BBSRC-NSF/BIO PTMeXchange: Globally harmonized re-analysis and sharing of data on post-translational modifications

Proteins are the key functional molecules in cells, performing multiple biological tasks. This includes catalysing reactions, providing structure to cellular components, signaling between different cells and regulating the production of other genes among many others. Proteins are composed of chains of individual amino acids that are formed initially into a long sequence, which forms into a strictly controlled 3D structure, giving the highly specific function to each protein. The advent of genome sequencing has transformed our ability to study these molecules into a "Big Data" discipline, coupled to advances in mass spectrometry and allied computing techniques. This particular branch of "'omics" is referred to as proteomics - the high-throughput study (identification and quantification) of all the proteins that can be detected in a given biological sample. Proteomics is used right across biological and biomedical research for profiling systems as varied as human, model organisms including plants, and infectious diseases/microbes, among many others.

Many biological functions are dependent on chemical modifications that proteins can undergo, called Post-translational Modifications (PTMs). Due to the occurrence of PTMs, one particular gene can produce a great number of different protein entities which can potentially have different biological functions. PTMs can provide a rapid mechanism for changing function, such as switching an enzyme (biological catalyst) "on" and "off". Due to their functional importance, sites of PTMs on proteins are frequently the targets for drug design, particularly against cancer.

In this grant, we will study, using high-quality data analysis pipelines, the occurrence of the main types of PTMs across hundreds of proteomics datasets in the public domain, involving human and the main model organisms (e.g. mouse, rat and the model plant Arabidopsis). Three world-leading bioinformatics resources are involved in this proposal, namely PRIDE and PeptideAtlas (proteomics resources), and UniProtKB (protein knowledge-base). We expect that UniProtKB will be the main resource to disseminate the outputs of the project to thousands of researchers, working in varied disciplines. We will also showcase possible research applications of this huge amount of data that will be generated, for example studying how PTMs have evolved in different groups of species. We will ensure that all the outputs of the project are disseminated via different training and outreach activities, including e.g. delivering workshops, training and online help/tutorials.

The types and sites of post-translational modifications (PTMs) on proteins are rich and diverse, providing cells with a rapid mechanism for adapting function under different conditions. PTMs are widely studied across all areas of fundamental and applied life sciences research. Proteomics approaches using mass spectrometry (MS) provide the sole high-throughput means to detect and localize protein PTMs. Despite their biological importance, PTM-relevant data is collated in the public domain via disparate resources, with a lack of data provenance. An efficient way to improve the situation is to make PTM information derived from proteomics approaches available through UniProtKB (http://www.uniprot.org/), the world-leading protein-knowledgebase. There are hundreds of relevant PTM proteomics datasets in the public domain since the proteomics community is now widely embracing open data policies (e.g. through the resources PRIDE and PeptideAtlas, part of the ProteomeXchange consortium).
We will develop and deploy in the cloud open and reproducible pipelines to re-analyse consistently hundreds of PTM relevant public datasets coming from human and the main model organisms. Complementary analysis approaches will be used: primarily standard protein database-based but also spectral library-based and open modification searches. Special attention will be devoted to ensuring that PTM localization is accurate and community guidelines will be developed with that goal in mind. These data will be widely disseminated to UniProtKB and other knowledge-bases (e.g. neXtProt) and made available at PRIDE, PeptideAtlas, and a new resource PTMeXchange. These new PTM data will be integrated across studies, to increase statistical power at an unprecedented scale and accuracy. Finally, we will perform several following demonstration studies to understand PTM motifs, function and evolution.

There is the potential for the following impacts:

- The biggest potential impact is on Pharma, within which there are many efforts in drug design to target cell signalling, and PTMs. The results will inevitably feed into improved understanding of processes and potentially generating new targets. There is also potential for indirect benefits in the biotech industry (improved understanding of PTMs in fungi) and Agrifood (PTMs on plants), e.g. derived through inference of site conservation from model organisms.

- Software vendors or pharmaceutical research and development teams will benefit, since we envisage they may wish to take up our software for local pipelines (e.g. deployed in their own cloud environments). It is important to highlight that all the software developed during the proposal will be open source.

- Research councils and charities funding research will benefit through the potential for increased impact of the mass spectrometry (MS)-based proteomics projects they fund, thanks to the re-analysis of public proteomics datasets and the integration of novel PTM proteomics data in UniProtKB.

- More broadly, as proteomics is a key technology in the Life Sciences, there is the potential for considerable indirect benefits across a wide range of areas in basic biology, biomedical and clinical science, as more value will be derived from datasets.

- Life scientists worldwide will be able to benefit from the training activities planned (both face-to-face and via on-line resources).

Staff employed will benefit:

- Further training in one key enabling technology for the BBSRC (proteomics) and exposure to a multi-disciplinary team, and to conferences, workshops and new national and International collaborations.

- Acquire skills needed to work with bioinformatics software in a cloud environment, something that is getting increasingly important with the growing size of datasets and the need of suitable IT infrastructure. The team will also use cutting edge machine learning methods in WP4, which are skills hugely in demand in academic research and industry.