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Title: Development Of NMR Methods For Metabolomics And Protein Resonance Assignments
Authors: Dubey, Abhinav
Advisors: Atreya, Hanudatta S
Pal, Debnath
Keywords: Nuclear Magnetic Resonance
Metabolomics
Protein NMR Spectroscopy
Pattern Recognition-Based Approach (PROMEB)
PROMEB Algorithm
Rapid Protein Resonance Assignments
Protein Resonance Assignments
Rapid NMR Assignments
Nuclear Magnetic Resonance-Based Metabolomics
ChemSMP
NMR
Submitted Date: 15-May-2016
Series/Report no.: G27742
Abstract: Nuclear Magnetic Resonance (NMR) spectroscopy is a quantitative, non-invasive and non-destructive technique useful in biological studies. By manipulating the magnetization of nuclei with non-zero spin, NMR gives insights into atomic level details. Application of NMR as a tool for discovering structure, understanding dynamics of bio-molecules such as proteins, metabolites, DNA, RNA and their interactions constitutes the field of bio-molecular NMR. In this thesis, new methods for rapid data analysis of NMR spectrum of proteins and metabolites are proposed. The first computational method, PROMEB (Pattern Recognition Based Assignment in Metabolomics) is useful for the identification and assignments of metabolites. This is an important step in metabolomics and is necessary for the discovery of new biomarkers. In NMR spectroscopy based studies, the conventional approach involves a database search, wherein chemical shifts are assigned to specific metabolites by use of a tolerance limit. This is inefficient because deviation in chemical shifts associated with pH or temperature variations, as well as missing peaks, impairs a robust comparison with the database. These drawbacks are overcome in PROMEB, which is a method based on matching the pattern of peaks of a metabolite in 2D [13C, 1H] HSQC NMR spectrum, rather than conventionally used absolute tolerance thresholds. A high success rate is obtained even in the presence of large chemical shift deviations such as 0.5 ppm in 1H and 3 ppm in 13C and missing peaks (up to 50%), compared to nearly no assignments obtained under these conditions with existing methods that employ a direct database search approach. The pattern recognition approach thus helps in identification and assignment of metabolites in-dependent of the pH, temperature, and ionic strength used, thereby obviating the need for spectral calibration with internal or external standards. Another computational method, ChemSMP(Chemical Shifts to Metabolic Path-ways), is described which facilitates the identification of metabolic pathways from a single two dimensional (2D) NMR spectrum. Typically in other approaches, this is done after relevant metabolites are identified to allow their mapping onto specific metabolic pathways. This task is daunting due to the complex nature of cellular processes and the difficulty in establishing the identity of individual metabolites. ChemSMP uses a novel indexing and scoring system comprised of a uniqueness score and a coverage score. Benchmarks show that ChemSMP has a positive prediction rate of > 90% in the presence of decluttered data and can sustain the same at 60 − 70% even in the presence of noise, such as deletions of peaks and chemical shift deviations. The method tested on NMR data acquired for a mixture of 20 amino acids shows a success rate of 93% in correct recovery of metabolic pathways. The third method developed is a new approach for rapid resonance assignments in proteins based on amino acid selective unlabeling. The method involves choosing a set of multiple amino acid types for selective unlabeling and identifying specific tripeptides surrounding the labeled residues from specific 2D NMR spectra in a combinatorial manner. The methodology directly yields sequence specific resonance assignments, without requiring a contiguously assigned stretch of amino acid residues to be linked, and is applicable to deuterated proteins. The fourth method involves a simple approach to rapidly identify amino acid types in proteins from a 2D NMR spectrum. The method is based on the fact that 13Cβ chemical shifts of different amino acid types fall in distinct spectral regions. By evolving the 13C chemical shifts in the conventional HNCACB or HN(CO)CACB type experiment for a single specified delay period, the phase of the cross peaks of different amino acid residues are modulated depending on their 13Cβ chemical shift values. Following this specified evolution period, the 2D HN projections of these experiments are acquired. The 13C evolution period can be chosen such that all residues belonging to a given set of amino acid types have the same phase pattern (positive or negative) facilitating their identification. This approach does not re-quire the preparation of any additional samples, involves the analysis of 2D [15N,1H] HSQC-type spectra obtained from the routinely used triple resonance experiments with minor modifications, and is applicable to deuterated proteins. Finally, the practical application of these methods for laboratory research is presented. PROMEB and ChemSMP is used to study cancer cell metabolism in previously unexplored oncogenic cell line. PROMEB helped in assigning a differential metabolite present at high concentration in cancer cell line compared to control non-cancerous cell line. ChemSMP revealed active metabolic pathways responsible for regulating energy homeostasis of cancer cells which were previously reported in literature. The two methods developed for rapid protein resonance assignments can be used in applications such as identifying active-site residues involved in ligand binding, phosphorylation, or protein-protein interactions. The phase modulated experiments will be useful for quick assignment of signals that shift during ligand binding or in combination with selective labeling/unlabeling approaches for identification of amino acid types to aid the sequential assignment process. Both the methodology was applied to two proteins: Ubiquitin (8 kDa) and L-IGFBP2 an intrinsically disordered protein (12 kDa), for demonstrating rapid resonance assignment using only set of 2D NMR experiments.
Abstract file URL: http://etd.ncsi.iisc.ernet.in/abstracts/3394/Abstract_Abhinav_Dubey.pdf
URI: http://hdl.handle.net/2005/2633
Appears in Collections:Mathematics (math)

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