Bibliographic record
Abstract
Maintaining protein homeostasis, i.e. a folded and functional proteome, depends on the efficient allocation of cellular protein quality control resources. Decline and dysregulation of protein homeostasis are directly associated to conditions of aging and neurodegeneration. Molecular chaperones as specialized protein quality control enzymes form the core of protein homeostasis. However, how chaperones selectively interact with their substrate proteins thus allocate their overall limited capacity remains poorly understood. Here, I present an integrated analysis of sequence and structural determinants that define interactions of the Saccharomyces cerevisiae Hsp70 Ssb. Structural homologues that differentially interact with Ssb for de novo folding were found to systematically differ in complexity of their folding landscapes, selective use of nonoptimal codons, and presence of short discriminative sequences. All analyzed characteristics contributed to the prediction of Ssb interactions in highly complementary manner, highlighting pervasive trade-offs in chaperone-assisted protein folding landscapes. However, short discriminative sequences were found to contribute by far the strongest signal towards explaining Ssb interactions. This observation suggested that some chaperone interactions may be directly programmed in the amino acid sequences rather than responding to folding challenges, possibly for regulatory advantages.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.000 |
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 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".