Lillian T. Chong

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.

This paper investigates how the D614G mutation in SARS-CoV-2 spike protein alters conformational dynamics and allosteric communication, leading to faster RBD opening and increased infectivity.

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

PRISM clusters molecular pathways using structural medoids and geometric dissimilarities, showing robust clustering across complex biomolecular transitions.

The paper evaluates a stress test for weighted ensemble simulations, leading to the development of a more efficient binless method for simulating ligand unbinding.

SHINE improves pathway ensemble analysis by integrating n-ary similarity, cheminformatics tools, and hierarchical clustering for MD simulations.

This paper develops a deep learning method to identify progress coordinates during weighted ensemble simulations, improving folding rate constant estimation by >3-fold in protein folding simulations.

This paper develops a reinforcement learning-based weighted ensemble method to identify progress coordinates for rare-event simulations and tests it on three benchmark systems.

WEDAP is a Python package that simplifies plotting molecular simulation data from conventional MD and weighted ensemble simulations, with examples using HIV-1 capsid protein data.