Tripleknock: predicting lethal effect of three-gene knockout in bacteria by deep learning

Investigating the lethal effect of multi-gene knockout is essential for discovering novel antibiotic targets and metabolic engineering. Unlike single genes or gene pairs, three-gene combinations involve more intricate interactions, making experimental screening time-consuming. Computational methods, particularly Genome-scale metabolic model (GEM)-based Flux balance analysis (FBA), requires constructing new GEMs from experimental data, limiting its use for new species. Moreover, using FBA for three-gene knockout screening could take several years. Therefore, a faster and GEM-independent approach is needed to facilitate genome-wide three-gene knockout screening. Here, we introduce Tripleknock for predicting the lethal effects of three-gene knockouts. Tripleknock was trained using genome-derived protein sequence features from Escherichia coli K-12 MG1655, and three-gene knockout simulations using FBA. The model uses a threshold of 90% reduction in cell growth to define lethal effect as the prediction output. Compared to FBA, Tripleknock yields predictions ~ 20 × faster and achieves an average cross-species F1 score of 0.77 on six Enterobacteriaceae pathogens (FBA-defined labels). To mitigate information leakage, we additionally evaluated Tripleknock under a strict gene-level disjoint Train/Val/Test split (genes left out). We further benchmarked against an essentiality-rule baseline and literature-curated (n = 37) E. coli triple perturbations for external validation; no false positives were observed among predicted lethal cases (FP = 0) in this small curated set. To the best of our knowledge, this provides a reproducible baseline and evaluation framework for bacterial triple-knockout lethality prediction.