Incredible

Below the heads, the molecule becomes highly flexible and density is lost. However, using #XLMS and #AlphaFold, we were able to model these regions and identify bend 1 (A1156) as an unwinding point, enabling a full-length structure. (8/11)

I've repeatedly heard computer people and laypeople say AlphaFold solved protein folding. It's maddening.

Paper of the day · 16.04.26
Evaluating zero-shot prediction of monomeric protein design success by AlphaFold, ESMFold, and ProteinMPNN.
De novo protein design has enabled the crea...
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#ProteinDesign #StructuralBiology #Bioinformatics #ProteinEngineering

La base de datos que revolucionó la biología estructural acaba de dar el salto definitivo: ya no solo predice proteínas solitarias, sino cómo se unen para formar verdaderos “equipos” funcionales.

Noticia en: www.ciencia1.com/2026/04/el-c...

#AlphaFold #IAenBiologia #Ciencia #DeepMind #NVIDIA

Very nice account: @alphafold.bsky.social both recognises the success and undeniable utility of AlphaFold for what it is, and boosts callouts of it being overhyped and of it being used as the AI bro's go-to example for hyping up "AI" in general.

Five stars for remaining grounded in reality.

AI helps create moving proteins Engineers used AI to design proteins that move, creating a route to more sophisticated molecular machines. In 2020, DeepMind’s AlphaFold essentially solved the “protein-folding problem,” establishing the 3D shape of protein molecules from the sequence of their constituent amino acids, and pushing forward material science and drug discovery. But the shape was static, and proteins’ roles are also determined by how they bend and flex: Enzyme proteins, for instance, catalyze reactions by forcing molecules together, and if they are too rigid or floppy, it won’t work. The new AI lets users describe a pattern of movement, and it will create a protein that can perform that movement; its creators hope it could lead to custom-made materials and more effective drugs.

go home everyone, AlphaFold solved the protein folding problem

(essentially is doing a lot of work there)

(this in a post from Semafor, which (appallingly) only links to a MIT press release about the relevant paper)

AlphaFold predates the current generation of AI. Effectiveness of cancer screenings still need to be evaluated.
And obviously I’m not talking about ML in general here, which should be apparent based on my original post.

💡AlphaFold's pLDDT scores can be visualised in Jalview as annotation tracks at the bottom - useful when comparing regions of high confidence across predicted protein structures in alignments.
▶️ For a guided tour of AlphaFold metrics in Jalview, watch: www.youtube.com/watch?v=WVCM...

Four images featuring Matt in his office, the film crew setting up a couple of shots and Max from Matt's lab

We’re delighted to share a ‘behind-the-scenes’ peek of SBS TV filming Matt Higgins @parasitematt.bsky.social and his group this week for a documentary on the Future of Medicine with AlphaFold. It will air in South Korea in early May 2026. Watch this space for more!

@mitsenkov.bsky.social , Senior Bioinformatician at AlphaFold DB, will be giving a keynote talk for EHA’s Computational Biology Training in Hematology programme, speaking with haematologists about structural biology and practical AlphaFold DB use-cases.

#AlphaFold #EHA2026

Run an MD simulation of any protein in the AlphaFold Protein Structure Database using AF-CALVADOS

Thanks to @sobuelow.bsky.social AF-CALVADOS is now on Colab

colab.research.google.com/github/KULL-...

Great presentation from @ulad-litvin.bsky.social @microbiologysociety.org annual conference: How far can AlphaFold go? Lessons from modelling virus–host protein interactions using AlphaFold3.

AlphaFold and Protein Folding: Not Dead Yet! The Frontier Is Conformational Ensembles Like the black knight in the classic Monty Python movie, grand scientific challenges such as protein folding are hard to finish off. Notably, AlphaFold is revolutionizing structural biology by bringin...

Even the one good example of 'AI' in research is overhyped: AlphaFold does not model folding! It predicts final static structures. It tells us nothing about protein dynamics, and we haven't actually fully solved the folding problem until we can model protein dynamics.

NASA’s Artemis II Crew Comes Home (Official Broadcast) YouTube video by NASA

Watch Live Now: NASA’s Artemis II Crew Comes Home (Official Broadcast)

www.youtube.com/live/nfhDuOH...

#science #artemis

dark blue background image with quote from Doeke Hekstra: "It is quite striking to watch ROCKET as it searches: The jumps it makes are unlike anything traditional methods would do.”

ROCKET is giving the Nobel Prize-winning AI-powered AlphaFold a serious upgrade in its ability to predict how proteins fold: https://bit.ly/4dUTZkG #science #biology #FlatironCCM

🍿

"Our results suggest that, at this stage, the main contribution of AI predictions is to provide quaternary structure models for experimentally identified PPIs." #alphafold

www.nature.com/articles/s41...

C'est quoi AlphaFold ? - @PolochSansGene-VODReplay #twitch #science #streamer #vulgarisation WIP

De la chiropratie pour protéines ou "C'est quoi AlphaFold ?" par @drpoloch.bsky.social
youtube.com/shorts/vKxMd... par @bestofvulgatwitch.bsky.social
#vulgarisation #sciences #biologie

C'est quoi AlphaFold ? - @PolochSansGene-VODReplay #twitch #science #streamer #vulgarisation WIP

@drpoloch.bsky.social nous explique le repliement des protéines et AlphaFold !

Retrouvez-la en stream sur Twitch :
www.twitch.tv/polochsansgene

Introducing ROCKET: An Upgrade to AlphaFold That Learns From Raw Experimental Data Introducing ROCKET: An Upgrade to AlphaFold That Learns From Raw Experimental Data on Simons Foundation

#FlatironCCM researchers have developed a new tool called ROCKET that extends the capabilities of AlphaFold by directly learning from raw experimental data: https://bit.ly/4dUTZkG #science #math

Designing Protein Binders Using the Generative Model Proteina-Complexa | NVIDIA Technical Blog Developing new protein-based therapies and catalysts involves the challenging task of designing protein binders, or proteins that bind to a target protein or small molecule. The search space for…

Worth sharing. Powerful.
Please share. #AIForHumanity

developer.nvidia.com/blog/designi...

Diagram titled ‘Protein–Drug Prediction Task Breakdown’ showing a hierarchical tree of modeling tasks. Top branches include apo monomer and protein–protein interactions, with sub-branches such as ensembles, induced-fit models, multimers, folded–unfolded complexes, and protein–ligand complexes with mutation effects on binding. Additional branches include potency, ADMET properties (e.g., solubility and blood–brain barrier penetration), and kinetics (e.g., koff). A legend indicates model maturity levels: solid lines (established), dashed lines (promising), and dotted lines (untapped).

More protein-ligand data are needed for AlphaFold-like models (& AI/ML) to enable prospective design!

Check out our piece in "Current Opinion in Structural Biology" – Equal parts a thank-you-letter to the PDB & summarizes how datasets will enable task-focused models!

Link: doi.org/10.1016/j.sb...

New paper from the Stern and Gronenborn labs on structure and evolution of aphid bicycle effector proteins.
We show how alphafold can generate accurate predictions for these rapidly evolving proteins and that these proteins are diverse in every possible way.

www.biorxiv.org/content/10.6...

AlphaFold as a prior: experimental structure determination conditioned on a pretrained neural network ROCKET improves experimental structure elucidation by integrating implicit structural knowledge from OpenFold, a trainable reimplementation of AlphaFold2, with X-ray crystallographic and cryo-electron...

AlphaFold as a prior: experimental structure determination conditioned on a pretrained neural network www.nature.com/articles/s41... rdcu.be/fbbiQ

Introducing ROCKET: An Upgrade to AlphaFold That Learns From Raw Experimental Data Introducing ROCKET: An Upgrade to AlphaFold That Learns From Raw Experimental Data on Simons Foundation

A new software tool called ROCKET is giving AlphaFold a serious upgrade in its ability to predict how proteins fold: www.simonsfoundation.org/2026/04/01/rocket-upgrad...

This series will guide you through accessing structural data programmatically with the Protein Data Bank in Europe (PDBe). The series also includes a final session on the AlphaFold Database (AFDB).

Webinar series organisers: Ajay Mishra, Dr. Sudakshina Ganguly, and Flaminia Zane at @ebi.embl.org.

Webinar series at EMBL-EBI. Accessing structural data programmatically with PDBe. 6 May - 17 June 2026.

Starting on 6 May, our next webinar series will focus on different levels of programmatic access at @pdbeurope.bsky.social and the #AlphaFold DB.

Registration for each webinar is free but essential: www.ebi.ac.uk/training/eve...

🧬🖥️📊

AI news of the day · 02.04.26
Introducing ROCKET: An Upgrade to AlphaFold That Learns From Raw Experimental Data - Simons Foundation
Introducing ROCKET: An Upgrade to AlphaFold That Learns From Raw Experimental Da...
Subnewsletter
#ProteinDesign #StructuralBiology #Bioinformatics #ProteinEngineering

Excellent work @alisiafadini.bsky.social et al. - exciting stuff!
#ROCKET #StructuralBiology #AlphaFold #ResearchSky #AcademicSky

We're a big step closer to automated determination of protein structures. The key? Having AlphaFold listen to experimental data. Great work, led by @alisiafadini.bsky.social and @minhuanli.bsky.social in an inspiring collaboration with @moalquraishi.bsky.social and @randyjread.bsky.social.