Abstract
We present an empirical method for identification of distinct structural motifs in proteins on the basis of experimentally determined backbone and 13Cβ chemical shifts. Elements identified include the N-terminal and C-terminal helix capping motifs and five types of β-turns: I, II, I′, II′ and VIII. Using a database of proteins of known structure, the NMR chemical shifts, together with the PDB-extracted amino acid preference of the helix capping and β-turn motifs are used as input data for training an artificial neural network algorithm, which outputs the statistical probability of finding each motif at any given position in the protein. The trained neural networks, contained in the MICS (motif identification from chemical shifts) program, also provide a confidence level for each of their predictions, and values ranging from ca 0.7–0.9 for the Matthews correlation coefficient of its predictions far exceed those attainable by sequence analysis. MICS is anticipated to be useful both in the conventional NMR structure determination process and for enhancing on-going efforts to determine protein structures solely on the basis of chemical shift information, where it can aid in identifying protein database fragments suitable for use in building such structures.
Similar content being viewed by others
References
Alexander PA, He Y, Chen Y, Orban J, Bryan PN (2009) A minimal sequence code for switching protein structure and function. Proc Natl Acad Sci USA 106:21149–21154
Asakura T, Taoka K, Demura M, Williamson MP (1995) The relationship between amide proton chemical shifts and secondary structure in proteins. J Biomol NMR 6:227–236
Aurora R, Rose GD (1998) Helix capping. Protein Sci 7:21–38
Aurora R, Srinivasan R, Rose GD (1994) Rules for α-helix termination by glycine. Science 264:1126–1130
Baldi P, Brunak S, Chauvin Y, Andersen CAF, Nielsen H (2000) Assessing the accuracy of prediction algorithms for classification: an overview. Bioinformatics 16:412–424
Baldwin RL, Rose GD (1999) Is protein folding hierarchic? II. Folding intermediates and transition states. Trends Biochem Sci 24:77–83
Becker OM, Karplus M (1997) The topology of multidimensional potential energy surfaces: theory and application to peptide structure and kinetics. J Chem Phys 106:1495–1517
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, Neal S, Wishart DS (2006) PREDITOR: a web server for predicting protein torsion angle restraints. Nucleic Acids Res 34:W63–W69
Bystroff C, Baker D (1998) Prediction of local structure in proteins using a library of sequence-structure motifs. J Mol Biol 281:565–577
Case DA (1998) The use of chemical shifts and their anisotropies in biomolecular structure determination. Curr Opin Struct Biol 8:624–630
Chou KC (2000) Prediction of tight turns and their types in proteins. Anal Biochem 286:1–16
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
Das R, Baker D (2008) Macromolecular modeling with Rosetta. Annu Rev Biochem 77:363–382
de Dios AC, Pearson JG, Oldfield E (1993) Secondary and tertiary structural effects on protein NMR chemical shifts: an ab initio approach. Science 260:1491–1496
Doreleijers JF, Nederveen AJ, Vranken W, Lin JD, Bonvin A, Kaptein R, Markley JL, Ulrich EL (2005) BioMagResBank databases DOCR and FRED containing converted and filtered sets of experimental NMR restraints and coordinates from over 500 protein PDB structures. J Biomol NMR 32:1–12
Dyson HJ, Rance M, Houghten RA, Lerner RA, Wright PE (1988) Folding of immunogenic peptide fragments of proteins in water solution. 1. Sequence requirements for the formation of a reverse turn. J Mol Biol 201:161–200
Eghbalnia HR, Wang LY, Bahrami A, Assadi A, Markley JL (2005) Protein energetic conformational analysis from NMR chemical shifts (PECAN) and its use in determining secondary structural elements. J Biomol NMR 32:71–81
Fuchs PFJ, Alix AJP (2005) High accuracy prediction of beta-turns and their types using propensities and multiple alignments. Proteins Struct Funct Bioinforma 59:828–839
Gronenborn AM, Clore GM (1994) Identification of N-terminal helix capping boxes by means of 13C chemical shifts. J Biomol NMR 4:455–458
Han B, Liu YF, Ginzinger SW, Wishart DS (2011) SHIFTX2: significantly improved protein chemical shift prediction. J Biomol NMR 50:43–57
Harper ET, Rose GD (1993) Helix stop signals in proteins and peptides: the capping box. Biochemistry 32:7605–7609
Heinig M, Frishman D (2004) STRIDE: a web server for secondary structure assignment from known atomic coordinates of proteins. Nucleic Acids Res 32:W500–W502
Henikoff S, Henikoff JG (1992) Amino-acid substitution matrices from protein blocks. Proc Natl Acad Sci USA 89:10915–10919
Hung LH, Samudrala R (2003) Accurate and automated classification of protein secondary structure with PsiCSI. Protein Sci 12:288–295
Hutchinson EG, Thornton JM (1994) A revised set of potential for β-turn formation in proteins. Protein Sci 3:2207–2216
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 13:199–211
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
Kaur H, Raghava GPS (2003) Prediction of beta-turns in proteins from multiple alignments using neural network. Protein Sci 12:627–634
Kirschner A, Frishman D (2008) Prediction of beta-turns and beta-turn types by a novel bidirectional Elman-type recurrent neural network with multiple output layers (MOLEBRNN). Gene 422:22–29
Kountouris P, Hirst JD (2010) Predicting beta-turns and their types using predicted backbone dihedral angles and secondary structures. BMC Bioinformatics 11:407
Matthews BW (1975) Comparison of predicted and observed secondary structure of T4 phage lysozyme. Biochim Biophys Acta 405:442–451
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
Pastore A, Saudek V (1990) The relationship between chemical shift and secondary structure in proteins. J Magn Reson 90:165–176
Pearson JG, Le HB, Sanders LK, Godbout N, Havlin RH, Oldfield E (1997) Predicting chemical shifts in proteins: Structure refinement of valine residues by using ab initio and empirical geometry optimizations. J Am Chem Soc 119:11941–11950
Petersen B, Lundegaard C, Petersen TN (2010) NetTurnP—neural network prediction of beta-turns by use of evolutionary information and predicted protein sequence features. PLoS One 5:e15079
Presta LG, Rose GD (1988) Helix signals in proteins. Science 240:1632–1641
Richardson JS (1981) The anatomy and taxonomy of protein structure. Adv Protein Chem 34:167–339
Richardson JS, Richardson DC (1988) Amino acid preferences for specific locations at the ends of alpha helices. Science 240:1648–1652
Rohl CA, Strauss CEM, Misura KMS, Baker D (2004) Protein structure prediction using rosetta. Meth Enzymol 383:66–93
Rose GD, Gierasch LM, Smith JA (1985) Turns in peptides and proteins. Adv Protein Chem 37:1–109
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
Sgourakis NG, Lange OF, DiMaio F, Andre I, Fitzkee NC, Rossi P, Montelione GT, Bax A, Baker D (2011) Determination of the structures of symmetric protein oligomers from NMR chemical shifts and residual dipolar couplings. J Am Chem Soc 133:6288–6298
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 (2010a) Prediction of Xaa-Pro peptide bond conformation from sequence and chemical shifts. J Biomol NMR 46:199–204
Shen Y, Bax A (2010b) SPARTA plus: 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, Delaglio F, Cornilescu G, Bax A (2009a) TALOS+: a hybrid method for predicting protein backbone torsion angles from NMR chemical shifts. J Biomol NMR 44:213–223
Shen Y, Vernon R, Baker D, Bax A (2009b) De novo protein structure generation from incomplete chemical shift assignments. J Biomol NMR 43:63–78
Shen Y, Bryan PN, He YN, Orban J, Baker D, Bax A (2010) De novo structure generation using chemical shifts for proteins with high-sequence identity but different folds. Protein Sci 19:349–356
Shepherd AJ, Gorse D, Thornton JM (1999) Prediction of the location and type of beta-turns in proteins using neural networks. Protein Sci 8:1045–1055
Sibanda BL, Blundell TL, Thornton JM (1989) Conformation of b-hairpins in protein structures: a systematic classification with applications to modelling by homology, electron density fitting and protein engineering. J Mol Biol 206:759–777
Spera S, Bax A (1991) Empirical correlation between protein backbone conformation and Ca and Cb 13C nuclear magnetic resonance chemical shifts. J Am Chem Soc 113:5490–5492
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
Wang YJ, Jardetzky O (2002) Probability-based protein secondary structure identification using combined NMR chemical-shift data. Protein Sci 11:852–861
Wang CC, Chen JH, Lai WC, Chuang WJ (2007) 2DCSi: identification of protein secondary structure and redox state using 2D cluster analysis of NMR chemical shifts. J Biomol NMR 38:57–63
Williamson MP (1990) Secondary structure dependent chemical shifts in proteins. Biopolymers 29:1428–1431
Wilmot CM, Thornton JM (1988) Analysis and prediction of the different types of b-turn in proteins. J Mol Biol 203:221–232
Wilmot CM, Thornton JM (1990) Beta-turns and their distortions: a proposed new nomenclature. Protein Eng 3:479–493
Wishart DS (2011) Interpreting protein chemical shift data. Prog Nucl Magn Reson Spectrosc 58:62–87
Wishart DS, Sykes BD (1994) The 13C chemical-shift index: a simple method for the identification of protein secondary structure using 13C chemical-shift data. J Biomol NMR 4:171–180
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, Watson MS, Boyko RF, Sykes BD (1997) Automated (1)H and (13)C chemical shift prediction using the BioMagResBank. J Biomol NMR 10:329–336
Acknowledgments
We thank Frank Delaglio for helpful comments and suggestions. This work was funded by the Intramural Research Program of the NIDDK and by the Intramural AIDS-Targeted Antiviral Program of the Office of the Director, NIH.
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
Shen, Y., Bax, A. Identification of helix capping and β-turn motifs from NMR chemical shifts. J Biomol NMR 52, 211–232 (2012). https://doi.org/10.1007/s10858-012-9602-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10858-012-9602-0