Molecular Dynamics Simulations of HLA-CW4-B2M-KIR2DL1 Protein and Homology Modeling of a Complex Associated with Psoriasis Disease (HLA-CW6-B2M-KIR2DS1)
Bibliographic record
Abstract
Abstract Molecular dynamics (MD) simulation was used to study the interactions of two immune proteins of HLA-Cw4-β2m-KIR2DL1 complex with small peptide QYDDAVYKL (nine amino acids) in an aqueous solution. This study aims to gain a detailed information about the conformational changes and the dynamics of the complex. The right parameters and force field for performing the MD simulations that was needed to calibrate the complex structure were determined. The non-bonded interactions (Electrostatic and van der Waals contributions), H-bond formation, and salt bridges between the ligand HLA-Cw4 and the receptor KIR2DL1 were estimated using the obtained MD trajectories. The buried surface area due to binding was calculated to get insight into the causes of specificity of receptor to ligand and explains mutations experiment. The study concluded that β2-microglobulin, one part of the complex, is not directly interacting with the peptide at the groove; therefore, it could be neglected from simulation. Our results showed that β2-microglobulin does not have any significant effect on the dynamics of the 3D-structure of the complex. This project will help in understanding to optimize candidate drug design, a small peptide that disrupts the interaction, for the optimal biological effect.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".