Barry H. Honig

This paper uses free energy perturbation (FEP) calculations to predict and improve the accuracy of Fc-FcRn binding energy changes due to mutations, incorporating conformational changes and coupling effects.

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

The paper shows Dpr4/DIP-{Theta} transneuronal interactions control branch-specific axon pruning in Drosophila mushroom body neurons, with N-Cadherin mediating this process.

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

This paper evaluates YF2, a CREBBP/p300 activator, showing it enhances cytotoxicity and immune responses in B-cell lymphoma by restoring acetylation and altering the immune microenvironment.

The paper shows that DIPs and Dprs interact in cis within the same cells, regulating trans interactions and influencing neural development in Drosophila.

The paper benchmarks free energy perturbation calculations for predicting mutation effects on protein-protein binding affinity and introduces an automated method to improve accuracy.

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 investigates how varying affinities of CAM interactions affect synaptic targeting and neuronal survival in Drosophila, finding different affinity requirements for these processes.

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.

This study identifies Robo2 as a synaptogenic molecule critical for excitatory synapse formation and spatial coding in hippocampal circuits.