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Record W4402899055 · doi:10.1021/acschembio.4c00286

Leveraging Covalency to Stabilize Ternary Complex Formation For Cell–Cell “Induced Proximity”

2024· review· en· W4402899055 on OpenAlexaff
Karolina Krygier, Anjalee N. Wijetunge, Arthur Srayeddin, Harrison M. McCann, Anthony F. Rullo

Bibliographic record

VenueACS Chemical Biology · 2024
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Degradation and Inhibitors
Canadian institutionsMcMaster University
Fundersnot available
KeywordsTernary complexTernary operationCellChemistryNanotechnologyBiophysicsCell biologyComputer scienceMaterials scienceBiologyBiochemistry

Abstract

fetched live from OpenAlex

Recent advances in the field of translational chemical biology use diverse “proximity-inducing” synthetic modalities to elicit new modes of “event driven” pharmacology. These include mechanisms of targeted protein degradation and immune clearance of pathogenic cells. Heterobifunctional “chimeric” compounds like Proteolysis TArgeting Chimeras (PROTACs) and Antibody Recruiting Molecules (ARMs) leverage these mechanisms, respectively. Both systems function through the formation of reversible “ternary” or higher-order biomolecular complexes. Critical to function are key parameters, such as bifunctional molecule affinity for endogenous proteins, target residence time, and turnover. To probe the mechanism and enhance function, covalent chemical approaches have been developed to kinetically stabilize ternary complexes. These include electrophilic PROTACs and Covalent Immune Recruiters (CIRs), the latter designed to uniquely enforce cell–cell induced proximity. Inducing cell–cell proximity is associated with key challenges arising from a combination of steric and/or mechanical based destabilizing forces on the ternary complex. These factors can attenuate the formation of ternary complexes driven by high affinity bifunctional/proximity inducing molecules. This Account describes initial efforts in our lab to address these challenges using the CIR strategy in antibody recruitment or receptor engineered T cell model systems of cell–cell induced proximity. ARMs form ternary complexes with serum antibodies and surface protein antigens on tumor cells that subsequently engage immune cells via Fc receptors. Binding and clustering of Fc receptors trigger immune cell killing of the tumor cell. We applied the CIR strategy to convert ARMs to covalent chimeras, which “irreversibly” recruit serum antibodies to tumor cells. These covalent chimeras leverage electrophile preorganization and kinetic effective molarity to achieve fast and selective covalent engagement of the target ternary complex protein, e.g., serum antibody. Importantly, covalent engagement can proceed via diverse binding site amino acids beyond cysteine. Covalent chimeras demonstrated striking functional enhancements compared to noncovalent ARM analogs in functional immune assays. We revealed this enhancement was in fact due to the increased kinetic stability and not concentration, of ternary complexes. This finding was recapitulated using analogous CIR modalities that integrate peptidic or carbohydrate binding ligands with Sulfur(VI) Fluoride Exchange (SuFEx) electrophiles to induce cell–cell proximity. Mechanistic studies in a distinct model system that uses T cells engineered with receptors that recognize covalent chimeras or ARMs, revealed covalent receptor engagement uniquely enforces downstream activation signaling. Finally, this Account discusses potential challenges and future directions for adapting and optimizing covalent chimeric/bifunctional molecules for diverse applications in cell–cell induced proximity.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.074
GPT teacher head0.345
Teacher spread0.271 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations5
Published2024
Admission routes1
Has abstractyes

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