Interaction Profile-Based Protein Classification of Death Domain
Bibliographic record
Abstract
Background: The increasing number of protein sequences and 3D structure obtained fromgenomic initiatives is leading many of us to focus on proteomics, and to dedicate our experimentaland computational efforts on the creation and analysis of information derived from 3D structure.In particular, the high-throughput generation of protein-protein interaction data from a feworganisms makes such an approach very important towards understanding the molecularrecognition that make-up the entire protein-protein interaction network. Since the generation ofsequences, and experimental protein-protein interactions increases faster than the 3D structuredetermination of protein complexes, there is tremendous interest in developing in silico methodsthat generate such structure for prediction and classification purposes. In this study we focused onclassifying protein family members based on their protein-protein interaction distinctiveness.Structure-based classification of protein-protein interfaces has been described initially by Ponstinglet al. [1] and more recently by Valdar et al. [2] and Mintseris et al. [3], from complex structures thathave been solved experimentally. However, little has been done on protein classification based onthe prediction of protein-protein complexes obtained from homology modeling and dockingsimulation.Results: We have developed an in silico classification system entitled HODOCO (Homologymodeling, Docking and Classification Oracle), in which protein Residue Potential InteractionProfiles (RPIPS) are used to summarize protein-protein interaction characteristics. This systemapplied to a dataset of 64 proteins of the death domain superfamily was used to classify eachmember into its proper subfamily. Two classification methods were attempted, heuristic andsupport vector machine learning. Both methods were tested with a 5-fold cross-validation. Theheuristic approach yielded a 61% average accuracy, while the machine learning approach yielded an89% average accuracy.Conclusion: We have confirmed the reliability and potential value of classifying proteins via theirpredicted interactions. Our results are in the same range of accuracy as other studies that classifyprotein-protein interactions from 3D complex structure obtained experimentally. While ourclassification scheme does not take directly into account sequence information our results are inagreement with functional and sequence based classification of death domain family members.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".