Abstract
NMR chemical shifts in proteins depend strongly on local structure. The program TALOS establishes an empirical relation between 13C, 15N and 1H chemical shifts and backbone torsion angles ϕ and ψ (Cornilescu et al. J Biomol NMR 13 289–302, 1999). Extension of the original 20-protein database to 200 proteins increased the fraction of residues for which backbone angles could be predicted from 65 to 74%, while reducing the error rate from 3 to 2.5%. Addition of a two-layer neural network filter to the database fragment selection process forms the basis for a new program, TALOS+, which further enhances the prediction rate to 88.5%, without increasing the error rate. Excluding the 2.5% of residues for which TALOS+ makes predictions that strongly differ from those observed in the crystalline state, the accuracy of predicted ϕ and ψ angles, equals ±13°. Large discrepancies between predictions and crystal structures are primarily limited to loop regions, and for the few cases where multiple X-ray structures are available such residues are often found in different states in the different structures. The TALOS+ output includes predictions for individual residues with missing chemical shifts, and the neural network component of the program also predicts secondary structure with good accuracy.
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References
Ando I, Kameda T, Asakawa N, Kuroki S, Kurosu H (1998) Structure of peptides and polypeptides in the solid state as elucidated by NMR chemical shift. J Mol Struct 441:213–230
Andreassen H, Bohr H, Bohr J, Brunak S, Bugge T, Cotterill RMJ, Jacobsen C, Kusk P, Lautrop B, Petersen SB, Saermark T, Ulrich K (1990) Analysis of the secondary structure of the human immunodeficiency virus (HIV) proteins p17, gp120, and gp41 by computer modeling based on neural network methods. J Acquir Immune Defic Syndr 3:615–622
Asakura T, Demura M, Date T, Miyashita N, Ogawa K, Williamson MP (1997) NMR study of silk I structure of Bombyx mori silk fibroin with N-15- and C-13-NMR chemical shift contour plots. Biopolymers 41:193–203
Berjanskii MV, Wishart DS (2005) A simple method to predict protein flexibility using secondary chemical shifts. J Am Chem Soc 127:14970–14971
Berjanskii MV, Wishart DS (2008) Application of the random coil index to studying protein flexibility. J Biomol NMR 40:31–48
Billeter M, Wagner G, Wuthrich K (2008) Solution NMR structure determination of proteins revisited. J Biomol NMR 42:155–158
Cai M, Huang Y, Zheng R, Wei SQ, Ghirlando R, Lee MS, Craigie R, Gronenborn AM, Clore GM (1998) Solution structure of the cellular factor BAF responsible for protecting retroviral DNA from autointegration. Nat Struct Biol 5:903–909
Case DA (1995) Calibration of ring-current effects in proteins and nucleic acids. J Biomol NMR 6:341–346
Castellani F, van Rossum BJ, Diehl A, Rehbein K, Oschkinat H (2003) Determination of solid-state NMR structures of proteins by means of three-dimensional N-15-C-13-C-13 dipolar correlation spectroscopy and chemical shift analysis. Biochemistry 42:11476–11483
Cavalli A, Salvatella X, Dobson CM, Vendruscolo M (2007) Protein structure determination from NMR chemical shifts. Proc Natl Acad Sci USA 104:9615–9620
Choy WY, Sanctuary BC, Zhu G (1997) Using neural network predicted secondary structure information in automatic protein NMR assignment. J Chem Inf Comput Sci 37:1086–1094
Cornilescu G, Delaglio F, Bax A (1999) Protein backbone angle restraints from searching a database for chemical shift and sequence homology. J Biomol NMR 13:289–302
Czinki E, Csaszar AG (2007) Empirical isotropic chemical shift surfaces. J Biomol NMR 38:269–287
Grishaev A, Tugarinov V, Kay LE, Trewhella J, Bax A (2008) Refined solution structure of the 82-kDa enzyme malate synthase G from joint NMR and synchrotron SAXS restraints. J Biomol NMR 40:95–106
Haigh CW, Mallion RB (1979) Ring current theories in nuclear magnetic resonance. Prog Nucl Magn Reson Spectrosc 13:303–344
Hare BJ, Prestegard JH (1994) Application of neural networks to automated assignment of NMR spectra of proteins. J Biomol NMR 4:35–46
Huang K, Andrec M, Heald S, Blake P, Prestegard JH (1997) Performance of a neural-network-based determination of amino acid class and secondary structure from H-1-N-15 NMR data. J Biomol NMR 10:45–52
Hung LH, Samudrala R (2003) Accurate and automated classification of protein secondary structure with PsiCSI. Protein Sci 12:288–295
Jones DT (1999) Protein secondary structure prediction based on position-specific scoring matrices. J Mol Biol 292:195–202
Kabsch W, Sander C (1983) Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers 22:2577–2637
Markley JL, Ulrich EL, Berman HM, Henrick K, Nakamura H, Akutsu H (2008) BioMagResBank (BMRB) as a partner in the Worldwide Protein Data Bank (wwPDB): new policies affecting biomolecular NMR depositions. J Biomol NMR 40:153–155
Meiler J (2003) PROSHIFT: protein chemical shift prediction using artificial neural networks. J Biomol NMR 26:25–37
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
Neal S, Berjanskii M, Zhang HY, Wishart DS (2006) Accurate prediction of protein torsion angles using chemical shifts and sequence homology. Magn Reson Chem 44:S158–S167
Parsons LM, Grishaev A, Bax A (2008) The periplasmic domain of TolR from haemophilus influenzae forms a dimer with a large hydrophobic groove: NMR solution structure and comparison to SAXS data. Biochemistry 47:3131–3142
Pons JL, Delsuc MA (1999) RESCUE: an artificial neural network tool for the NMR spectral assignment of proteins. J Biomol NMR 15:15–26
Ramirez BE, Voloshin ON, Camerini-Otero RD, Bax A (2000) Solution structure of DinI provides insight into its mode of RecA inactivation. Protein Sci 9:2161–2169
Rost B, Sander C (1993) Prediction of protein secondary structure at better than 70 percent accuracy. J Mol Biol 232:584–599
Saito H (1986) Conformation-dependent C13 chemical shifts—a new means of conformational characterization as obtained by high resolution solid state C13 NMR. Magn Reson Chem 24:835–852
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, 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
Spera S, Bax A (1991) Empirical correlation between protein backbone conformation and Cα and Cβ 13C nuclear magnetic resonance chemical shifts. J Am Chem Soc 113:5490–5492
Tugarinov V, Choy WY, Orekhov VY, Kay LE (2005) Solution NMR-derived global fold of a monomeric 82-kDa enzyme. Proc Natl Acad Sci USA 102:622–627
Ulmer TS, Ramirez BE, Delaglio F, Bax A (2003) Evaluation of backbone proton positions and dynamics in a small protein by liquid crystal NMR spectroscopy. J Am Chem Soc 125:9179–9191
Vila JA, Villegas ME, Baldoni HA, Scheraga HA (2007) Predicting C-13(alpha) chemical shifts for validation of protein structures. J Biomol NMR 38:221–235
Vila JA, Aramini JM, Rossi P, Kuzin A, Su M, Seetharaman J, Xiao R, Tong L, Montelione GT, Scheraga HA (2008) Quantum chemical C-13(alpha) chemical shift calculations for protein NMR structure determination, refinement, and validation. Proc Natl Acad Sci USA 105:14389–14394
Villegas ME, Vila JA, Scheraga HA (2007) Effects of side-chain orientation on the C-13 chemical shifts of antiparallel beta-sheet model peptides. J Biomol NMR 37:137–146
Wagner G, Pardi A, Wuthrich K (1983) Hydrogen-bond length and H-1-Nmr chemical-shifts in proteins. J Am Chem Soc 105:5948–5949
Wang YJ, Jardetzky O (2002) Probability-based protein secondary structure identification using combined NMR chemical-shift data. Protein Sci 11:852–861
Williamson MP, Asakura T (1993) Empirical comparisons of models for chemical-shift calculation in proteins. J Magn Reson B 101:63–71
Williamson MP, Kikuchi J, Asakura T (1995) Application of H1 NMR chemical shifts to measure the quality of protein structures. J Mol Biol 247:541–546
Wishart DS, Sykes BD, Richards FM (1991) Relationship between nuclear magnetic resonance chemical shift and protein secondary structure. J Mol Biol 222:311–333
Wishart DS, Sykes BD, Richards FM (1992) The chemical shift index: a fast and simple method for the assignment of protein secondary structure through NMR spectroscopy. Biochemistry 31:1647–1651
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 36:496–502
Xu XP, Case DA (2001) Automated prediction of N-15, C-13(alpha), C-13(beta) and C-13′ chemical shifts in proteins using a density functional database. J Biomol NMR 21:321–333
Acknowledgments
We thank Alex Grishaev for carrying out the MSG calculation with the new TALOS+ backbone angle restraints. This work was funded by the Intramural Research Program of the NIDDK, NIH. G.C. was supported by NIH grants P41RR02301 (BRTP/NCRR) and P41GM66326 (NIGMS)
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Shen, Y., Delaglio, F., Cornilescu, G. et al. TALOS+: a hybrid method for predicting protein backbone torsion angles from NMR chemical shifts. J Biomol NMR 44, 213–223 (2009). https://doi.org/10.1007/s10858-009-9333-z
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DOI: https://doi.org/10.1007/s10858-009-9333-z