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#alphafold

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Наука & Инновации

💥 Революция в биохимии! ИИ научился предсказывать и даже создавать белки 🧬. AlphaFold и Rosetta меняют всё: от медицины до экологии. Это начало новой эры в науке! 🔬🤖 #AlphaFold #ИИ #биохимия #наука #технологии #NobelPrize #AI #биотех #proteinfolding #DeepMind

Подробнее: scientia-et-innovatio.blogspot

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Keiran Rowell

youtube.com/watch?v=P_fHJIYENd

Veritasium just released a great video of #ProteinFolding algorithms and #Alphafold as party of the story.

Veritasium releases some of the best science videos around: accessible, clear and accurate. It's a great service to communicating this breakthrough to everyone.

#ScientificComputing #Bioinformatics #AI #MachineLearning #ScienceCommunication #ProteinDesign

P.S. shame about the anthropomorphic hype at the end

YouTubeWhat if all the world's biggest problems have the same solution?By Veritasium
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davidak

@malteengeler ich würde sagen #KI lässt sich auch in einer besseren Welt ohne #Kapitalismus sinnvoll einsetzen.

Gerade wenn es nicht nur um #GenAI geht, sondern auch um die kürzlichen Durchbrüche bei Reasoning (#OpenAI o3), sehe ich riesiges Potential für z.B. die Wissenschaft. Siehe #AlphaFold

Allerdings schaffen wir es nicht in einem kapitalistischen System die Organisationen mit dem Gemeinwohl zu alignen, geschweige denn deren Modelle und wir enden in einer Dystopie (mehr als eh schon 💀).

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Dave nλ=2dsinθ

"AI protein-prediction tool AlphaFold3 is now open source" ... almost.

"Anyone can now download the AlphaFold3 software code and use it non-commercially. But for now, only scientists with an academic affiliation can access the training weights on request"

Stop gatekeeping you priiiiickssss.

#StructuralBiology @strucbio #Alphafold

nature.com/articles/d41586-024

www.nature.comAI protein-prediction tool AlphaFold3 is now open sourceThe code underlying the Nobel-prize-winning tool for modelling protein structures can now be downloaded by academics.
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The Japan Times

What’s incredible about the work done across both Baker’s lab and Google DeepMind is the pace of progress. We’re getting closer to a place where the drug-discovery process is more efficient and successful. That’s something to celebrate. japantimes.co.jp/commentary/20 #commentary #worldnews #alphafold #deepmind #google #nobelprize #drugs #medicine #healthcare #ai #chemistry

The Japan Times · Google's DeepMind Nobel Prize showcases AI’s medical potentialBy Lisa Jarvis
Continued thread
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Victoria Stuart 🇨🇦 🏳️‍⚧️

2024 Nobel Prize - Chemistry: computational chemistry
nobelprize.org/prizes/chemistr
* David Baker, UWash: computational protein design
* Demis Hassabis, John Jumper, DeepMind: protein structure prediction

Chemistry Nobel to dev. of AlphaFold AI that predicts protein structures
nature.com/articles/d41586-024

* old.reddit.com/r/MachineLearni
* technologyreview.com/2024/10/0
* news.ycombinator.com/item?id=4

NobelPrize.orgThe Nobel Prize in Chemistry 2024The Nobel Prize in Chemistry 2024 was divided, one half awarded to David Baker "for computational protein design", the other half jointly to Demis Hassabis and John Jumper "for protein structure prediction"
#2024Nobel#ComputationalBiology#ProteinDesign
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RiffReporter

Der #Nobelpreis für #Chemie geht in diesem Jahr an drei Proteinforscher. Zwei davon haben ein #KI-Modell entwickelt, das die 3D-Struktur von Proteinen vorhersagt. Über die Software #Alphafold und wie sie die #Biologie revolutioniert: Christian J. Meier #Archiv riffreporter.de/de/technik/alp

RiffReporter · Künstliche Intelligenz macht die Form von Proteinen sichtbarBy Dr. Christian J. Meier
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Matt Hodgkinson

Juan Mateos-Garcia of Google DeepMind wants equitable use of their tools like AlphaFold. They solved protein structure prediction in 2020 and this is helpful for drug discovery, but researchers in LMIC have barriers to adoption.

Used OpenAlex & PDB to study 20.6K papers citing AlphaFold. LMIC researchers are underrepresented, but study of diseases affecting LMICs is overrepresented - esp. work on natural products.

Equitable adoption There is evidence of an adoption gap LMICs and LMLICs seem underrepresented in AlphaFold vs. baseline Also visible when we focus on PDB research
Equitable impact AF disease research more focused on LMIC diseases 20% AF papers focus on diseases (vs. 34% in baseline) Share of AF in diseases with big LMIC impact 36% higher than baseline
User interviews We interviewed 10 LMIC AlphaFold users in India, Pakistan, South Africa, Tunisia, Ghana and Brazil. They focus on diseases with big LMIC impacts including Malaria, infectious diseases, Antimicrobial Resistance. AlphaFold impacts: Lowers barriers to entry Accelerates research Attracts talent to structural biology Barriers to adoption: Accessibility (IT infrastructure and skills) Lack of information about AF/its relevance Weak research ecosystem "So it reduces reliance on costly experiments too. Often in a laboratory setting, there's a lot of troubleshooting, a lot of experiments that fail. And using an online capability such as AlphaFold for this deep understanding would definitely be beneficial in reducing the reliance on costly experiments" "Indian Universities are not teaching AlphaFold because it's a very new concept and they spend the whole curriculum on teaching the basics." "I think one important factor is to make sure that people understand that AlphaFold can be like used like a database, which was not clear for a lot of people"
#ICSSI#ScienceofScience#AItools
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Thiago Carvalho

'Determining how 11,269 enzyme structures catalysing 361 different metabolic reactions evolved over 400 million years alongside their molecular functions, we report that metabolism has shaped the structural evolution of enzymes at different levels: the organisms overall metabolism; the topological organisation of the metabolic network; and each enzymes molecular properties.'
#Preprint #Evolution #StructuralBiology #AlphaFold
biorxiv.org/content/10.1101/20

bioRxiv · The Role of Metabolism in Shaping Enzyme Structures Over 400 Million Years of EvolutionThe functions of cells and proteins depend on their biochemical microenvironment. In order to understand how biochemical constraints shaped protein structural evolution, we coupled the extensive genetic and metabolic data from the Saccharomycotina subphylum with the capability of AlphaFold2 to systematically predict structures from sequence. Determining how 11,269 enzyme structures catalysing 361 different metabolic reactions evolved over 400 million years alongside their molecular functions, we report that metabolism has shaped the structural evolution of enzymes at different levels: the organisms overall metabolism; the topological organisation of the metabolic network; and each enzymes molecular properties. For example, structural evolution depends on each enzymes reaction mechanism, on the variability rather than the amount of metabolic flux, and on biosynthetic cost. Evolutionary cost-optimization is stronger on highly abundant enzymes and acts differently on different structural domains, with the exception of small-molecule binding sites, which are prioritised over other structural domains and lack cost-optimisation. Finally, while enzyme surfaces are less constrained, surface residues can also be exposed to positive selection for the co-evolution of protein-protein interaction sites. Accessing AlphaFolds power to predict protein structures systematically and across species barriers, facilitating the integration of protein structures with functional genomics, we were thus able to map biological constraints which shape protein structural evolution at scale and over long timelines. ### Competing Interest Statement MR and AZ are founders and shareholders of Eliptica Ltd. JLS is an advisor for ForensisGroup Inc. AR is a scientific consultant for LifeMine Therapeutics, Inc.
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Dave nλ=2dsinθ

Open letter to Nature editors complaining about the lack of code availability for AF3 (Publishing code is normally a prerequisite for publishing in Nature)

(Yes, I am fully aware of the irony of using a Google form to do this - not my idea, just sharing.)

Boosts Welcome!

#StructuralBiology #Alphafold #Crystallography #CryoEM #NMR @strucbio

docs.google.com/forms/d/e/1FAI

Google DocsLetter to the Editor: AlphaFold3 We are submitting the follow as a Letter to the Editor and will post the text immediately on Zenodo. If you would like to endorse to this letter, please fill out the form below. Authors: Stephanie A. Wankowicz, UCSF Pedro Beltrao, ETH Benjamin Cravatt, Scripps Roland Dunbrack, FCCC Anthony Gitter, UW Madison Kresten Lindorff-Larsen, Copenhagen Sergey Ovchinnikov, MIT Nicholas Polizzi, DFCI/HMS Brian K. Shoichet, UCSF James S. Fraser, UCSF The publication of AlphaFold2 was a breakthrough moment for structural biology. Its impact has been far-ranging. Structure predictions for individual proteins opened new avenues for understanding biological systems and small molecule drug discovery. Large-scale prediction studies enabled evolutionary analyses and genetic variant interpretations. The open code was extended and modified for new methods and applications in protein design and protein-protein assembly prediction. These examples, among many, demonstrate how subsequent research and benchmarks have been made possible because the code and models were open and downloadable. For these reasons, we were disappointed with the lack of code, or even executables accompanying the publication of AlphaFold3 in Nature. Although AlphaFold3 expands AlphaFold2’s capacities to include small molecules, nucleic acids, and chemical modifications, it was released without the means to test and use the software in a high-throughput manner. This does not align with the principles of scientific progress, which rely on the ability of the community to evaluate, use, and build upon existing work. The high-profile publication advertises capabilities that remain locked behind the doors of the parent company. In this publication, several deviations from our community's standards stand out. First, the absence of available code compromises peer review, a cornerstone of scientific publication and a standard typically upheld by journals. Indeed, one of us (RD) was a reviewer, and despite repeated requests, he was not given access to code during the review. Second, the model's limited availability on a hosted web server, capped at ten predictions per day, restricts the scientific community's capacity to verify the broad claims of the findings or apply the predictions on a large scale. Specifically, the inability to make predictions on novel organic molecules akin to chemical probes and drugs, one of the central claims of the paper, makes it impossible to test or use this method. Finally, the pseudocode released will require months of effort to turn into workable code that approximates the performance, wasting valuable time and resources. Even if such a reimplementation is attempted, restricted access raises questions about whether the results could be fully validated. Computational costs of machine learning approaches are becoming prohibitive for academic institutions, owing to the high costs of training the models, leaving much computational research and potential breakthroughs in the hands of for-profit companies. While companies have the right to capitalize on their innovations, using the imprimatur of academic publications without the possibility of reproducing the results, far less building on them, subverts the enterprise. The amount of disclosure in the AlphaFold3 publication is appropriate for an announcement on a company website (which, indeed, the authors used to preview these developments), but it fails to meet the scientific community’s standards of being usable, scalable, and transparent. This moment can motivate our community to raise the bar of openness and transparency to accelerate scientific progress. When journals fail to enforce their written policies about making code available to reviewers1 and alongside publications2, they demonstrate how these policies are applied inequitably and how editorial decisions do not align with the needs of the scientific community. While there is an ever-changing landscape of how science is performed and communicated, journals should uphold their role in the community by ensuring that science is reproducible upon dissemination, regardless of who the authors are. AI approaches now directly impact biological discovery and human health. Fully realizing their potential will require not only technical breakthroughs but also open and collaborative efforts to build on others’ findings, ​​as is foundational in all scientific research. 1)https://web.archive.org/web/20240511023627/https://www.nature.com/nature-portfolio/editorial-policies/reporting-standards 2)https://web.archive.org/web/20240511023855/https://www.nature.com/nature/for-authors/initial-submission
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