How to Deploy and Interpret AlphaFold2 with Minimal Compute
It is no longer news that one of the steepest challenges in biology has been leveled by a London-based artificial intelligence outfit – DeepMind. They won the Critical Assessment of protein-Structure Prediction 14 edition (CASP14) with a ground truth score of 90. DeepMind proceeded to publish a landmark paper in the summer of 2021.
Highly accurate protein structure prediction with AlphaFold – Nature
DeepMind named their protein-folding platform Alphafold (updated version -AlphaFold v2.3.0 at the time of writing). They released the source code on GitHub for open access. However, deploying AlphaFold2 open source code on GitHub requires humongous computational resources; downloading the database requires 12 vCPUs, 85 GB RAM, a 100 GB boot disk, 3 TB disk, and an A100 GPU. Also, the user must be vast in Linux, and deploy docker containers and other dependencies. The purpose of this article is to guide readers through seamless alternative tools available to tap into the AlphaFold miracle.
This article is of medium length (no pun intended), so please stay with me.
I will be discussing:
· How AlphaFold works
· Performance evaluation metrics
· The EMBL-EBI method
· The Colab notebook method
· AlphaFold2's limitations
· Conclusion
So how does AlphaFold2 work?
Integrally, AlphaFold2 consists of a trained multiple sequence alignment (MSA), paired residues, and PDB templates of 100000 known protein structures (validated experimentally by NMR, X-ray crystallography, cryo-EM) from metagenomic databases. The AlphaFold2 evoformer, a 48-block neural network, was built based on concepts derived from large language models (LLM), tokenization, transformers, and attention.
The evoformer outputs MSA and pair representations which is fed into the structure prediction module. These blocks employ invariant point attention to predict the single representation copy of the first row of the MSA, which is consequently funnelled to predict the
