Quantum study of mutational effect in binding of efavirenz to HIV‐1 RT
Bibliographic record
Abstract
Full quantum mechanical computational study has been carried out to study binding of efavirenz (EFZ), a second generation FDA approved nonnucleoside inhibitor, to HIV-1 reverse transcriptase (RT) and its K103N and Y181C mutants using the MFCC (molecular fractionation with conjugate caps) method. The binding interaction energies between EFZ and each protein fragment are calculated using a combination of HF/3-21G, B3LYP/6-31G* and MP2/6-31G* ab initio levels. The present computation shows that Efavirenz binds to HIV-1 RT predominantly through strong electrostatic interaction with the Lys101 residue. The small loss of binding to K103N mutant by Efavirenz can be attributed to a slightly weakened attractive interaction between the drug and Lys101 due to a conformational change of mutation. The small loss of binding to Y181C mutant by efavirenz can be attributed to the Glu698 residue moving closer to EFZ due to conformational change, which results in an increase of repulsive energy relative to the wild type (WT). The binding of efavirenz-derived DPC961 to HIV-1 RT is enhanced by an additional attractive interaction to residue Hid235 and reduced repulsion to Glu698, resulting in an increase of binding energy by about 4 kcal/mol.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".