People are overestimating Alphafold and it’s a problem
AlphaFold isn’t saying that, that it is you interpreting the AlphaFold output. Very low pLDDTs are very strongly correlated with flexible or disordered regions. You can’t get high resolution structural info on highly flexible or disordered regions with crystallography or other techniques, so your interpretation would be wrong anyway.
AlphaFold 3 is a Fantastic Breakthrough and Deserves all the
AlphaFold is helping researchers build models, but it can’t yet simply find all protein states. F@H has always focused on molecular dynamics, while Rosetta@Home (a BOINC project) looked for protein structures and has been more supplanted by AlphaFold. F@H also open sources all its results and was probably used to train AlphaFold.
Best AlphaFold Posts - Reddit
Seems like DeepMind just caused the ImageNet moment for protein folding. Blog post isn't that deeply informative yet (paper is promised to appear soonish). Seems like the improvement over the first version of AlphaFold is mostly usage of transformer/attention mechanisms applied to residue space and com
alphafold - Reddit
2022年1月29日 · I have used Alphafold to determine the structures for a protein of my interest. While the confidence score is low for the over all prediction, I am curious to know if the secondary structures are accurate. I don’t have much concern about the exact folding of the protein but am concerned if each secondary structure is accurate.
[D] DeepMind's AlphaFold 2 Explained! AI Breakthrough in
2020年12月1日 · DeepMind solves a 50-year old problem in Protein Folding Prediction. AlphaFold 2 improves over DeepMind's 2018 AlphaFold system with a new architecture and massively outperforms all competition. In this Video, we take a look at how AlphaFold 1 works and what we can gather about AlphaFold 2 from the little information that's out there.
Google DeepMind: AlphaFold 3 predicts the structure and ... - Reddit
In a paper published in Nature, we introduce AlphaFold 3, a revolutionary model that can predict the structure and interactions of all life’s molecules with unprecedented accuracy. For the interactions of proteins with other molecule types we see at least a 50% improvement compared with existing prediction methods, and for some important ...
AlphaFold: a solution to a 50-year-old grand challenge in biology
2020年11月30日 · Alphafold 2018 didn't do any atomic simulation or movement. It learnt statistical patterns in the data. It certainly demonstrated that there is a lot more structural and evolutionary data in sequence alignments than people had previously managed to access. It's not clear that it learnt much about the process of folding though.
AlphaFold3 was just announced : r/bioinformatics - Reddit
"we describe our AlphaFold 3 model with a substantially updated diffusion-based architecture, which is capable of joint structure prediction of complexes including proteins, nucleic acids, small molecules, ions, and modified residues" the possibilities are endless Too excited to see how it will change the structural bioinformatics
Announcing AlphaFold 3: our state-of-the-art AI model for
2024年5月8日 · Yes, AlphaFold has been involved in several recent medical breakthroughs and advancements: Accelerating drug discovery for neglected diseases: The Drugs for Neglected Diseases Initiative (DNDi) is using AlphaFold to help identify new drug candidates for diseases like Chagas disease and leishmaniasis that disproportionately affect developing ...
How important of a breakthrough is DeepMind's AlphaFold?
2021年11月28日 · AlphaFold might need MD relaxations before the correct active site is found, although I decided against MD because docking results were good enough already. --- To find out the best fit for your research question, the easiest may be to search publications on your class of enzymes and see what homology modeling software was used.