Abstract
While chemical shifts are invaluable for obtaining structural information from proteins, they also offer one of the rare ways to obtain information about protein dynamics. A necessary tool in transforming chemical shifts into structural and dynamic information is chemical shift prediction. In our previous work we developed a method for 4D prediction of protein 1H chemical shifts in which molecular motions, the 4th dimension, were modeled using molecular dynamics (MD) simulations. Although the approach clearly improved the prediction, the X-ray structures and single NMR conformers used in the model cannot be considered fully realistic models of protein in solution. In this work, NMR ensembles (NMRE) were used to expand the conformational space of proteins (e.g. side chains, flexible loops, termini), followed by MD simulations for each conformer to map the local fluctuations. Compared with the non-dynamic model, the NMRE+MD model gave 6–17% lower root-mean-square (RMS) errors for different backbone nuclei. The improved prediction indicates that NMR ensembles with MD simulations can be used to obtain a more realistic picture of protein structures in solutions and moreover underlines the importance of short and long time-scale dynamics for the prediction. The RMS errors of the NMRE+MD model were 0.24, 0.43, 0.98, 1.03, 1.16 and 2.39 ppm for 1Hα, 1HN, 13Cα, 13Cβ, 13CO and backbone 15N chemical shifts, respectively. The model is implemented in the prediction program 4DSPOT, available at http://www.uef.fi/4dspot.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Andrec M, Snyder DA, Zhou Z, Young J, Montelione GT, Levy RM (2007) A large data set comparison of protein structures determined by crystallography and NMR: statistical test for structural differences and the effect of crystal packing. Proteins 69:449–465
Angyan AF, Szappanos B, Perczel A, Gaspari Z (2010) CoNSEnsX: an ensemble view of protein structures and NMR-derived experimental data RID D-9861-2011 RID D-9594-2011. BMC Struct Biol 10:39
Baskaran K, Brunner K, Munte CE, Kalbitzer HR (2010) Mapping of protein structural ensembles by chemical shifts. J Biomol NMR 48:71–83
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
Breiman L (2001) Random forests. Mach Learn 45:5–32
Case DA, Darden TA, Cheatham TEI, Simmerling CL, Wang J, Duke RE, Luo R, Crowley M, Walker RC, Zhang W, Merz KM, Wang B, Hayik S, Roitberg A, Seabra G, Kolossvary I, Wong KF, Paesani F, Vanicek J, Wu X, Brozell SR, Steinbrecher T, Gohlke H, Yang L, Tan C, Mongan J, Hornak V, Cui G, Mathews DH, Seetin MG, Sagui C, Babin V, Kollman PA (2008) AMBER 10. University of California, San Francisco
Cavalli A, Salvatella X, Dobson CM, Vendruscolo M (2007) Protein structure determination from NMR chemical shifts. Proc Natl Acad Sci USA 104:9615–9620
Dedios AC, Oldfield E (1994) Chemical-shifts of carbonyl carbons in peptides and proteins. J Am Chem Soc 116:11485–11488
Han B, Liu Y, Ginzinger SW, Wishart DS (2011) SHIFTX2: significantly improved protein chemical shift prediction. J Biomol NMR 50:43–57
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
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
Krzeminski M, Fuentes G, Boelens R, Bonvin AM (2009) MINOES: a new approach to select a representative ensemble of structures in NMR studies of (partially) unfolded states. Application to Δ25-PYP. Proteins 74:895–904
Lehtivarjo J, Hassinen T, Korhonen SP, Peräkylä M, Laatikainen R (2009) 4D prediction of protein 1H chemical shifts. J Biomol NMR 45:413–426
Li D, Brueschweiler R (2010) Certification of molecular dynamics trajectories with NMR chemical shifts. J Phys Chem Lett 1:246–248
Liu X, Ren Y, Zhou P, Shang Z (2011) Prediction of protein 13Cα NMR chemical shifts using a combination scheme of statistical modeling and quantum-mechanical analysis. J Mol Struct 995:163–172
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 132:1220–1221
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 H, Wishart DS (2003) Rapid and accurate calculation of protein 1H, 13C and 15N chemical shifts. J Biomol NMR 26:215–240
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
Parker LL, Houk AR, Jensen JH (2006) Cooperative hydrogen bonding effects are key determinants of backbone amide proton chemical shifts in proteins. J Am Chem Soc 128:9863–9872
Robustelli P, Kohlhoff K, Cavalli A, Vendruscolo M (2010) Using NMR chemical shifts as structural restraints in molecular dynamics simulations of proteins. Structure 18:923–933
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 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 105:4685–4690
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
Wang L, Markley JL (2009) Empirical correlation between protein backbone 15N and 13C secondary chemical shifts and its application to nitrogen chemical shift re-referencing. J Biomol NMR 44:95–99
Wishart DS, Case DA (2001) Use of chemical shifts in macromolecular structure determination. Methods Enzymol 338:3–34
Wishart DS, Bigam CG, Holm A, Hodges RS, Sykes BD (1995) 1H, 13C and 15N random coil NMR chemical shifts of the common amino acids. I. Investigations of nearest-neighbor effects. J Biomol NMR 5:67–81
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:W496–W502
Xu XP, Case DA (2001) Automated prediction of 15N, 13Cα, 13Cβ and 13C’ chemical shifts in proteins using a density functional database. J Biomol NMR 21:321–333
Acknowledgments
This work was supported by the The National Doctoral Programme in Informational and Structural Biology (ISB). The Finnish IT Center for Science (CSC) is acknowledged for providing the computational resources.
Author information
Authors and Affiliations
Corresponding author
Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Lehtivarjo, J., Tuppurainen, K., Hassinen, T. et al. 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 (2012). https://doi.org/10.1007/s10858-012-9609-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10858-012-9609-6