Comparative Study on Analytical Methods for Phage-Specific Structures

Abstract

Bacteriophages (phages) are the “killers” and “regulators” of bacteria. In addition to being the foundation of life science research, phages are also an extremely promising alternative approach to solve the antibiotic crisis. The analysis of phage-specific structures is helpful for understanding host recognition and infection, and plays a key role in the study of viral evolution, new antibacterial applications, and biological tool development. However, the current bottlenecks in phage structure analysis are evident in two aspects: one is the gap between the static conformations extracted by structural biology methods and the dynamic functional changes of proteins in the natural infection environment; the other is that the strict requirements of advanced characterization technology for sample integrity and homogeneity, cumbersome procedures and high cost limit its characterization throughput and popularization in use. To overcome the dynamic functionality and throughput limitations of traditional structural analysis, people begin to explore computational methods: Basic Local Alignment Search Tool (BLAST) directly screens homologous proteins to infer functions; Convolutional Neural Networks (CNN) can directly predict structures and features of proteins based on their sequences or mass spectra; GNN is good at simulating protein interaction networks and their dynamic changes; and integrated platforms like DeepSP-GIN are the trend of future integration of multiple deep learning models to achieve high-throughput automatic analysis. This article compared and analyzed several commonly used data analysis methods in different situations, and summarized the applicable scenarios and effects of each method.