7 papers
SwitchCraft: A Programmatic Framework for Designing State-Switching Proteins
Bowen Jing, Mihir Bafna, Anisha Parsan +5
Multistate mechanisms underlie many of the complex functions observed in natural proteins. The ability to rationally design multistate proteins would have transformative implicatio…
Learning residue level protein dynamics with multiscale Gaussians
Mihir Bafna, Bowen Jing, Bonnie Berger
Many methods have been developed to predict static protein structures, however understanding the dynamics of protein structure is essential for elucidating biological function. Whi…
AI-based Methods for Simulating, Sampling, and Predicting Protein Ensembles
Bowen Jing, Bonnie Berger, Tommi Jaakkola
Advances in deep learning have opened an era of abundant and accurate predicted protein structures; however, similar progress in protein ensembles has remained elusive. This review…
Generative Modeling of Molecular Dynamics Trajectories
Bowen Jing, Hannes Stärk, Tommi Jaakkola +1
Molecular dynamics (MD) is a powerful technique for studying microscopic phenomena, but its computational cost has driven significant interest in the development of deep learning-b…
AlphaFold Meets Flow Matching for Generating Protein Ensembles
Bowen Jing, Bonnie Berger, Tommi Jaakkola
The biological functions of proteins often depend on dynamic structural ensembles. In this work, we develop a flow-based generative modeling approach for learning and sampling the…
Equivariant Scalar Fields for Molecular Docking with Fast Fourier Transforms
Bowen Jing, Tommi Jaakkola, Bonnie Berger
Molecular docking is critical to structure-based virtual screening, yet the throughput of such workflows is limited by the expensive optimization of scoring functions involved in m…