Protein Language Model Supervised Precise and Efficient Protein Backbone Design Method

ABSTRACT Proteins are essential macromolecules that play crucial roles in nearly every type of biological function. Most of the protein functions are determined by their position topologies, indicating that new functional proteins might be generated by designing their tertiary structures initially. Over the past two decades, numerous energy-based and machine learning algorithms have been proposed for de novo protein design. However, optimizing these models to achieve a balanced performance among three critical aspects, namely sample fidelity, diversity, and generation speed, remains a challenging task on account of the constrained residue connections and hydrophobic interactions. Here we used the protein language model (pLM) as supervision to develop two complementary deep learning algorithms, Generative Protein Design by Language Model-Inpainting (GPDL-Inpainting) and GPDL-Hallucination, that can sufficiently represent and generate the protein structures and sequences. We compared the models with the state-of-the-art protein design models (RFdiffusion, RF Inpainting, and RF Halluciantion) and demonstrated that our methods can generate more designable structures with more diversity and higher computation efficiency. We also explored the possibility of the combination of hallucination and inpainting which might further improve the model efficiency and diversity. The source code of GPDL is available at https://github.com/sirius777coder/GPDL .