The intrinsic disorder status of the human hepatitis C virus proteome
Bibliographic record
Abstract
Many viral proteins or their biologically important regions are disordered as a whole, or contain long disordered regions. These intrinsically disordered proteins/regions do not possess unique structures and possess functions that complement the functional repertoire of "normal" ordered proteins and domains, with many protein functional classes being heavily dependent on the intrinsic disorder. Viruses commonly use these highly flexible regions to invade the host organisms and to hijack various host systems. These disordered regions also help viruses in adapting to their hostile habitats and to manage their economic usage of genetic material. In this article, we focus on the structural peculiarities of proteins from human hepatitis C virus (HCV) and use a wide spectrum of bioinformatics techniques to evaluate the abundance of intrinsic disorder in the completed proteomes of several human HCV genotypes, to analyze the peculiarities of disorder distribution within the individual HCV proteins, and to establish potential roles of the structural disorder in functions of ten HCV proteins. We show that the intrinsic disorder or increased flexibility is not only abundant in these proteins, but is also absolutely necessary for their functions, playing a crucial role in the proteolytic processing of the HCV polyprotein, the maturation of the individual HCV proteins, and being related to the posttranslational modifications of these proteins and their interactions with DNA, RNA, and various host proteins.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 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".