Seger, E. | bioRxiv Science

bioRxiv2026

Leveraging AI and structural proteomics for rational design of a KAT6A degrader

While targeted protein degraders such as PROTACs are a clinically proven therapeutic strategy, the discovery of novel degraders remains hampered by trial-and-error process. To address this challenge, we developed the AIMS platform, which combines structural proteomics with AI models for rational PROTAC design. AIMS is an end-to-end toolkit for PROTAC optimization, encompassing structure solving using proteomics and AI, prediction of ADME and degradation properties, and prospective ranking of compound design ideas. Altogether, this integrated platform successfully enabled the multi-parameter optimization of a potent and bioavailable in vivo validated KAT6A degrader, establishing a versatile framework for PROTAC development across various targets. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=71 SRC="FIGDIR/small/727609v1_ufig1.gif" ALT="Figure 1"> View larger version (17K): org.highwire.dtl.DTLVardef@13596d0org.highwire.dtl.DTLVardef@140500eorg.highwire.dtl.DTLVardef@147e585org.highwire.dtl.DTLVardef@12dbdfe_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2025

A Novel Algorithm for the Harmonization of Pan-cancer Proteomics

Proteomic characterization of cancer tissues holds the potential to advance therapeutic options and reveal novel biomarkers by unlocking insights available only on the proteome level. However, proteomics data analysis is greatly challenged by systematic technical variability in experimental protocols, instrumentation and data processing, restricting comparisons between studies. With the continued and unprecedented growth of proteomics datasets, a comprehensive strategy for harmonizing these datasets is necessary to enable large-scale integrative analyses. Herein, we describe a novel framework for pan-cancer harmonization and imputation, which offers the scientific community an updated approach to this challenge. Rather than relying on a single batch-effect correction algorithm, our multi-step approach accurately addresses critical systematic differences with custom-tailored solutions, including standardized reanalysis of raw data and an autoencoder for pan-cancer integration. By introducing a suite of benchmarks, we bridge the critical gap in reliable harmonization evaluation. Using this framework, we created a harmonized pan-cancer dataset and demonstrated its superiority over existing solutions and previous pan-cancer harmonization efforts. We further demonstrated its utility in revealing prognostic markers, estimating indication-wide biomarker prevalence, and facilitating target discovery for cancer subtypes. We expect our work to provide powerful tools supporting proteomics research for precision cancer medicine.

bioinformatics↗