A global protein interaction network of Leishmania donovani
Bibliographic record
Abstract
Leishmania donovani is the causative agent of visceral leishmaniasis, a tropical disease affecting millions worldwide. While proteomic studies of Leishmania species have been conducted, the organization of protein-protein interaction (PPI) networks in L. donovani remains largely unexplored. Here, we present a protein interaction network for L. donovani generated through size-exclusion chromatography coupled with mass spectrometry (SEC-MS) and computational analysis. We quantified 3468 proteins with high confidence, of which approximately 70 % are conserved across the Tritryps (Trypanosoma brucei, T. cruzi, and L. donovani). The resulting network contains 1509 nodes and 16,095 interactions, exhibiting scale-free topology and covering key cellular machineries such as the proteasome, ribosome, translation initiation complexes, and BBSome. Remarkably, most annotated Leishmania complexes remained intact within the network, highlighting its high quality. Paralogs within L. donovani frequently interacted with each other, a phenomenon observed at a higher rate than reported in different organisms. Beyond structural organization, the network also provided interaction-based evidence that functionally contextualizes previously uncharacterized or poorly annotated proteins. Complexes involved in mRNA metabolism and flagellar assembly revealed novel components supported by conserved interaction patterns, underscoring the biological utility of the network for functional inference. Our study provides the first experimentally derived, large-scale interaction network specific to L. donovani, offering critical insights into the parasite's molecular architecture. All interaction data are available through our dedicated database at https://2025.trypsnetdb.org.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".