All Publications


  • An all-atom protein generative model. bioRxiv : the preprint server for biology Chu, A. E., Cheng, L., Nesr, G. E., Xu, M., Huang, P. S. 2023

    Abstract

    Proteins mediate their functions through chemical interactions; modeling these interactions, which are typically through sidechains, is an important need in protein design. However, constructing an all-atom generative model requires an appropriate scheme for managing the jointly continuous and discrete nature of proteins encoded in the structure and sequence. We describe an all-atom diffusion model of protein structure, Protpardelle, which instantiates a "superposition" over the possible sidechain states, and collapses it to conduct reverse diffusion for sample generation. When combined with sequence design methods, our model is able to co-design all-atom protein structure and sequence. Generated proteins are of good quality under the typical quality, diversity, and novelty metrics, and sidechains reproduce the chemical features and behavior of natural proteins. Finally, we explore the potential of our model conduct all-atom protein design and scaffold functional motifs in a backbone- and rotamer-free way.

    View details for DOI 10.1101/2023.05.24.542194

    View details for PubMedID 37292974

    View details for PubMedCentralID PMC10245864

  • Infomax Neural Joint Source-Channel Coding via Adversarial Bit Flip Song, Y., Xu, M., Yu, L., Zhou, H., Shao, S., Yu, Y., Assoc Advancement Artificial Intelligence ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE. 2020: 5834-5841