Insulin-Like Growth Factor II mRNA Binding Protein 1 Associates with Gag Protein of Human Immunodeficiency Virus Type 1, and Its Overexpression Affects Virus Assembly
Bibliographic record
Abstract
The assembly of human immunodeficiency virus type 1 (HIV-1) particles is driven by viral Gag protein. This function of Gag not only benefits from its self-multimerization property but also depends on its interaction with a number of cellular factors such as TSG101 and ALIX/AIP1 that promote virus budding and release from cell surfaces. However, interaction with Gag also allows some cellular factors such as APOBEC3G and Trim5alpha to access viral replication machinery and block viral replication. In this study, we report a new HIV-1 Gag-binding factor named insulin-like growth factor II mRNA binding protein 1 (IMP1). Gag-IMP1 interaction requires the second zinc finger of the nucleocapsid (NC) domain of Gag and the KH3 and KH4 domains of IMP1. A fourfold reduction of HIV-1 infectivity was seen with overexpression of the wild-type IMP1 and its mutant that is able to interact with Gag but not with overexpression of IMP1 mutants exhibiting Gag-binding deficiency. The decreased viral infectivity was further shown as a result of diminished viral RNA packaging, abrogated Gag processing on the cellular membranes, and impeded maturation of virus particles. Together, these results demonstrate that IMP1 interacts with HIV-1 Gag protein and is able to block the formation of infectious HIV-1 particles.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".