Abstract
Chemical shift frequencies represent a time-average of all the conformational states populated by a protein. Thus, chemical shift prediction programs based on sequence and database analysis yield higher accuracy for rigid rather than flexible protein segments. Here we show that the prediction accuracy can be significantly improved by averaging over an ensemble of structures, predicted solely from amino acid sequence with the Rosetta program. This approach to chemical shift and structure prediction has the potential to be useful for guiding resonance assignments, especially in solid-state NMR structural studies of membrane proteins in proteoliposomes.
References
Asbury T, Quine JR, Achuthan S, Hu J, Chapman MS, Cross TA, Bertram R (2006) PIPATH: an optimized algorithm for generating alpha-helical structures from PISEMA data. J Magn Reson 183(1):87–95
Berardi MJ, Shih WM, Harrison SC, Chou JJ (2011) Mitochondrial uncoupling protein 2 structure determined by NMR molecular fragment searching. Nature 476(7358):109–113. doi:10.1038/nature10257
Bonneau R, Tsai J, Ruczinski I, Chivian D, Rohl C, Strauss CE, Baker D (2001) Rosetta in CASP4: progress in ab initio protein structure prediction. Proteins Suppl 5:119–126. doi:10.1002/prot.1170
Bradley P, Misura KM, Baker D (2005) Toward high-resolution de novo structure prediction for small proteins. Science 309(5742):1868–1871. doi:10.1126/science.1113801
Cavalli A, Salvatella X, Dobson CM, Vendruscolo M (2007) Protein structure determination from NMR chemical shifts. Proc Natl Acad Sci USA 104(23):9615–9620. doi:10.1073/pnas.0610313104
Clayden NJ, Williams RJP (1982) Peptide group shifts. J Magn Reson 49(3):383–396. doi:10.1016/0022-2364(82)90252-9
Cornilescu G, Delaglio F, Bax A (1999) Protein backbone angle restraints from searching a database for chemical shift and sequence homology. J Biomol NMR 13(3):289–302
Dalgarno DC, Levine BA, Williams RJ (1983) Structural information from NMR secondary chemical shifts of peptide alpha C-H protons in proteins. Biosci Rep 3(5):443–452
Das R, Baker D (2008) Macromolecular modeling with Rosetta. Annu Rev Biochem 77:363–382. doi:10.1146/annurev.biochem.77.062906.171838
Das R, Qian B, Raman S, Vernon R, Thompson J, Bradley P, Khare S, Tyka MD, Bhat D, Chivian D, Kim DE, Sheffler WH, Malmstrom L, Wollacott AM, Wang C, Andre I, Baker D (2007) Structure prediction for CASP7 targets using extensive all-atom refinement with Rosetta at home. Proteins 69(Suppl 8):118–128. doi:10.1002/prot.21636
Das BB, Nothnagel HJ, Lu GJ, Son WS, Tian Y, Marassi FM, Opella SJ (2012) Structure determination of a membrane protein in proteoliposomes. J Am Chem Soc 134(4):2047–2056. doi:10.1021/ja209464f
De Angelis AA, Howell SC, Nevzorov AA, Opella SJ (2006) Structure determination of a membrane protein with two trans-membrane helices in aligned phospholipid bicelles by solid-state NMR spectroscopy. J Am Chem Soc 128(37):12256–12267. doi:10.1021/ja063640w
de Dios AC, Pearson JG, Oldfield E (1993) Secondary and tertiary structural effects on protein NMR chemical shifts: an ab initio approach. Science 260(5113):1491–1496
Delaglio F, Kontaxis G, Bax A (2000) Protein structure determination using molecular fragment replacement and NMR dipolar couplings. J Am Chem Soc 122:2142–2143
Gross K-H, Kalbitzer HR (1988) Distribution of chemical shifts in 1H nuclear magnetic resonance spectra of proteins. J Magn Reson 76(1):87–99. doi:10.1016/0022-2364(88)90203-x
Han B, Liu Y, Ginzinger SW, Wishart DS (2011) SHIFTX2: significantly improved protein chemical shift prediction. J Biomol NMR 50(1):43–57. doi:10.1007/s10858-011-9478-4
Hopf TA, Colwell LJ, Sheridan R, Rost B, Sander C, Marks DS (2012) Three-dimensional structures of membrane proteins from genomic sequencing. Cell 149(7):1607–1621. doi:10.1016/j.cell.2012.04.012
Howell SC, Mesleh MF, Opella SJ (2005) NMR structure determination of a membrane protein with two transmembrane helices in micelles: MerF of the bacterial mercury detoxification system. Biochemistry 44(13):5196–5206. doi:10.1021/bi048095v
Iwadate M, Asakura T, Williamson MP (1999) C alpha and C beta carbon-13 chemical shifts in proteins from an empirical database. J Biomol NMR 13(3):199–211
Kohlhoff KJ, Robustelli P, Cavalli A, Salvatella X, Vendruscolo M (2009) Fast and accurate predictions of protein NMR chemical shifts from interatomic distances. J Am Chem Soc 131(39):13894–13895. doi:10.1021/ja903772t
Li D-W, Bruschweiler R (2009) Certification of molecular dynamics trajectories with NMR chemical shifts. J Phys Chem Lett 1(1):246–248. doi:10.1021/jz9001345
London RE, Wingad BD, Mueller GA (2008) Dependence of amino acid side chain 13C shifts on dihedral angle: application to conformational analysis. J Am Chem Soc 130(33):11097–11105. doi:10.1021/ja802729t
Luginbuhl P, Szyperski T, Wuthrich K (1995) Statistical basis for the use of 13C chemical shifts in protein structure determination. J Magn Reson B 109(2):229–233. doi:10.1006/jmrb.1995.0016
Marassi FM, Opella SJ (2003) Simultaneous assignment and structure determination of a membrane protein from NMR orientational restraints. Protein Sci 12(3):403–411. doi:10.1110/ps.0211503
Marks DS, Colwell LJ, Sheridan R, Hopf TA, Pagnani A, Zecchina R, Sander C (2011) Protein 3D structure computed from evolutionary sequence variation. PLoS ONE 6(12):e28766. doi:10.1371/journal.pone.0028766
Markwick PR, Cervantes CF, Abel BL, Komives EA, Blackledge M, McCammon JA (2010) Enhanced conformational space sampling improves the prediction of chemical shifts in proteins. J Am Chem Soc 132(4):1220–1221. doi:10.1021/ja9093692
Meiler J (2003) PROSHIFT: protein chemical shift prediction using artificial neural networks. J Biomol NMR 26(1):25–37. doi:5121785
Mittag T, Forman-Kay JD (2007) Atomic-level characterization of disordered protein ensembles. Curr Opin Struct Biol 17(1):3–14. doi:10.1016/j.sbi.2007.01.009
Moseley HN, Sperling LJ, Rienstra CM (2010) Automated protein resonance assignments of magic angle spinning solid-state NMR spectra of beta1 immunoglobulin binding domain of protein G (GB1). J Biomol NMR 48(3):123–128. doi:10.1007/s10858-010-9448-2
Mulder FA (2009) Leucine side-chain conformation and dynamics in proteins from 13C NMR chemical shifts. ChemBioChem 10(9):1477–1479. doi:10.1002/cbic.200900086
Neal S, Nip AM, Zhang H, Wishart DS (2003) Rapid and accurate calculation of protein 1H, 13C and 15 N chemical shifts. J Biomol NMR 26(3):215–240
Nielsen JT, Eghbalnia HR, Nielsen NC (2012) Chemical shift prediction for protein structure calculation and quality assessment using an optimally parameterized force field. Prog Nucl Magn Reson Spectrosc 60:1–28. doi:10.1016/j.pnmrs.2011.05.002
Osapay K, Case DA (1991) A new analysis of proton chemical shifts in proteins. J Am Chem Soc 113(25):9436–9444. doi:10.1021/ja00025a002
Park SH, Das BB, Casagrande F, Tian Y, Nothnagel HJ, Chu M, Kiefer H, Maier K, De Angelis A, Marassi FM, Opella SJ (2012) Structure of the chemokine receptor CXCR1 in phospholipid bilayers. Nature (in press)
Pastore A, Saudek V (1990) The relationship between chemical shift and secondary structure in proteins. J Magn Reson 90(1):165–176. doi:10.1016/0022-2364(90)90375-j
Raman S, Lange OF, Rossi P, Tyka M, Wang X, Aramini J, Liu G, Ramelot TA, Eletsky A, Szyperski T, Kennedy MA, Prestegard J, Montelione GT, Baker D (2010) NMR structure determination for larger proteins using backbone-only data. Science 327(5968):1014–1018. doi:10.1126/science.1183649
Saito H (1986) Conformation-dependent 13C chemical shifts: a new means of conformational characterization as obtained by high-resolution solid-state 13C NMR. Magn Reson Chem 24(10):835–852. doi:10.1002/mrc.1260241002
Sharma M, Yi M, Dong H, Qin H, Peterson E, Busath DD, Zhou HX, Cross TA (2010) Insight into the mechanism of the influenza A proton channel from a structure in a lipid bilayer. Science 330(6003):509–512. doi:10.1126/science.1191750
Shen Y, Bax A (2007) Protein backbone chemical shifts predicted from searching a database for torsion angle and sequence homology. J Biomol NMR 38(4):289–302. doi:10.1007/s10858-007-9166-6
Shen Y, Bax A (2010) modest improvement in empirical NMR chemical shift prediction by means of an artificial neural network. J Biomol NMR 48(1):13–22. doi:10.1007/s10858-010-9433-9
Shen Y, Lange O, Delaglio F, Rossi P, Aramini JM, Liu G, Eletsky A, Wu Y, Singarapu KK, Lemak A, Ignatchenko A, Arrowsmith CH, Szyperski T, Montelione GT, Baker D, Bax A (2008) Consistent blind protein structure generation from NMR chemical shift data. Proc Natl Acad Sci USA 105(12):4685–4690. doi:10.1073/pnas.0800256105
Shen Y, Vernon R, Baker D, Bax A (2009) De novo protein structure generation from incomplete chemical shift assignments. J Biomol NMR 43(2):63–78. doi:10.1007/s10858-008-9288-5
Spera S, Bax A (1991) Empirical correlation between protein backbone conformation and C. alpha. and C. beta. 13C nuclear magnetic resonance chemical shifts. J Am Chem Soc 113(14):5490–5492. doi:10.1021/ja00014a071
Steele RA, Opella SJ (1997) Structures of the reduced and mercury-bound forms of MerP, the periplasmic protein from the bacterial mercury detoxification system. Biochemistry 36(23):6885–6895. doi:10.1021/bi9631632
Szilagyi L, Jardetzky O (1989) Proton chemical shifts and secondary structure in proteins. J Magn Reson 83(3):441–449. doi:10.1016/0022-2364(89)90341-7
Tian F, Valafar H, Prestegard JH (2001) A dipolar coupling based strategy for simultaneous resonance assignment and structure determination of protein backbones. J Am Chem Soc 123(47):11791–11796
Tycko R, Hu KN (2010) A Monte Carlo/simulated annealing algorithm for sequential resonance assignment in solid state NMR of uniformly labeled proteins with magic-angle spinning. J Magn Reson 205(2):304–314. doi:10.1016/j.jmr.2010.05.013
Vila JA, Villegas ME, Baldoni HA, Scheraga HA (2007) Predicting 13Calpha chemical shifts for validation of protein structures. J Biomol NMR 38(3):221–235. doi:10.1007/s10858-007-9162-x
Vila JA, Aramini JM, Rossi P, Kuzin A, Su M, Seetharaman J, Xiao R, Tong L, Montelione GT, Scheraga HA (2008) Quantum chemical 13C(alpha) chemical shift calculations for protein NMR structure determination, refinement, and validation. Proc Natl Acad Sci USA 105(38):14389–14394. doi:10.1073/pnas.0807105105
Vila JA, Arnautova YA, Martin OA, Scheraga HA (2009) Quantum-mechanics-derived 13Calpha chemical shift server (CheShift) for protein structure validation. Proc Natl Acad Sci USA 106(40):16972–16977. doi:10.1073/pnas.0908833106
Villegas ME, Vila JA, Scheraga HA (2007) Effects of side-chain orientation on the 13C chemical shifts of antiparallel beta-sheet model peptides. J Biomol NMR 37(2):137–146. doi:10.1007/s10858-006-9118-6
Wang Y, Jardetzky O (2004) Predicting 15 N chemical shifts in proteins using the preceding residue-specific individual shielding surfaces from phi, psi i-1, and chi 1 torsion angles. J Biomol NMR 28(4):327–340. doi:10.1023/B:JNMR.0000015397.82032.2a
Wishart DS, Sykes BD, Richards FM (1991) Relationship between nuclear magnetic resonance chemical shift and protein secondary structure. J Mol Biol 222(2):311–333
Wishart DS, Arndt D, Berjanskii M, Tang P, Zhou J, Lin G (2008) CS23D: a web server for rapid protein structure generation using NMR chemical shifts and sequence data. Nucleic Acids Res 36(Web Server issue):496–502. doi:10.1093/nar/gkn305
Xu XP, Case DA (2001) Automated prediction of 15 N, 13Calpha, 13Cbeta and 13C′ chemical shifts in proteins using a density functional database. J Biomol NMR 21(4):321–333
Yarov-Yarovoy V, Schonbrun J, Baker D (2006) Multipass membrane protein structure prediction using Rosetta. Proteins 62(4):1010–1025. doi:10.1002/prot.20817
Acknowledgments
This research was supported by grants from the National Institutes of Health (R21GM094727; P01AI074805). It utilized the Biotechnology Research Center for NMR Molecular Imaging of Proteins at UCSD (P41EB002031).
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Tian, Y., Opella, S.J. & Marassi, F.M. Improved chemical shift prediction by Rosetta conformational sampling. J Biomol NMR 54, 237–243 (2012). https://doi.org/10.1007/s10858-012-9677-7
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DOI: https://doi.org/10.1007/s10858-012-9677-7