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Record W4409632156 · doi:10.1158/1538-7445.am2025-4499

Abstract 4499: Drug target ID and binding site mapping in complex cellular environments using LiP-MS

2025· article· en· W4409632156 on OpenAlexaff
Martin Soste, Polina Shichkova, Roland Bruderer, Matevz Stefancic, Daniel Redfern, Francesca Cavallo, Kuhulika Bhalla, Helen E. Burston, Prasamit S. Baruah, Roland Hjerpe, Stuart Thomson, Allan M. Jordan, Yuehan Feng

Bibliographic record

VenueCancer Research · 2025
Typearticle
Languageen
FieldChemistry
TopicMass Spectrometry Techniques and Applications
Canadian institutionsCégep de Saint-Laurent
Fundersnot available
KeywordsBinding siteComputational biologyDrugChemistryBiophysicsBiologyPharmacologyBiochemistry

Abstract

fetched live from OpenAlex

Abstract Identifying drug targets within cellular contexts is critical to advancing both target-based and phenotypic drug discovery, particularly for small molecule degraders, such as molecular glues (MG). In most common cases of MG, target identification involves two key mechanism of actions (MoA): determining specific protein target(s) that are engaged by the E3 ligase when a drug binds the E3 ligase, and, conversely, identifying the E3 ligase (adaptor) that is recruited to form the ternary complex when the drug directly targets the protein itself. Limited proteolysis combined with mass spectrometry (LiP-MS) has emerged as a robust, label-free method for deconvoluting small molecule and peptide targets directly within cell lysates, avoiding the need for compound modification or cell line manipulation. This approach leverages a non-specific protease under controlled conditions to detect conformational changes or steric hindrance induced by drug-protein interactions or protein-protein interactions. Using quantitative MS, LiP monitors over 250, 000 peptides, representing more than 8, 000 proteins in a mammalian cell line, allowing for proteome-wide profiling of drug-target interactions. A comprehensive concentration-response study spanning seven concentrations, combined with machine learning-based LiP-scoring, provides a precise ranking of targets and predicts binding sites with peptide-level resolution. Beyond global target identification, we also established a high-throughput HR-LiP workflow that offers detailed peptide-level insights into small molecule-protein interactions directly in cell lysates from cell lines overexpressing target proteins or complexes. This workflow bypasses the need for protein purification, mitigating risks of protein truncation or misfolding. To evaluate LiP-MS’ performance on molecular glue MoAs, we performed global target ID experiments using two well-characterized models: the cyclin K degraders CR8 and SR-4835 and the GSPT1 degraders MRT-2359 and Eragidomide. While the cyclin K degraders bind directly to CDK12, inducing its interaction with DDB1 and leading to the degradation of cyclin K within the CDK12 complex, the GSPT1 compounds engage CRBN, which in turn recruits and degrades GSPT1 as a neosubstrate. Applying HR-LiP, we mapped the binding site of the BRD4 inhibitor JQ1 on full-length BRD4, a large protein challenging to study via standard methods, using a mammalian transient overexpression system. Similarly, we located binding sites for EGFR inhibitors gefitinib and afatinib on the membrane-bound EGFR protein. Additionally, we pinpointed the SMER28 binding site on the hexameric ATPase VCP, an autophagy enhancer, demonstrating the method's applicability to large protein complexes. In conclusion, LiP-MS provides a versatile toolbox for identifying drug targets and mapping binding sites within complex cellular environments. Citation Format: Martin Soste, Polina Shichkova, Roland Bruderer, Matevz Stefancic, Daniel Redfern, Francesca Cavallo, Kuhulika Bhalla, Helen Burston, Prasamit S. Baruah, Roland Hjerpe, Stuart Thomson, Allan Jordan, Yuehan Feng. Drug target ID and binding site mapping in complex cellular environments using LiP-MS [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 4499.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.003

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 head0.077
GPT teacher head0.383
Teacher spread0.306 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations0
Published2025
Admission routes1
Has abstractyes

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