Nature｜给天然蛋白做减法与加法：Raygun 如何同时控制替换、插入和删除
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TL;DR - Raygun (Duke/UCSD, Nature 2026) is a template-guided protein sequence editor that compresses any-length ESM-2 residue embeddings into a fixed 50×1,280 probabilistic latent, letting users generate variants of a natural protein at an arbitrary target length — so substitutions, insertions and deletions happen jointly in one sampling step. It matters because most real protein engineering starts from a working protein that needs to be shortened or restructured, not designed de novo.
- Architecture: ESM-2 (650M) embeddings → T-Blocks (transformer + 1D conv) → Reduction to 50 pooled blocks treated as a template-specific Gaussian; noise scales the covariance (tested ~0.01–6, quality degrades past ~2.2), and a Repetition layer expands the latent to the user's target length. ~701M trainable params, trained self-supervised on only ~80k UniRef50 sequences; a separate embedding→sequence decoder is >99% accurate. Losses: embedding reconstruction + sequence cross-entropy + a size-invariance/replication term.
- Single-step sampling rather than multi-step diffusion: ~0.3 s per sequence on an A100, claimed ~100× faster than multi-step de novo generation — but that covers generation only, not the pLL/Pfam/AlphaFold3/TM-score screening cascade that follows.
- Results: within ±10% length change, median TM-score ≈0.78; mTOR shortened by 500+ residues kept ~0.7 TM-score, while heavily extended CCR1 fell to ~0.45. Across Pfam families spanning four SCOP classes, 50.65% of candidates retained the domain (~14.75% above matched random baselines), and known active/binding sites were preserved above the background sequence-retention rate without any explicit functional annotation.
- Caveats the authors state: indels are an emergent consequence of whole-sequence regeneration, so positional control is weak; the Gaussian latent is an approximation (Shapiro–Wilk rejects normality, median statistic ~0.96); and function retention still depends heavily on downstream screening, with extreme miniaturization and gain-of-function remaining unsolved. Wet-lab validation was done on fluorescent proteins, TurboID and EGF.
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Nature｜给天然蛋白做减法与加法：Raygun 如何同时控制替换、插入和删除
TL;DR - Raygun (Duke/UCSD, Nature 2026) is a template-guided protein sequence editor that compresses any-length ESM-2 residue embeddings into a fixed 50×1,280 probabilistic latent, letting users generate variants of a natural protein at an arbitrary target length — so substitutions, insertions and deletions happen jointly in one sampling step. It matters because most real protein engineering starts from a working protein that needs to be shortened or restructured, not designed de novo.
- Architecture: ESM-2 (650M) embeddings → T-Blocks (transformer + 1D conv) → Reduction to 50 pooled blocks treated as a template-specific Gaussian; noise scales the covariance (tested ~0.01–6, quality degrades past ~2.2), and a Repetition layer expands the latent to the user's target length. ~701M trainable params, trained self-supervised on only ~80k UniRef50 sequences; a separate embedding→sequence decoder is >99% accurate. Losses: embedding reconstruction + sequence cross-entropy + a size-invariance/replication term.
- Single-step sampling rather than multi-step diffusion: ~0.3 s per sequence on an A100, claimed ~100× faster than multi-step de novo generation — but that covers generation only, not the pLL/Pfam/AlphaFold3/TM-score screening cascade that follows.
- Results: within ±10% length change, median TM-score ≈0.78; mTOR shortened by 500+ residues kept ~0.7 TM-score, while heavily extended CCR1 fell to ~0.45. Across Pfam families spanning four SCOP classes, 50.65% of candidates retained the domain (~14.75% above matched random baselines), and known active/binding sites were preserved above the background sequence-retention rate without any explicit functional annotation.
- Caveats the authors state: indels are an emergent consequence of whole-sequence regeneration, so positional control is weak; the Gaussian latent is an approximation (Shapiro–Wilk rejects normality, median statistic ~0.96); and function retention still depends heavily on downstream screening, with extreme miniaturization and gain-of-function remaining unsolved. Wet-lab validation was done on fluorescent proteins, TurboID and EGF.