A Slice-based $^{13}$C-detected NMR Spin System Forming and Resonance Assignment Method

Nuclear magnetic resonance NMR spectroscopy is attracting more attention in the field of computational structural biology. Till recently, $^1$H-detected experiments are the dominant NMR technique used due to the high sensitivity of $^1$H nuclei. However, the current availability of high magnetic fields and cryogenically cooled probe heads allow researchers to overcome the low sensitivity of $^{13}$C nuclei. Consequently, $^{13}$C-detected experiments have become a popular technique in different NMR applications especially resonance assignment and structure determination of large proteins. In this paper, we propose the first spin system forming method for $^{13}$C-detected NMR spectra. Our method is able to accurately form spin systems based on as few as two $^{13}$C-detected spectra, CBCACON, and CBCANCO. Our method picks slices from the more trusted spectrum and uses them as feedback to direct the slice picking in the less trusted one. This feedback leads to picking the accurate slices that consequently helps to form better spin systems. We tested our method on a real dataset of ‘Ubiquitin’ and a benchmark simulated dataset consisting of 12 proteins. We fed our spin systems as inputs to a genetic algorithm to generate the chemical shift assignment, and obtained 92 percent correct chemical shift assignment for Ubiquitin. For the simulated dataset, we obtained an average recall of 86 percent and an average precision of 88 percent. Finally, our chemical shift assignment of Ubiquitin was given as an input to CS-ROSETTA server that generated structures close to the experimentally determined structure.

[1]  K Wüthrich,et al.  Automated sequence-specific NMR assignment of homologous proteins using the program GARANT , 1996, Journal of biomolecular NMR.

[2]  Faming Liang,et al.  Bayesian Peak Picking for NMR Spectra , 2013, Genom. Proteom. Bioinform..

[3]  Robert Powers,et al.  A common sense approach to peak picking in two-, three-, and four-dimensional spectra using automatic computer analysis of contour diagrams , 1991 .

[4]  Xiang Wan,et al.  CISA: Combined NMR Resonance Connectivity Information Determination and Sequential Assignment , 2007, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[5]  Brian E Coggins,et al.  PACES: Protein sequential assignment by computer-assisted exhaustive search , 2003, Journal of biomolecular NMR.

[6]  Yang Shen,et al.  Building native protein conformation from NMR backbone chemical shifts using Monte Carlo fragment assembly , 2007, Protein science : a publication of the Protein Society.

[7]  Arash Bahrami,et al.  Probabilistic Interaction Network of Evidence Algorithm and its Application to Complete Labeling of Peak Lists from Protein NMR Spectroscopy , 2009, PLoS Comput. Biol..

[8]  Xin Gao,et al.  Recent Advances in Computational Methods for Nuclear Magnetic Resonance Data Processing , 2013, Genom. Proteom. Bioinform..

[9]  G. Montelione,et al.  Automated analysis of protein NMR assignments using methods from artificial intelligence. , 1997, Journal of molecular biology.

[10]  M. Billeter,et al.  MUNIN: A new approach to multi-dimensional NMR spectra interpretation , 2001, Journal of biomolecular NMR.

[11]  Martin Billeter,et al.  MUNIN: Application of three-way decomposition to the analysis of heteronuclear NMR relaxation data** , 2001, Journal of biomolecular NMR.

[12]  Xin Gao,et al.  An automated framework for NMR resonance assignment through simultaneous slice picking and spin system forming , 2014, Journal of biomolecular NMR.

[13]  Michele Vendruscolo,et al.  Protein structure determination from NMR chemical shifts , 2007, Proceedings of the National Academy of Sciences.

[14]  Xin Gao,et al.  Combining automated peak tracking in SAR by NMR with structure-based backbone assignment from 15N-NOESY , 2012, BMC Bioinformatics.

[15]  Xin Gao,et al.  Towards Automated Structure-Based NMR Resonance Assignment , 2010, RECOMB.

[16]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[17]  K. Hatada,et al.  Introduction to NMR Spectroscopy , 2004 .

[18]  Ivano Bertini,et al.  A method for Cα direct-detection in protonless NMR , 2007 .

[19]  R. Tycko,et al.  A Monte Carlo/simulated annealing algorithm for sequential resonance assignment in solid state NMR of uniformly labeled proteins with magic-angle spinning. , 2010, Journal of magnetic resonance.

[20]  Simon A. Corne,et al.  An artificial neural network for classifying cross peaks in two-dimensional NMR spectra , 1992 .

[21]  Xin Gao,et al.  PICKY: a novel SVD-based NMR spectra peak picking method , 2009, Bioinform..

[22]  Xin Gao Mathematical Approaches to the NMR Peak-Picking Problem , 2012 .

[23]  Zhi Liu,et al.  WaVPeak: picking NMR peaks through wavelet-based smoothing and volume-based filtering , 2012, Bioinform..

[24]  David S. Wishart,et al.  CS23D: a web server for rapid protein structure generation using NMR chemical shifts and sequence data , 2008, Nucleic Acids Res..

[25]  Xin Gao,et al.  Towards Fully Automated Structure-Based NMR Resonance Assignment of 15N-Labeled Proteins From Automatically Picked Peaks , 2011, J. Comput. Biol..

[26]  Kurt Wüthrich,et al.  Sequence-specific NMR assignment of proteins by global fragment mapping with the program Mapper , 2000, Journal of biomolecular NMR.

[27]  Wen-Lian Hsu,et al.  GANA—a genetic algorithm for NMR backbone resonance assignment , 2005, 2005 IEEE Computational Systems Bioinformatics Conference - Workshops (CSBW'05).

[28]  Wen-Lian Hsu,et al.  RIBRA - An Error-Tolerant Algorithm for the NMR Backbone Assignment Problem , 2006, J. Comput. Biol..

[29]  W. M. Westler,et al.  A relational database for sequence-specific protein NMR data , 1991, Journal of biomolecular NMR.

[30]  Gerard J. Kleywegt,et al.  A versatile approach toward the partially automatic recognition of cross peaks in 2D 1H NMR spectra , 1990 .

[31]  Rochus Keller,et al.  AutoLink: automated sequential resonance assignment of biopolymers from NMR data by relative-hypothesis-prioritization-based simulated logic. , 2005, Journal of magnetic resonance.

[32]  M. Zweckstetter,et al.  Mars - robust automatic backbone assignment of proteins , 2004, Journal of biomolecular NMR.

[33]  M. Billeter,et al.  Automated peak picking and peak integration in macromolecular NMR spectra using AUTOPSY. , 1998, Journal of magnetic resonance.

[34]  D. Case,et al.  Use of chemical shifts in macromolecular structure determination. , 2002, Methods in enzymology.

[35]  D. Baker,et al.  De novo protein structure generation from incomplete chemical shift assignments , 2009, Journal of biomolecular NMR.

[36]  Xin Gao,et al.  Median Modified Wiener Filter for nonlinear adaptive spatial denoising of protein NMR multidimensional spectra , 2015, Scientific Reports.

[37]  Wolfgang Bermel,et al.  13C Direct‐detection biomolecular NMR , 2008 .

[38]  Oliver F. Lange,et al.  Consistent blind protein structure generation from NMR chemical shift data , 2008, Proceedings of the National Academy of Sciences.

[39]  Ivano Bertini,et al.  13C-detected protonless NMR spectroscopy of proteins in solution , 2006 .

[40]  Guohui Lin,et al.  CISA: Combined NMR Resonance Connectivity Information Determination and Sequential Assignment , 2007, TCBB.

[41]  Claudio Nicolini,et al.  Neural networks for the peak-picking of nuclear magnetic resonance spectra , 1993, Neural Networks.

[42]  Franco M Montevecchi,et al.  Median‐modified Wiener filter provides efficient denoising, preserving spot edge and morphology in 2‐DE image processing , 2009, Proteomics.

[43]  Torsten Herrmann,et al.  Automated sequence-specific protein NMR assignment using the memetic algorithm MATCH , 2008, Journal of biomolecular NMR.

[44]  Sequence specific resonance assignment via Multicanonical Monte Carlo search using an ABACUS approach , 2008, Journal of biomolecular NMR.

[45]  Zhi Liu,et al.  Automatic Peak Selection by a Benjamini-Hochberg-Based Algorithm , 2013, PloS one.

[46]  Kurt Wüthrich,et al.  Nuclear magnetic resonance in protein research , 1974, Experientia.

[47]  H. Kalbitzer,et al.  A general Bayesian method for an automated signal class recognition in 2D NMR spectra combined with a multivariate discriminant analysis , 1995, Journal of biomolecular NMR.

[48]  Manfred Spraul,et al.  Cryogenically cooled probes—a leap in NMR technology , 2005 .

[49]  Shuai Cheng Li,et al.  IPASS : Error Tolerant NMR Backbone Resonance Assignment by Linear Programming , 2009 .

[50]  A. Rouh,et al.  Bayesian signal extraction from noisy FT NMR spectra , 1994, Journal of Biomolecular NMR.

[51]  Kurt Wüthrich,et al.  GARANT-a general algorithm for resonance assignment of multidimensional nuclear magnetic resonance spectra , 1997, J. Comput. Chem..