Computational Investigation of Signaling Regimens using Proteomics Data
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We present computational methods addressing three key challenges in the quest to construct a more complete picture of protein signaling pathways, namely, confident identification of proteins in a sample, functional classification of large-scale proteomics data, and characterization of the dynamic conformational changes in protein structures. First, we develop a probabilistic protocol for identification of short peptide fragments characterized by tandem mass-spectromety (MS/MS). A machine learning procedure for correctly matching peptides with mass spectra was constructed. Further, using a probabilistic framework, a method for protein identification based on the peptide predictions was proposed and tested. Second, a genome-wide functional classification protocol for identifying dual-specificity membrane- and protein-binding domains was developed. We demonstrate that reversible membrane binding is a key component in spatially regulation protein interaction networks and further propose a mechanistic classification of dual-specificity binding. As an extension of this model, we build a knowledge-mining procedure for learning the general mechanisms of membrane-binding, using C1, C2, and PH domains as test-beds. Last, we present a method for modeling the changes in single molecule dynamics induced by a signaling event as a discrete state Markov Chain model. Specifically, we use the partial unfolding of so-called mechanical proteins by way of steered molecular dynamics to demonstrate how the protein energy landscape is altered when different external mechanical forces are applied.