๐๐น๐ฝ๐ต๐ฎ๐๐ผ๐น๐ฑ ๐๐ป๐ฑ ๐ฃ๐ฟ๐ผ๐๐ฒ๐ถ๐ป ๐๐ผ๐น๐ฑ๐ถ๐ป๐ด
Protein folding was a problem for 50 years. Scientists wanted to know how amino acid chains become 3D shapes. These shapes determine what a protein does. In 2020, DeepMind solved this with AlphaFold.
Proteins have too many possible shapes. A search would take longer than the age of the universe. Nature finds the shape in milliseconds. AlphaFold mimics this.
The system works in steps.
- It searches databases for related proteins.
- It finds residues changing together.
- It builds a map of distances.
- It refines the 3D coordinates over several loops.
This changes drug design.
- Old methods took 6 to 18 months per target.
- AlphaFold finds a starting structure in one hour.
- One company found a drug candidate in 12 months. This usually takes 5 years.
We now have shapes for 214 million proteins. This is nearly 100 percent of known proteins. You find new enzymes for plastic waste or carbon capture.
You need a GPU to run this. Memory use grows fast as proteins get longer. A 10GB GPU fails on proteins over 800 residues.
Use these tools to start.
- ColabFold: Easy setup on Google Colab.
- ESMFold: Fast but less accurate.
- AlphaFold Database: Search pre-made shapes.
- Chai-1: An open weight option.
You do not need a PhD. Use Python to fold proteins. The data is ready. Build something new.
Optional learning community: https://t.me/GyaanSetuAi