efam: an expanded, metaproteome-supported HMM profile database of viral protein families

Hybrid sequencing reveals the genome of a Chrysochromulina parva virus and highlight its distinct replication strategy

Chrysochromulina parva (C. parva) is a eukaryotic freshwater haptophyte algae found in lakes and rivers worldwide. It is known to be infected by viruses, yet knowledge of the diversity and activity of these viruses is still very limited. Based on sequences of PCR-amplified DNA polymerase B (polB) gene fragments, Chrysochromulina parva virus BQ1 (CpV-BQ1) was the first known lytic agent of C. parva, and was considered a member of the virus family Phycodnaviridae, order Algavirales. However, the genome of a different C. parva-infecting virus (CpV-BQ2, or Tethysvirus ontarioense) from another virus family, the Mesomimiviridae, order Imitervirales, was the first sequenced. Here, we report the complete genome sequence of the putative phycodnavirus CpV-BQ1, accession PQ783904. The complete CpV-BQ1 genome sequence is 165,454 bp with a GC content of 32.32% and it encodes 193 open reading frames. Phylogenetic analyses of several virus hallmark genes including the polB, the late gene transcription factor (VLTF-3), and the putative A32-like virion packaging ATPase (Viral A32) all demonstrate that CpV-BQ1 is most closely related to other viruses in the phylum Megaviricetes within the order Algavirales, family Phycodnaviridae.

Tertiary Structures of Haseki Tick Virus Nonstructural Proteins Are Similar to Those of Orthoflaviviruses

Currently, a large number of novel tick-borne viruses potentially pathogenic to humans are discovered. Studying many of them by classical methods of virology is difficult due to the absence of live viral particles or a sufficient amount of their genetic material. In this case, the use of modern methods of bioinformatics and synthetic and structural biology can help. Haseki tick virus (HSTV) is a recently discovered tick-borne unclassified ssRNA(+) virus. HSTV-positive patients experienced fever and an elevated temperature. However, at the moment, there is no information on the tertiary structure and functions of its proteins. In this work, we used AlphaFold 3 and other bioinformatic tools for the annotation of HSTV nonstructural proteins, based on the principle that the tertiary structure of a protein is inextricably linked with its molecular function. We were the first to obtain models of tertiary structures and describe the putative functions of HSTV nonstructural proteins (NS3 helicase, NS3 protease, NS5 RNA-dependent RNA-polymerase, and NS5 methyltransferase), which play a key role in viral genome replication. Our results may help in further taxonomic identification of HSTV and the development of direct-acting antiviral drugs, POC tests, and vaccines.

Improving viral annotation with artificial intelligence

Viruses of bacteria, "phages," are fundamental, poorly understood components of microbial community structure and function. Additionally, their dependence on hosts for replication positions phages as unique sensors of ecosystem features and environmental pressures. High-throughput sequencing approaches have begun to give us access to the diversity and range of phage populations in complex microbial community samples, and metagenomics is currently the primary tool with which we study phage populations. The study of phages by metagenomic sequencing, however, is fundamentally limited by viral diversity, which results in the vast majority of viral genomes and metagenome-annotated genomes lacking annotation. To harness bacteriophages for applications in human and environmental health and disease, we need new methods to organize and annotate viral sequence diversity. We recently demonstrated that methods that leverage self-supervised representation learning can supplement statistical sequence representations for remote viral protein homology detection in the ocean virome and propose that consideration of the functional content of viral sequences allows for the identification of similarity in otherwise sequence-diverse viruses and viral-like elements for biological discovery. In this review, we describe the potential and pitfalls of large language models for viral annotation. We describe the need for new approaches to annotate viral sequences in metagenomes, the fundamentals of what protein language models are and how one can use them for sequence annotation, the strengths and weaknesses of these models, and future directions toward developing better models for viral annotation more broadly.

Birth of protein folds and functions in the virome

The rapid evolution of viruses generates proteins that are essential for infectivity and replication but with unknown functions, due to extreme sequence divergence1. Here, using a database of 67,715 newly predicted protein structures from 4,463 eukaryotic viral species, we found that 62% of viral proteins are structurally distinct and lack homologues in the AlphaFold database2,3. Among the remaining 38% of viral proteins, many have non-viral structural analogues that revealed surprising similarities between human pathogens and their eukaryotic hosts. Structural comparisons suggested putative functions for up to 25% of unannotated viral proteins, including those with roles in the evasion of innate immunity. In particular, RNA ligase T-like phosphodiesterases were found to resemble phage-encoded proteins that hydrolyse the host immune-activating cyclic dinucleotides 3',3'- and 2',3'-cyclic GMP-AMP (cGAMP). Experimental analysis showed that RNA ligase T homologues encoded by avian poxviruses similarly hydrolyse cGAMP, showing that RNA ligase T-mediated targeting of cGAMP is an evolutionarily conserved mechanism of immune evasion that is present in both bacteriophage and eukaryotic viruses. Together, the viral protein structural database and analyses presented here afford new opportunities to identify mechanisms of virus-host interactions that are common across the virome.

Identification of mobile genetic elements with geNomad

Identifying and characterizing mobile genetic elements in sequencing data is essential for understanding their diversity, ecology, biotechnological applications and impact on public health. Here we introduce geNomad, a classification and annotation framework that combines information from gene content and a deep neural network to identify sequences of plasmids and viruses. geNomad uses a dataset of more than 200,000 marker protein profiles to provide functional gene annotation and taxonomic assignment of viral genomes. Using a conditional random field model, geNomad also detects proviruses integrated into host genomes with high precision. In benchmarks, geNomad achieved high classification performance for diverse plasmids and viruses (Matthews correlation coefficient of 77.8% and 95.3%, respectively), substantially outperforming other tools. Leveraging geNomad's speed and scalability, we processed over 2.7 trillion base pairs of sequencing data, leading to the discovery of millions of viruses and plasmids that are available through the IMG/VR and IMG/PR databases. geNomad is available at https://portal.nersc.gov/genomad .

Large language models improve annotation of prokaryotic viral proteins

Viral genomes are poorly annotated in metagenomic samples, representing an obstacle to understanding viral diversity and function. Current annotation approaches rely on alignment-based sequence homology methods, which are limited by the paucity of characterized viral proteins and divergence among viral sequences. Here we show that protein language models can capture prokaryotic viral protein function, enabling new portions of viral sequence space to be assigned biologically meaningful labels. When applied to global ocean virome data, our classifier expanded the annotated fraction of viral protein families by 29%. Among previously unannotated sequences, we highlight the identification of an integrase defining a mobile element in marine picocyanobacteria and a capsid protein that anchors globally widespread viral elements. Furthermore, improved high-level functional annotation provides a means to characterize similarities in genomic organization among diverse viral sequences. Protein language models thus enhance remote homology detection of viral proteins, serving as a useful complement to existing approaches.

Interrogating the viral dark matter of the rumen ecosystem with a global virome database

The diverse rumen virome can modulate the rumen microbiome, but it remains largely unexplored. Here, we mine 975 published rumen metagenomes for viral sequences, create a global rumen virome database (RVD), and analyze the rumen virome for diversity, virus-host linkages, and potential roles in affecting rumen functions. Containing 397,180 species-level viral operational taxonomic units (vOTUs), RVD substantially increases the detection rate of rumen viruses from metagenomes compared with IMG/VR V3. Most of the classified vOTUs belong to Caudovirales, differing from those found in the human gut. The rumen virome is predicted to infect the core rumen microbiome, including fiber degraders and methanogens, carries diverse auxiliary metabolic genes, and thus likely impacts the rumen ecosystem in both a top-down and a bottom-up manner. RVD and the findings provide useful resources and a baseline framework for future research to investigate how viruses may impact the rumen ecosystem and digestive physiology.

Large language models improve annotation of viral proteins

Viral sequences are poorly annotated in environmental samples, a major roadblock to understanding how viruses influence microbial community structure. Current annotation approaches rely on alignment-based sequence ho-mology methods, which are limited by available viral sequences and sequence divergence in viral proteins. Here, we show that protein language model representations capture viral protein function beyond the limits of remote sequence homology by targeting two axes of viral sequence annotation: systematic labeling of protein families and function identification for biologic discovery. Protein language model representations capture protein functional properties specific to viruses and expand the annotated fraction of ocean virome viral protein sequences by 37%. Among unannotated viral protein families, we identify a novel DNA editing protein family that defines a new mobile element in marine picocyanobacteria. Protein language models thus significantly enhance remote homology detection of viral proteins and can be utilized to enable new biological discovery across diverse functional categories.

VirClust-A Tool for Hierarchical Clustering, Core Protein Detection and Annotation of (Prokaryotic) Viruses

Recent years have seen major changes in the classification criteria and taxonomy of viruses. The current classification scheme, also called "megataxonomy of viruses", recognizes six different viral realms, defined based on the presence of viral hallmark genes (VHGs). Within the realms, viruses are classified into hierarchical taxons, ideally defined by the phylogeny of their shared genes. To enable the detection of shared genes, viruses have first to be clustered, and there is currently a need for tools to assist with virus clustering and classification. Here, VirClust is presented. It is a novel, reference-free tool capable of performing: (i) protein clustering, based on BLASTp and Hidden Markov Models (HMMs) similarities; (ii) hierarchical clustering of viruses based on intergenomic distances calculated from their shared protein content; (iii) identification of core proteins and (iv) annotation of viral proteins. VirClust has flexible parameters both for protein clustering and for splitting the viral genome tree into smaller genome clusters, corresponding to different taxonomic levels. Benchmarking on a phage dataset showed that the genome trees produced by VirClust match the current ICTV classification at family, sub-family and genus levels. VirClust is freely available, as a web-service and stand-alone tool.

Rational Design of Profile HMMs for Sensitive and Specific Sequence Detection with Case Studies Applied to Viruses, Bacteriophages, and Casposons

Profile hidden Markov models (HMMs) are a powerful way of modeling biological sequence diversity and constitute a very sensitive approach to detecting divergent sequences. Here, we report the development of protocols for the rational design of profile HMMs. These methods were implemented on TABAJARA, a program that can be used to either detect all biological sequences of a group or discriminate specific groups of sequences. By calculating position-specific information scores along a multiple sequence alignment, TABAJARA automatically identifies the most informative sequence motifs and uses them to construct profile HMMs. As a proof-of-principle, we applied TABAJARA to generate profile HMMs for the detection and classification of two viral groups presenting different evolutionary rates: bacteriophages of the Microviridae family and viruses of the Flavivirus genus. We obtained conserved models for the generic detection of any Microviridae or Flavivirus sequence, and profile HMMs that can specifically discriminate Microviridae subfamilies or Flavivirus species. In another application, we constructed Cas1 endonuclease-derived profile HMMs that can discriminate CRISPRs and casposons, two evolutionarily related transposable elements. We believe that the protocols described here, and implemented on TABAJARA, constitute a generic toolbox for generating profile HMMs for the highly sensitive and specific detection of sequence classes.

New Microviridae isolated from Sulfitobacter reveals two cosmopolitan subfamilies of single-stranded DNA phages infecting marine and terrestrial Alphaproteobacteria

The Microviridae family represents one of the major clades of single-stranded DNA (ssDNA) phages. Their cultivated members are lytic and infect Proteobacteria, Bacteroidetes, and Chlamydiae. Prophages have been predicted in the genomes from Bacteroidales, Hyphomicrobiales, and Enterobacteriaceae and cluster within the 'Alpavirinae', 'Amoyvirinae', and Gokushovirinae. We have isolated 'Ascunsovirus oldenburgi' ICBM5, a novel phage distantly related to known Microviridae. It infects Sulfitobacter dubius SH24-1b and uses both a lytic and a carrier-state life strategy. Using ICBM5 proteins as a query, we uncovered in publicly available resources sixty-five new Microviridae prophages and episomes in bacterial genomes and retrieved forty-seven environmental viral genomes (EVGs) from various viromes. Genome clustering based on protein content and phylogenetic analysis showed that ICBM5, together with Rhizobium phages, new prophages, episomes, and EVGs cluster within two new phylogenetic clades, here tentatively assigned the rank of subfamily and named 'Tainavirinae' and 'Occultatumvirinae'. They both infect Rhodobacterales. Occultatumviruses also infect Hyphomicrobiales, including nitrogen-fixing endosymbionts from cosmopolitan legumes. A biogeographical assessment showed that tainaviruses and occultatumviruses are spread worldwide, in terrestrial and marine environments. The new phage isolated here sheds light onto new and diverse branches of the Microviridae tree, suggesting that much of the ssDNA phage diversity remains in the dark.

The Use of Bacteriophages in Biotechnology and Recent Insights into Proteomics

Phages have certain features, such as their ability to form protein-protein interactions, that make them good candidates for use in a variety of beneficial applications, such as in human or animal health, industry, food science, food safety, and agriculture. It is essential to identify and characterize the proteins produced by particular phages in order to use these viruses in a variety of functional processes, such as bacterial detection, as vehicles for drug delivery, in vaccine development, and to combat multidrug resistant bacterial infections. Furthermore, phages can also play a major role in the design of a variety of cheap and stable sensors as well as in diagnostic assays that can either specifically identify specific compounds or detect bacteria. This article reviews recently developed phage-based techniques, such as the use of recombinant tempered phages, phage display and phage amplification-based detection. It also encompasses the application of phages as capture elements, biosensors and bioreceptors, with a special emphasis on novel bacteriophage-based mass spectrometry (MS) applications.

The International Virus Bioinformatics Meeting 2022

The International Virus Bioinformatics Meeting 2022 took place online, on 23-25 March 2022, and has attracted about 380 participants from all over the world. The goal of the meeting was to provide a meaningful and interactive scientific environment to promote discussion and collaboration and to inspire and suggest new research directions and questions. The participants created a highly interactive scientific environment even without physical face-to-face interactions. This meeting is a focal point to gain an insight into the state-of-the-art of the virus bioinformatics research landscape and to interact with researchers in the forefront as well as aspiring young scientists. The meeting featured eight invited and 18 contributed talks in eight sessions on three days, as well as 52 posters, which were presented during three virtual poster sessions. The main topics were: SARS-CoV-2, viral emergence and surveillance, virus-host interactions, viral sequence analysis, virus identification and annotation, phages, and viral diversity. This report summarizes the main research findings and highlights presented at the meeting.