Theoretical investigation of the interactions in binding pocket of Reverse Transcriptase
Bibliographic record
Abstract
Interactions in proteins have been studied using several chemical information techniques including quantum chemical methods that are applied to truncated systems composed of the ligand molecule and the surrounding amino acids of the receptor. In this work we adopt an approach to study these interactions accounting for as many as possible explicit solvent molecules and without the need of a fragmented calculation. Furthermore, we embed our quantum chemical calculations within a molecular dynamics framework that enables a fundamentally fast system for quantum molecular dynamic simulations (QCMD). Central to this new system for QCMD is the tight binding QC system, newly developed in our laboratories, and which combined with the MD paradigm results in an ultra accelerated QCMD method for protein-ligand interaction evaluations. We have applied our newly developed method to the Nevirapine (NVP)-Reverse Transcriptase (RT) system. We show how the proposed method leads us to new findings. The advanced QCMD was applied to a system of RT with NVP and it has led to the knowledge of specific groups and atoms that interact with surrounding amino acids of RT and help in drug binding. The information derived from this calculation may be used in designing drugs for NVP resistant virus strains that have binding capability like NVP.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".