Protein structure prediction with recurrent neural network and convolutional neural network: a case study

Bioinformatics comprises the computer system's technology for numerous purposes, including storage, retrieval, manipulation, prediction, and distribution of information attributed to biological macromolecules such as deoxyribonucleic acid (DNA), ribonucleic acid (RNA), and proteins. In the bioinformatics category, protein structure prediction (PSP) depends on the fundamental protein structure divided into four main types: primary, secondary, tertiary, and quaternary. The utmost focus of bioinformatics is not just accuracy on prediction tasks but the interpretation of the underlying biological processes. The biggest challenge in computational biology is understanding the intricate reliance between protein structure and sequence. Deep learning (DL), with the aid of accessible tools, data and impactful computational resources, has created a revolution in numerous fields, including PSP. In recent years, various DL and machine learning methods have been employed for PSP at various levels of detail. Convolutional neural networks and recurrent neural networks (RNNs) have emerged as popular DL approaches. In this chapter, we explore the evolution of PSP from simple statistical methods from the past to the highly intensive and sophisticated computational DL algorithms of the last few decades. We also discuss a few case studies, along with the challenges to aid researchers in predicting the protein structure with these DL algorithms.