Protein metabolite identification

The previous chapters have dealt mainly with LC/MS analysis involving short run times, many samples, and relatively small numbers of compounds in samples. What about samples containing very complex compound mixtures, for example, natural products, samples from biomarker discovery, protein digests, and QA/QC method development or metabolite identification samples requiring detection of every component Such workflows often require several analysis steps with different columns and different mobile phases and pH values to increase the separation probability by changing the selectivities of individual runs. 

The marriage of HPLC to mass spectrometry (MS), now developed into a mature instrumentation, continues to greatly impact many of the separation sciences, especially in pharmaceutical analysis where it has been used in new drug discovery [23,24] and in drug metabolite identification [25-27]. HPLC-MS has also made an impact on lipid research, providing a convenient approach to the analysis of phospholipids and fatty acids [28,29]. It has also greatly benefited the field of proteomics [30-34], especially analysis of protein structure and function. 

With the increasing number, diversity, and complexity of compounds being analyzed, UPLC presents the possibility to extend and expand the utility of separation science. Today, UPLC is widely used for metabolite identification analysis of natural products and herbal medicines pharmacokinetic, toxicity, degradation, bioanalysis, and bioequivalence studies quality control and in drug discovery determination of pesticides and separation of various pharmaceutical-related small organic molecules, proteins, and peptides. UPLC is also used for impurity profiling, method development, and validation performed in quality assurance and quality control laboratories [46,47,56-69]. 

Identification of a unique sequence of nucleotide bases within the DNA molecule, ranging from <100 to several thousand nucleotides, can serve as a highly specific tool for identification purposes (Nadal et al., 1996). Further, these techniques provide a direct comparison at the gene level and do not rely on the detection of secondary products (proteins, metabolites) whose expressions may be affected by the physiological status of the cells. 

Griffiths, H.R, Lunec, J. and Blake, D.R. (1992). Oxygen radical-induced fluorescence in proteins identification of the fluorescent tryptophan metabolite N formyl kynurenine as a biological index of radical damage. Amino Acids 3, 183-194. 

Since 1988, the methods that we use to isolate cDNAs of alkaloid biosynthesis have become ever more facile and sensitive, allowing for more efficient cDNA identification. We do not, however, yet understand enough about the cellular localization of alkaloid formation or about the nature of the catalysts to move completely away from enzymology and biochemistry and to use only molecular genetic techniques to dissect these biosynthetic pathways. Even our most recently successful cDNA isolations and identifications involved classical protein purification. We are beginning now to use proteomics and EST sequencing to identify natural product biosynthetic cDNAs, but these approaches are more feasible when a specialized cell/tissue type in which secondary metabolite biosynthetic pathways are active, can be isolated and used as a protein or RNA source. 

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