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.

Highlights

  • Nature uses certain languages to describe biological sequences such as DNA, RNA, and proteins

  • We propose this as a general-purpose representation for protein sequences that can be used in a wide range of bioinformatics problems, including protein family classification, protein interaction prediction, structure prediction, motif extraction, protein visualization, and domain identification

  • We demonstrated that ProtVec can be used as an informative and dense representation for biological sequences in protein family classification, and obtained an average family classification accuracy of 93%

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Summary

Introduction

Nature uses certain languages to describe biological sequences such as DNA, RNA, and proteins. We propose an unsupervised data-driven distributed representation for biological sequences This method, called bio-vectors (BioVec) in general and protein-vectors (ProtVec) for proteins, can be applied to a wide range of problems in bioinformatics, such as protein visualization, protein family classification, structure prediction, domain extraction, and interaction prediction. In this approach, each biological sequence is embedded in an n-dimensional vector that characterizes biophysical and biochemical properties of sequences using neural networks. We will analyze the biophysical and the biochemical properties of this representation qualitatively and quantitatively To further evaluate this feature extraction method, we apply it in classification of 7,027 protein families of 324,018 protein sequences in Swiss-Prot.

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