Recognition of disease associated posttranslationally modified proteins through proteomics and bioinformatics methods
Abstract
There are multiple challenges in biomarker discovery. This thesis will discuss various ways in which these obstacles can be overcome and show three case studies implementing strategies for doing so. Currently there is great deal of research on improving instrument sensitivity and dynamic range for the analysis of complex samples. There is no consensus on which proteomics tool can best identify and quantify the complete proteome of any tissue or bodily fluid. The most commonly used strategy is shut gun proteomics. This is a "bottom-up" approach in which protein samples are digested and proteins identified by mass spectrometry (MS). One common obstacle in proteomics is the complexity of the sample. In bottom-up proteomics a peptide mixture of crude plasma can contain hundreds of thousands of peptides, which impedes 100% sequence coverage of identified proteins. Because total sequence coverage is hard to achieve in such complex biological samples a lot of information is lost about posttranslational modifications (PTMs). It is well documented that different cellular environments affect the machinery of the cell along with the proteins produced. Aberrations in protein PTMs are often the result of an underlying disease state and in many cases they serve as biomarkers or drug targets. Proteomics as opposed to genomics can capture these posttranslational modifications. For this reason our approach was to study general PTM features known to be involved in the disease that could be easily selected through immunoaffinity methods. This not only reduces the complexity of the sample but also targets proteins with PTMs known to be involved in disease. This proteomics approach along with pathway analysis tools could bring to light protein signatures of a disease state and help in the discovery of new biomarkers for diagnosis, prognosis or prediction of patient treatment outcome.
Degree
Ph.D.
Advisors
Regnier, Purdue University.
Subject Area
Analytical chemistry|Biochemistry|Bioinformatics
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