How Does AlphaFold Work? The Science Explained for Biologists
AlphaFold changed structural biology — but most explanations of how it works are written for machine learning engineers, not biologists. This article explains the core ideas in biological terms: what information AlphaFold uses, what it learned from, and why its predictions are so much better than anything that came before.
The key insight: evolution encodes structure
Every AlphaFold explanation eventually comes back to one idea: evolution is a massive experiment in protein folding, and the results of that experiment are encoded in the sequences of billions of known proteins.
Proteins that perform the same function in distantly related organisms have similar structures — even when their sequences have diverged significantly over hundreds of millions of years. Why? Because mutations that destroy the fold are selected against. Sequences that maintain the fold survive. Over evolutionary time, the sequences in any protein family carry a hidden record of which parts are structurally essential, which residues must stay in contact, and which can vary freely.
AlphaFold learned to read that record. Its training data was not a set of rules written by biologists, but the entire Protein Data Bank — over 170,000 experimentally determined structures — cross-referenced with the evolutionary information in hundreds of millions of known protein sequences. It learned, empirically, the mapping from evolutionary patterns to 3D structure.
Multiple sequence alignments — reading evolutionary memory
The first thing AlphaFold does with your input sequence is search large sequence databases — UniRef90, BFD, MGnify — for evolutionary relatives. It collects all the sequences it finds, aligns them into a multiple sequence alignment (MSA), and uses that alignment as primary input alongside the sequence itself.
Mouse M K T A Y I A K Q R Q I S F V K S H F S
Zebrafish M K T A Y V A K Q R E I S F V K A H F S
Yeast M R T A H I S K Q K Q V T F L K N H Y T
E. coli – – T A Y I A K Q – Q I S F – K S H – S
The MSA encodes two kinds of structural information. Conservation: positions that are identical or near-identical across all species are structurally or functionally essential — mutating them disrupts the fold or destroys activity. Co-variation: positions that vary together across species are often in physical contact in 3D — when one mutates, the other compensates to preserve the interaction.
Co-evolution: how contacts leave a signal
The co-evolution concept is worth dwelling on because it’s the biological heart of why AlphaFold works. Two residues that are in direct physical contact in the protein’s 3D structure tend to co-evolve — when a mutation at one position would disrupt the contact, a compensating mutation at the other position is selected to restore it.
stable interaction
different residues, same contact
By analyzing the statistical patterns of correlated mutations across hundreds of thousands of sequences in the MSA, AlphaFold can infer which pairs of residues are likely to be in contact in 3D space. These predicted contacts constrain the structure — they function like a sparse set of distance measurements that the model uses to guide the fold.
The Evoformer: AlphaFold’s core architecture
AlphaFold2’s central innovation is the Evoformer — a neural network module that simultaneously processes two representations: the MSA (which encodes evolutionary information) and a pairwise distance matrix (which encodes spatial relationships between residues).
The key idea is that these two representations are processed together, not separately. Information flows between them through attention mechanisms — mathematical operations that allow each position in the sequence to “attend to” and be influenced by every other position simultaneously, weighted by how relevant each pair is for predicting the structure.
You don’t need to understand the mathematics to grasp the biological meaning: the Evoformer is learning to ask and answer questions like “if residues 45 and 112 are co-evolving, and residues 112 and 78 are co-evolving, what does that imply about the relative positions of residues 45 and 78?” — propagating structural constraints across the entire length of the sequence in parallel.
This is repeated through 48 layers of the Evoformer. Each layer refines the representation, resolving ambiguities and propagating information until the final layer produces a detailed picture of which residues are near each other and in what geometry.
The full AlphaFold2 pipeline
pLDDT scores: how AlphaFold reports confidence
AlphaFold doesn’t just predict structure — it predicts how confident it is in each residue’s position. The pLDDT score (predicted Local Distance Difference Test) is a per-residue score from 0 to 100. It estimates how well the predicted position of each residue would match an experimental structure, if one existed.
Critically, low pLDDT doesn’t always mean AlphaFold made an error — it often means the residue is genuinely disordered and has no single stable position to predict. A disordered loop that flaps freely in solution will have low pLDDT because AlphaFold correctly recognized it has no well-defined structure, not because the prediction is wrong.
In PyMOL, pLDDT is stored in the B-factor column of AlphaFold PDB files. To color a structure by confidence: spectrum b, blue_white_red, minimum=50, maximum=100 — blue regions are high confidence, red are low.
AlphaFold3: what changed and why it matters
- Predicts single protein chains
- Multimer extension for protein complexes
- Single deterministic structure output
- Requires deep MSA for best accuracy
- Available free — database + ColabFold
- Best-in-class for single-chain proteins
- Predicts complexes natively — protein + DNA, RNA, ligands, ions
- Generates multiple diverse conformations
- Handles modified residues and covalent modifications
- State-of-the-art for protein-ligand complexes
- Available via web server (usage limits apply)
- Commercial use restrictions on model weights
The architectural shift from Evoformer to diffusion is the most important technical change in AlphaFold3. Diffusion models — the same class used for image generation — work by learning to denoise: starting from random noise and iteratively refining toward a plausible structure. This allows AlphaFold3 to generate multiple different plausible structures for the same input, capturing conformational diversity that AlphaFold2’s single-output approach misses.
For practical purposes: use AlphaFold2 for single protein chains where you need the most accurate prediction. Use AlphaFold3 when you need to predict how a protein interacts with DNA, RNA, a small molecule ligand, or another protein.
How AlphaFold works in one paragraph
AlphaFold2 works by extracting evolutionary information from a multiple sequence alignment of protein homologs, identifying co-evolving residue pairs as signals of spatial contacts, and using a deep neural network (Evoformer) to translate that evolutionary information into predicted 3D coordinates. It was trained on the entire Protein Data Bank — learning empirically what evolutionary patterns correspond to what structures. The pLDDT score reports per-residue confidence; high-pLDDT regions are reliable, low-pLDDT regions are probably disordered. AlphaFold3 extends this to molecular complexes using a diffusion architecture, making it the current best tool for predicting how proteins interact with ligands, DNA, and RNA.