Steven Daniel Pelech

Professor

Research Classification

Cell Signaling
Neurological Diseases
Cell Signaling and Cancer
Cellular Differentiation
Cellular Division
Enzymes and Proteins
Functional and Structural Proteomics
Genomics and Proteomics
Proteomics
Bioinformatics
Diabetes
Alzheimer's Disease
Parkinson's Disease
Neuronal Communication and Neurotransmission

Research Interests

Protein Microarrays
Protein Kinases
Protein Phosphatases
Protein phosphorylation
Mitosis
Meiosis
Cell signalling
Signal transduction

Relevant Degree Programs

 

Research Methodology

Antibody Microarrays
Cell/Tissue Lysate Microarrays
Peptide Microarrays
Peptide Synthesis
Antibody Production
Multi-immunoblotting
Kinase assays

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Doctoral students
Any time / year round

Investigation of protein kinase signalling networks in diverse experimental model systems, including growth factor-treated human cancer cell lines and meiotic maturation of sea star and frog oocytes. Investigations of protein kinase signalling networks in biopsied human tissues from patients with cancer, neurological disorders and diabetes. Development of microarray technology for monitoring protein expression, covalent modification, protein-protein interactions and protein-drug interactions. As a hybrid academic-industrial laboratory, the research seeks to support both basic and applied research goals.

I am open to hosting Visiting International Research Students (non-degree, up to 12 months).
I am interested in hiring Co-op students for research placements.

Graduate Student Supervision

Doctoral Student Supervision (Jan 2008 - May 2019)
Investigations of the origin, regulation, and substrate specificities of protein kinases in the human kinome (2015)

The eukaryotic protein kinases (ePKs) constitute one of the largest families of enzymes encoded by eukaryotic genomes. They regulate all cellular processes by transducing inter- and intracellular signals via the phosphorylation of specific protein substrates. The primary sequences of ePK catalytic domains are highly conserved, indicating a common ancestry. They all share a conserved catalytic core for their phosphotransferase function and often a common activation mechanism by phosphorylation of a variable segment known as the activation T-loop. Starting from a manually aligned and annotated map of 492 typical human protein kinase domains, I first explored the origin of ePKs using a modified BLAST method in various species. Comparisons of primary, secondary and tertiary structures supported the hypothesis that protein kinases and choline kinases evolved from an ancient aminoacyl-tRNA ligase. Secondly, I studied the functional roles of two extremely conserved phosphosites that exist ubiquitously in the activation T-loops of most protein-serine/threonine kinases. The extensively examined extracellular signal-regulated kinases (ERKs) 1/2 from the mitogen-activated protein kinase family were used as a model in this study. I discovered that both Thr-207 and Tyr-210 from human ERK1 were essential for regulation of its phosphotransferase activity. Autophosphorylation of Thr-207 played an inhibitory role to control ERK1 activity after the initial phosphorylation and activation by MEK1. This may serve as a general mechanism for kinase autoinhibition. I also examined the substrate specificities of more than 200 human protein kinases with peptide microarrays populated with semi-optimal substrate sequences. The resultant data were used to expand the training datasets for development of next generation protein kinase substrate predictive algorithms. Additionally, I describe a novel method to produce a more unbiased polyclonal generic phosphotyrosine antibody than the monoclonal antibodies that are commonly used to enrich and track tyrosine-phosphorylated proteins. The tools and knowledge resulting from this research should enable improvements in the characterization of protein kinases and establishing their linkages to a variety of human diseases for the development of better diagnostic tests and therapeutic drugs. This work also provides basic insights into the evolution of life and the specific architecture of protein kinase-based signalling systems.

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Modelling human cell protein phosphorylation networks (2015)

Defects in cell signalling networks are linked to over 400 human diseases. My thesis research aimed to model these networks in more detail to facilitate understanding of their architecture and operations under normal and pathological conditions. The various protein levels in diverse normal human cell and tissue types were inferred from their mRNA expressions, and their up/down-regulation was also investigated in about 300 human cancer cell lines and 50 types of human cancers. This was based on meta-analyses of gene microarray measurements deposited in the USA National Center for Biotechnology Information's Gene Expression Omnibus database. I identified proteins that were commonly or uniquely expressed in normal and cancerous human cells and tissues. The co-expression patterns of proteins were used to predict potential interactions, but there was not a strong correlation between high co-expression and actual direct protein-protein interactions documented in the scientific literature. With respect to the post-translational regulation of proteins, my research efforts primarily targeted protein phosphorylation, which is the most predominant type of reversible covalent modification of proteins. Complex protein phosphorylation networks emerge through the interplay of protein kinases, protein phosphatases, phosphorylation site-dependent binding proteins, and their phosphoprotein substrates. I modelled the interactions of protein kinases with substrate proteins and inhibitory compounds. Nearly a million human phosphosites were predicted, and each of these was tested in silico as substrates for 500 human protein kinases. The interactions of over 550 known protein kinase inhibitory drugs with the 500 protein kinases were also tested. These predicted interactions were compared with empirical data from other on-line protein-protein and protein-drug interaction databases. The human phosphosites were also analysed with respect to their protein conservation in over 20 other diverse species, and it was found that threonine phosphosites in protein kinases that were activatory were particularly well conserved in evolution. Finally, probabilistic graphical models were developed to model the most probable structure of substrate phosphosites for specific protein kinases. The discussed probabilistic graphical model, gave more theory justifications for the protein-protein interaction modelling that was presented in the earlier parts of the thesis.

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