Use of an Integrated Approach Involving AlphaFold Predictions for the Evolutionary Taxonomy of Duplodnaviria Viruses

Abstract

The evaluation of the evolutionary relationships is exceptionally important for the taxonomy of viruses, which is a rapidly expanding area of research. The classification of viral groups belonging to the realm Duplodnaviria, which include tailed bacteriophages, head-tailed archaeal viruses and herpesviruses, has undergone many changes in recent years and continues to improve. One of the challenging tasks of Duplodnaviria taxonomy is the classification of high-ranked taxa, including families and orders. At the moment, only 17 of 50 families have been assigned to orders. The evaluation of the evolutionary relationships between viruses is complicated by the high level of divergence of viral proteins. However, the development of structure prediction algorithms, including the award-winning AlphaFold, encourages the use of the results of structural predictions to clarify the evolutionary history of viral proteins. In this study, the evolutionary relationships of two conserved viral proteins, the major capsid protein and terminase, representing different viruses, including all classified Duplodnaviria families, have been analysed using AlphaFold modelling. This analysis has been undertaken using structural comparisons and different phylogenetic methods. The results of the analyses mainly indicated the high quality of AlphaFold modelling and the possibility of using the AlphaFold predictions, together with other methods, for the reconstruction of the evolutionary relationships between distant viral groups. Based on the results of this integrated approach, assumptions have been made about refining the taxonomic classification of bacterial and archaeal Duplodnaviria groups, and problems relating to the taxonomic classification of Duplodnaviria have been discussed.

Using AlphaFold Predictions in Viral Research

“…As of January 2023, 19,499 GenBank sequences, assigned to class Caudoviricetes , contained 1,731,815 coding regions, 67% of which were annotated as hypothetical proteins. In some cases, BLAST search and HMM-HMM motif comparisons fail to assign a function to proteins encoded in phage genomes, but analysis of fold of AF2-derived structures can assist to clarify this function [ 55 ].…”

“…It seems that no large-scale studies have been published on the accuracy of modelling using AF2 compared with the predictions of other algorithms. However, comparing the predicted average local distance difference test (lDDT) score of the 54 AF2-derived models of the major capsid protein and ATPase subunit of phage terminase indicated an impressive level of accuracy of the predictions [ 55 ]. Interestingly, structural predictions of more conserved terminase were more accurate than those of major capsid protein, (terminase lDDT mean: 0.988, median: 0.996; major capsid protein lDDT mean: 0.907, median: 0.929).…”

“…AlphaFold modelling and subsequent clustering have been used in taxonomic studies of archaeal viruses [ 56 ]. Clustering using AlphaFold showed interesting and often biologically meaningful results [ 55 ]. Clustering using structures predicted by AlphaFold showed interesting and often biologically meaningful results ( Figure 7 ).…”

“…Most of these limitations apply to studies that involve experimentally determined structures, but, hypothetically, they could be exacerbated by structural prediction errors. Therefore, predicting the effectiveness of using AlphaFold for the analysis of structural similarity and evolutionary history, based only on the similarity of the predicted structures, seems to be a difficult task [ 55 ].…”

Deep Learning-based structural and functional annotation of Pandoravirus hypothetical proteins

“…The availability of AlphaFold 2 (AF2; ref) and the AlphaFold Protein Structure Database () provide new opportunities for structure based function annotation (eg) and detection of novel protein families (Bordin et al ., 2023; Durairaj et al ., 2023; Hernandez et al ., 2023). Notably, viral proteins are currently largely absent from the AlphaFold Database, somewhat complicating their study: nevertheless, AF2 models have proved useful for virus phylogenetic analysis (Evseev et al ., 2023) and function annotation (Say et al ., 2023).…”