We present ABACUS-R, a method based on deep learning for designing amino acid sequences that autonomously fold into a given target backbone. This method predicts the sidechain type of a central residue from its 3D local environment by using an encoder-decoder network trained with a multi-task learning strategy. The environmental features encoded by the network include the types but not the conformations of the sidechains of surrounding residues. This eliminates the needs for reconstructing and optimizing sidechain structures, and drastically simplifies the sequence design process. Thus iteratively applying the encoder-decoder to different central residues is able to produce self-consistent overall sequences for a target backbone. Extensive results of wet experiments, including five structures solved by X-ray crystallography, show that ABACUS-R outperforms state-of-the-art energy function-based de novo sequence design methods by significant margins in success rate and design precision. ABACUS-R constitutes a robust tool for wide applications in protein design and protein engineering.
In de novo deisgn of protein backbones with deep generative methods, the designability or physical plausibility of the generated backbones needs to be emphasized. Here we report SCUBA-D, a method using denoising diffusion with priors of non-zero means to transform a low quality initial backbone into a high quality backbone. SCUBA-D has been developed by gradually adding new components to a basic denoising diffusion module to improve the physical plausibility of the denoised backbone. It comprises a module that uese one-step denoising to generate prior backbones, followed by a high resolution denoising diffusion module, in which structure diffusion is assisted by the simultaneous diffusion of a language model representation of the amino acid sequence. To ensure high physical plausibility of the denoised output backbone, multiple generative adversarial network (GAN)-style discriminators are used to provide additional losses in training. We have computationally evaluated SCUBA-D by applying structure prediction to amino acid sequences designed on the denoised backbones. The results suggest that SCUBA-D can generate high quality backbones from initial backbones that contain noises of various types or magnitudes, such as initial backbones coarsely sketched to follow certain overall shapes, or initial backbones comprising well-defined functional sites connected by unknown scaffolding regions.
In the version of this article initially published, reference 64 (Liu, Y. et al. ABACUS-R: Rotamer-free protein sequence design based on deep learning and self-consistency (Code Ocean, 2022); https://doi.org/10.24433/CO.3351944.v1) was omitted from the reference list and the Code availability section. It has been restored to the HTML and PDF versions of the article.
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