Decoding complex interactomes of macromolecules
Gergő Gógl | 3IA Fellow
This project aims to decode complex interactomes using AI-based tools that integrate quantitative biochemical data with advanced computational modeling. Building on recent breakthroughs in affinity interactomics —which can measure millions of affinities of macromolecular interactions— the project seeks to overcome current limitations in interpreting large-scale interaction networks. By developing novel AI-driven methods, Gergo's team will identify recurring short linear motifs, infer hidden network topologies, and predict binding affinities of experimentally so-far uncharted interactions, particularly within intrinsically disordered regions often implicated in cancer. Combining experimental data generation and machine learning, this interdisciplinary effort will reveal how mutations rewire cellular signaling, uncover novel cancer driver mechanisms, and establish a new paradigm for quantitative systems biochemistry at the interface of interactomics and artificial intelligence.
