PESIN Conjugates for Multimodal Imaging: Can Multimerization Compensate Charge Influences on Cell Binding Properties? A Case Study
Bibliographic record
Abstract
Recently, anionic charges were found to negatively influence the in vitro gastrin-releasing peptide receptor (GRPR) binding parameters of dually radioisotope and fluorescent dye labeled GRPR-specific peptide dimers. From this, the question arose if this adverse impact on in vitro GRP receptor affinities could be mitigated by a higher valency of peptide multimerization. For this purpose, we designed two different hybrid multimodal imaging units (MIUs), comprising either one or two click chemistry-compatible functional groups and reacted them with PESIN (PEG3-BBN7–14, PEG = polyethylene glycol) dimers to obtain a dually labeled peptide homodimer or homotetramer. Using this approach, other dually labeled peptide monomers, dimers, and tetramers can also be obtained, and the chelator and fluorescent dye can be adapted to specific requirements. The MIUs, as well as their peptidic conjugates, were evaluated in terms of their photophysical properties, radiolabeling efficiency with 68Ga and 64Cu, hydrophilicity, and achievable GRP receptor affinities. Here, the hydrophilicity and the GRP receptor binding affinities were found to be especially strongly influenced by the number of negative charges and peptide copies, showing logD (1-octanol-water-distribution coefficient) and IC50 (half maximal inhibitory concentration) values of −2.2 ± 0.1 and 59.1 ± 1.5 nM for the homodimer, and −1.9 ± 0.1 and 99.8 ± 3.2 nM for the homotetramer, respectively. From the obtained data, it can be concluded that the adverse influence of negatively charged building blocks on the in vitro GRP receptor binding properties of dually labeled PESIN multimers can, at least partly, be compensated for by the number of introduced peptide binding motives and the used molecular design.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".