ELRIG-Forum 2026: Abstracts

Dr. Alexander Hergovich, Toulouse, France

Targeted protein degradation (TPD) and the targeting of protein-protein interactions (PPIs) in a wider sense have emerged as novel drug discovery and development avenues. These approaches can tackle ‘undruggable’ disease drivers (aka provide unprecedented angles for the targeting of disease drivers that are not classically targetable). After providing a broader overview of the TPD & PPI fields, the talk will focus on molecular glues and PROTACs (Proteolysis targeting chimerics) with an emphasis on high throughput screening and subsequent lead identification approaches. The clinical validation status of molecular glues and PROTACs will be summarized with other TPD advances in the clinic.

Prof. Dr. Manuel Kaulich, Vivlion GmbH, Frankfurt

Systematic mapping of genetic interactions provides a powerful route to uncover functional gene networks and actionable vulnerabilities. While single-gene CRISPR screens identify essential genes, they fail to resolve buffering relationships, paralog dependencies, and higher-order pathway organization. Combinatorial CRISPR screening overcomes these limitations by directly interrogating pairwise genetic perturbations at scale. In my presentation, I will outline experimental and computational strategies for extracting functional gene modules from combinatorial CRISPR datasets, with a particular focus on the ubiquitin-proteasome system (UPS). Using Vivlion’s optimised dual-guide RNA libraries and scalable screening platforms, we generate quantitative genetic interaction maps in human cells. These interaction profiles enable the identification of synthetic lethal and buffering relationships and reveal connectivity patterns characteristic of protein complexes. By integrating high-content phenotyping with network-based computational analysis, combinatorial interaction signatures can be leveraged to infer active UPS complexes and assign poorly characterized genes to defined functional modules. This approach further facilitates mechanistic studies of targeted protein degraders and supports target deconvolution and combination strategies in early drug discovery. Combinatorial genetics thus provides a systematic framework to move from perturbation data to functional complex-level insights, enabling the extraction of biologically and pharmacologically relevant modules that remain inaccessible to single-gene screening approaches.

Dr. Stephan Kirchmaier, Promega, Walldorf

Confirming that a compound binds its intended target within living cells is essential for successful drug discovery yet remains technically challenging for novel or poorly characterized proteins. Traditional methods present significant limitations: biochemical thermal shift assays require purified proteins and lack cellular context. Live-cell tracer-based target engagement assays deliver excellent quantitative data but require known ligands for assay development—a significant barrier when pursuing first-in-class programs against targets lacking suitable tool compounds.

We present a novel live-cell target engagement platform that enables quantitative assessment of compound–target binding without requiring known ligands or purified proteins.

Validation across more than 20 target classes demonstrates broad applicability spanning diverse cellular compartments and protein families, including historically challenging targets. The streamlined workflow is fully compatible with high-throughput screening formats. This platform empowers discovery teams to identify and validate hits against novel targets earlier in the pipeline, generate reliable cellular potency data for medicinal chemistry optimization, and build confidence in target engagement before committing to downstream studies.

Dr. Ricardo Cunha, Institut für Umwelt & Energie, Technik & Analytik e. V. (IUTA)

In response to the increasing complexity of instrumental analysis data, current vendor processing software face user adoption challenges due to complicated user interfaces, the need to manually transfer data between software packages for specific purposes, and the lack of actual data processing algorithms. The open-source community is constantly striving to fill the gaps in vendor software with innovative data processing algorithms, but this often results in an overwhelming selection of loose scripts or even processing concepts in scientific publications that are useless to the power user. Non-target analysis (NTA) in environmental studies is an example where proprietary software lacks flexibility for different use cases, but the open-source community has developed innovative algorithms to mitigate this lack of flexibility. We therefore present the StreamFind R library to address these challenges by encouraging the development and integration of open-source algorithms into a harmonized platform for assembling interoperable analytical data processing workflows. At the same time, StreamFind aims to improve users' data literacy by providing a flexible, transparent and standard solution for gaining better insights from data. StreamFind is designed to be data agnostic, meaning that users can process different types of data (e.g., liquid chromatography (LC) coupled to UV, high resolution mass spectrometry (HRMS) and Raman spectroscopy (RS) data, tabular data and sensor data acquired via open communication protocols such as LADS OPC UA) within the same interface. We have developed a standardized and data agnostic workflow representation that follows FAIR principles. The workflow standardization will be demonstrated for NTA based on LC-HRMS and for the identification and quantification of monoclonal antibodies using LC-UV and LC-HRMS, respectively. In addition, quality assessment of (bio)pharmaceuticals using an innovative LC-RS dataset combined with machine learning will be shown to demonstrate the interoperability and potential of the workflows in StreamFind.

The StreamFind R library is available for installation from the ODEA project's GitHub repository (https://github.com/odea-project/StreamFind), accompanied by extensive documentation, tutorials and examples. Collaborative contributions to the project and the integration of additional open-source algorithms are encouraged, fostering a collective effort to advance data processing.

Dr. Thorsten Mosler, Leader Proximity Proteomics, Uni Frankfurt

Molecular glue degraders represent a rapidly expanding class of small molecules that reprogram E3 ubiquitin ligases to ubiquitinate and degrade disease-relevant proteins. Despite their therapeutic potential, the rational design of molecular glues remains challenging, underscoring the need for unbiased discovery strategies to identify new chemical targets. To address this challenge, we developed ProxiCapture, an affinity-based proteomics workflow that models the systemic behavior of molecular glues by combining purified CRBN-ΔHBD protein with native cell or tissue lysates. Systematic application of ProxiCapture across eight cancer cell lines, three maturation states of immune cells, and paired primary healthy and tumor tissues, revealed a comprehensive atlas of pomalidomide interactors, including previously uncharacterized targets. These findings reveal that degrader-dependent interactors of CRBN are context-dependent, requiring broad, physiologically and systemically anchored sampling to uncover the full “glueable” proteome. Taken together, this study establishes a scalable platform that accelerates molecular glue discovery by capturing cell- and tissue-specific recruitment profiles and predicting system-wide degrader effects.