ABLE

Classifying proteins into their respective enzyme class is an interesting question for researchers for a variety of reasons. The open source Protein Data Bank (PDB) contains more than 1,60,000 structures, with more being added everyday. In this project we developed an attention-based bidirectional-LSTM model (ABLE) which was trained on oversampled data generated by SMOTE to analyse and classify a protein into one of the six enzyme classes or a negative class using only the primary structure of the protein described as a string by the FASTA sequence as an input. We achieve the highest F1-score of 0.834 using our proposed model on a dataset of proteins from the Protein Data Bank. We baseline our model against seventeen other machine learning and deep learning models, including a Convolutional Neural Network, Long Short Term Memory Network, Bidirectional Long Short Term Memory Network, and Gated Recurrent Unit Network. We perform extensive experimentation and statistical testing to corroborate our results.

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