Lisa Otten

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.

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.