Analysis of relevant physicochemical properties in obligate and non-obligate protein-protein interactions
Bibliographic record
Abstract
Identification and analysis of types of protein-protein interactions (PPI) is an important problem in molecular biology because of its key role in many biological processes in living cells. In this paper, we focus on obligate and non-obligate complexes, their prediction and analysis. We propose a feature selection scheme called MRMR <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">pro</sup> which is based on Minimum Redundancy Maximum Relevance (MRMR) to focus on the most discriminative and relevant properties to distinguish between these two types of complexes. Our prediction approach uses desolvation energies of pairs of atoms or amino acids present in the interfaces of such complexes. Our results on two well-known datasets confirm that MRMR <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">pro</sup> leads to significant improvements on performance by finding more relevant features for prediction. Furthermore, the prediction performance of our biologically guided feature selection methods demonstrate that hydrophobic amino acids are more discriminating than hydrophilic and amphipathic amino acids to distinguish between obligate and non-obligate complexes.
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".