De novo protein identification in mammalian sperm using in situ cryoelectron tomography and AlphaFold2 docking
Bibliographic record
Abstract
Abstract Understanding molecular mechanisms of cellular pathways requires knowledge of the identities of participating proteins, their cellular localization and their 3D structures. Contemporary workflows typically require multiple techniques to identify target proteins, track their localization using fluorescence microscopy, followed by in vitro structure determination. To identify mammal-specific sperm proteins and understand their functions, we developed a visual proteomics workflow to directly address these challenges. Our in situ cryo-electron tomography and subtomogram averaging provided 6.0 Å resolution reconstructions of axonemal microtubules and their associated proteins. The well-resolved secondary and tertiary structures allowed us to computationally match, in an unbiased manner, novel densities in our 3D reconstruction maps with 21,615 AlphaFold2-predicted protein models of the mouse proteome. We identified Tektin 5, CCDC105 and SPACA9 as novel microtubule inner proteins that form an extensive network crosslinking the lumen of microtubule and existing proteins. Additional biochemical and mass spectrometry analyses helped validate potential candidates. The novel axonemal sperm structures identified by this approach form an extensive interaction network within the lumen of microtubules, suggesting they have a role in the mechanical and elastic properties of the microtubule filaments required for the vigorous beating motions of flagella.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".