PepSite: prediction of peptide-binding sites from protein surfaces
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Complex biological functions emerge through intricate protein-protein interaction networks. An important class of protein-protein interaction corresponds to peptide-mediated interactions, in which a short peptide stretch from one partner interacts with a large protein surface from the other partner. Protein-peptide interactions are typically of low affinity and involved in regulatory mechanisms, dynamically reshaping protein interaction networks. Due to the relatively small interaction surface, modulation of protein-peptide interactions is feasible and highly attractive for therapeutic purposes. Unfortunately, the number of available 3D structures of protein-peptide interfaces is very limited. For typical cases where a protein-peptide structure of interest is not available, the PepSite web server can be used to predict peptide-binding spots from protein surfaces alone. The PepSite method relies on preferred peptide-binding environments calculated from a set of known protein-peptide 3D structures, combined with distance constraints derived from known peptides. We present an updated version of the web server that is orders of magnitude faster than the original implementation, returning results in seconds instead of minutes or hours. The PepSite web server is available at http://pepsite2.russelllab.org.
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.
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 | 0.001 | 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 it