Ayu: a machine intelligence tool for identification of extracellular proteins in the marine secretome

Microbes are the engines driving the elemental cycles. In order to interact with their environment and the community, microbes secrete proteins into the environment (known collectively as the secretome), where they remain active for prolonged periods of time. Despite the environmental relevance of microbes, our knowledge of the marine secretome remains limited due to a lack of effective in silico methods for the study of secreted proteins. An alternative approach to characterise the secretome is to combine modern machine learning tools with the evolutionary adaptation changes of the proteome to the marine environment. In this study, we identify and describe adaptations of marine extracellular proteins, which vary between phyla, resulting in differences in ATP costs, amino acid composition and nitrogen and sulphur content. We develop ‘Ayu’, a machine prediction tool that does not employ homology-based predictors and achieves better and quicker performance than current state-of-the-art software. When applied to oceanic samples (Tara Oceans dataset), our method was able to recover more than double the proteins compared to the most widely used method to identify secreted proteins. The application of this tool to open ocean samples allows better characterisation of the composition of the marine secretome.