Protein residue networks from a local search perspective
Bibliographic record
Abstract
Proteins have been abstracted as a network of interacting amino acids and much attention has been paid to the small-world property of such networks, which we call protein residue networks (PRNs). Hitherto, a global search strategy such as breadth-first search (BFS) is commonly used to measure the average path length of PRNs. We propose that a local search strategy is more appropriate because the inverse relationship between clustering and average path length in a local search better fits the notion that amino acids get closer to each other as a protein becomes more compact. This inverse relationship is also observed in data from a molecular dynamics (MD) simulation of a protein unfolding. To study local search on PRNs, we devised a greedy local search algorithm called EDS and compared the characteristics of BFS paths with EDS paths. While they are different in terms of variation in path length, search cost and link usage, they exhibit similarities in terms of hierarchy and centrality. We argue that the differences are preferable as they make EDS paths a better model of intra-protein communication. The similarities are also preferable as they imply the transferability of existing methods based on BFS centrality. Clustering coupled with strong transitivity helps to keep EDS paths short on PRNs by creating a store of potential short-cut edges. The ready availability of PRN edges that can act as short-cuts help EDS avoid backtracking. The number of short-cut edges scales linearly with protein size. Short-cut edges are enriched with short-range contacts, see higher usage (are more central), have stronger local clustering but weaker local community structure, and effect larger EDS path dilation. Throughout the paper, network statistics for PRNs from an MD simulation are reported to support our findings, and to observe how the network statistics change as a protein folds.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".