Near-real-time protein structures change science:

It means:
→ Next-gen protein AI data waves
→ Interactive protein design loops (DMTA in hours)
→ Proteome-scale insights with fewer resources

It means the bottleneck doesn't have to be compute.

It's close (preprint below).

It looks like each per‑residue latent captures almost exclusively local information

When the authors perturb the latent of a single residue, only that residue’s reconstruction quality changes, while others stay intact

🧬 Introducing La‑Proteina:

a partially‑latent flow‑matching model that co‑generates sequence + all‑atom structure for proteins up to 800 aa 🧬

Side‑chains live in latents, backbone explicit → 75 % codesign & SOTA motif scaffolds 🔥

Boltz-2 just dropped: open-source AI that predicts both protein complex folds ✚ binding affinities in one shot 🚀

This is a win for protein AI, but let's not forget MSAs, the bioinformatics backbone many structure models lean on.

MMseqs2-GPU is available as a downloadable NVIDIA NIM microservice (MSA-Search)!

Friends,

Real-time, accurate protein structure prediction has never felt so imminent.

Code: github.com/soedinglab/m...
Publication: www.biorxiv.org/content/10.1...
Blog: developer.nvidia.com/blog/boost-a...
Press: blogs.nvidia.com/blog/japan-s...