Identifying protein binding functionality of protein family sequences by Aligned Pattern clusters
Bibliographic record
Abstract
A basic task in protein analysis is to discover a set of sequence patterns that reflect the function of a protein family. This set of sequence patterns contains non-exact significant residue associations. Currently, the existing combinatorial methods are computationally expensive and probabilistic methods require richer representation of the amino acid associations. To undertake this task, we create a synthesized pattern representation called an Aligned Pattern (AP) Cluster that identifies the residue associations in the binding segment and the site variations in the aligned residues. In this paper, our algorithm identifies the binding segments for two protein families: the Cytochrome Complex and the Ubiquitin protein families. For each of the experiments, the AP Clusters obtained correspond to protein binding segments including a few beyond those identified by the other protein databases, PROSITE and pFam. Furthermore, the columns of aligned sites that exist only as a single value in the AP Clusters also corresponds to the binding residues. Additional information retained by the AP Clusters can reveal the amino acid residues of interest, thus averting time-consuming simulations and experimentation.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".