Towards Protein Tertiary Structure Prediction Using LSTM/BLSTM

Determining the native structure of a protein, given its primary sequence is one of the most demanding tasks in computational biology. Traditional protein structure prediction methods are laborious and involve vast conformation search space. Contrarily, deep learning is a rapidly evolving field with outstanding performance at problems where there are complicated relationships between input features and desired outputs. Various deep neural network architectures such as recurrent neural networks, convolution neural networks, deep feed-forward neural networks are becoming popular for solving problems in protein science. This work mainly concentrates on prediction of three-dimensional structure of proteins from the given primary sequences using deep learning techniques. Long short-term memory (LSTM) and bidirectional LSTM (BLSTM) neural network architectures are used for predicting protein tertiary structures from primary sequences. The result shows that single-layer BLSTM networks fed with primary sequence and position-specific scoring matrix data gives better accuracy compared to LSTM and two-layer BLSTM models. This study may get benefited to the computational biologists working in the area of protein structure prediction.