Challenging the Existing Model of the Hexameric HIV-1 Gag Lattice and MA Shell Superstructure: Implications for Viral Entry
Bibliographic record
Abstract
Despite type 1 human immunodeficiency virus (HIV-1) being discovered in the early 1980s, significant knowledge gaps remain in our understanding of the superstructure of the HIV-1 matrix (MA) shell. Current viral assembly models assume that the MA shell originates via recruitment of group-specific antigen (Gag) polyproteins into a hexagonal lattice but fails to resolve and explain lattice overlapping that occurs when the membrane is folded into a spherical/ellipsoidal shape. It further fails to address how the shell recruits, interacts with and encompasses the viral spike envelope (Env) glycoproteins. These Env glycoproteins are crucial as they facilitate viral entry by interacting with receptors and coreceptors located on T-cells. In our previous publication, we proposed a six-lune hosohedral structure, snowflake-like model for the MA shell of HIV-1. In this article, we improve upon the six-lune hosohedral structure by incorporating into our algorithm the recruitment of complete Env glycoproteins. We generated the Env glycoprotein assembly using a combination of predetermined Env glycoprotein domains from X-ray crystallography, nuclear magnetic resonance (NMR), cryoelectron tomography, and three-dimensional prediction tools. Our novel MA shell model comprises 1028 MA trimers and 14 Env glycoproteins. Our model demonstrates the movement of Env glycoproteins in the interlunar spaces, with effective clustering at the fusion hub, where multiple Env complexes bind to T-cell receptors during the process of viral entry. Elucidating the HIV-1 MA shell structure and its interaction with the Env glycoproteins is a key step toward understanding the mechanism of HIV-1 entry.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".