COMBINING HUERISTIC APPROACHES WITH MACHINE LEARNING TO RECOMMEND POST TRANSLATIONAL MODIFICATIONS FOR STUDY
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English, Nolan
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Abstract
Post-translational modifications (PTMs) alter the chemistry of amino acid residues within translated proteins and thereby have the potential to expand the function and complexity of the proteome beyond the limits of the genome. Since the advent of high-throughput protein sequencing by mass spectrometry, hundreds of different types of PTMs have been discovered enabling cell signaling, protein degradation, DNA regulation, and nearly every other cellular function. However, the rate at which PTM data are generated far surpasses the rate at which it is being curated and/or processed for interpretation. Today, more than 2 million PTMs contributing to over 400 types of modifications exist within the public domain, however far fewer PTMs are thought to have a function. Even less have their functional context understood due to the high burden of experimental evidence needed to uncover functionality. In this seminar I will describe the development of SAPH-ire, a machine learning model meant to recommend potentially functional PTMs for experimental investigation. I will present this model as part of a general data, analytics, and visualization approach meant to close the gap between PTM detection and study.
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Date
2022-04-28
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Dissertation