raman spectra of those amino acids are compared with their experimental spectra and the major bands are assigned. The results are in good agreement with experiment. We have also verified the DFT results with Quantum mechanical molecular mechanics (QM/MM) results for some amino acids. QM/MM results are very similar with the DFT results. Although the theoretical calculation of individual amino acids are feasible, but the calculated Raman spectrum of whole protein molecule is difficult or even quite impossible task, since it relies on lengthy and costly quantum-chemical computation. However, we have tried to simulate the Raman spectrum of whole protein by adding the proportionate contribution of the Raman spectra of each amino acid present in this protein. In DFT calculations, only the contributions of disulphide bonds between cysteines are included but the contribution of the peptide and hydrogen bonds have not been considered. We have recorded the Raman spectra of bovine insulin using micro-Raman set up. The experimental spectrum is found to be very similar with the resultant simulated Raman spectrum with some exceptions. Ó 2014 Elsevier B.V. All rights reserved.
Introduction
⇑ Corresponding author. Tel.: +91 33 24734971; fax: +91 33 24732805. E-mail address: [email protected] (G.B. Talapatra). http://dx.doi.org/10.1016/j.saa.2014.03.044 1386-1425/Ó 2014 Elsevier B.V. All rights reserved.
In recent years laser Raman spectroscopy is widely used as a protein, peptides and amino acids sensing tool. It provides important structural information on conformational changes of protein
346
B. Tah et al. / Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 129 (2014) 345–351
[1–5]. Changes between native and denatured insulin in the solid form as well as the spectral features of proinsulin and insulin fibrils were studied previously [3]. Computation biology [6] and molecular biology [7] is very interesting field for modern research. Our group is actively involved in studying Raman and SERS of many important bio molecules and carried out theoretical calculations using DFT and ab initio methods to get the vibrational frequencies and their detail structural information [8–11]. Many other researchers are also involved in this type of project [12–14]. The building blocks of a protein are the amino acids, having common backbone consisting of an amine group at one terminus and a carboxyl group at the other. They are different in their side chains, which give amino acids their chemical properties. These groups branch from the a-carbon separating the two terminal groups. To the best of our knowledge, the simulated Raman spectrum of whole protein molecule by DFT method is very rare or it is quite impossible till date for larger sized protein like insulin [15]. Insulin, an essential protein to control the glucose level into the blood stream, contains 51 amino acids in one monomer unit, thus the calculations quickly become very costly in terms of computational time and computer resources. There are twenty amino acids in nature. The primary structure of proteins consists of linear and unbranched polypeptides of different amino acids. The sequence and combination of amino acids are different for different protein. It is to be noted that in insulin, the individual amino acids are in their L-stereo isomeric form. In this paper, we have carried out QM calculation by DFT of each individual amino acid present in insulin and compared with their experimental Raman spectra. Major bands are also assigned. Moreover, to get the simulated Raman spectra of insulin, the proportionate contribution of Raman spectra of each amino acid was algebraically added, without considering the peptide and hydrogen bonds. Here we have also neglected some effects: (i) interactions between the neighbouring molecules, (ii) intermolecular H-bonding between amino acids, (iii) inhomogeneous excitation of amino acids. Homogeneous excitation of all amino acids may not be possible by using laser light. The surface lying amino acids may excite more than the inner amino acids. Even with these restrictions, the simulated results are in good agreement with experiment. We have also performed QM/MM calculations and verified DFT results of some individual amino acid in insulin.
Experimental section and theoretical calculations Chemical and procedure The bovine pancreas insulin was purchased from Sigma Aldrich. The insulin powder was placed on a quartz slide. Then the Raman spectra were collected using micro-Raman set up.
Instrumentation A Raman triple spectrometer (Model-T64000) fitted with holographic gratings of 1800 grooves/mm and a back illuminated CCD (Synapse) were used for Raman spectra recording. It was equipped with open stage Olympus microscope with 10, 50, and 100 objectives. The samples were placed under the microscope on a glass slide. It was excited with 514.5 nm radiation from a Spectra Physics Ar+ ion laser (Model-Stabilite 2017) source with 6 mW power at the sample. The integration time was 10 s and the number of accumulation was 5 to get better signal to noise ratio. Measurements were performed over the spectral range to cover the wave number 400–1800 cm 1 with a resolution of about 2 cm 1.
Theoretical calculations Insulin is a globular protein consisting of chain A and chain B. Schematic diagram is shown in Fig. 1. Fig. 1(a) shows the structure of bovine insulin monomer taken from PDB 2A3G. Fig. 1(b) displays that chain A contains 21 and chain B contains 30 amino acid residues. The both chains are connected by two disulfide inter-chain bridges between A7Cys and B7Cys and between A20Cys and B19Cys, while a third intra-chain bridge binds A6Cys and A11Cys of the chain A. [16,17] The A chain have two segments of a-helix (A1Gly-A8Ala) and (A13Leu-A16Leu), whereas, B chain has a central segment of a-helix (B9Ser-B19Cys) [15]. Fig. 1(c) shows the population of different amino acids present in bovine insulin molecule. Its most interesting characteristics are the ability to form different structures, including dimers, tetramers, and hexamers. Insulin is very large molecule and the hydrogen bonding effected only in the amide region and COOH region. The peptide bond between these two regions attached one amino acid with another. The theoretical calculations were carried out using Gaussian 09 code operated in the windows operating system [18,19]. Optimization of the molecular structures of different amino acids and the calculations of the vibrational frequencies for the optimized structures were done by DFT. Since Raman intensities are known to be sensitive to diffuse augmentation of the basis set [20–22]. The B3LYP hybrid density functional [23] and 6311++ G (d, p) [24] basis set were used in the DFT calculations. Cartesian displacement and calculated vibrational modes of different amino acids have been displayed using Gauss View-05 software. Many researchers performed theoretical calculations by using this basis set, since the scaled vibrational frequencies have seen to coincide with the experimentally observed values with acceptable deviations for it [25]. The theoretically estimated vibrational frequencies of all the different probable forms of the molecule as obtained from the B3LYP/6-311++ G (d, p) level of calculations were presented using appropriate scaling factor. Additionally, the calculated vibrational frequencies have been clarified by means of the potential energy distribution (PED) analysis of all the fundamental vibration modes by using VEDA 4 program [26–28]. Hybrid quantum-mechanics and molecular-mechanics (QM/ MM) method [29] was employed to account for the effects of the neighbouring residues on the Raman spectra of different aminoacids calculated from the DFT method. In QM/MM method, the region of interest in large macromolecules is treated with QM methods while including the influence of its surrounding environment at the MM level [30,31]. To the crystal structure of the protein (chain A and B of the insulin monomer), hydrogen atoms were added and the system was subjected to a short minimization employing CHARMM22 force field [32] and NAMD program [33]. The resulting structure was subjected to QM/MM optimizations and the Raman vibrational spectra were computed at the QM/ MM optimized structure. Since the experimental Raman spectrum of insulin is dominated by the contributions arising from Phe, Tyr, and Cys side chains (see Table 4), three QM/MM calculations were performed keeping one of the above amino acid side chains in the QM region and the rest of the system in the MM region. The bond between Ca and Cb of the corresponding amino acid served as the boundary between QM and MM regions. The valency of the terminal QM atoms were fulfilled by using Hydrogen as link atoms [34] and the interaction between the QM and MM regions were calculated using the so-called electronic embedding scheme [35] The QM region was treated with DFT-B3LYP functional and 6-311++G (d, p) basis set, while the MM region was treated with the AMBER force field [36]. The ONIOM method which employs a subtractive QM/MM scheme to evaluate the properties of the multi-level
B. Tah et al. / Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 129 (2014) 345–351
347
Fig. 1. (a) Structure of bovine insulin monomer (PDB 2A3G) (b) the amino acid sequence with inter- and intra-chain disulfide bridges and the a-helix segments of insulin (c) population of different amino acids present in bovine insulin monomer.
Table 1 Band assignment for simulated spectra of Phenylalanine (Fig. 2). Simulation (cm DFT
1
)
487 576 627 713 864 961 1001 1033 1074 1203 1266 1355 1468 1597 1618
Simulation (cm QM/MM
1
)
Experiment (cm
1
) [25]
495
Ring def. {bend. CCC ( 11), twist. HCCC ( 10), out CCC (55)} Ring breath. {bend. CCC (41)} Ring def. {bend. CCC (80)} COO wag. (32), ring breath {CCC str. (12), CH wag. (11)} NH2 wag* (18), CH wag. of ring (10) HNC bend. (16) Sym. ring C–C str. (43) Ring def. {CC str. (35), bend. HCC (21)} CH wag. ( 15) Ring breath. {CC str. (32)} CH wag. (31) Ring CH rock. (37) CH2 sciss. (74) Asym. ring C–C str. ( 64) In phase C2–C3 and C5–C6 str. (62)
632 722 800 1005 1014
1001 1034
1220 1264 1334 1486 1608 1629
1212
Proposed band assignment (PED%)
1602
Stretching: str.; deformation: def.; scissors: sciss.; rocking: rock.; twisting: twist.; wagging: wag.; bending: bend.; asymmetric: assy.; symmetric: sym.; breathing: breath. Negative sign in PED indicates the anti clock wise or out of phase motion. * Band is only present in QM simulation.
system is used in the present work [37,38]. All QM/MM calculations were carried out using Gaussian 09 program. Results and discussion After generating the Raman spectra of each amino acid, the individual scale factors have evaluated by comparing with experimental data from previous literature [25]. To illustrate briefly, discussion on only three main amino acid residues (Phenylalanine, Tyrosine, and Cystine) are presented in the main manuscript. The simulated Raman spectra of remaining amino acid residues and their band assignments are shown in SI (Figs. S1–S13 and Tables S1–S13). Phenylalanine (Phe) Phenylalanine (Phe) is one of the important amino acids present in insulin molecule. It has some characteristics band. Fig. 2 shows the simulated Raman spectra of L-Phe in the range 400–1700 cm 1. In order to obtain a considerably better agreement with the experimental data, scaling factor has to be used [39–41]. The
observed disagreement between the theory and experiment could be a consequence of the anharmonicity and of the general tendency of the quantum chemical methods to overestimate the force constants at the exact equilibrium geometry [42]. Nevertheless, after applying the respective scaling factors on the ab initio and DFT normal mode calculations, as one can see from Table 1, the theoretical calculations reproduce the experimental data well and allow us to assign the vibrational modes. Here we use the experimental results published in previous literature [25] to calculate the scale factor. The value of the scale factor in DFT calculation is 0.98 for Phe. The vibrational modes due to the benzene ring of Phe, appear at 1618, 1355, 1203, 1033, 1001 cm 1 from the QM calculations, which are very similar to those obtained from QM/ MM calculations as well as the experimental results [25]. The Cartesian displacements and normal modes of some selected vibrations of Phe residue are shown in Fig. 3. Tyrosine (Tyr) Tyrosine (Tyr) is also an important amino acid in insulin. Due to the absence of tryptophan, Tyrosine dominates the florescence
348
B. Tah et al. / Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 129 (2014) 345–351 Table 2 Band assignment for simulated spectra of Tyrosine (Fig. 4). Simulation (cm DFT
1
)
481 642 699 773 837 859 987 1064 1160 1199 1253 1278 1314 1336 1458 1621
Simulation (cm QM/MM
1
)
1
Experiment (cm
) [25]
Proposed band assignment (PED%)
496 637
C–C str. (36), ring def. Ring def. {bend. CCC ( 66), bend. CCO ( 10)} Ring def. {twist. CCCC (30), twist. HCCC (11)} Ring breath., {str. CC ( 12)}, COO wag.* ( 12) Ring breath. {str. CC ( 39)}, NH2 wag. (12), CH2 wag. (12) Ring CH wag. (15) CH2 rock. (34) HNC wag. (14) CH and OH of ring wag. (49) C–C str. (48), ring CH bend. (12) CO in ring str. ( 66), CH of ring wag. (17) CH wag. ( 30) Bend. HCC (38) CH wag. (47), ring CH bend. (13) CH2 sciss. (80) In phase C2–C3 and C5–C6 str. (77)
802 847 843 989 [43] 1172 1199 1267 [44]
1258
1326 1343 1479 1614
1613
Stretching: str.; deformation: def.; scissors: sciss.; rocking: rock.; twisting: twist.; wagging: wag.; bending: bend.; asymmetric: assy.; symmetric: sym.; breathing: breath. Negative sign in PED indicates the anti clock wise or out of phase motion. * Band is only present in QM simulation.
Table 3 Band assignment for simulated spectra of Cystine (Fig. 6). Simulation (cm DFT
1
)
Simulation (cm QM/MM
481 526 604 661 693 750 802 880 966 1098 1133 1205 1251 1341 1417 1450 1468 1643
1
)
Experiment (cm
501
1
) [25]
Proposed band assignment (PED%)
499 [44]
S–S str. (83) CCO bend. (34) C–S str. (53) C–S str. (21) COO- def. ( 13) C–C str. (16), COO sciss. (21) NH2 wag. (45) C–C str. (34) C–C str. (48), CH2 rock. ( 12) C–N str. (46) NH2 wag (12) NH2 twist. ( 10), CH2 wag. (37) CH wag. (48) CH wag. ( 19), CH2 wag. (37) CH wag. (48) CH sciss. (41), NH2 rock. (29) CH2 wag. (63) CH2 sciss. (82) NH2 sciss. (67)
671 720
836 1066 1091
1067 1140
1371 1429 1476
Stretching: str.; deformation: def.; scissors: sciss.; rocking: rock.; twisting: twist.; wagging: wag.; bending: bend.; asymmetric: assy.; symmetric: sym.; breathing: breath. Negative sign in PED indicates the anti clock wise or out of phase motion.
Table 4 Band assignments for the insulin spectra. The comparison between the experimental and theoretical result have been shown here with reference (Fig. 8). Our experiment (cm
1
)
Other experiment (cm
1
) [3]
Simulation (cm QM/MM
512 643
515 644
495 637
829 852 899 962 1003 1032 1123 1174 1205 1270 1336 1448 1607 1659 1336
832 854 900 963 1004 1032 1128 1177 1212 1270 1344 1450 1607 1662 1344
802 843
1014 1045
1
)
Simulation (cm DFT 481 641 718 837 859
1220
1001 1034 1116 1175 1198
1613
1335 1456 1619 1335
1
)
Proposed band assignment S–S str. Tyr COO wag. Tyr Tyr C–C str. C–C str. Phe Phe ring def. C–N str. Tyr Tyr and Phe Amide III (a helical) C–H def. CH2 def. Phe and Tyr Amide I (a helical) C–H def.
B. Tah et al. / Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 129 (2014) 345–351
Fig. 2. The simulated Raman spectrum of Phenylalanine.
behaviour of insulin. Moreover, in Raman spectra of insulin, some peaks are the characteristic peaks of Tyr. Fig. 4 shows the simulated Raman spectra of L-Tyr in the range 400–1700 cm 1. After comparing the simulated data with the experimental data [25], we have calculated the scale factor nearly equal to 0.98 for DFT calculation. In QM calculation of Tyr, the peaks around 837, 1199, 1621 cm 1 [25] originates from benzene ring vibrations which are comparable with the QM/MM results. From this observation, we can conclude that the benzene ring vibrations of Phe and Tyr residues show peaks at different position. The reason of this dissimilarity is due to difference in their side chains. In DFT calculation the peak at 773 cm 1 is due to mixture of amine, carboxylate and CH vibrations; whereas, 987 cm 1 band is originated due to the rocking vibration of CH2. The peaks at 1278, 1336 cm 1 is arising from CH vibration. The Table 2 shows the band assignment of simulated Raman spectra of Tyr and the corresponding peaks of experimental data [25,43,44]. The Cartesian displacements and normal modes of some selected vibrations of Tyr residue are shown in Fig. 5.
Cystine (Cys) In insulin, Cysteine is the only amino acid, which contains sulphur atom. There are three-disulphide bonds between Cys molecules (Fig. 1(a)). Fig. 6 shows the simulated Raman spectra of two Cys, which are connected by an S–S bond. We do not use scale factor as the experimental [25] and simulated data are comparable. In case of Raman spectra of Cys by DFT calculation, the peak 481 cm 1 is due to the vibration of S–S bond, which appears at 495 cm 1 in the QM/MM calculation. Due to S–S vibration, the peak in QM/MM calculation is originated around 495 cm 1.
349
Fig. 4. The simulated Raman spectrum of Tyrosine.
In the experimental spectra of insulin, this bond appeared around 512 cm 1. Whereas, the peaks at 661, 693 and 880 cm 1 are due to C–S stretching, COO deformation and C–C stretching respectively [25]. The band at 1098 cm 1 originated for C–N stretching vibration [22]. The peaks around 1133 and 1450 cm 1 are due to NH2 and CH2 wagging respectively. Table 3 shows the band assignment of simulated Raman spectra of Cys and the corresponding peaks of experimental data [25]. The Cartesian displacements and normal modes of some selected vibrations of Cys residue are shown in Fig. 7. Insulin Fig. 8 shows the experimental (Curve-a) and simulated (Curveb) Raman spectra of bovine insulin. They are very much similar in nature. The vibrational frequencies in cm l with assignments as well as a comparison between the experimental and theoretical results are given in Table 4. It is to be noted that our experimental spectrum is similar with the result of others [3]. Careful observation of Fig. 8 shows interesting spectral similarity between experimental and calculation along with two dissimilar regions indicated by shaded areas. We assume that the spectral changes observed here are mainly due to neglect of interactions between the neighbouring molecules and the contribution of intermolecular H-bonding between amino acids. The first spectral change is in the region (700–800 cm 1) where a peak at 718 cm 1 appeared in the QM calculated result due to the wagging of COO group, which did not appear in our experimental study. Amino acids are molecules containing an amine group (NH2), a carboxylic acid group (COOH), a hydrogen atom (H) and one of the twenty different organic groups. In protein, one amino acid is connected with other amino acid by a peptide bond when the carboxyl
Fig. 3. Cartesian displacements and normal modes of some selected vibrations of Phenylalanine residue. The numbers in the parentheses referred to the experimental value of the assigned band.
350
B. Tah et al. / Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 129 (2014) 345–351
Fig. 5. Cartesian displacements and normal modes of some selected vibrations of Tyrosine residue. The numbers in the parentheses referred to the experimental value of the assigned band.
Fig. 6. The simulated Raman spectrum of Cystine.
Fig. 7. Cartesian displacements and normal modes of some selected vibrations of Cystine residue. The numbers in the parentheses referred to the experimental value of the assigned band.
group (COOH) of one amino acid reacts with the amino group (NH2) of the other amino acid, causing the release of a molecule
Fig. 8. show the experimental (Curve-a) and QM calculated Raman spectra (Curveb) of bovine insulin.
of water (H2O). In calculation, we have not considered the effect of peptide bonds between different amino acids. Therefore, it is expected that the peak for COO wagging vibration in the experimental spectra should not appear. The second dissimilarity arises nearly 1659 cm 1 recorded in experiment, is the signature of protein’s secondary structure, assigned as amide-I (a helical) [3]. This band has been attributed to have contribution from secondary structure of insulin and several H-bonding in a-helix region that have not been considered in calculated results. Thus, in calculation, only the summation of the proportionate contribution of amino acids could not give the exact Raman spectra of insulin molecule. However, except these dissimilarities, the simulated and experimental spectra of insulin are very much similar. The observed peak 512 cm 1 in experimental spectra of insulin is due to S–S (disulfide bond between two Cys molecules) stretching vibration. The QM simulated spectrum shows a peak 481 cm 1 (for QM/MM 495 cm 1) after considering the S–S bond between Cys molecules. This shifting in simulated spectrum may be responsible for not considering the inter-chain effect between chain A and chain B, connected each other by S–S bond. The peak around 643, 829, 852and 1174 cm 1 is due to the vibration of Tyr residue [3,45]. The corresponding vibration can be observed in spectra obtained from QM calculation of Tyr (Fig. 4) and the peaks are 641, 837, 859 and 1175 cm 1. In experimental data, the bands around 1003 and 1032 cm 1 are originated from the vibrations of Phe [25]. In the QM simulated spectra of Phe these peaks are at 1001 and 1033 cm 1 (Fig. 2). Whereas the peaks around 1205, 1609 cm 1 in experimentally obtained Raman
B. Tah et al. / Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 129 (2014) 345–351
spectra, is originated due the mixture vibration of Phe and Tyr [3]. The subsequent peaks in QM simulated spectra (Figs. 2 and 4) are observed at 1198 and 1619 cm 1 respectively. In experimental spectra, the C–N vibration mode is responsible for the peak at 1123 cm 1 [3]. The corresponding peak for QM calculated spectra is at 1116 cm 1. In the experimental data the band around 1336 and 1448 cm 1 are originated due to the CH and CH2 deformation respectively. The vibrational frequencies obtained from QM/MM calculations are in good agreement with those obtained from DFT calculations and the experimental values. Conclusion All spectra obtained by QM and QM/MM calculation were compared with experimental Raman spectra. Both results were in good agreement with the experimental observation. All major bands in spectra are assigned. These amino acids are the different components of insulin molecule. By adding the proportionate contribution of simulated Raman spectra of each amino acid we get a resultant simulated spectrum. This is very similar to insulin Raman spectra with some exceptions. All the exceptions are categorically discussed. All these spectra can be used to identify an amino acid. This report gives an important database of Raman spectra of amino acids which can be useful in bio-chemical field of application. Acknowledgments The authors acknowledge Joydeep Chowdhury, Department of Physics, Sammilani Mahavidyalaya, Baghajatin Station, E.M. Bypass, Kolkata-700075, India for his valuable suggestions. The authors also thank DST, Government of India (Project No-SR/S2/ CMP-0079/2010(G)) for partial financial support. Thanks also go to the authority of the IACS for providing central instrumental facilities of micro-Raman system. Appendix A. Supplementary material Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.saa.2014.03.044. References [1] N.C. Maiti, M.M. Apetri, M.G. Zagorski, P.R. Carey, V.E. Anderson, J. Am. Chem. Soc. 126 (2004) 2399–2408. [2] C. Ortiz, D. Zhang, A.E. Ribbe, Y. Xie, D. Ben-Amotz, Biophys. Chem. 128 (2007) 150–155. [3] N.T. Yu, C.S. Liu, D.C. O’shea, J. Mol. Biol. 70 (1972) 117–132. [4] M. Mahato, P. Pal, B. Tah, M. Ghosh, G.B. Talapatra, J. Colloids Surf. B 88 (2011) 141–149. [5] V.P. Drachev, M.D. Thoreson, E.N. Khaliullin, V. Jo Davisson, V.M. Shalaev, J. Phys. Chem. B 108 (2004) 18046–18052. [6] M. Levitt, A. Warshel, Nature 253 (1975) 694–698.
351
[7] M. Levitt, Nature 224 (1969) 759–763. [8] J. Chowdhury, J. Sarkar, R. De, M. Ghosh, G.B. Talapatra, Chem. Phys. 330 (2006) 172–183. [9] J. Chowdhury, J. Sarkar, T. Tanaka, G.B. Talapatra, J. Phys. Chem. C 112 (2008) 227–239. [10] S. Chandra, J. Chowdhury, M. Ghosh, G.B. Talapatra, J. Phys. Chem. A 116 (2012) 10934–10947. [11] S. Chandra, J. Chowdhury, M. Ghosh, G.B. Talapatra, J. Colloids Inter. Sci. 399 (2013) 33–45. [12] S. Yamamoto, J. Kaminsky, P. Bour, Anal. Chem. 84 (2012) 2440–2451. [13] M. Xie, Y. Qi, Y. Hu, J. Phys. Chem. A 115 (2011) 3060–3067. [14] M. Xie, G. Zhu, Y. Hu, H. Gu, J. Chem. Phys. C 115 (2011) 20596–20602. [15] Y. Qi, Y. Hu, M. Xie, D. Xing, H. Gu, J. Raman Spectrosc. 42 (2011) 1287–1293. [16] J.L. Whittingham, D.J. Scott, K. Chance, A. Wilson, J. Finch, J. Brange, G.G. Dodson, J. Mol. Biol. 318 (2002) 479–490. [17] S. Mangialardo, F. Piccirilli, A. Perucchi, P. Dore, P. Postorino, J. Raman Spectrosc. 43 (2012) 692–700. [18] A. Atac, M. Karabacak, C. Karaca, E. Kose, Spectrochim. Acta Part A 85 (2012) 145–154. [19] M.J. Frisch, G.W. Trucks, H.B. Schlegel, G.E. Scuseria, M.A. Robb, J.R. Cheeseman, G. Scalmani, V. Barone, B. Mennucci, G.A. Petersson, H. Nakatsuji, M. Caricato, X. Li, H.P. Hratchian, A.F. Izmaylov, J. Bloino, G. Zheng, J.L. Sonnenberg, M. Hada, M. Ehara, K. Toyota, R. Fukuda, J. Hasegawa, M. Ishida, T. Nakajima, Y. Honda, O. Kitao, H. Nakai, T. Vreven, J.A. Montgomery Jr., J.E. Peralta, F. Ogliaro, M. Bearpark, J.J. Heyd, E. Brothers, K.N. Kudin, V.N. Staroverov, R. Kobayashi, J. Normand, K. Raghavachari, A. Rendell, J.C. Burant, S.S. Iyengar, J. Tomasi, M. Cossi, N. Rega, J.M. Millam, M. Klene, J.E. Knox, J.B. Cross, V. Bakken, C. Adamo, J. Jaramillo, R. Gomperts, R.E. Stratmann, O. Yazyev, A.J. Austin, R. Cammi, C. Pomelli, J.W. Ochterski, R.L. Martin, K. Morokuma, V.G. Zakrzewski, G.A. Voth, P. Salvador, J.J. Dannenberg, S. Dapprich, A.D. Daniels, O. Farkas, J.B. Foresman, J.V. Ortiz, J. Cioslowski, D.J. Fox, GAUSSIAN 09, Revision A.1, Gaussian Inc., Wallingford, CT, 2009. [20] J.R. Cheeseman, M.J. Frisch, J. Chem. Theory Comput. 7 (2011) 3323–3334. [21] D. Rappoport, F. Furche, J. Chem. Phys. 126 (2007) 1–6. [22] M.D. Halls, H.B. Schlegel, J. Chem. Phys. 111 (1999) 8819. [23] J.P. Perdew, Y. Wang, Phys. Rev. B 45 (1992) 13244–13249. [24] G. Shakila, S. Periandy, J. Atom. Mol. Opt. Phys. 201 (2011) 1–10. [25] S. Stewart, P.M. Fredericks, Spectrochim. Acta Part A 55 (1999) 1641–1660. [26] M.H. Jamróz, Jan Cz. Dobrowolski, Robert Brzozowski, J. Mol. Struct. 787 (2006) 172–183. [27] M.H. Jamróz, Spectrochim. Acta A 114 (2013) 220–230. [28] Ç. Çırak, Y. Sert, F. Ucun, Spectrochim. Acta A 92 (2012) 406–414. [29] Warshel, M. Levitt, J. Mol. Biol. 103 (1976) 227–249. [30] D. Bakowies, W. Thiel, J. Phys. Chem. 110 (1996) 10580–10594. [31] H. Lin, D.G. Truhlar, Theor. Chem. Acc. 117 (2007) 185–199. [32] J.P. Foster, F. Weinhold, J. Phys. Chem. B 102 (1998) 3586–3616. [33] J.C. Phillips, R. Braun, W. Wang, J. Gumbart, E. Tajkhorshid, E. Villa, C. Chipot, R.D. Skeel, L. Kale, K. Schulten, J. Comput. Chem. 26 (2005) 1781–1802. [34] N. Reuter, A. Dejaegere, B. Maigret, M. Karplus, J. Phys. Chem. A 104 (2000) 1720–1735. [35] J. Kstner, S. Thiel, H. M Senn, P. Sherwood, W. Thiel, J. Chem. Theory Comput. 3 (2007) 1064–1072. [36] P.K. Weiner, P.A. Kollman, J. Comput. Chem. 2 (1981) 287–303. [37] S. Dapprich, I. Komaromi, K. Byun, K. Morokuma, M. Frisch, J. Mol. Struct. (Theochem) 461 (1999) 1–21. [38] T. Vreven, K.S. Byun, I. Komaromi Jr., S. Dapprich, J.A. Montgomery, K. Morokuma, M.J. Frisch, J. Chem. Theory Comput. 2 (2006) 815–826. [39] A.P. Scott, L. Radom, J. Phys. Chem. 100 (1996) 16502–16513. [40] M.W. Wang, Chem. Phys. Lett. 256 (1996) 391–399. [41] J. Sarkar, J. Chowdhury, P. Pal, G.B. Talapatra, Vib. Spec. 41 (2006) 90–96. [42] G. Rauhut, P. Pulay, J. Phys. Chem. 99 (1995) 3093–3100. [43] L.I. Grace, R. Cohen, T.M. Dunn, D.M. Lubman, M.S. de Vries, J. Mol. Spec. 215 (2002) 204–219. [44] G. Zhu, X. Zhu, Q. Fan, X. Wan, Spectrochim. Acta Part A 78 (2011) 1187–1195. [45] W. Qian, W. Zhao, S. Krimm, J. Mol. Struct. 250 (1991) 89–102.
