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PubMed2026

LAMBDA: a prophage detection benchmark for genomic language models.

Transformer-based genomic sequence models represent an emerging frontier in computational biology. Yet, their embeddings have not yet shown the same level of predictive power as natural and protein language models, highlighting a gap between current implementations and theoretical promise. Existing benchmarks for DNA language models primarily focus on classifying regulatory elements in eukaryotic genomes, leaving open the fundamental question of whether these models learn sequence-level features across whole genomes. We introduce LAMBDA, a benchmark designed to rigorously evaluate genome language model embeddings through phage-bacteria sequence discrimination across four categories of increasing complexity: probing tasks, fine-tuning assessments, diagnostic tests, and genome-wide prophage detection. Our comprehensive analysis of current genomic language models provides insight into the importance of training data selection relative to model size, the need for domain-specific training, and the capabilities and limitations of genomic language models for detecting prophage sequences. This benchmark represents a challenging genomic annotation task in the bacterial domain and addresses a key computational problem with direct relevance to microbiology and medicine.

Prophages↗

PubMed2026

Gabija restricts phage circularization and DNA replication.

Anti-bacteriophage systems such as restriction-modification and CRISPR-Cas have DNA substrate specificity mechanisms that enable the identification of invaders. How Gabija, a highly prevalent nuclease-helicase antiphage system, limits phage replication while executing self- vs. non-self-discrimination remains unknown. Here, we show that phage-encoded DNA end-binding proteins that antagonize host RecBCD sensitize phages to Gabija. When targeting a temperate lambda-like phage in Pseudomonas aeruginosa, Gabija prevents phage genome circularization and subsequent replication. DNA end-binding complexes, including a phage exonuclease and a single-stranded DNA (ssDNA)-annealing protein or GamMu dimers that prevent loading of the host repair complex RecBCD, are necessary and sufficient to license phage and plasmid sensitivity to Gabija. Mutant escape phages lacking these DNA end-binding proteins become protected from Gabija by RecBCD translocation activities. RecBCD activity on the bacterial genome, presumably whenever it is linearized, also prevents Gabija from targeting self-DNA. Therefore, we propose that Gabija antagonizes the circularization and replication of linear DNA devoid of RecBCD as a mechanism to identify and antagonize foreign invaders.

DNA Replication↗

PubMed2025

CRISPRi-mediated repression of three cI repressors induces the expression of three related Neisseria gonorrhoeae bacteriophages.

The Neisseria gonorrhoeae FA1090 isolate encodes nine prophage islands (Ngoɸ1-9). Ngoɸ1-3 contain genes consistent with a Siphoviridae-dsDNA bacteriophage (phage). Saturating transposon-sequencing screens using two different N. gonorrhoeae isolates predicted that multiple prophage genes were essential, including three putative transcriptional repressors: ngo0479 (present in Ngoɸ1), ngo1116 (present in Ngoɸ2), and ngo1630 (present in Ngoɸ3). All three genes display homology to the Lambda phage cI, a regulator important for maintaining the lysogenic state and inhibiting lytic induction, but these proteins are not close paralogs. Using a Neisseria lactamica-derived Type I-C CRISPR-interference system, we show that these cI orthologs are essential, as the knockdown of each gene results in bacterial death. We determined that the repression of the three cI orthologs resulted in the significant induction of phage gene expression. Finally, we detected Siphoviridae-like phage particles released from N. gonorrhoeae following repression of ngo0479, ngo1116, or ngo1630. We hypothesize that these cI orthologs are critical for preventing phage lytic infection and cell death and allow N. gonorrhoeae to benefit from the carriage and expression of prophage genes.IMPORTANCEBacteriophage, or phage, are bacteria-infecting viruses and are the most abundant natural entities in the world. Here, we report that Neisseria gonorrhoeae's three most complete double-stranded DNA prophage islands each encode essential and related transcriptional repressors. CRISPRi-mediated repression of these transcriptional repressors leads to a significant increase in prophage gene expression and phage induction. This study marks an important initial step in studying the interaction between N. gonorrhoeae and its resident phage.

Neisseria gonorrhoeae↗

PubMed2026

A conserved distal-tail helical extension defines a tailspike attachment architecture in Gram-negative siphophages.

Rapid growth of bacteriophage genome collections has outpaced functional annotation of tail-tip proteins, limiting comparative analysis of host-recognition structures. Starting from a shared distal-tail gene organization in the Salmonella phages 9NA and Jersey, I developed a morphogenetic bioinformatic framework integrating gene synteny, sequence comparison, profile hidden Markov model (HMM) screening, structural evidence, structure-aware searching, and AlphaFold modeling. Comparison with the experimentally characterized lambda and Sf11 tail assemblies identified a predominantly alpha-helical C-terminal extension of the distal-tail (DT) protein associated with tailspike attachment, termed the distal-tail helical extension (DT-helix). Screening 541,986 proteins from 5167 complete NCBI RefSeq tailed-phage genomes, followed by evidence-based evaluation of sequence, genomic context, and structural architecture, identified 165 curated DT-helical-extension-associated phages. Their DT proteins segregated into six sequence groups. In the four principal multi-member groups, cognate tailspikes showed group-specific conservation in proximal N-terminal regions but substantially greater downstream diversity, consistent with sequence constraint at the DT-tailspike attachment boundary. A complementary ProstT5/Foldseek search supported the established groups but revealed no convincing additional highly divergent family. Together with the experimentally characterized Sf11 attachment interface, these findings define a recurrent morphogenetic architecture linking conserved distal-tail scaffolds to more variable receptor-binding proteins across siphophages infecting Gram-negative bacteria. Although universal exchangeability is not established, the identified scaffold-receptor-binding boundaries provide a framework for molecular characterization and rational phage engineering. Accession-level information for the 165 curated phages is available through PhageTailDB.

Viral Tail Proteins↗

PubMed2026

LAMBDA: A Prophage Detection Benchmark for Genomic Language Models.

Transformer-based genomic sequence models represent an emerging frontier in computational biology. Yet, their embeddings have not yet shown the same level of predictive power as natural and protein language models, indicating a gap between current implementations and theoretical promise. Existing benchmarks for DNA language models primarily focus on classifying regulatory elements in eukaryotic genomes, leaving open the fundamental question of whether these models learn sequence-level features across whole genomes. We introduce LAMBDA, a benchmark designed to rigorously evaluate genome language model embeddings through phage-bacteria sequence discrimination across four categories of increasing complexity: probing tasks, fine-tuning assessments, diagnostic tests, and genome-wide prophage detection. Our comprehensive analysis of current genomic language models provides novel insights into the importance of training data quality relative to model size, the need for domain-specific training, and the application of genomic language models for detecting prophage sequences. This benchmark represents a challenging genomic annotation task in the bacterial domain and addresses a key computational problem with direct relevance to microbiology and medicine.

DNA language model↗