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ProteinMPNN

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2

RESEARCH · 1 SOURCE · Google DeepMind

SynthID Bio: watermarking AI-designed proteins while fine‑tuning AlphaFold 3

The authors introduce SynthID Bio, a family of watermarking methods that embeds imperceptible signatures into protein sequences and predicted 3D coordinates so the watermark is verifiable on synthesized proteins; in wet‑lab tests on three targets (VEGF‑A, SARS‑CoV‑2 RBD, PD‑L1) watermarked protein binders retained hit rates, binding affinities, and sequence diversity comparable to unwatermarked designs. For folding, the approach fine‑tunes part of AlphaFold 3’s diffusion network to make predicted coordinates carry a detectable signature while preserving prediction accuracy and structural feature distributions.

8.0

RESEARCH · 1 SOURCE · arXiv cs.AI

Mirror-Score: calibrated, inference-only scoring and public benchmark for D-peptide/L-protein design (arXiv:2609.36057v1)

This paper introduces Mirror-Score, a calibrated inference-only scoring framework and a public benchmark (31 crystal complexes, 18 with known affinities) for heterochiral D-peptide/L-protein complexes, with code and data at the linked GitHub. The authors show that raw ProteinMPNN negative log-likelihood (NLL) is an unreliable affinity ranker across families (pooled Spearman rho = 0.19, with sign reversals between targets) and instead highlight Boltz-2 mirror-space cofolding confidence and interface pLDDT as more informative for family-matched ranking (e.g., structure-level leave-one-out Spearman rho = 0.90 on the viral-entry family), while noting limited cross-family transfer at current sample sizes.

6.0