Biology subjects

Scheller, I.

Publications and source records attributed to Scheller, I..

4 recordsLinked to original sources

bioRxiv2026

Discovery of CDK4-selective molecular glue degraders by high-throughput proteomics

Molecular glue degraders (MGDs) are proximity-inducing molecules that promote the destruction of disease-causing proteins by stabilizing novel interfaces between E3 ubiquitin ligases and target proteins. The rational design of MGDs remains exceptionally challenging, historically relying on serendipitous discoveries. Here, we deployed a high-throughput, mass spectrometry (MS)-based screen evaluating thousands of cereblon (CRBN)-directed compounds to expedite the identification of novel neosubstrates. This workflow led to the discovery of NE26394, a first-in-class MGD that selectively eliminates cyclin-dependent kinase 4 (CDK4), a critical oncogenic driver of cell cycle progression. Mechanistically, NE26394-induced CDK4 recognition by CRBN depends on the co-recruitment of endogenous INK4 family proteins. In CDK4-dependent cancer models, NE26394 effectively mimics the anti-proliferative RB-E2F pathway perturbations induced by clinical CDK4 inhibitors, rendering it an attractive candidate for further preclinical development.

biochemistry↗

bioRxiv2026

Integrated proteomic screening reveals design principles of CRBN molecular glue degraders

Cereblon (CRBN)-based molecular glue degraders (MGDs) induce the degradation of diverse disease-relevant proteins, underscoring their broad therapeutic potential. Here we systematically expand the CRBN neosubstrate landscape using a target-agnostic discovery approach. By integrating deep proteomic and ubiquitinomic profiling of a 960-compound library, we identify compound-induced ubiquitination and depletion of over 230 endogenous proteins. Among these, 124 represent previously unreported CRBN neosubstrates, with over half lacking a predicted G-loop degron. We provide this dataset via an interactive resource, NeosubstratesDB. Complementary cellular and biochemical assays mechanistically define the interaction domain of IRAK1 and establish G-loop-dependent degradation for BCL6. Interpretable machine learning (iML) integrating proteomic profiles with chemical structures highlights key molecular fingerprints driving neosubstrate selectivity for targets such as CSNK1A1, ZFP91 and WEE1. Together, these findings significantly expand the repertoire of CRBN neosubstrates and provide a framework for rational design of next-generation MGDs.

biochemistry↗

bioRxiv2025

PROTRIDER: Protein abundance outlier detection from mass spectrometry-based proteomics data with a conditional autoencoder

Structured abstractO_ST_ABSMotivationC_ST_ABSDetection of gene regulatory aberrations enhances our ability to interpret the impact of inherited and acquired genetic variation for rare disease diagnostics and tumor characterization. While numerous methods for calling RNA expression outliers from RNA-sequencing data have been proposed, the establishment of protein expression outliers from mass spectrometry data is lacking. ResultsHere, we propose and assess various modeling approaches to call protein expression outliers across three datasets from rare disease diagnostics and oncology. We use as independent evidence the enrichment for outlier calls in matched RNA-seq samples and the enrichment for rare variants likely disrupting protein expression. We show that controlling for hidden confounders and technical covariates, while simultaneously modeling the occurrence of missing values, is largely beneficial and can be achieved using conditional autoencoders. Moreover, we find that the differences between experimental and fitted log-transformed intensities by such models exhibit heavy tails that are poorly captured with the Gaussian distribution and report stronger statistical calibration when instead using the Students t-distribution. Our resulting method, PROTRIDER, outperformed baseline approaches based on raw log-intensities Z-scores or on differential expression analysis with limma. The application of PROTRIDER reveals significant enrichments of AlphaMissense pathogenic variants in protein expression outliers. Overall, PROTRIDER provides a method to confidently identify aberrantly expressed proteins applicable to rare disease diagnostics and cancer proteomics. Availability and ImplementationPROTRIDER is freely available at github.com/gagneurlab/PROTRIDER and also available on Zenodo under the DOI zenodo.15569781. ContactJulien Gagneur: gagneur at in.tum.de

bioinformatics↗

bioRxiv2019

Detection of aberrant splicing events in RNA-seq data with FRASER

Aberrant splicing is a major cause of rare diseases, yet its prediction from genome sequence remains in most cases inconclusive. Recently, RNA sequencing has proven to be an effective complementary avenue to detect aberrant splicing. Here, we developed FRASER, an algorithm to detect aberrant splicing from RNA sequencing data. Unlike existing methods, FRASER captures not only alternative splicing but also intron retention events. This typically doubles the number of detected aberrant events and identified a pathogenic intron retention in MCOLN1. FRASER automatically controls for latent confounders, which are widespread and substantially affect sensitivity. Moreover, FRASER is based on a count distribution and multiple testing correction, reducing the number of calls by two orders of magnitude over commonly applied z score cutoffs, with a minor sensitivity loss. The application to rare disease diagnostics is demonstrated by reprioritizing a pathogenic aberrant exon truncation in TAZ from a published dataset. FRASER is easy to use and freely available.

bioinformatics↗