Proteogenomics

Image of a eukaryote cell illustrating how proteins are made: DNA in the nucleus is read by RNA polymerase, then ribosomes in the cytoplasm produce an amino acid strand that folds into a functional protein.

The main idea behind the proteogenomic approach is to identify peptides by comparing MS/MS data to protein databases that contain predicted protein sequences. The protein database is generated in a variety of ways through the utilization of genomic and transcriptomic data. Below are some of the ways in which protein databases are generated:

Proteogenomics can be applied in different ways. One application is the improvement of gene annotations in various organisms. Gene annotation involves discovering genes and their functions.[9] Proteogenomics has become especially useful in the discovery and improvement of gene annotations in prokaryotic organisms. For example, various microorganisms have had their genomic annotation studied through the proteogenomic approach including, Escherichia coli, Mycobacterium, and multiple species of Shewanella bacteria. [10]

Besides improving gene annotations, proteogenomic studies can also provide valuable information about the presence of programmed frameshifts, N-terminal methionine excision, signal peptides, proteolysis and other post-translational modifications.[3][7] Proteogenomics has potential applications in medicine, especially to oncology research. Cancer occurs through genetic mutations such as methylation, translocation, and somatic mutations. Research has shown that both genomic and proteomic information are needed to understand the molecular variations that lead to cancer.[2][11] Proteogenomics has aided in this through the identification of protein sequences that may have functional roles in cancer.[12] A specific example of this occurred in a study involving colon cancer that resulted in the discovery of potential targets for cancer treatment.[2] Proteogenomics has also led to personalized cancer targeting immunotherapies, where antibody epitopes for cancer antigens are predicted using proteogenomics to create medicines that act on the patient's specific tumor.[13] In addition to treatment, proteogenonomics may provide insight into cancer diagnosis. In studies involving colon and rectal cancer, proteogenomics was utilized to identify somatic mutations. The identification of somatic mutations in patients could be used to diagnose cancer in patients. In addition to direct applications in cancer treatment and diagnosis, a proteogenomic approach can be used to study proteins that result in resistance to chemotherapy.[11]

Proteogenomics may offer methods of peptide identification without having the disadvantage of incomplete or inaccurate protein databases faced by proteomics;however, there are incurring challenges with the proteogenomic approach.[1] One of the biggest challenges of proteogenomics is the sheer size of protein databases generated. statistically, a large protein database is more likely to result in the incorrect matching of the data from the protein database to the MS/MS data, this issue can hinder the identification of new peptides. False positives are also an issue through proteogenomic approaches. false positives can occur as a result of extremely large protein data bases where miss-matched data leads to incorrect identification. Another issue is the incorrect matching of MS/MS spectra to protein sequence data that corresponds to a similar peptide instead of the actual peptide. There are cases of receiving data of a peptide located at multiple gene sites, this can lead to data that can be interpreted in different ways. Despite these challenges, there are ways to reduce many of the errors that occur. For example, when dealing with a very large protein database, one could compare the identified novel peptide sequences to all of the sequences within the database and then compare the post translational modifications. Next it can be determined if the two sequences represent the same peptide or if they are two different peptides.[1]