Diana Murray

The paper integrates three computational methods to predict E. coli protein-protein interactions and functional networks, identifying over 20K high-confidence interactions.

The paper introduces PIONEER2, a method that improves protein-protein interface prediction by integrating structural homology and deep learning, outperforming AlphaFold3 in certain scenarios.

The paper uses large-scale perturbation profiles to analyze drug mechanisms of action and polypharmacology across multiple tumor subtypes and tissues.

PrePPI predicts protein-protein interactions on a proteome-wide scale using structural and non-structural clues, providing a database of ~1.3 million human PPIs.

ZIPPI evaluates protein-protein interaction models using sequence co-evolution and conservation, showing promise for genome-wide application in E. coli and potential for human interactomes.

The paper uses phosphoproteomic time-series data and the VESPA algorithm to analyze drug resistance mechanisms in colon cancer cells, revealing adaptive signaling rewiring confirmed by CRISPRko assays.

PrePCI predicts protein-compound interactions using structural and chemical similarity, providing a database of over 5 billion interactions with associated probabilities.

The paper introduces an algorithm to reconstruct context-specific molecular interaction maps centered on oncoproteins, validating it with KRAS in lung adenocarcinoma and identifying novel synthetic lethal proteins.