Applications of SAMDI Mass Spectrometry for High-Throughput Reaction Monitoring on Peptide ArraysPublic
This work combines the use of high-throughput mass spectrometry with peptide arrays for to monitor reactions on peptides. The Mrksich lab introduced a high-throughput, label-free, biochemical assay that relies on self-assembled monolayers on gold and matrix-assisted laser desorption/ionization mass spectrometry, termed SAMDI-MS. This dissertation introduces new applications of SAMDI-MS and peptide arrays and illustrates the many advantages of this technique for studying reactions on peptides. Chapter 2 describes the use of SAMDI-MS and peptide arrays for the identification of new sequence-selective reactions of peptides. Selective reactions on peptides and proteins are important for studying protein function and preparing protein-based materials. Using this technique, we discover and characterize the sequence-selective acetylation of peptides containing histidine and tyrosine by acetic anhydride and demonstrate the advantages of using peptide arrays with a label- free analysis method to discover peptide-modifying reactions. We then illustrate the use of SAMDI-MS and phosphorylated peptide arrays to gain a systems- level understanding of phosphatase activity in cell lysates. Phosphorylation is the most prominent post-translational modification on proteins. While phosphorylation plays important roles in many cellular processes, understanding of the regulation of phosphorylation is limited. Kinases and phosphatases work cooperatively to regulate phosphorylation; however, research has primarily focused on kinases due to the technical challenges of studying phosphatase activity. In Chapter 3, we show that phosphatase activity is specific and may play a significant role in the regulation of global phosphorylation. Lastly, we apply machine learning to high-throughput data acquired from SAMDI-based peptide arrays to improve the efficiency of peptide array design. One of the main challenges of high-throughput experiments is analysis of large data sets. In Chapter 4, we describe the use of machine learning to predict peptide signal-to-noise to design arrays consisting of highly detectable peptides. This allows for higher quality data collection, and therefore more reliable results for future peptide array experiments. The use of peptide arrays has significantly increased the capabilities of protein research, and SAMDI-MS offer many advantages for the continued expansion of new applications for peptide arrays. This dissertation illustrates the many advantages of SAMDI-MS and peptide arrays for novel biochemical applications.
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