Protein Visualizer 2.0: Intuitive and Interactive Visualization of Protein Topology and Co/Post-Translational Modifications
Bibliographic record
Abstract
Topology and post-translational modifications (PTMs) are critical features in studying the structures and functions of proteins. Several popular and detailed protein visualization algorithms are available for such studies, such as PyMOL, NCBI's iCn3D, UniProt PTM viewing, and Protter. However, none of the studies depict the special relationships between key structural features such as N-glycosylation, disulfide bonds, potential N-glycosylation sites (free sequons), potential disulfide bonds (free cysteine sites), and protein topology in a way conducive to detailed analyses. We introduce Protein Visualizer 2.0 (https://sfu-sun-lab.github.io/protein-visualizer-2.0/), a web-based tool that visualizes these features and the topology of all human proteins. This tool allows users to readily assess potential conflicts among predicted protein topology and known co/post-translational modifications. This tool also helps to reveal hidden relationships among displayed structural features.
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 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.035 | 0.027 |
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".