Extended Data Fig. 5: Protein backbone generation without condition or with biased secondary structure (SS) distributions. | Nature Methods

Extended Data Fig. 5: Protein backbone generation without condition or with biased secondary structure (SS) distributions.

From: De novo protein design with a denoising diffusion network independent of pretrained structure prediction models

Extended Data Fig. 5

(a) The distribution of the mutual TM-scores between the set of backbones unconditionally generated by SCUBA-D. (b) Histograms of the proportions of residues in the α helix state (upper panel) and of residues in the β strand state (lower panel) for the set of unconditionally generated backbones with SCUBA-D (blue) and for a set of natural protein structures (salmon), which comprised PDB structures of resolutions higher than 2.0 Å, of mutual sequence identities below 40%, and of 100 to 500 residues in length. The proportions were calculated on individual backbones. The histograms represent the normalized frequencies of backbones with proportions in specific bins. (c) Scattering plot of the recovery rates of the input secondary structure (SS) states versus the scRMSDs for the set of 225 backbones generated using the 25 input structures. Each input structure was composed according to the SS distribution of a natural backbone. The gray box indicates the region with scRMSD < 2.0 Å and SS recovery rate > 70%. (d) The scRMSDs of the backbones generated with biased SS distributions and the SS recovery rate. For each SS distribution, 9 backbones were generated and evaluated, one data point in the plots corresponding to one designed backbone. The results for three different classes of SS distributions (all-α, all-β, and mixed αβ) are displayed in different plots. Within each plot, results biased towards the same SS distribution are numbered the same and displayed in the same column. Results for different SS distributions were arranged from left to right in an ascending order of the corresponding chain lengths.

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