Kyle Tretina
@allthingsapx.bsky.social
690 followers 690 following 40 posts
Product Marketing Lead @NVIDIA | PhD @UMBaltimore | omics, immuno/micro, AI/ML | 🇺🇸🇸🇰 | Posts are my own views, not those of my employer.
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Reposted by Kyle Tretina
martinsteinegger.bsky.social
MMseqs2-GPU sets new standards in single query search speed, allows near instant search of big databases, scales to multiple GPUs and is fast beyond VRAM. It enables ColabFold MSA generation in seconds and sub-second Foldseek search against AFDB50. 1/n
📄 www.nature.com/articles/s41...
💿 mmseqs.com
GPU-accelerated homology search with MMseqs2 - Nature Methods
Graphics processing unit-accelerated MMseqs2 offers tremendous speedups for homology retrieval from metagenomic databases, query-centered multiple sequence alignment generation for structure predictio...
www.nature.com
allthingsapx.bsky.social
Preprint:
Highly efficient protein structure prediction on NVIDIA RTX Blackwell and Grace-Hopper
nvda.ws/4n4xzz9

Visit the NVIDIA Digital Biology Labs website to find more information like this:
t.co/R9ufEZrGEA
nvda.ws
allthingsapx.bsky.social
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).
allthingsapx.bsky.social
Does anyone here care about biomolecular AI? Who should I follow?
allthingsapx.bsky.social
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
allthingsapx.bsky.social
🧬 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 🔥
allthingsapx.bsky.social
I'm at ICML 2025!

DM me if you want to chat.

@icmlconf.bsky.social #ICML2025 #icml25 #BioNeMo
allthingsapx.bsky.social
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.
allthingsapx.bsky.social
MMseqs2-GPU is available as a downloadable NVIDIA NIM microservice (MSA-Search)!
Reposted by Kyle Tretina
karstenkreis.bsky.social
📢📢 "Proteina: Scaling Flow-based Protein Structure Generative Models"

#ICLR2025 (Oral Presentation)

🔥 Project page: research.nvidia.com/labs/genair/...
📜 Paper: arxiv.org/abs/2503.00710
🛠️ Code and weights: github.com/NVIDIA-Digit...

🧵Details in thread...

(1/n)
allthingsapx.bsky.social
I’m @neurips24! Let’s chat😁
Reposted by Kyle Tretina
lindorfflarsen.bsky.social
"Are there at least 3 simulations per simulation condition with statistical analysis?"

From @commsbio.bsky.social's "Reliability and reproducibility checklist for molecular dynamics simulations" (doi.org/10.1038/s420...)

IMO the number 3 is meaningless and could equally well be 1 or 1000
Reposted by Kyle Tretina
franknoe.bsky.social
Super excited to preprint our work on developing a Biomolecular Emulator (BioEmu): Scalable emulation of protein equilibrium ensembles with generative deep learning from @msftresearch.bsky.social ch AI for Science.

www.biorxiv.org/content/10.1...
allthingsapx.bsky.social
DiffDock was the first time a traditional drug discovery simulation task was represented as a generative AI task AFAIK.

Recent DiffDock versions + other DL models are advancing rapidly + solving real problems for researchers.

Let's have a balanced conversation about it.
arxiv.org/abs/2412.02889
Deep-Learning Based Docking Methods: Fair Comparisons to Conventional Docking Workflows
The diffusion learning method, DiffDock, for docking small-molecule ligands into protein binding sites was recently introduced. Results included comparisons to more conventional docking approaches, wi...
arxiv.org
Reposted by Kyle Tretina
martinpacesa.bsky.social
#CASP16 results are in! Template-based VFold seems to be lead method for nucleic acid structure prediction! AlphaFold2 and 3 still seem to be best methods for protein monomer and complex prediction.
allthingsapx.bsky.social
Please add me to this starter pack. I'd appreciate it!
allthingsapx.bsky.social
Please add me to this starter pack. I'd appreciate it.
allthingsapx.bsky.social
Please add me to this starter pack. I'd appreciate it.
allthingsapx.bsky.social
Please add me to this starter pack. I'd appreciate it.
allthingsapx.bsky.social
Please add me to your starter packs. I'd appreciate it. Nice to meet you!