Perspectives of machine learning on protein structure prediction and function

The experimental techniques for determining protein structure using electron microscopy and other various methods. These are all laborious, time-consuming processes that call for knowledge. Using data gathered in the protein libraries and computational approaches, scientists employ prediction tools to make the process less time-consuming. Machine learning techniques have piqued the community's interest in structure prediction in recent years. The majority strategies off machine learning helps in protein structure prediction and function focus on co-evolutionary techniques. The quantity of homologous protein sequences present in the databases affects how accurate these methods are. Many proteins experience difficulty with the prediction challenge, particularly those with a dearth of sequence homologs. Without using any assumptions, deep learning techniques enable the extraction of complex features from protein sequence data. Protein structures that are accurately predicted are used to create antibodies, study protein-protein interactions, and develop new drugs. This article reviews methods for predicting protein structure and function using machine learning.