Exploring the conformational dynamics of <scp>PD1</scp> in complex with different ligands: What we can learn for designing novel <scp>PD1</scp> signaling blockers?
Bibliographic record
Abstract
Activation of T cells triggers the expression of regulatory molecules like the programmed cell death 1 (PD1) protein. The association of PD1 with the natural ligands PDL1 and PDL2 induces an inhibitory signal that prevents T cells from proliferating and exerting effector functions. However, little is known about how the binding of the ligands induce the PD1 inhibitory signal over T cells effector functions. Here, we explore the dynamics of PD1 free, and in complex with different PDL1 variants as well as the therapeutic antibodies nivolumab and pembrolizumab in order to assess the conformational changes in PD1 related to the signaling process. Our simulations suggest a pre-conformational selection mechanism for the binding of the different PDL1 variants, while an induced-fit model fits better for the molecular recognition process of the therapeutic antibodies. A deep analysis of the changes on PD1 movement upon the binding to different ligands revealed that as larger is the difference in the conformation adopted by loop C'D with respect to the complex with PDL1 is higher the ligand ability to reduce the PD1 inhibitory signaling. This behavior suggests that targeting specific conformations of this loop can be useful for designing therapies able to recover T cells effector functions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".