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====== Running AlphaFold at scale ======

By Joran Martijn, March 2026

==== A brief history of AlphaFold ====

The challenge of predicting protein structures from amino acid sequences has long been a significant issue in biochemistry, and many groups have taken a crack at it for several decades.

In 2016 Google's **DeepMind** began exploring protein folding as part of its research into using AI for biological problems. AlphaFold made its first notable appearance in the 13th **Critical Assessment of Protein Structure Prediction** (CASP13) in December 2018. It showcased its ability to predict protein structures with remarkable accuracy, outperforming other methods.

In 2020, DeepMind released **AlphaFold2**, which significantly improved upon the original model. It utilized new neural network architectures and training techniques, demonstrating astonishing accuracy in predictions. During CASP14, AlphaFold2 solved protein structures at a level comparable to experimental techniques.

In July 2021, DeepMind published the **AlphaFold Protein Structure Database**, providing over 350,000 predicted protein structures for the scientific community. This database has since been a vital resource for researchers worldwide.

**AlphaFold3** was released in 2024. It's main development relative to its predecessor was that it can predict structures of protein complexes with non-protein molecules (DNA, RNA, ions, lipids, Fe-S clusters, small molecule ligands, post-transcriptional modifications, chemical modifications of nucleic acids). It can also predict structures without proteins, such as ss- and dsDNA and ssRNA chains.

==== AlphaFold theory ====

The main tenet of AlphaFold is to use evolutionary signals in protein sequences to inform the reconstruction of the three-dimensional structure of proteins. The idea is that amino acid **residues of the same protein that co-evolve together, are very likely to be close to each other in 3D space**. Conversely, pairs that do not co-evolve are thought to be further away from each other.

The exact details are a bit murky but I was able to understand the following:
  * AF is a multicomponent AI system that uses deep-learning to train its neural network models
  * It was trained on experimentally verified structures from the PDB

AlphaFold consists of two phases:

* 1A. Compare input protein sequence with those in **publically available databases**
  * 1B. Construct a **multiple sequence alignment**
  * 1C. Construct a **pair representations**, where for each residue-vs-residue in an all-vs-all comparison the degree of co-evolution is quantified

The neural network (called **Evoformer** in AF2, **Pairformer** in AF3) allows for continuous flow between the MSA and the pair representations. It interprets and updates both these things.

* 2. A structure module in AF2, or 'diffusion module' in AF3, that uses the information extracted from phase 1 to infer the 3D protein structure

==== Running AlphaFold on Perun ====

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