Word embeddings for protein sequence analysis

Nowadays, the ability to predict protein functions directly from amino acid sequences alone remains a major biological challenge. The understanding of protein properties and functions is extremely important and can have a wide range of biotechnological and medical applications. High-level representations from the field of deep learning can provide new alternatives to address these problems, particularly Natural Language Processes methods, such as word embeddings (WE), which have shown particular success when applied to protein sequence analysis. Here, a software tool that eases the implementation of WE models toward protein representation and classification is presented. This tool was validated using two protein classification problems namely, the identification of plant ubiquitylation sites and lysine crotonylation sites, and further used to explore enzyme functional annotation. Several WE were tested and fed to different Machine and Deep Learning networks. Overall, WE achieved good results being even competitive with state-of-the-art, reinforcing the idea that language-based methods can be applied with success to a wide range of protein classification problems.