ProtVec: A Continuous Distributed Representation of Biological Sequences

We propose a new approach for representing biological sequences. This method, named protein-vectors or ProtVec for short, can be utilized in bioinformatics applications such as family classification, protein visualization, structure prediction, disordered protein identification, and protein-protein interaction prediction. Using the Skip-gram neural networks, protein sequences are represented with a single dense n-dimensional vector. This method was evaluated by classifying protein sequences obtained from Swiss-Prot belonging to 7,027 protein families where an average family classification accuracy of $94\%\pm 0.03\%$ was obtained, outperforming existing family classification methods. In addition, our model was used to predict disordered proteins from structured proteins. Two databases of disordered sequences were used: the DisProt database as well as a database featuring the disordered regions of nucleoporins rich with phenylalanine-glycine repeats (FG-Nups). Using support vector machine classifiers, FG-Nup sequences were distinguished from structured Protein Data Bank (PDB) sequences with 99.81\% accuracy, and unstructured DisProt sequences from structured DisProt sequences with 100.0\% accuracy. These results indicate that by only providing sequence data for various proteins into this model, information about protein structure can be determined with high accuracy. This so-called embedding model needs to be trained only once and can then be used to ascertain a diverse set of information regarding the proteins of interest. In addition, this representation can be considered as pre-training for various applications of deep learning in bioinformatics.