Matteo Cagiada

🇮🇹, Computational biophysicist, Novo Nordisk Foundation Postdoc fellow at @UniofOxford / @UCPH_Research

[Matteo Cagiada @CagiadaMatteo]

My first full contribution from my time in @OPIGlets is out!
We present created FlAbDab and FTCRDab: two large-scale, MD datasets to study flexibility in immune receptors. Find all the details and links to the databases in the preprint manuscript:
https://t.co/B7Bf4xQFGX

[Kresten Lindorff-Larsen @LindorffLarsen]

AF2χ: Predicting protein side-chain rotamer distributions with AlphaFold2

[Matteo Cagiada @CagiadaMatteo]

If you want to hear more about this project, you can join me on June 26 at the next 39th Annual Symposium of The Protein Society (San Francisco), where I will present the results of this work!

[Matteo Cagiada @CagiadaMatteo]

I am happy to share that our paper "Predicting absolute protein folding stability using generative models"(https://t.co/xg72d23zf0) has been awarded the Protein Science BEST PAPER 2024 by @ProteinSociety !

[proteinsociety @ProteinSociety]

Congratulations to the winners of the 2024 Protein Science Best Paper: Jessica Lusty Beech, from Montana State University, USA and Matteo Cagiada from the University of Copenhagen, Denmark.
Protein Science - Wiley Online Library

[Nature Computational Science @NatComputSci]

📢Out now! Charlotte M. Deane and colleagues discuss the need for a renewed focus on data and validation for advancing the potential of machine learning in small-molecule drug discovery. @OPIGlets https://t.co/qSS9J7EnUy

➡️https://t.co/lwxHhbo2h9

[The Nobel Prize @NobelPrize]

The 2024 #NobelPrize laureates in chemistry Demis Hassabis and John Jumper have successfully utilised artificial intelligence to predict the structure of almost all known proteins.

In 2020, Hassabis and Jumper presented an AI model called AlphaFold2. With its help, they have been able to predict the structure of virtually all the 200 million proteins that researchers have identified. Since their breakthrough, AlphaFold2 has been used by more than two million people from 190 countries. Among a myriad of scientific applications, researchers can now better understand antibiotic resistance and create images of enzymes that can decompose plastic.

Read more about their story: https://t.co/nWxcZs6wqC

[The Nobel Prize @NobelPrize]

BREAKING NEWS
The Royal Swedish Academy of Sciences has decided to award the 2024 #NobelPrize in Chemistry with one half to David Baker “for computational protein design” and the other half jointly to Demis Hassabis and John M. Jumper “for protein structure prediction.”

[Brotman Baty @BrotmanBaty]

MARK YOUR CALENDAR: The next Variant Effects Seminar Series is Tuesday, October 1, 9 to 10 am (Pacific). @saorisakaue @harvardmed & @Priyank67495046 @UCSF will present.
@varianteffects

https://t.co/mJT10Gqb3F

[Kresten Lindorff-Larsen @LindorffLarsen]

New preprint w @TKSchulze who analysed cellular abundance (VAMP-seq) data for ~32,000 variants of six proteins

We find that much of the variation can be explained and predicted by a burial-dependent substitution matrix

Lots more goodies in the paper

https://t.co/axsUhazLZ8

[Oxford Protein Informatics Group (OPIG) @OPIGlets]

New research led by @LucyVost has just been released on @biorxiv:

"Improving Structural Plausibility in 3D Molecule Generation via Property-Conditioned Training with Distorted Molecules"

https://t.co/H38stqbn0i

[Matteo Cagiada @CagiadaMatteo]

@CovinoLab Thank you Roberto!

[Matteo Cagiada @CagiadaMatteo]

What an amazing day at yesterday UCPH SCIENCE PhD graduation ceremony! 🎓
And I’m also humbled and excited to announce that I’ve been honored with the PhD Award of the year at SCIENCE 2024.

[Matteo Cagiada @CagiadaMatteo]

Thank you to everyone who has supported me along the way, especially my supervisor @LindorffLarsen, my friends and colleagues at UCPH!

[Tanishq Mathew Abraham, Ph.D. @iScienceLuvr]

Technical Report of HelixFold3 for Biomolecular Structure Prediction

The PaddleHelix research team at Baidu have released their AlphaFold3 replication under an open-source noncommercial license. Performance approaches that of AlphaFold3.

abs: https://t.co/hCleAXSDSX
website: https://t.co/lHMu3oc5jw
github: https://t.co/jxZr7fMH0Z

[Oxford Protein Informatics Group (OPIG) @OPIGlets]

OPIG DPhil student @GemmaLGordon led work to build and analyse "PLAbDab-nano: a database of camelid and shark nanobodies from patents and the literature". Just released on bioRxiv and available as an OPIG webapp:

Preprint: https://t.co/OzbefvcyKB
Webapp: https://t.co/5yvBJ4IRyn

[Alex Rives @alexrives]

We have trained ESM3 and we're excited to introduce EvolutionaryScale.

ESM3 is a generative language model for programming biology. In experiments, we found ESM3 can simulate 500M years of evolution to generate new fluorescent proteins.

Read more: https://t.co/iAC3lkj0iV

[Matteo Cagiada @CagiadaMatteo]

Exciting news🤩! (code and weights are available also!)

[Mohammed AlQuraishi @MoAlQuraishi]

ESM3 is out from EvolutionaryScale! 98B params (~GPT3 scale). Multimodal over sequence, structure, and function with cool design applications. Trained on variable masking ratios and decodes proteins iteratively. Some work on alignment too. Looks exciting! https://t.co/T6yvJf4xel

[Matteo Cagiada @CagiadaMatteo]

@LindorffLarsen Congrats Kresten!! 🥳