Deep Metric Learning for Proteomics

Deep learning has become an innovative tool for predicting the properties of a protein. However, obtaining an accurate predictive model using deep learning methods typically requires a large amount of labelled data, which is expensive and time-consuming to accumulate. Even when optimised, these algorithms are often black boxes, which make it challenging to interpret the decision-making processes that lead to the final prediction. Therefore, there is a demand for innovative modelling techniques that overcome these drawbacks within the space of bioinformatic deep learning. To address these issues, we have designed a modelling scheme that utilises techniques from computer vision. Specifically, we explore how triplet-networks can form a robust model architecture that is capable of learning and ranking proteins from just a few labelled examples. We evaluate our model on a variety of downstream tasks, including peak absorption wavelength, enantioselectivity, plasma membrane localisation, and thermostability. The embedded representations produced by this method show considerable improvement when compared to previous baseline models. Finally, to emphasise that this is an example of white-box deep learning, we visualised the features produced by the algorithm to gain a better understanding as to how the network reaches its prediction for each protein property.