Using Intrinsic X-ray Absorption Spectral Differences To Identify and Map Peptides and Proteins
Bibliographic record
Abstract
The intrinsic variation in the near-edge X-ray absorption fine structure (NEXAFS) spectra of peptides and proteins provide an opportunity to identify and map them in various biological environments, without additional labeling. In principle, with sufficiently accurate spectra, peptides (<50 amino acids) or proteins with unusual sequences (e.g., cysteine- or methionine-rich) should be differentiable from other proteins, since the NEXAFS spectrum of each amino acid is distinct. To evaluate the potential for this approach, we have developed X-SpecSim, a tool for quantitatively predicting the C, N, and O 1s NEXAFS spectra of peptides and proteins from their sequences. Here we present the methodology for predicting such spectra, along with tests of its precision using comparisons to the spectra of various proteins and peptides. The C 1s, N 1s, and O 1s spectra of two novel antimicrobial peptides, Indolicidin (ILPWKWPWWPWRR-NH2) and Sub6 (RWWKIWVIRWWR-NH2), as well as human serum albumin and fibrinogen are reported and interpreted. The ability to identify, differentiate, and quantitatively map an antimicrobial peptide against a background of protein is demonstrated by a scanning transmission X-ray microscopy study of a mixture of albumin and sub6.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".