Scientists are actively researching ways to make phage therapies more effective. This includes efforts to engineer phages to expand their host range and aid in bacterial defense system evasion. More at Molecule of the Month: pdb101.rcsb.org/motm/318

Theoretically, it shouldn't. All it predicts is: P(binding mode | it binds). Aka. assuming that it DOES bind, how confident are you about the predicted model of how/where it binds.

🚀 Excited to share our new work: Absolute Stability Predictor! 📊: forms.gle/4ZnXZSnTBvaykkAi9 Built the MGnify Stability Dataset (1.8M measurements) and developed stability prediction models, together with @grocklin, @KotaroTsuboyama, @sokrypton, and teams.

Catalogue entries for more than 100 antibodies sold by the research services and supply company Thermo Fisher Scientific contain images that have apparently been manipulated, according to a pair of science sleuths. go.nature.com/4wZqGEE

De novo design of DNA origami with a generative diffusion model 1 Generative SNUPI introduces diffusion-model-based inverse design for DNA origami: given a user-defined line-based target geometry, it generates base-pair-level 3D structures that are physically plausible, then automatically produces scaffold routing and staple sequences for experimental fabrication. 2 A key bottleneck in generative DNA origami—lack of large standardized structural datasets—is addressed by training on simulated equilibrium conformations: 450 wireframe 2HB designs (216 2D, 234 3D) whose base-pair coordinates were generated with the SNUPI multiscale model. 3 The generative core is a denoising diffusion probabilistic model operating on base-pair coordinates as a point-cloud-like representation, implemented with a scalable graph Transformer using random graph construction and SE(3)-aware geometric handling to avoid alignment during training. 4 To follow a target shape, the model uses conditional guidance based on optimal transport: classifier-style gradients derived from Wasserstein Distance (WD) bias diffusion sampling so generated structures converge toward the provided geometry, improving shape fidelity and routing success. 5 Across 100 diverse conditional generations (hundreds to ~15,000 base pairs), the WD to the target drops from widely varying initial values (192.69–2178.54 nm) to a low final average of 2.21 ± 1.32 nm, indicating consistent convergence to the intended geometry across sizes and complexities. 6 The pipeline goes beyond shape generation by integrating a deterministic routing program: generated geometries are converted into loop representations, spanning trees, scaffold routes, and staple sets (20–60 nt), with bond-length regularization (0.34 ± 0.05 nm), and export to atomic models via CNDO → oxDNA → PDB post-processing. 7 Generative SNUPI also embeds fast, in-workflow physics evaluation using SNUPI-based simulation to predict equilibrium shapes and flexibility (RMSD, RMSF) without heavy molecular dynamics; for 100 designs, many cluster around RMSD 2.49 ± 1.29 nm and average RMSF 1.72 ± 0.15 nm, enabling pre-experimental screening. 8 Experimental validation shows the simulation-guided design loop is actionable: a “Face 1” dog design predicted to have locally high RMSF folds with high monomer yield yet shows AFM distortion; adding edges to stiffen flexible regions (“Face 2”) improves AFM agreement and reduces RMSD (4.07 ± 0.48 nm to 3.45 ± 0.35 nm). 9 The framework supports functional free-form mechanics and assembly: auxetic metastructures (rotating triangle, re-entrant) are designed and experimentally transformed open→closed using junction gaps plus site-specific connectors, achieving mean enclosed-area reductions of 34.9% and 47.3%; modular dog face/body components with matched curved interfaces assemble into dimers with >65% yield across combinations. 💻Code: github.com/SSDL-SNU/Generati… 📜Paper: doi.org/10.1038/s41467-026-7… #DNANanotechnology #DNAOrigami #GenerativeAI #DiffusionModels #InverseDesign #ComputationalBiology #Biophysics #Nanorobotics #StructuralBiology #MachineLearning

Proteins are the machinery of life. Scientists have cataloged billions of protein sequences—but their biology is still mostly unknown. Today we're releasing a world model of protein biology: a scientific engine for prediction, design, and discovery that consists of ESMFold2, ESMC, and ESM Atlas. Together, they're helping to open up a new way for researchers to design proteins and speed up scientific discovery. Our mission is to cure or prevent disease. To do that, we need to accelerate science. That's why we're releasing all three openly. bit.ly/3PGf1dk

1/8 🚨 New preprint from the @SternbergLab & Jinek labs! CRISPR-associated transposases (CASTs) insert large DNA cargoes at precise genomic locations — no double-strand breaks needed.

What happens when you let frontier LLMs design proteins, and then synthesize and test them in a wet lab? We ran a protein design competition with @muni_bio where AI agents competed against humans to design molecules that bind TREM2, a key receptor linked to Alzheimer’s. Results: GPT 5.2 and Grok 4.1 both placed in the top 5, with molecules showing strong binding to TREM2 when tested in our lab.