Proteome-wide comparison of tertiary protein structures reveals molecular mimicry in Plasmodium-human interactions
Bibliographic record
Abstract
Introduction Molecular mimicry is a strategy used by parasites to evade the host’s immune system and facilitate transmission to a new host. To date, high-throughput examples of molecular mimicry have been limited to comparing protein sequences. However, recent advances in the prediction of tertiary structural models, led by Deepmind’s AlphaFold, enable the comparison of thousands of proteins from parasites and their hosts at the structural level, allowing for the identification of more mimics. Here, we present the first proteome-level search for tertiary structure similarity between proteins from Plasmodium falciparum, a malaria-causing parasite, and humans. Methods We assembled a database of experimentally-characterized protein tertiary structures (from the Protein Data Bank) and AlphaFold-generated protein tertiary structures from P. falciparum, human, and 15 negative control species, i.e., species not infected by P. falciparum. We aligned human and control structures to the parasite structures using Foldseek. Results We identified molecular mimicry in three proteins that have been previously proposed as mediators of Plasmodium-human interactions. By extending this approach to all P. falciparum proteins, we identified an additional 41 potential mimics that are supported by additional experimental data. Discussion Our findings demonstrate a valuable application of AlphaFold-derived tertiary structural models, and we discuss key considerations for its effective use in other host-parasite systems.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".