Daniel M. Zuckerman

This study uses computational simulations to reveal ion permeation mechanisms and dynamic conductance modulation in gap junction channels formed by connexin proteins.

This study compares AI tools for predicting protein structural ensembles, finding they generate diverse but distinct conformational distributions, with limited coverage of experimental structures and minimal response to known mutations.

The paper describes a method to generate equilibrium protein structure ensembles using physics-based computations to harmonize AI-generated outputs.

The paper develops MMIST, a computational approach linking live-cell morphodynamics to gene expression changes during cell state transitions.

The paper designs proteins with controlled dissociation rates by introducing strained excited states, enabling rapid cytokine receptor dissociation.

This paper characterizes a seven-state binding network in dynein assembly using 39 ITC experiments and Bayesian inference, revealing previously unrecognized binding states and balancing cooperativities.

This paper improves Bayesian parameter inference for ITC by resolving prior ambiguity through multi-dataset analysis and hierarchical modeling, enhancing accuracy in binding parameter estimation.

The paper uses delay embedding and VAE features to predict single-cell dynamics of a molecular reporter from unlabeled live-cell imaging data.

This paper introduces a morphodynamical trajectory embedding method to analyze live-cell imaging data, revealing ligand-specific cell state transitions and improving trajectory modeling compared to snapshot-based analysis.