In silico mass spectrometry for biologists: Tools and resources for next-generation proteomics

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Proteins are the key functional molecules in cells, performing multiple biological tasks. This includes catalysing reactions, providing structure to cellular components, signalling between different cells and regulating the production of other genes as transcription factors. The recent advent of genome sequencing has transformed our ability to study these molecules into a "Big Data" discipline, coupled to advances in mass spectrometry (MS) and allied computing techniques. This particular branch of the "'omics" is referred to as proteomics - the high-throughput study (identification and importantly, quantification) of all the proteins that can be detected in a given biological sample. For example, by discovery of the proteins that are more abundant in different life cycle stages (during development or during ageing) ,may give us clues as to which biological pathways control these processes. Proteomics is used right across biological and biomedical research for profiling systems as varied as plants, model organisms, infectious diseases/microbes, chronic disease of humans and animals, among many others. Currently, the primary technology used in proteomics is MS. Each assay (or scan) in a given MS run (one given experiment) provides us information about which proteins are present in our samples, by studying the peptides generated from them using a defined enzyme (e.g. trypsin). In the mass spectrometer, each peptide is broken up, and the instrument reports the masses of the different fragments in so called mass spectra. In the most traditional and most widely-used proteomics approaches nowadays, called 'data dependent acquisition' (DDA) techniques, only the most abundant peptides are measured by the instrument, and a lot of the remaining peptides are simply not detected and/or measured. This leaves the possibility that invaluable biological information is simply missed, which informs on the relative level of proteins in the cell. Recently, a novel group of proteomic approaches are starting to be used which can overcome some of the limitations of DDA approaches, known as Data Independent Acquisition (DIA) methods. Excitingly, these methods capture a near-complete digital record of the proteome in that experiment, but require more sophisticated software tools to mine these DIA maps. Relatively few groups are expert in their use, limiting the potential of the community to analyse the growing numbers of DIA data sets. Additionally, the current software tools are not yet robust enough, nor available on user-friendly web-based platforms that the average biologist can use. In this project, we will develop and build open software able to analyse proteomics datasets generated using these novel DIA proteomics approaches in a robust manner, so they can be used in the future by anyone in the community. This will be achieved by making the software available on the European Bioinformatics Institute's "cloud" IT infrastructure. When the project finishes, the generated software pipelines will be ready to be deployed in other similar infrastructures in the UK and internationally. We will also improve and refine current analysis methods by using proteomics data already made available in the public domain, by extending existing collections of mass spectra called spectral libraries. This will support a rich portfolio of (re)analysis methods for the user base, with 'plug and play' components, that also includes support for detection of so called post-translational modifications (PTMs), which are notoriously difficult to identify otherwise. The project outputs will greatly benefit a wide-range of biological and biomedical researchers interested in proteomic techniques for interrogation of samples - even if they don't have access to mass spectrometers. We will ensure this is disseminated via delivering workshops, training and online help/tutorials.