Cascading 1D-Convnet Bidirectional Long Short Term Memory Network with Modified COCOB Optimizer: A Novel Approach for Protein Secondary Structure Prediction

Abstract

The functional properties of proteins play a key job in numerous organic procedures. These functional properties rely on the structure of the specific protein. Prediction of protein structure can be performed by experimental setup as well as by computational methods. In addition to statistics and probabilistic approaches, the computational methods also include artificial intelligence-based techniques. We designed a model which consists of 1D-Convnet and modified recurrent neural network with modified continuous coin betting optimizer for the prediction of protein structure. In the modified continuous coin betting (COCOB) algorithm the probability of getting a head or a tail is based on tossing the coin twice for getting the outcome of a coin flip. It shows significant improvement in gradient calculation. As per our knowledge this is the first approach to predict protein secondary structure using modified COCOB optimization in deep learning domain. We have performed this experiment on Nvidia DGX station having four GPU core. We have used bidirectional architecture of long short term memory for this research work. We designed a 1D-Convnet model and assessed it on CB513 and CullPDB dataset. Further we combined 1D-Convnet model with bidirectional long short term memory to assess CB513 and CullPDB dataset. Both the models are evaluated by Q8 and Q3 accuracy. We compared COCOB and modified COCOB on our model and found that modified COCOB performs better than COCOB. However, our 1D-Convnet-BLSTM model achieves 76.55% (Q3) and 74.45% (Q8) accuracy on CB513 dataset and 68.91% (Q3) and 75.04% (Q8) accuracy on CullPDB dataset.