About the Project
Project Description
Protein-protein interactions (PPIs) are fundamental molecular events that regulate cellular behaviour. These biophysical associations, mediated via stretches of amino acid sequences, can be stable or transient and dynamically regulated by post translational modifications and protein topology. Changes in protein sequence, post-translational state, and abundance are key attributes that modulate PPIs. PPI networks encapsulate information on cellular organisation and functional relationships. To date, large numbers of cancer mutations have been exposed through genomics; however, it remains unclear how most cancer genes function and exert influence on disease processes through protein networks. Protein immunoprecipitation in combination with mass spectrometry (IP-MS) is a powerful approach to identify endogenous protein interactions, however it is not suitable for high throughput global characterization of protein interactions. As an alternative approach, protein crosslinking (XL) using bifunctional chemical reagents that stabilize proximal protein interfaces followed by proteolytic cleavage and MS identification has the potential to map protein interactions in a global and high throughput fashion with some degree of structural resolution (Yu & Huang, 2018).
Recently, we established approaches for global interactome analysis using co-fraction correlation analysis (Hillier 2017) and large-scale protein expression correlations (Roumeliotis 2017). The later approach was used to construct a protein relationship network in colorectal cancer cell lines, revealing how prominent cancer driver mutations can lead to collateral effects on proteins they are associated with through cohesive PPIs. Applying protein correlation analysis on patient samples, we revealed that distinct molecular remodelling events underpinned clinical trajectories that could guide the selection of candidates as biomarkers for patient stratification or therapeutic intervention (Chen et al 2020). To extend the functional and structural characterisation of cancer driver mutations in a global and high throughput fashion, here we propose to develop a quantitative approach to map protein interactions on global scale at high throughput. The method will be used to chart the native protein interaction landscapes across cancer cell lines, generating information on protein-protein interaction (PPI) surfaces. A deeper understanding of how somatic mutations affect protein structure, function and rewiring of cellular networks, will provide mechanistic insights and underpins new intervention strategies. Specifically, we will establish quantitative global interactome analysis and we will develop data analysis methods to identify differential network properties across a panel of cancer cell lines. We will generate de novo interactome networks that will be annotated using public experimental data to discriminate known and novel interactions and to perform associations with cellular phenotypes. Additionally, using distance constraints we will compare the identified interaction interfaces with model protein structures from structural databases. Overall, with this project we aim to provide a global differential interaction map across a panel of cancer cells at structural resolution with implications in cancer cell behaviour prediction.
Keywords
- Mass spectrometry proteomics
- Protein interactions
- Cancer cell profiling
Candidate profile
Candidates must have a First class or Upper Second class BSc Honours/MSc in biology, biochemistry, or chemistry.
How to apply
To view the full project proposal and details on how to apply using our online recruitment portal, please go to icr.ac.uk/phds. Please ensure that you read and follow the application instructions very carefully.
Please note we only accept applications via the online application system apply.icr.ac.uk.
Applications close at 11:55pm UK time on 14 November 2021.
References
- Yu, C. & Huang, L. (2018) Cross-Linking Mass Spectrometry: An Emerging Technology for Interactomics and Structural Biology. Anal Chem, 90(1), 144-165.
- Roumeliotis, T. I., Williams, S. P., Goncalves, E., Alsinet, C., Del Castillo Velasco-Herrera, M., Aben, N., Ghavidel, F. Z., Michaut, M., Schubert, M., Price, S., Wright, J. C., Yu, - L., Yang, M., Dienstmann, R., Guinney, J., Beltrao, P., Brazma, A., Pardo, M., Stegle, O., Adams, D. J., Wessels, L., Saez-Rodriguez, J., McDermott, U. & Choudhary, J. S. (2017) Genomic Determinants of Protein Abundance Variation in Colorectal Cancer Cells. Cell Rep, 20(9), 2201-2214.
- Chen YJ, Roumeliotis TI, Chang YH, Chen CT, Han CL, Lin MH, Chen HW, Chang GC, Chang YL, Wu CT, Lin MW, Hsieh MS, Wang YT, Chen YR, Jonassen I, Ghavidel FZ, Lin - ZS, Lin KT, Chen CW, Sheu PY, Hung CT, Huang KC, Yang HC, Lin PY, Yen TC, Lin YW, Wang JH, Raghav L, Lin CY, Chen YS, Wu PS, Lai CT, Weng SH, Su KY, Chang WH, Tsai PY, Robles AI, Rodriguez H, Hsiao YJ, Chang WH, Sung TY, Chen JS, Yu SL, Choudhary JS, Chen HY, Yang PC, Chen YJ.( 2020) Proteogenomics of Non-smoking Lung Cancer in East Asia Delineates Molecular Signatures of Pathogenesis and Progression. Cell. 182(1):226-244.
- Hillier C, Pardo M, Yu L, Bushell E, Sanderson T, Metcalf T, Herd C, Anar B, Rayner JC, Billker O, Choudhary JS*. (2019) Landscape of the Plasmodium Interactome Reveals Both Conserved and Species-Specific Functionality. Cell Rep. 28(6):1635-1647