New peptide identification method to cope with unexpected modifications.

14th November 2014

Current methods of identifying proteins are based on breaking down proteins into constituent smaller peptides, and matching patterns of peptide fragments to corresponding patterns from known peptides, or to theoretical predictions of these patterns. Where a database match is not possible, de novo sequencing of the amino acids in the peptides can be used to identify peptides, and modern techniques promise continual improvements.

But one problem that holds back even more advanced techniques for identifying proteins in complex mixtures or accurate quantification of protein levels is the cellular modification of proteins, after being made. The modifications are often critical to their biological properties. Proteins also can be modified during digestion, or food processing, potentially affecting their effects on our health, or making them more or less allergenic. In the field of protein identification, such modifications can prevent proteins being identified, or worse misidentified. Existing identification techniques essentially rely on matching to databases that include modification information. But there are hundreds of different modifications that have been identified, and more we don’t know of yet, so the searches only relate to a small selection of known types of modification.

Dr Thomas Wilhelm from the Institute of Food Research (IFR), and Dr Alexandra Jones from the Sainsbury Laboratory (now University of Warwick) have published a new method to overcome these problems. Their Mass Spectrometry-Peak Shift Analysis (MS-PSA) can rapidly identify potential protein modifications, and can be used to complement other protein identification techniques, improving both their speed and sensitivity. The technique involves taking spectra, and using techniques inspired by frequent pattern mining to find spectra that are related, for example due to their modifications. Crucially, the technique does not rely on pre-existing databases and peptide identification methods, although these can be exploited to aid interpretation of MS-PSA results.

An important field of application of this new technique is peptide glycosylation analysis. It is estimated that over 70% of proteins are glycosylated across kingdoms, often affecting their biological function. Alterations in glycosylation have been associated with different pathological conditions in humans and play an increasingly important role in biomarker discovery including inflammatory and cancer diseases. Protein glycosylation is not limited to Eukaryotes and gaining insights into bacterial glycosylation is crucial to understand the impact of bacteria on health and disease and to underpin glycoengineering approaches for the expression of therapeutic molecules.