Prediction of biological protein–protein interactions using atom‐type and amino acid properties
Bibliographic record
Abstract
Identification and analysis of types of biological protein-protein interactions and their interfaces to predict obligate and non-obligate complexes is a problem that has drawn the attention of the research community in the past few years. In this paper, we propose a prediction approach to predict these two types of complexes. We use desolvation energies - amino acid and atom type - of the residues present in the interface. The prediction is performed via two state-of-the-art classification techniques, namely linear dimensionality reduction (LDR) and support vector machines (SVM). The results on a newly compiled data set, namely BPPI, which is a joint and modified version of two well-known data sets consisting of 213 obligate and 303 non-obligate complexes, show that the best prediction is achieved with SVM (76.94% accuracy) when using desolvation energies of atom-type features. Also, the proposed approach outperforms the previous solvent accessible area-based approaches using SVM (75% accuracy) and LDR (73.06% accuracy). Moreover, a visual analysis of desolvation energies in obligate and non-obligate complexes shows that a few atom-type pairs are good descriptors for these types of complexes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".