Kyle Tretina

Kyle Tretina

@allthingsapx.bsky.social

Product Marketing Lead @NVIDIA | PhD @UMBaltimore | omics, immuno/micro, AI/ML | 🇺🇸🇸🇰 | Posts are my own views, not those of my employer.

742 followers694 following40 posts

Kyle Tretina reposted

Nate Corley
@ncorley.bsky.social

1/n Crossover episode! We just released RFD4-Proteina, our latest protein design model - and the result of a year-long collaboration between the Baker Lab and NVIDIA GenAIR (the name says it all…). I hope you’ll agree - the results are astonishing. 🌐 Website: rfd4-proteina.org

Kyle Tretina reposted

Martin Steinegger 🇺🇦
@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

nature.comGPU-accelerated homology search with MMseqs2 - Nature MethodsGraphics processing unit-accelerated MMseqs2 offers tremendous speedups for homology retrieval from metagenomic databases, query-centered multiple sequence alignment generation for structure predictio...
Kyle Tretina
@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).

Kyle Tretina
@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 🔥

Kyle Tretina
@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

Kyle Tretina
@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.

Kyle Tretina
@allthingsapx.bsky.social

To learn more about the impact of MMseqs2-GPU and test it out (for free), here's a blog I wrote: developer.nvidia.com/blog/acceler...

developer.nvidia.comAccelerated Sequence Alignment for Protein Science with MMseqs2-GPU and NVIDIA NIM | NVIDIA Technical BlogProtein sequence alignment—comparing protein sequences for similarities—is fundamental to modern biology and medicine. It illuminates gene functions by reconstructing evolutionary relationships…
Kyle Tretina
@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

arxiv.orgDeep-Learning Based Docking Methods: Fair Comparisons to Conventional Docking WorkflowsThe 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...

Kyle Tretina reposted

Martin Pacesa
@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.

Kyle Tretina
@allthingsapx.bsky.social

If you're building AI models for drug discovery, you should check out the newly open-source #BioNeMo Framework: code: github.com/NVIDIA/bione... paper: arxiv.org/abs/2411.10548 docs: docs.nvidia.com/bionemo-fram... explainer: t.co/7MOamSChGN

github.comGitHub - NVIDIA/bionemo-framework: BioNeMo Framework: For building and adapting AI models in drug discovery at scaleBioNeMo Framework: For building and adapting AI models in drug discovery at scale - NVIDIA/bionemo-framework

Kyle Tretina reposted

Anshul Kundaje
@anshulkundaje.bsky.social

developer.nvidia.com/blog/boost-a... Boost Alphafold2 Protein Structure Prediction with GPU-Accelerated MMseqs2 Nice improvements in speed

developer.nvidia.comBoost Alphafold2 Protein Structure Prediction with GPU-Accelerated MMseqs2 | NVIDIA Technical BlogThe ability to compare the sequences of multiple related proteins is a foundational task for many life science researchers. This is often done in the form of a multiple sequence alignment (MSA)…
Kyle Tretina
@allthingsapx.bsky.social

Cheers to some exceptional researchers Christian Dallago, Alejandro Chacon, Bertil Schmidt, Milot Mirdita, Martin Steinegger and Felix Kallenborn and others! Read more details in the blog at the top of this thread, and the preprint will be on BioRxiv soon. 6/

Kyle Tretina
@allthingsapx.bsky.social

This acceleration is a game-changer for drug discovery. It enables scientists to perform many workflows faster, including those for target ID, protein function studies, lead optimization, drug resistance, precision medicine, vaccine design research, and many others. 3/🧵

Kyle Tretina
@allthingsapx.bsky.social

Accelerated computing is revolutionizing protein structure prediction, starting with lightning-fast multiple sequence alignments. Read more about #AlphaFold and #MMseqs2GPU: developer.nvidia.com/blog/boost-a...

developer.nvidia.comBoost Alphafold2 Protein Structure Prediction with GPU-Accelerated MMseqs2 | NVIDIA Technical BlogThe ability to compare the sequences of multiple related proteins is a foundational task for many life science researchers. This is often done in the form of a multiple sequence alignment (MSA)…

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