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Title: DeepProteomics: Protein family classification using Shallow and Deep Networks

Paper content:
The knowledge regarding the function of proteins is necessary as it gives a clear picture of biological processes.
Nevertheless, there are many protein sequences found and added to the databases but lacks functional annotation.
The laboratory experiments take a considerable amount of time for annotation of the sequences.
This arises the need to use computational techniques to classify proteins based on their functions.
In our work, we have collected the data from Swiss-Prot containing 40433 proteins which is grouped into 30 families.
We pass it to recurrent neural network(RNN), long short term memory(LSTM) and gated recurrent unit(GRU) model and compare it by applying trigram with deep neural network and shallow neural network on the same dataset.
Through this approach, we could achieve maximum of around 78% accuracy for the classification of protein families.
Proteins are considered to be essentials of life because it performs a variety of functions to sustain life.
It performs DNA replication, transportation of molecules from one cell to another cell, accelerates metabolic reactions and several other important functions carried out within an organism.
Proteins carry out these functions as specified by the informations encoded in the genes.
Proteins are classified into three classes based on their tertiary structure as globular, membrane and fibrous proteins.
Many of the globular proteins are soluble enzymes.
Membrane proteins enables the transportation of electrically charged molecules past the cell membranes by providing channels.
Fibrous proteins are always structural.
Collagen which is a fibrous protein forms the major component of connective tissues.
Escherichia coli cell is partially filled by proteins and 3% and 20% fraction of DNA and RNA respectively contains proteins.
All of this contributes in making proteomics as a very important field in modern computational biology.
It is therefore becoming important to predict protein family classification and study their functionalities to better understand the theory behind life cycle.Proteins are polymeric macromolecules consisting of amino acid residue chains joined by peptide bonds.
And proteome of a particular cell type is a set of proteins that come under the same cell type.
Proteins is framed using a primary structure represented as a sequence of 20-letter alphabets which is associated with a particular amino acid base subunit of proteins.
Proteins differ from one another by the arrangement of amino acids intent on nucleotide sequence of their genes.
This results in the formation of specific 3D structures by protein folding which determines the unique functionality of the proteins.
The primary structure of proteins is an abstracted version of the complex 3D structure but retains sufficient information for protein family classification and infer the functionality of the families.Protein family consists of a set of proteins that exhibits similar structure at sequence as well as molecular level involving same functions.
The lack of knowledge of functional information about sequences in spite of the large number of sequences known, led to many works identifying family of proteins based on primary sequences BID0 BID1 BID2 .
Dayhoff identified the families of numerous proteins BID3 .
Members of the same protein family can be identified using sequence homology which is defined as the evolutionary relatedness.
It also exhibits similar secondary structure through modular protein domains which further group proteins families into super families BID4 .
These classifications are listed in database like SCOP BID5 .
Protein family database (Pfam) BID6 is an extremely large source which classify proteins into family, domain, repeat or motif.
Protein classification using 3D structure is burdensome and require complex techniques like X-ray crystallography and NMR spectroscopy.
This led to the works BID7 BID8 BID9 which uses only primary structure for protein family classification.
In this work we use data from Swiss-Prot for protein family classification and obtain a classification accuracy of about 96%.In
our work we gathered family information of about 40433 protein sequences in Swiss-Prot from Protein family database(Pfam), which consists of 30 distinct families. The
application of keras embedding and n-gram technique is used with deep learning architectures and traditional machine learning classifiers respectively for text classification problems in the cyber security BID33 , BID34 , BID35 , BID36 , BID37 . By
following, we apply keras word embedding and pass it to various deep neural network models like recurrent neural network(RNN), long short term memory(LSTM) and gated recurrent unit(GRU) and then compare it performance by applying trigram with deep and shallow neural networks for protein family classification. To
verify the model used in our work, we test it over dataset consisting of about 12000 sequences from the same database.The rest of the part of this paper are organized as follows. Section
2 discusses the related work, Section 3 provides background details of deep learning architecture, Section 4 discusses the proposed methodology, Section 5 provides results and submissions and at last the conclusion and future work directions are placed in Section 6.
In our work we have analyzed the performance of different recurrent models like RNN, LSTM and GRU after applying word embedding to the sequence data to classify the protein sequences to their respective families.
We have also compared the results by applying trigram with deep neural network and shallow neural network.
Neural networks are preferred over traditional machine learning models because they capture optimal feature representation by themselves taking the primary protein sequences as input and give considerably high family classification accuracy of about 96%.Deep
neural networks architecture is very complex therefore, understanding the background mechanics of a neural network model remain as a black box and thus the internal operation of the network is only partially demonstrated. In the
future work, the internal working of the network can be explored by examining the Eigenvalues and Eigenvectors across several time steps obtained by transforming the state of the network to linearized dynamics BID32 .

assistant: text: Proteins, amino-acid sequences, machine learning, deep learning, recurrent neural network(RNN), long short term memory(LSTM), gated recurrent unit(GRU), deep neural networks