Application of the AlphaFold2 Protein Prediction Algorithm Based on Artificial Intelligence

Abstract

As the expression products of genes and macromolecules in living organisms, proteins are the main material basis of life activities. They exist widely in various cells and have various functions such as catalysis, cell signaling and structural support, playing a key role in life activities and functional execution. At the same time, the study of protein can better grasp the life activities from the molecular level, and has important practical significance for disease management, new drug development and crop improvement. Due to advances in high-throughput sequencing technology, protein sequence data has grown exponentially. The protein function prediction problem can be seen as a multi-label binary classification problem by extracting the features of a given protein and mapping them to the protein function label space. A variety of data sources can be mined to obtain protein function prediction features, such as protein sequence, protein structure, protein family, protein interaction network, etc. The initial steps are classical sequence-based methods, such as BLAST, which calculate the similarity between protein sequences and transmit annotations between proteins whose similarity scores exceed a specific threshold. This method has great limitations for protein function prediction without sequence similarity. Therefore, this paper analyzes the development prospect of bioanalysis and artificial intelligence through the application status and realization path of AlphaFold2 protein prediction algorithm based on artificial intelligence.