AlphaFold 3 Embraces All Biochemistry DeepMind’s AlphaFold 3 enhances 3D biomolecular modeling.

May 15, 2024
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3 min read
AlphaFold 3 Embraces All Biochemistry: DeepMind’s AlphaFold 3 enhances 3D biomolecular modeling.

The latest update of DeepMind’s AlphaFold model is designed to find the structures of not just proteins but all biologically active molecules as well as interactions between them.

What’s new: Google announced AlphaFold 3, which models the 3D shapes of biomolecules including proteins, DNA, RNA, and ligands (molecules that bind to proteins or DNA, which includes antibodies and many drugs) in any combination. AlphaFold Server provides access for noncommercial uses (with some limitations). Unlike earlier versions, AlphaFold 3 is not open source.

Key insight: Given a sequence of amino acids (the building blocks of proteins), the previous version of AlphaFold drew on an existing knowledge of amino acid structures, computed their locations and angles, and assembled them like Lego blocks. To adapt the system for molecules that aren’t made of amino acids, AlphaFold 3 represents them as collections of individual atoms and uses a generative model to find their positions in space.

How it works: Given a list of molecules, AlphaFold 3 generates their joint 3D structure, revealing how they fit together. Several transformers hone embeddings of proteins and amino acids, while a diffusion model (also a transformer) processes embeddings of atoms. The team trained the system on five datasets including ground truth protein, DNA, and RNA structures interactions in the Protein Data Bank. They also trained it on protein shapes computed by AlphaFold 2; that model’s explicit knowledge of amino acid structures helped overcome AlphaFold 3’s tendency to hallucinate in some instances. Among the key processes:

  • Given a protein’s amino acid sequence, a molecule’s set of atoms, or any combination thereof, AlphaFold 3 first represents each common amino acid, nucleotide, and individual atom (that isn’t a part of a common amino acid or nucleotide) with a single token. 
  • For each token, the system draws on existing databases to compute a variety of features, which fall into five categories: (i) per-token features like position, (ii) features of proteins in the Protein Data Bank, (iii) features of a given molecule, (iv) features derived from a genetic search (for example, whether two amino acid sequences appear to be related evolutionarily) and (v) features that describe chemical bonds between two tokens. 
  • Given these features, a transformer produces a single embedding that represents all tokens and pairwise embeddings that represent relationships between each pair of tokens. A second transformer refines the pairwise embeddings based on known molecules that share subsequences of amino acids or nucleotides with the input. A third transformer further refines the embeddings.
  • Given the features, embeddings, and a noisy point cloud of atoms, the diffusion model removes the noise. (That is, it learned to modify the atoms’ positions to match those in their dataset.)
  • AlphaFold 3 learned to optimize seven additional loss terms, including one that minimized the difference between the predicted and actual length of bonds between molecules and another that minimized the difference between predicted and actual distances between pairs of atoms.

Results: On PoseBusters, a database of protein and protein-molecule shapes, AlphaFold 3 successfully found the shapes of about 77 percent of examples, while AutoDock Vina (a non-learning program that models molecular interactions) achieved about 53 percent. On a Protein Data Bank evaluation set, AlphaFold 3 successfully found about 84 percent of protein shapes, while AlphaFold Multimer 2.3 (an update of AlphaFold 2) found 83 percent. Modeling protein-protein interactions, AlphaFold 3 achieved 77 percent, while AlphaFold Multimer 2.3 achieved 67 percent, according to DockQ (a metric for the quality of such interactions).

Behind the news: The original AlphaFold solved one of the most challenging problems in molecular biology by figuring out how long chains of amino acids would fold, giving scientists clear targets for designing new bioactive molecules. Google spun off Isomorphic Labs to apply AlphaFold 2 to drug discovery. That company will use AlphaFold 3 and control commercial access to it.

Why it matters: AlphaFold 3 is a triumph of machine learning. It extends the utility of the previous version beyond proteins, and it computes with unprecedented accuracy how biological molecules will combine, allowing for a more comprehensive understanding of how drugs interact with the body. Its ability to predict how antibodies will bind to proteins could help stave off future pandemics and other illnesses.

We’re thinking: Although Isomorphic Labs retains control of AlphaFold 3, biologists said the information in the paper is enough for other researchers to develop similar systems. We look forward to open versions!


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