In silico analysis of the binding of anthelmintics to Caenorhabditis elegans P-glycoprotein 1
Bibliographic record
Abstract
Macrocyclic lactones (ML) are important anthelmintics used in animals and humans against parasite nematodes, but their therapeutic success is compromised by the spread of ML resistance. Some ABC transporters, such as p-glycoproteins (Pgps), are selected and overexpressed in ML-resistant nematodes, supporting a role for some drug efflux proteins in ML resistance. However, the role of such proteins in ML transport remains to be clarified at the molecular level. Recently, Caenorhabditis elegans Pgp-1 (Cel-Pgp-1) has been crystallized, and its drug-modulated ATPase function characterized in vitro revealed Cel-Pgp-1 as a multidrug transporter. Using this crystal structure, we have developed an in silico drug docking model in order to study the binding of ML and other anthelmintic drugs to Cel-Pgp-1. All tested ML bound with high affinity in a unique site, within the inner chamber of the protein, supporting that ML may be transported by Cel-Pgp-1. Interestingly, interacting residues delineate a ML specific fingerprint involving H-bonds, including T1028. In particular, benzofurane and spiroketal moieties bound to specific sub-sites. When compared with the aglycone ML, such as moxidectin and ivermectin aglycone, avermectin anthelmintics have significant higher affinity for Cel-Pgp-1, likely due to the sugar substituent(s) that bind to a specific area involving H-bonds at Y771. Triclabendazole, closantel and emodepside bound with good affinities to different sub-sites in the inner chamber, partially overlapping with the ML binding site, suggesting that they could compete for Cel-Pgp-1-mediated ML transport. In conclusion, this work provides novel information on the role of nematode Pgps in transporting anthelmintics, and a valuable tool to predict drug-drug interactions and to rationally design new competitive inhibitors of clinically-relevant nematode Pgps, to improve anthelmintic therapeutics.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".