The goal of this application is to develop new computational methods to profile protein expression and phosphorylation changes in response to signaling pathways and disease states, directly supporting studies of melanoma and prostate cancer carried out in the laboratories of three collaborators. Shotgun proteomics using multidimensional LC/MSMS approaches that are based on peptide gas phase fragmentation, such as MuDPIT, have proven effective in idenfitying proteins in complex samples. However, there are serious limitations with respect to depth of sampling proteins in complex mixtures, accuracy of assigning peptide sequences to MSMS spectra, ambiguities in distinguishing protein isoforms, quantification of protein abundances, and characterization of posttranslational modifications, such as phosphorylation. In addition, methods are needed to handle problems arising with complex mixtures, such as peaks that overlap in mass and elution, peptides eluting in many fractions during multidimensional separation, and clustering of peptides/proteins based on multivariate measurements. The proposed experiments will develop new computational tools to create an integrated software system which will address these goals.
The specific aims are to (1) develop computational tools for quantifying changes in protein abundances from samples fractionated by multidimensional LC, (2) increase the accuracy of peptide and protein identifications by improving algorithms for theoretical MS/MS spectral predictions, (3) develop statistical and computational methods to improve phosphopeptide analyses in complex samples, and (4) develop an Image Recognition Neural Network strategy for clustering peptide and phosphopeptide features within multidimensional datasets between many samples. Completion of these aims will address outstanding unsolved obstacles in shotgun proteomics and provide robust computational tools to achieve accurate and sensitive protein profiling, assessment of differential phosphorylation, integration of multivariate datasets from multiple platforms and samples, and new algorithms for rapid delineation of disease discriminators in proteomics datasets. As these tools are developed, they will be applied to three projects involving proteomics for basic and clinical cancer research, profiling molecular changes in cancer cells, tissues, and fluids for cancer biomarker discovery. Data collection for all three of projects will be carried out using LTQ-Orbitrap and 4000 QTrap mass spectrometry instruments available in our biomolecular mass spectrometry core facility, where investigators will access the software under development for data reduction. This will provide continual feedback from investigators about results and experiences, which will allow the team to respond by troubleshooting software and adding further analytical capabilities for the needs of real-world samples.
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