  Generative models of macromolecules carry abundant and impactful implications
for industrial and biomedical efforts in protein engineering. However, existing
methods are currently limited to modeling protein structures or sequences,
independently or jointly, without regard to the interactions that commonly
occur between proteins and other macromolecules. In this work, we introduce
MMDiff, a generative model that jointly designs sequences and structures of
nucleic acid and protein complexes, independently or in complex, using joint
SE(3)-discrete diffusion noise. Such a model has important implications for
emerging areas of macromolecular design including structure-based transcription
factor design and design of noncoding RNA sequences. We demonstrate the utility
of MMDiff through a rigorous new design benchmark for macromolecular complex
generation that we introduce in this work. Our results demonstrate that MMDiff
is able to successfully generate micro-RNA and single-stranded DNA molecules
while being modestly capable of joint modeling DNA and RNA molecules in
interaction with multi-chain protein complexes. Source code:
https://github.com/Profluent-Internships/MMDiff.
