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Computational Investigation of Signaling Regimens using Proteomics Data

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posted on 11.06.2014, 00:00 by Morten Källberg
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.



Lu, Hui



Degree Grantor

University of Illinois at Chicago

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Committee Member

Dai, Yang Stroscio, Michael Min, Jung-Hyun Xu, Jinbo

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