Upon starvation cells undergo autophagy, a cellular degradation pathway important in the turnover of whole organelles and long lived proteins. Starvation-induced protein degradation has been regarded as an unspecific bulk degradation process. We studied global protein dynamics during amino acid starvation-induced autophagy by quantitative mass spectrometry and were able to record nearly 1500 protein profiles during 36 h of starvation. Cluster analysis of the recorded protein profiles revealed that cytosolic proteins were degraded rapidly, whereas proteins annotated to various complexes and organelles were degraded later at different time periods. Inhibition of protein degradation pathways identified the lysosomal/ autophagosomal system as the main degradative route.

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LC MS/MS has become an established technology in proteomic studies, and with the maturation of the technology the bottleneck has shifted from data generation to data validation and mining. To address this bottleneck we developed Experimental Peptide Identification Repository (EPIR), which is an integrated software platform for storage, validation, and mining of LC MS/MS-derived peptide evidence. EPIR is a cumulative data repository where precursor ions are linked to peptide assignments and protein associations returned by a search engine (e.g. Mascot, Sequest, or PepSea). Any number of datasets can be parsed into EPIR and subsequently validated and mined using a set of software modules that overlay the database. These include a peptide validation module, a protein grouping module, a generic module for extracting quantitative data, a comparative module, and additional modules for extracting statistical information. In the present study, the utility of EPIR and associated software tools is demonstrated on LC MS/MS data derived from a set of model proteins and complex protein mixtures derived from MCF-7 breast cancer cells. Emphasis is placed on the key strengths of EPIR, including the ability to validate and mine multiple combined datasets, and presentation of protein-level evidence in concise, nonredundant protein groups that are based on shared peptide evidence. Molecular & Cellular Proteomics 3:1023-1038, 2004.LC MS/MS has become a well-established technology for large-scale protein characterization in proteomic research (1). Briefly, proteins in the sample are digested with an enzyme, typically trypsin, because the tryptic peptides are more compatible with MS/MS analysis. The mass spectrometer is coupled to a reverse-phase LC unit, which reduces sample complexity and increases concentration of the peptides during MS acquisition. Throughout the LC MS/MS analysis, peptides are isolated and fragmented by CID to generate sequence-dependent MS/MS information, and finally the data is matched against a sequence database using a search engine. In a typical LC MS/MS acquisition, hundreds to thousands of precursor ions are subjected to MS/MS. As LC MS/MS can now be performed in a fully automated fashion, a new challenge faces investigators: the ability to generate LC MS/MS data outpaces the ability to analyze it (2).One of the initial challenges when analyzing LC MS/MS data is the assignment of peptides to precursor ions. Currently, this is typically achieved by statistical algorithms that match a theoretical peak list with the measured peak list and include the cross-correlative Sequest algorithm (3) and probability-based algorithms such as Mascot (4). The number of incorrect peptide assignments made using probabilistic or cross-correlative algorithms alone can become an issue, as different peptides may have overlapping or even identical fragmentation patterns (e.g. Leu/Ile substitutions). This issue is particularly valid for large LC MS/MS datasets and/or when a high sensitivity (i.e. the true positive rate) is requir...