Development and Application of Large-Scale Protein Folding Stability Analysis in Drug Target Identification and Disease Biomarker Discovery
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In the past decade, several mass spectrometry-based proteomic techniques have been developed for the large-scale analysis of protein folding stabilities. The main focus of this dissertation is to develop and apply these large-scale protein folding stability approaches in drug target identification and disease biomarker discovery. One goal of this work is to develop a novel chemo-selection strategy to improve the bottom-up proteomics readout in proteome-wide limited proteolysis experiments. Another goal of this work is to apply these methods to the target identification of two drugs with known mode of action, and to the biomarker discovery of Parkinson’s disease.
Described in the first part of the dissertation is the development of a chemo-selective enrichment strategy to isolate the semi-tryptic peptides generated in mass spectrometry-based applications of limited proteolysis methods. The method is termed Semi-Tryptic Peptide Enrichment Strategy for Proteolysis Procedures (STEPP). The STEPP-PP workflow was evaluated in two proof-of-principle drug target identification experiments involving two well-studied drugs, cyclosporin A and geldanamycin. The STEPP-LiP workflow was evaluated in one proof-of-principle experiment on identification of protein conformational changes between a breast cancer cell line, MCF-7, and a normal cell line, MCF-10A. The STEPP protocol increased the number of semitryptic peptides detected in the LiP and PP experiments by 5- to 10-fold. The STEPP protocol not only increases the proteomic coverage, but also increases the amount of structural information that can be gleaned from limited proteolysis experiments. Moreover, the protocol also enables the quantitative determination of ligand binding affinities. When coupled to a one-pot data acquisition strategy, the one-pot STEPP-PP technique was found to have a very low false positive rate (i.e., 0.09%) in a proof-of-principle drug target identification experiments involving cyclosporin A and a yeast lysate.
The second part of this dissertation describes the application of protein folding stability approaches to the identification of protein targets of subglutinol A (a natural immunosuppressant) and manassantin A (a natural product with anti-cancer activity).
In the suglutinol A study, a combination of SPROX, TPP, CPP and STEPP-PP strategies was used to identified two consistent protein hits, deoxycytidine kinase (DCK) and exportin-2 (XPO2), from more than 2000 assayed proteins in a 2B4T cell lysate. The binding of DCK with subglutinol A was validated using a targeted gel-based pulse proteolysis experiment. A set of chemical biology experiments were performed to uncover the relation of this interaction with subglutinol A’s mode of action. It was shown that neither of the kinase activity, expression level or phosphorylation modification level of DCK was alternated by subglutinol A. However, the nuclear transportation of DCK was blocked by subgltutinol A. This reduction of DCK level in the cell nucleus possibly leads to the observed reduction of nuclear dCMP pool and the halted proliferation of sublgutinol A treated T cells.
In the manassantin A study, a combination of STEPP-LiP, STEPP-PP, one-pot STEPP-PP, one-pot SPROX and one-pot TPP strategies were performed to identify the protein target of the drug in a hypoxia-treated HEK293T cell lysate. These experiments assayed over 4000 proteins and found 4 protein hits for further validation of their interaction with manassantin A.
The third part of describes the utilization of the SPROX method to characterize the progression of PD in a mouse model of the disease in which the human α-synuclein protein with an A53T mutation was overexpressed. The thermodynamic stabilities of proteins in brain tissue cell lysates from Huα-Syn(A53T) transgenic mice were profiled at three time points including at 1 Month (n=9), at 6 Months (n=7), and at the time (between 9 and 16 Months) a mouse became Symptomatic (n=8). The thermodynamic stability profiles generated here on over 300 proteins were compared to the thermodynamic stability profiles generated on the same proteins from similarly aged wild-type mice using a two-way ANOVA analysis. A group of 22 proteins were identified with age-related protein stability changes, and a group of 11 proteins were found to be differential stabilized in the Huα-Syn(A53T) transgenic mouse model. The proteins differentially stabilized in the disease mouse model could potentially be used as Parkinson’s disease biomarkers upon further validation.
Disease biomarker discovery
Drug target identifcation
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