Prediction of crystal packing and biological protein-protein interactions
Bibliographic record
Abstract
Prediction of protein-protein interactions are important to understand any biological processes. The structural models of the complexes resulting from these interactions are necessary to understand those processes at the molecular level. X-ray crystallography is the most popular method to determine the three dimensional structures of protein complexes. However, some of the observed interactions in the structures of protein complexes determined by X-ray crystallography are crystal packing contacts and are not biologically relevant. Thus, it is important to discriminate between biologically relevant interactions and crystal packing contacts. We propose a classification approach to predict these two types of complexes. Our approach has two main features. Firstly, we have calculated various interface property features from the quaternary structures of these interactions. Various features are extracted for each complex, namely number-based and area-based amino acid compositions. Secondly, these features are treated as the input features of the classifiers. The classification is performed with support vector machines (SVM) and linear dimensionality reduction (LDR) coupled with Bayesian classifiers. The results on a standard benchmark dataset of crystal packing and biological protein complexes show increasing prediction accuracy when compared.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".