GAMA Miguel Angel

I recorded ~8h introducing the main algorithms for protein design: from classical approaches to protein language models, AlphaFold, ESMFold, MPNN, diffusion models and more :)
youtu.be/wKUYtAt87d4T...

Slides
drive.google.com/file/d/1EPLj...

English is available only via auto-translated subtitles

I’ve recorded ~8h explaining the architectures of AlphaFold, AF2 & AF3, as well as the context needed to understand their development, applications and limitations :)
youtu.be/_jDRr5BcTaY

Slides
drive.google.com/file/d/1i4QE...

English is available only via auto-translated subtitles

2/2 I’ve reviewed many courses, yet few give evolution the importance it deserves. They acknowledge it, but rarely go beyond algorithms like AlphaFold. Understanding evolution helps us understand how our models are biased and how to mitigate those biases.

The 7th lecture is available on YouTube :) We will review how proteins emerge and diversify throughout evolution, considering mutations and molecular interactions
youtu.be/qaypRS8SX5M

Slides
drive.google.com/file/d/1BfQd...

English is available only via auto-translated subtitles

Resurrected nitrogenases recapitulate canonical N-isotope biosignatures over two billion years - Nature Communications The study shows that nitrogenase enzymes have maintained stable isotope signatures over billions of years, revealing how ancient microbes shaped Earth’s nitrogen cycle and offering a new experimental ...

Our new paper, out today! We resurrected ancient nitrogenases first used by life on Earth 3 billion years ago. We combined synthetic biology and geology & validated their chemical #biosignature in rocks that helps reveal ancient life on Earth!(and beyond!)

Link: www.nature.com/articles/s41...

💪 NEW VIDEO:
Flying over the A-band of an atomic-scale model of a vertebrate muscle sarcomere. Let's explore the molecular mechanics that make your muscles work.

Rendered using @bradyajohnston.bsky.social 's molecular nodes

Model based on the incredible work of the @raunser-lab.bsky.social lab

The 6th lecture is now available on YouTube :) We’ll review how proteins adopt their 3D shape, how they perform their functions and how their activity is regulated
youtu.be/cZs8XtVYa5A

Slides
drive.google.com/file/d/1TpPj...

English is available only via auto-translated subtitles

The fifth lecture of the course is now available on YouTube :) We’ll review amino acid chemistry and how we organize and classify proteins
youtu.be/gE6qXwpBP_s

Slides
drive.google.com/file/d/1F99V...

For now, the English version is only available through the automatic translation of the subtitles

The fourth lecture of the course is now available on YouTube :) We will review how Transformers and modern LLMs work
youtu.be/vUpb6O6T2yQ

Slides
drive.google.com/file/d/1y2Vj...

For now, the English version is only available through the automatic translation of the subtitles.

Thanks!

Where is the preprint? 🧐

The third lecture of the course is now available on YouTube :) We will review how neural networks work.
youtu.be/pAgL7NsCUMU

Slides
drive.google.com/file/d/1cazt...

For now, the English version is only available through the automatic translation of the subtitles.

The second lecture of the course is now available on YouTube :) We will review what AI is, its subfields and how to train a model.
youtu.be/Xx80O85-5rI

Slides
drive.google.com/file/d/1i-Jo...

For now, the English version is only available through the automatic translation of the subtitles.

The first lecture of the course is now available on YouTube :)
youtu.be/uMkZzKbnoJI

Slides
drive.google.com/file/d/1uDwe...

For now, the English version is only available through the automatic translation of the subtitles.

Sorry, i forgot to add the webpage of the course 😅

miangoaren.github.io/teaching/pro...

By the way, if anyone has recommendations on how to translate the classes (e.g., the audio) into English, they’re very welcome. My YouTube account doesn’t have the stats required to use automatic dubbing 😢

GitHub - miangoar/AI-driven-protein-design: Resources for learning AI-driven protein design Resources for learning AI-driven protein design. Contribute to miangoar/AI-driven-protein-design development by creating an account on GitHub.

3/3 I also created a repository with +300 tools, +70 databases and +130 courses related to protein science, bioinformatics and data science, aimed at facilitating learning. Once all classes have been published, the slides will be available for download :)

github.com/miangoar/AI-...

2/3 The course includes +800 freely available slides, and starting next monday, I will publish one video per day. For example, the AlphaFold lecture is ~7.4 hours long and includes 148 slides, in which I cover the architectures of AF1, AF2 and AF3 as well as their applications.

🧵1/3 I created this free 37-hour course, distributed across 10 lectures, to introduce AI-based protein design. For more information about the course and its specific topics, please visit the official course page:

Biomedical and life science articles by female researchers spend longer under review Women are underrepresented in academia, especially in STEMM fields, at top institutions, and in senior positions. This study analyzes millions of biomedical and life science articles, revealing that f...

Cost of being female lead/corresponding author in biomedical sciences: "[T]he median amount of time spent under review is 7.4%–14.6% longer for female-authored articles than for male-authored articles" even in disciplines where women well-represented. #AcademicSky

journals.plos.org/plosbiology/...

😮 OMG! Congratulations Milot !

Mirdita Lab - Laboratory for Computational Biology & Molecular Machine Learning Mirdita Lab builds scalable bioinformatics methods.

My time in @martinsteinegger.bsky.social's group is ending, but I’m staying in Korea to build a lab at Sungkyunkwan University School of Medicine. If you or someone you know is interested in molecular machine learning and open-source bioinformatics, please reach out. I am hiring!
mirdita.org

www.biorxiv.org/content/10.6... most proteins are multi domain but our understanding of how architectures evolve is limited - we mapped 40K chitinases and show a stepwise “grammar” of domain gain/loss and localisation changes that predicts ecological strategy and physiological function.

I just want to create hype and say that I made a 10-class course to introduce people to AI-driven protein design. It’s around 750 slides and will be freely available for anyone who wants to use them and, most importantly, improve them. Stay tuned :)

Even the five most abundant folds account for ~31% of all domains in the PDB. For more information on these superfolds check out

Protein superfamilies and domain superfolds
pubmed.ncbi.nlm.nih.gov/7990952/

1/2 If you think that the Protein Data Bank is a representative DB, it is not. The data is highly biased. The CATH suggests that there are 1,472 protein folds, yet among the ~600k domains present in the PDB, ~39% are represented by the 10 most abundant folds (AKA superfolds).

Thrilled to introduce TEDLH, a library of profile HMMs built from TED’s structure-derived domain annotations of the AFDB.
Explore 765K HMMs capturing CATH superfamily diversity.

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

🕷️Spiders in your blood? Don’t worry, these are protein molecules of C4BP protein, part of your immune system. Just like real spiders, they grab prey with their “legs.” Watch them in action! Intrigued, check the paper of @tkadava.bsky.social doi.org/10.1016/j.mc... @bijvoet-centre.bsky.social

YouTube video by Schmidt Ocean The Origins of Complex Life | Searching for the Asgards

youtu.be/Jy_boPdY0zo?...

Proteins are dynamic structures, but structural biology often shows them as static snapshots. Inspired by long-exposure photography and generative art, I built ProteinCHAOS, an artistic tool inspired by molecular dynamics to capture protein flexibility over time, much like long-exposure images.