Computational Methods for Improved Peptide and Protein Identification in Proteomics

Shotgun proteomics is an analytical method used to identify proteins from complex mixtures such as a whole-cell lysates. This method utilizes high-resolution mass spectrometers, proteolysis, and fractionation techniques in order to maximize the number and quantities of proteins being detected. Knowing the identity and abundance of proteins in a cell provides insights into cell functioning, and how cells respond to external stimuli. Our lab uses proteomics to further understanding of signaling networks and how these are dysregulated during melanoma progression. Computer algorithms are an essential aspect of shotgun proteomics in order to match hundreds of thousands of spectra to the peptide sequences from which they came. The most productive peptide identification methods search databases of protein sequences, looking for the best peptide spectrum matches, but these methods can be plagued by false positives and false negatives. I designed and implemented MSPlus, software which increases sensitivity and specificity in peptide identification by using physicochemical filters and consensus scoring between multiple database search programs, approaches which are now commonly in use. After peptides are identified, they must be mapped back to the proteins from which they derive, a non-trivial task in the human proteome with its extensive alternative splicing, gene duplication, and post-translational processing. I designed and implemented IsoformResolver, software which accurately and efficiently infers proteins from peptides using a pre-calculated peptide-centric reformatted protein database. Proteins are reported in the context of protein groups, a concise representation which allows experimentalists to see the most likely proteins in the context of all possible proteins for which there is mass spectrometry evidence. This novel representation minimizes the protein volatility inherent to the more common protein-centric output. Finally I examine the capabilities and limits of shotgun proteomics. I introduce a tier-based representation of protein abundances, and investigate how the abundances vary by protein class and at different sampling depths. I compare proteomics and transcriptomics results, and investigate to what extent proteomics can be used to identify members which distinguish cell states

IsoformResolver: A Peptide-Centric Algorithm for Protein Inference

When analyzing proteins in complex samples using tandem mass spectrometry of peptides generated by proteolysis, the inference of proteins can be ambiguous, even with well-validated peptides. Unresolved questions include whether to show all possible proteins vs a minimal list, what to do when proteins are inferred ambiguously, and how to quantify peptides that bridge multiple proteins, each with distinguishing evidence. Here we describe IsoformResolver, a peptide-centric protein inference algorithm that clusters proteins in two ways, one based on peptides experimentally identified from MS/MS spectra, and the other based on peptides derived from an <i>in silico</i> digest of the protein database. MS/MS-derived protein groups report minimal list proteins in the context of all possible proteins, without redundantly listing peptides. <i>In silico</i>-derived protein groups pull together functionally related proteins, providing stable identifiers. The peptide-centric grouping strategy used by IsoformResolver allows proteins to be displayed together when they share peptides in common, providing a comprehensive yet concise way to organize protein profiles. It also summarizes information on spectral counts and is especially useful for comparing results from multiple LC–MS/MS experiments. Finally, we examine the relatedness of proteins within IsoformResolver groups and compare its performance to other protein inference software