Data Fusion and Inductive Transfer for Organelle Proteomics

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A protein is localised in its intended sub-cellular location in order to function and interact with its correct binding partners and substrates. The determination of sub-cellular protein location is particularly desirable to biologists for two reasons. First, it can assist elucidation of a protein's role within the cell, as proteins are spatially organised according to their function and specificity of their molecular interactions. Second, it refines knowledge of cellular processes by pinpointing certain activities to specific organelles. Organelle proteomics, the systematic study of proteins and their assignments to organelles, is a rapidly growing field and many high throughput approaches have been developed to date which result in protein subcellular locations. One reason for the increase in growth of the field is that there is significant correlation between disease classes and sub-cellular localisations. It is well established that loss of functional effects in diseases can be attributed to abnormal protein localisations. For example, in many types of carcinoma cells, nuclear-cytoplasmic transport, essential for normal cell function, has been found to be defective as a result of abnormal protein localisations. The datasets produced in general are high quality rich sources of data and are have been mined using a variety of statistical methods and are beginning to be explored through the use of more robust machine learning (ML) methods which have shown to yield significant improvements in protein-organelle predictions over previous statistical methods. However, there are still inherent issues that limit the optimal application of such contemporary ML methods: (1) limited number of proteins and features/channels available, (2) limited number of organelle markers and (3) limited number of organelle classes.In this proposal we aim to improve protein-organelle association via the application of state-of-the-art ML methods in which we wish to exploit all sources of information available to accurately assign a protein to its sub-cellular compartment. In addition to the high-quality data produced from experimental (high-throughput mass spectrometry-based) methods we wish to develop a toolkit that will enable the exploitation of other data sources on which to predict protein localisation such as protein amino acid sequences, protein-protein interaction partners, conserved signal peptide motifs and imaging data on which to strengthen the predictions made on high quality gradient-based data. Many researchers have already found that in many situations training statistical models on multiple related data sources is better than training models on each data source individually.The development of a framework that will enable the fusion and transfer of knowledge from multiple data sources will lead to the creation of optimal organelle proteomics datasets which will be deposited in a public access proteomics data repository (PRIDE). The toolkit will be freely available for the use of the whole proteomics community.The work proposed in this grant will be implemented by a multidisciplinary team bringing together expertise in state-of-the-art gradient-based proteomics approaches (Lilley), contemporary machine learning methods (Holden and Trotter), bioinformatics and code development (Gatto), and applied mathematics (Simpson). The majority of this team have worked together previously on organelle proteomics bioinformatics projects that have resulted in the release of new toolkits for organelle proteomics data analysis.