A Deep Learning Approach for Prediction of Protein Secondary Structure

Abstract

The secondary structure of a protein is critical for establishing a link between the protein primary and tertiary structures. For this reason, it is important to design methods for accurate protein secondary structure prediction. Most of the existing computational techniques for protein structural and functional prediction are based on machine learning with shallow frameworks. Different deep learning architectures have already been applied to tackle protein secondary structure prediction problem. In this study, deep learning based models, i.e., convolutional neural network and long short-term memory for protein secondary structure prediction were proposed. The input to proposed models is amino acid sequences which were derived from CulledPDB dataset. Hyperparameter tuning with cross validation was employed to attain best parameters for the proposed models. The proposed models enables effective processing of amino acids and attain approximately 87.05% and 87.47% Q 3 accuracy of protein secondary structure prediction for convolutional neural network and long short-term memory models, respectively.

Transformer Encoder with Protein Language Model for Protein Secondary Structure Prediction

“…These interactions play a crucial role in determining the function of proteins [2]. To fully comprehend the relationship between the tertiary structures of a protein, it is important to predict its secondary structure [3]. The use of efficient techniques to forecast protein structures has become essential in closing the disparity between the number of recognized protein sequences and the determined structures due to the limitations that experimental procedures entail, such as time requirements and the substantial costs involved [4].…”