#SaprotHub: Making #protein #modeling accessible to all biologists:
-create & train own #DeepLearning models without the need for advanced #MachineLearning & coding expertise
https://doi.org/10.1101/2024.05.24.595648
#DIYbio #bioinformatics #CompChem #AI #ML #KI #LLM #AF2 #ESMfold #ColabFold #sciece #research
I use Alphafold (actually colabfold) and am happy to answer questions, but I'm just a user. There are people with more expertise than me here.
Hashtags might find the right people
#StructurePrediction #alphafold #alphafold2 #colabfold #ProteinStructure #StructuralBiology #ProteinPrediction #protein #bioinformatics
The University of Washington School of Medicine, #Columbia, and #MIT researchers released RF Diffusion, an #OpenSource artificial intelligence (#AI) program, through #ColabFold, part of #Google's Colaboratory. The code is also available from #GitHub.
#EdTech #HigherEducation #EdTechChat
https://www.ipd.uw.edu/2023/03/rf-diffusion-now-free-and-open-source/
But our XLs also capture identities of + localise interfaces in >2000 binary PPIs! Many of these were known from previous high throughput #interactomics, but others were predicted, or a hop & skip away. Again - these are PPIs in native seqs, subcell locales, PTMs, cofactors.
Using #ColabFold, Xabier Vazquez Campos submitted >500 of our XLd PPIs + 400 random pairs to Multimer-v2 modelling. All types of XLd PPIs had larger predicted interfaces than those for random pairs. We also found diferences in interfaces for PPIs detected by diff interactome techniques.
Bose-Einstein-Kondensat
MCDuncanLab
Apereo Foundation
Pete Carlton
tarabartolec