bioRxiv Science⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.64898/2026.09.29.755270

BactMentha: a database of annotated bacteria-host protein interactions

Bergogne, L.·Boujeant, M.·Iannuccelli, M.·Bergon, A.·Fernandez-Macgregor, J.·Brun, C.·Licata, L.·Zanzoni, A.
View original source ↗Export metadata ↓

Abstract

Bacterial pathogens ensure their successful dissemination by employing specialized secretion systems to deliver a wide range of virulence proteins that interact with specific host proteins to modulate the response during infection. These interactions are often mediated by host-like elements, like short linear motifs, which can interfere with host interaction interfaces, enabling bacterial secreted proteins to compete and displace native partners of the targeted proteins. Several resources provide access to bacteria-host protein-protein interaction data. However, some are neither fully functional or up to date. Moreover, most of these databases lack virulence protein annotations, thus hampering hypothesis formulation based on protein interaction analyses. In this context, we have developed BactMentha, a unified resource that integrates bacterial-host protein-protein interactions for three organisms (human, mouse, and rat) with virulence protein annotations and bacterial strain clinical relevance information. BactMentha provides easy access to available experimentally identified binding regions of the interaction partners, complemented with putative interaction interfaces based on predicted structure complexes and on the detection of host-like elements on bacterial protein sequences interacting with human proteins. BactMentha currently contains 11 776 unique protein interactions between 2970 bacterial proteins, of which 684 have at least one virulence annotation, and 5088 host proteins. Experimentally identified binding regions are available for 532 bacteria-host interactions, most of which involve human proteins (440 interactions). Predicted interaction interfaces enabled a 3.7-fold increase in known and putative binding regions in the bacteria-human protein interaction data. By providing easy access to functionally annotated and clinically relevant protein interaction data, enriched with experimentally and predicted interaction interfaces, BactMentha can be instrumental in formulating novel hypotheses and speeding up wet lab research on the molecular mechanisms underlying pathogenic bacterial infections.

Explore related subjects

bioinformaticsPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Bergogne, L., Boujeant, M., Iannuccelli, M., Bergon, A., Fernandez-Macgregor, J., Brun, C., Licata, L., Zanzoni, A.. 2026-10-05. BactMentha: a database of annotated bacteria-host protein interactions. https://doi.org/10.64898/2026.09.29.755270

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

Auditing Confidence-Calibrated Benchmarking of Single-Cell Perturbation Models

Single-cell perturbation models are increasingly assessed for both predictive performance and calibrated uncertainty under distribution shift. We constructed such an evaluation harness (two datasets, Norman 2019 and Replogle 2022; four split types over six dataset-split combinations; three models, scVI, CPA and a linear Ridge baseline; three uncertainty quantification methods, MC dropout, quantile regression and split conformal prediction) and then audited it against null baselines it had never included. Most of its headline results measure something other than what their metric names claim. The harness supplied each test cell to the model as input and then scored the model on reproducing that same cell's expression, so an identity predictor returning the input unchanged attains a perturbation-effect correlation of 1.0 on all six splits: any model given the target cell is measured against this echo ceiling rather than against a null. Against a constant train-mean predictor, scVI wins on 99.7% of Norman cells but only 0.2% of Replogle cells. One split held out sequencing batches rather than perturbations; another scores no genuinely novel perturbation after label collapse; unseen perturbations are silently replaced with a negative-control sentinel at inference; and the most convention-sensitive decision-value entry moves 29.2 percentage points under a library-size convention that changes that row's all-gene MSE by 0.01%. Because a validation budget is spent on a ranked shortlist rather than on a marginal interval, we also score each uncertainty signal by the DE-gene error it avoids in the top k: the worst-calibrated method is the best ranker, and a benchmark reporting only coverage orders the two backwards. We document six failure modes, each with its mechanism, the diagnostic that exposes it, and a detection procedure transferable to other harnesses. One result survives, with a boundary condition: split-conformal coverage calibrated on K562 residuals under-covers on RPE1 (0.900 to 0.847), and marginal coverage at the nominal 0.900 is compatible with covering 0.271 to 0.342 of the DE-gene intervals a prioritization decision would rest on. We continue to recommend conformal prediction, recalibrated per cellular context and reported per gene set rather than marginally.

bioinformatics↗
bioRxiv2026

DCAF: Unifying Data Validity, Evaluation Validity and Predictive Modeling for Single-cell Perturbation Simulation

Single-cell perturbation profiling is limited by cost and throughput, while computational methods remain constrained by inconsistent evaluation and underperformance relative to simple baselines. Here we present DCAF, a unified framework for single-cell perturbation simulation that that jointly addresses data validity, evaluation validity and predictive modeling. Among its three components, DCAF-data addresses the single-measurement limitation by reconstructing pre- and post-perturbation status using entropy-regularized optimal transport, and is demonstrated on both cell line and multi-cell type datasets. DCAF-criterion evaluates log fold changes in non-silenced genes to improve sensitivity to model error and robustness to zero inflation noise, validated on synthetic datasets. DCAF-model combines JEPA with explicit modeling for regulatory-dynamics modeling for interpretable prediction. Across benchmarks, DCAF outperforms state-of-the-art deep-learning methods, as well as mean and linear baselines in cross-gene, cross-cell-type and cross-region generalization. In adult Mouse brain, DCAF recapitulates known cell-type-specific regulatory heterogeneity of Dgcr8 knockdown at both cell type and gene levels. DCAF thus provides a rigorous and biologically grounded foundation for reliable perturbation simulation across development, disease and therapeutic discovery.

bioinformatics↗
bioRxiv2026

Phase-Based Frequency Tracking and Analysis of Vocalisations in the Genus Saxicola

Acoustic analysis provides valuable tools for studying bird vocalisations, but quantitative characterisation of call structure remains challenging when frequency and amplitude vary jointly over time. We present a phase-based frequency tracking method that represents amplitude as a function of instantaneous frequency and use it to characterise alarm calls in the genus Saxicola. The method enables the extraction of acoustic features describing the frequency trajectory and temporal structure of individual calls. We apply the method to a dataset of 801 calls from 49 recordings representing 15 species, and use principal component analysis (PCA) and robust PCA to investigate patterns of acoustic variation across species, recordings and geographic regions. The results demonstrate the feasibility of the approach and reveal structured patterns of acoustic similarity and variation among the calls analysed. Robust PCA concentrates the variance of the low-rank representation into fewer principal components than conventional PCA applied to the original feature matrix, providing a complementary representation of the dominant acoustic structure. The observed patterns illustrate the potential of quantitative acoustic characterisation for comparative studies of vocal variation. However, uneven species representation and uncertainty in individual identity and taxonomic labels limit the biological conclusions that can be drawn from the present dataset. The method provides a foundation for further acoustic research, in which broader sampling and integration with independent genetic and morphological evidence could help investigate variation within and among species.

bioinformatics↗