Continuous Distributed Representation of Biological Sequences for Deep Proteomics and Genomics

Abstract

We introduce a new representation and feature extraction method for biological sequences. Named bio-vectors (BioVec) to refer to biological sequences in general with protein-vectors (ProtVec) for proteins (amino-acid sequences) and gene-vectors (GeneVec) for gene sequences, this representation can be widely used in applications of deep learning in proteomics and genomics. In the present paper, we focus on protein-vectors that can be utilized in a wide array of bioinformatics investigations such as family classification, protein visualization, structure prediction, disordered protein identification, and protein-protein interaction prediction. In this method, we adopt artificial neural network approaches and represent a protein sequence with a single dense n-dimensional vector. To evaluate this method, we apply it in classification of 324,018 protein sequences obtained from Swiss-Prot belonging to 7,027 protein families, where an average family classification accuracy of 93%±0.06% is obtained, outperforming existing family classification methods. In addition, we use ProtVec representation to predict disordered proteins from structured proteins. Two databases of disordered sequences are 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 are distinguished from structured protein sequences found in Protein Data Bank (PDB) with a 99.8% accuracy, and unstructured DisProt sequences are differentiated from structured DisProt sequences with 100.0% accuracy. These results indicate that by only providing sequence data for various proteins into this model, accurate information about protein structure can be determined. Importantly, this model needs to be trained only once and can then be applied to extract a comprehensive set of information regarding proteins of interest. Moreover, this representation can be considered as pre-training for various applications of deep learning in bioinformatics. The related data is available at Life Language Processing Website: http://llp.berkeley.edu and Harvard Dataverse: http://dx.doi.org/10.7910/DVN/JMFHTN.

Fig 1. Protein sequence splitting.

In order to prepare the training data, each protein sequence will be represented as three sequences (1, 2, 3) of 3-grams.

Fig 2. Normalized distributions of biochemical and biophysical properties in protein-space.

In these plots, each point represents a 3-gram (a word of three residues) and the colors indicate the scale for each property. Data points in these plots are projected from a 100-dimensional space a 2D space using t-SNE. As it is shown words with similar properties are automatically clustered together meaning that the properties are smoothly distributed in this space.

Fig 3. Visualization of protein sequences using ProtVec can characterize FGNUPs versus Disport disordered sequences and structured sequences.

Column (a) compares FG Nup sequences 2D histogram (at the bottom) with 2D histogram of FG Nup disordered regions (on top). Column (b) compares 2D histogram two random sets of structured sequences with the same average length as the FG-Nups. Column (c) compares between 2D histogram of DisProt sequences (at the bottom) and 2D histogram of DisProt disordered regions (on top).

Fig 4. Classification of FG-Nups versus PDB structured sequences.

In this figure, each point presents a protein projected into a 2D space.