  The classification of amino acids and their sequence analysis plays a vital
role in life sciences and is a challenging task. This article uses and compares
state-of-the-art deep learning models like convolution neural networks (CNN),
long short-term memory (LSTM), and gated recurrent units (GRU) to solve
macromolecule classification problems using amino acids. These models have
efficient frameworks for solving a broad spectrum of complex learning problems
compared to traditional machine learning techniques. We use word embedding to
represent the amino acid sequences as vectors. The CNN extracts features from
amino acid sequences, which are treated as vectors, then fed to the models
mentioned above to train a robust classifier. Our results show that word2vec as
embedding combined with VGG-16 performs better than LSTM and GRU. The proposed
approach gets an error rate of 1.5%.
