Shell palaeoproteomics: First application of peptide mass fingerprinting for the rapid identification of mollusc shells in archaeology
Bibliographic record
Abstract
Molluscs were one of the most widely-used natural resources in the past, and their shells are abundant among archaeological findings. However, our knowledge of the variety of shells that were circulating in prehistoric times (and thus their socio-economic and cultural value) is scarce due to the difficulty of achieving taxonomic determination of fragmented and/or worked remains. This study aims to obtain molecular barcodes based on peptide mass fingerprints (PMFs) of intracrystalline proteins, in order to obtain shell identification. Palaeoproteomic applications on shells are challenging, due to low concentration of molluscan proteins and an incomplete understanding of their sequences. We explore different approaches for protein extraction from small-size samples (<20 mg), followed by MALDI-TOF-MS analysis. The SP3 (single-pot, solid-phase) sample preparation method was found to be the most successful in retrieving the intracrystalline protein fraction from seven molluscan shell taxa, which belong to different phylogenetic groups, possess distinct microstructures and are relevant for archaeology. Furthermore, all the shells analysed, including a 7000-year-old specimen of the freshwater bivalve Pseudunio, yielded good-quality distinctive spectra, demonstrating that PMFs can be used for shell taxon determination. Our work suggests good potential for large-scale screening of archaeological molluscan remains. SIGNIFICANCE: We characterise for the first time the peptide mass fingerprints of the intracrystalline shell protein fraction isolated from different molluscan taxa. We demonstrate that these proteins yield distinctive PMFs, even for shells that are phylogenetically related and/or that display similar microstructures. Furthermore, we extend the range of sample preparation approaches for "shellomics" by testing the SP3 method, which proved to be well-suited to shell protein extraction from small-size and protein-poor samples. This work thus lays the foundations for future large-scale applications for the identification of mollusc shells and other invertebrate remains from the archaeological and palaeontological records.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".