This is the code for this video on Youtube by Siraj Raval on DeepMind AlphaFold. This is a re-implemention of Sheng and Jinbo's deep leanring model on protein contacts prediction, which is a breakthrough in protein structure prediction. However, unfortunately they did not opensource their codes and models. So its a re-implementation of their methods based on the paper. As their paper reported, deep learning model can significantly improve the accuracy of contacts prediction.
Coding Challenge - Due Date, Feb, 20 at 12 PM PST - Replicate AlphaFold as best you can. Post your github links in the comment section of the video! I'll give a shoutout to the top 2 entries. I'll send the winner a brand new Nvidia Titan RTX GPU. You can also DM @Sirajraval on twitter or send it to hello@sirajraval.com , use the tag #alphafoldchallenge on social media.
This challenge is open to anyone, anywhere in the world.
- Good Documentation
- Working, readable code
Wizards, see page 11 of this paper and DeepMind's blog post for details on the AlphaFold algorithm. 2 Residual networks are used. Multiple methods are attempted.
This code is one very related example to AlphaFold. Here are others that you can build off of
- https://github.com/carlosmartinezvillar/3DCNNFolds
- https://github.com/igemsoftware2017/AiGEM_TeamHeidelberg2017/blob/master/DeeProtein/DeeProtein_README.md
- https://github.com/pfnet-research/BMI219-2017-ProteinFolding
- https://github.com/5bingstar/Deep-learning-for-contact_map_v2
- https://github.com/Illumina/PrimateAI
Do the best you can! I'm looking for well documented code. Good luck!