Abstract
The combination of the wide availability of protein backbone and side-chain NMR chemical shifts with advances in understanding of their relationship to protein structure makes these parameters useful for the assessment of structural-dynamic protein models. A new chemical shift predictor (PPM) is introduced, which is solely based on physical–chemical contributions to the chemical shifts for both the protein backbone and methyl-bearing amino-acid side chains. To explicitly account for the effects of protein dynamics on chemical shifts, PPM was directly refined against 100 ns long molecular dynamics (MD) simulations of 35 proteins with known experimental NMR chemical shifts. It is found that the prediction of methyl-proton chemical shifts by PPM from MD ensembles is improved over other methods, while backbone Cα, Cβ, C′, N, and HN chemical shifts are predicted at an accuracy comparable to the latest generation of chemical shift prediction programs. PPM is particularly suitable for the rapid evaluation of large protein conformational ensembles on their consistency with experimental NMR data and the possible improvement of protein force fields from chemical shifts.
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References
Beauchamp KA, Lin YS, Das R, Pande VS (2012) Are protein force fields getting better? A systematic benchmark on 524 diverse NMR measurements. J Chem Theory Comput 8:1409–1414
Berendsen HJC, van der Spoel D, van Drunen R (1995) GROMACS—a message-passing parallel molecular-dynamics implementation. Comput Phys Commun 91:43–56
Berjanskii MV, Wishart DS (2005) A simple method to predict protein flexibility using secondary chemical shifts. J Am Chem Soc 127:14970–14971
Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE (2000) The protein data bank. Nucleic Acids Res 28:235–242
Brüschweiler R, Case DA (1994) Adding harmonic motion to the Karplus relation for spin–spin coupling. J Am Chem Soc 116:11199–11200
Buck M, Bouguet-Bonnet S, Pastor RW, MacKerell AD (2006) Importance of the CMAP correction to the CHARMM22 protein force field: dynamics of hen lysozyme. Biophys J 90:L36–L38
Cavalli A, Salvatella X, Dobson CM, Vendruscolo M (2007) Protein structure determination from NMR chemical shifts. Proc Natl Acad Sci USA 104:9615–9620
Duan Y, Wu C, Chowdhury S, Lee MC, Xiong G, Zhang W, Yang R, Cieplak P, Luo R, Lee T, Caldwell J, Wang J, Kollman P (2003) A point-charge force field for molecular mechanics simulations of proteins based on condensed-phase quantum mechanical calculations. J Comput Chem 24:1999–2012
Haigh CW, Mallion RB (1972) New tables of ring current shielding in proton magnetic-resonance. Org Magn Res 4:203–228
Haigh CW, Mallion RB (1979) Ring current theories in nuclear magnetic-resonance. Prog NMR Spectrosc 13:303–344
Han B, Liu YF, Ginzinger SW, Wishart DS (2011) SHIFTX2: significantly improved protein chemical shift prediction. J Biomol NMR 50:43–57
Hess B, Kutzner C, van der Spoel D, Lindahl E (2008) GROMACS 4: algorithms for highly efficient, load-balanced, and scalable molecular simulation. J Chem Theory Comput 4:435–447
Hornak V, Abel R, Okur A, Strockbine B, Roitberg A, Simmerling C (2006) Comparison of multiple amber force fields and development of improved protein backbone parameters. Proteins 65:712–725
Jorgensen WL, Chandrasekhar J, Madura JD, Impey RW, Klein ML (1983) Comparison of simple potential function for simulating liquid water. J Chem Phys 79:926–935
Klepeis JL, Lindorff-Larsen K, Dror RO, Shaw DE (2009) Long-timescale molecular dynamics simulations of protein structure and function. Curr Opin Struct Biol 19:120–127
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 131:13894–13895
Korzhnev DM, Religa TL, Banachewicz W, Fersht AR, Kay LE (2010) A transient and low-populated protein-folding intermediate at atomic resolution. Science 329:1312–1316
Lange OF, van der Spoel D, de Groot BL (2010) Scrutinizing molecular mechanics force fields on the submicrosecond timescale with NMR data. Biophys J 99:647–655
Lehtivarjo J, Hassinen T, Korhonen SP, Perakyla M, Laatikainen R (2009) 4D prediction of protein H-1 chemical shifts. J Biomol NMR 45:413–426
Lehtivarjo J, Tuppurainen K, Hassinen T, Laatikainen R, Perakyla M (2012) Combining NMR ensembles and molecular dynamics simulations provides more realistic models of protein structures in solution and leads to better chemical shift prediction. J Biomol NMR 52:257–267
Li DW, Brüschweiler R (2010a) NMR-based protein potentials. Angew Chem 49:6778–6780
Li DW, Brüschweiler R (2010b) Certification of molecular dynamics trajectories with NMR chemical shifts. J Phys Chem Lett 1:246–248
Li DW, Brüschweiler R (2011) Iterative optimization of molecular mechanics force fields from NMR data of full-length proteins. J Chem Theory Comput 7:1773–1782
Lindahl E, Hess B, van der Spoel D (2001) GROMACS 3.0: a package for molecular simulation and trajectory analysis. J Mol Model 7:306–317
Lindorff-Larsen K, Best RB, Vendruscolo M (2005) Interpreting dynamically-averaged scalar couplings in proteins. J Biomol NMR 32:273–280
Lindorff-Larsen K, Piana S, Palmo K, Maragakis P, Klepeis JL, Dror RO, Shaw DE (2010) Improved side-chain torsion potentials for the Amber ff99SB protein force field. Proteins 78:1950–1958
Long D, Li DW, Walter KFA, Griesinger C, Brüschweiler R (2011) Toward a predictive understanding of slow methyl group dynamics in proteins. Biophys J 101:910–915
Markwick PRL, Bouvignies G, Blackledge M (2007) Exploring multiple timescale motions in protein GB3 using accelerated molecular dynamics and NMR spectroscopy. J Am Chem Soc 129:4724–4730
Markwick PR, Showalter SA, Bouvignies G, Brüschweiler R, Blackledge M (2009) Structural dynamics of protein backbone phi angles: extended molecular dynamics simulations versus experimental (3) J scalar couplings. J Biomol NMR 45:17–21
Markwick PRL, 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 132:1220–1221
McConnell HM (1957) Theory of nuclear magnetic shielding in molecules. I. Long-range dipolar shielding of protons. J Chem Phys 27:226–229
Meiler J, Prompers JJ, Peti W, Griesinger C, Brüschweiler R (2001) Model-free approach to the dynamic interpretation of residual dipolar couplings in globular proteins. J Am Chem Soc 123:6098–6107
Moon S, Case DA (2007) A new model for chemical shifts of amide hydrogens in proteins. J Biomol NMR 38:139–150
Neal S, Nip AM, Zhang HY, Wishart DS (2003) Rapid and accurate calculation of protein H-1, C-13 and N-15 chemical shifts. J Biomol NMR 26:215–240
Osapay K, Case DA (1991) A new analysis of proton chemical-shifts in proteins. J Am Chem Soc 113:9436–9444
Robustelli P, Stafford KA, Palmer AG (2012) Interpreting protein structural dynamics from NMR chemical shifts. J Am Chem Soc 134:6365–6374
Rosato A, Aramini JM, Arrowsmith C, Bagaria A, Baker D, Cavalli A, Doreleijers JF, Eletsky A, Giachetti A, Guerry P, Gutmanas A, Guntert P, He YF, Herrmann T, Huang YPJ, Jaravine V, Jonker HRA, Kennedy MA, Lange OF, Liu GH, Malliavin TE, Mani R, Mao BC, Montelione GT, Nilges M, Rossi P, van dS, G, Schwalbe H, Szyperski TA, Vendruscolo M, Vernon R, Vranken WF, de V, S, Vuister GW, Wu B, Yang YH, Bonvin AMJJ (2012) Blind testing of routine, fully automated determination of protein structures from NMR data. Structure 20:227–236
Ruschak AM, Religa TL, Breuer S, Witt S, Kay LE (2010) The proteasome antechamber maintains substrates in an unfolded state. Nature 467:868–871
Sahakyan AB, Vranken WF, Cavalli A, Vendruscolo M (2011) Structure-based prediction of methyl chemical shifts in proteins. J Biomol NMR 50:331–346
Shen Y, Bax A (2007) Protein backbone chemical shifts predicted from searching a database for torsion angle and sequence homology. J Biomol NMR 38:289–302
Shen Y, Bax A (2010) SPARTA+: a modest improvement in empirical NMR chemical shift prediction by means of an artificial neural network. J Biomol NMR 48:13–22
Shen Y, Lange O, Delaglio F, Rossi P, Aramini JM, Liu GH, Eletsky A, Wu YB, 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 105:4685–4690
Shen Y, Vernon R, Baker D, Bax A (2009) De novo protein structure generation from incomplete chemical shift assignments. J Biomol NMR 43:63–78
Showalter SA, Brüschweiler R (2007) Validation of molecular dynamics simulations of biomolecules using NMR spin relaxation as benchmarks: application to the AMBER99SB force field. J Chem Theory Comput 3:961–975
Showalter SA, Johnson E, Rance M, Brüschweiler R (2007) Toward quantitative interpretation of methyl side-chain dynamics from NMR by molecular dynamics simulations. J Am Chem Soc 129:14146–14147
Trbovic N, Kim B, Friesner RA, Palmer AG (2008) Structural analysis of protein dynamics by MD simulations and NMR spin-relaxation. Proteins 71:684–694
Ulrich EL, Akutsu H, Doreleijers JF, Harano Y, Ioannidis YE, Lin J, Livny M, Mading S, Maziuk D, Miller Z, Nakatani E, Schulte CF, Tolmie DE, Kent Wenger R, Yao H, Markley JL (2008) BioMagResBank. Nucleic Acids Res 36:D402–D408
Van der Spoel D, Lindahl E, Hess B, Groenhof G, Mark AE, Berendsen HJC (2005) GROMACS: fast, flexible, and free. J Comput Chem 26:1701–1718
Vila JA, Arnautova YA, Martin OA, Scheraga HA (2009) Quantum-mechanics-derived C-13(alpha) chemical shift server (CheShift) for protein structure validation. Proc Natl Acad Sci USA 106:16972–16977
Vogeli B, Ying JF, Grishaev A, Bax A (2007) Limits on variations in protein backbone dynamics from precise measurements of scalar couplings. J Am Chem Soc 129:9377–9385
Wickstrom L, Okur A, Simmerling C (2009) Evaluating the performance of the ff99SB force field based on NMR scalar coupling data. Biophys J 97:853–856
Xu XP, Case DA (2001) Automated prediction of 15N, 13Calpha, 13Cbeta and 13C′ chemical shifts in proteins using a density functional database. J Biomol NMR 21:321–333
Xu XP, Case DA (2002) Probing multiple effects on 15N, 13C alpha, 13C beta, and 13C′ chemical shifts in peptides using density functional theory. Biopolymers 65:408–423
Xue Y, Ward JM, Yuwen TR, Podkorytov IS, Skrynnikov NR (2012) Microsecond time-scale conformational exchange in proteins: using long molecular dynamics trajectory to simulate NMR relaxation dispersion data. J Am Chem Soc 134:2555–2562
Zhang F, Brüschweiler R (2002) Contact model for the prediction of NMR N-H order parameters in globular proteins. J Am Chem Soc 124:12654–12655
Zhang H, Neal S, Wishart DS (2003) RefDB: a database of uniformly referenced protein chemical shifts. J Biomol NMR 25:173–195
Acknowledgments
We thank Art Palmer for stimulating discussions. This work was supported by grant MCB-0918362 of the National Science Foundation.
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Li, DW., Brüschweiler, R. PPM: a side-chain and backbone chemical shift predictor for the assessment of protein conformational ensembles. J Biomol NMR 54, 257–265 (2012). https://doi.org/10.1007/s10858-012-9668-8
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DOI: https://doi.org/10.1007/s10858-012-9668-8