Identification of Bioactive Conformational Space of Flexible Endogenous Lipid Mediator Lysophosphoserine Based on Accessible Spaces and Activities of Conformationally Restricted Analogues
Bibliographic record
Abstract
It is difficult to identify the bioactive conformations of flexible endogenous ligands that have a large number of rotatable bonds, but one approach is to introduce conformational restrictions. Then, by comparing the accessible conformational space and bioactivity of the analogs, it should be possible to estimate the bioactive conformational space. Here, we applied this strategy to identify the bioactive conformation of lysophosphatidylserine (LysoPS(18:1)), an endogenous lipidic ligand of the G-protein-coupled receptor GPR34. We synthesized LysoPS analogues conformationally restricted with respect to the glycerol moiety and sampled the accessible conformation spaces of two active and two inactive analogues by means of long-duration replica exchange molecular dynamics (REMD) and metadynamics (MTD) simulations. The conformational populations of these analogues and LysoPS itself were sampled with respect to two dihedral angles along the glycerol moiety. We found that REMD sampling of the whole molecule and MTD-based estimation of the free energy landscape gave similar results. Furthermore, the dihedral angles of LysoPS(18:1) in the previously identified docking pose with GPR34 were consistent with the range of dihedral angles in bioactive ligand conformations evaluated from the REMD and MTD calculations. Thus, at least with respect to the two dihedral angles of the glycerol moiety, the GPR34-bound conformation of LysoPS(18:1) corresponds well to some of the available unbound ligand conformations. Our findings are consistent with the “conformational selection” model of receptor recognition. This methodology for predicting the bioactive ligand conformation of flexible ligands should be helpful for rational drug design.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".