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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".