@inproceedings{
pao-huang2023scalable,
title={Scalable Multimer Structure Prediction using Diffusion Models},
author={Peter Pao-Huang and Bowen Jing and Bonnie Berger},
booktitle={NeurIPS 2023 Generative AI and Biology (GenBio) Workshop},
year={2023},
url={https://openreview.net/forum?id=Qrx0Dt3RBg}
}
Accurate protein complex structure modeling is a necessary step in understanding the behavior of biological pathways and cellular systems. While some works have attempted to address this challenge, there is still a need for scaling existing methods to larger protein complexes. To address this need, we propose a novel diffusion generative model (DGM) that predicts large multimeric protein structures by learning to rigidly dock its chains together. Additionally, we construct a new dataset specifically for large protein complexes used to train and evaluate our DGM. We substantially improve prediction runtime and completion rates while maintaining competitive accuracy with current methods.
Scalable Multimer Structure Prediction using Diffusion Models
Peter Pao-Huang Bowen Jing Bonnie Berger