Abstract
In this paper we apply machine learning methods for predicting protein interactions in fungal secretion pathways. We assume an inter-species transfer setting, where training data is obtained from a single species and the objective is to predict protein interactions in other, related species. In our methodology, we combine several state of the art machine learning approaches, namely, multiple kernel learning (MKL), pairwise kernels and kernelized structured output prediction in the supervised graph inference framework. For MKL, we apply recently proposed centered kernel alignment and p-norm path following approaches to integrate several feature sets describing the proteins, demonstrating improved performance. For graph inference, we apply input-output kernel regression (IOKR) in supervised and semi-supervised modes as well as output kernel trees (OK3). In our experiments simulating increasing genetic distance, Input-Output Kernel Regression proved to be the most robust prediction approach. We also show that the MKL approaches improve the predictions compared to uniform combination of the kernels. We evaluate the methods on the task of predicting protein-protein-interactions in the secretion pathways in fungi, S.cerevisiae, baker’s yeast, being the source, T. reesei being the target of the inter-species transfer learning. We identify completely novel candidate secretion proteins conserved in filamentous fungi. These proteins could contribute to their unique secretion capabilities.
Highlights
Protein secretion is a fundamental cellular process that is required for transporting proteins into cellular compartments, the cell surface and the external space of the cell as well as for covalent modification i.e. disulphide bond formation and glycosylation of proteins
We introduce a framework of machine learning methods that can be used for predicting physical or functional protein-protein interaction or more specific biological networks i.e. metabolic pathways depending on what type of training labels are used
We evaluate the Protein-protein interaction (PPI) prediction in the cross-species transfer learning from S. cerevisiae to T. reesei
Summary
Protein secretion is a fundamental cellular process that is required for transporting proteins into cellular compartments, the cell surface and the external space of the cell as well as for covalent modification i.e. disulphide bond formation and glycosylation of proteins. As can be expected from its central role, the protein secretion machinery is conserved in eukaryotes. Fundamental research to unravel its functioning has been carried out in the fungus Saccharomyces cerevisiae [1]. The baker’s yeast S. cerevisiae of the subphylum Saccharomycotina does not naturally secrete large amounts of proteins unlike the filamentous fungi of the subphylum Pezizomycotina. For example the Pezizomycotina Trichoderma reesei (Hypocrea jecorina) is able to secrete its native cellulase proteins with yields of over 100 g/l in industrial.
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