Positions of cysteine residues reveal local clusters and hidden relationships to Sequons and Transmembrane domains in Human proteins
Bibliographic record
Abstract
Membrane proteins often possess critical structural features, such as transmembrane domains (TMs), N-glycosylation, and disulfide bonds (SS bonds), which are essential to their structure and function. Here, we extend the study of the motifs carrying N-glycosylation, i.e. the sequons, and the Cys residues supporting the SS bonds, to the whole human proteome with a particular focus on the Cys positions in human proteins with respect to those of sequons and TMs. As the least abundant amino acid residue in protein sequences, the positions of Cys residues in proteins are not random but rather selected through evolution. We discovered that the frequency of Cys residues in proteins is length dependent, and the frequency of CC gaps formed between adjacent Cys residues can be used as a classifier to distinguish proteins with special structures and functions, such as keratin-associated proteins (KAPs), extracellular proteins with EGF-like domains, and nuclear proteins with zinc finger C2H2 domains. Most importantly, by comparing the positions of Cys residues to those of sequons and TMs, we discovered that these structural features can form dense clusters in highly repeated and mutually exclusive modalities in protein sequences. The evolutionary advantages of such complementarity among the three structural features are discussed, particularly in light of structural dynamics in proteins that are lacking from computational predictions. The discoveries made here highlight the sequence-structure-function axis in biological organisms that can be utilized in future protein engineering toward synthetic biology.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".