From PDB to AlphaFold

Guy Salvesen and Giovanni Paternostro have spoken with Adam Godzik. Adam is the Bruce D. and Nancy B. Varner Presidential Endowed Chair in Cancer Research at the UC Riverside School of Medicine, Division of Biomedical Sciences. Adam was closely involved from an early stage in CASP, as a participant, and in the Joint Center for Structural Genomics (JCSG), one of the centers supported by the Protein Structure Initiative (PSI).

He sent the following comments:

                  I think that the success of PDB was driven by it being built by the crystallographic community itself, it was an effort from within, not from outside. It became widely accepted relatively early in its history, definitely before I got into the field. 

                  There was another development in bioinformatics that enabled AlphaFold – residue-residue interaction predictions from MSA (work of Debora S. Marks, for instance https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0028766) or a more general contact map prediction field…

Genentech and DeepMind are examples of start-ups that achieved major scientific advances, while parallel efforts by academic groups and by large companies on the same problems were not successful.

A comparative analysis might better show the reasons behind their achievements.

The research on DeepMind is described in the timeline, while the research on Genentech is based on published historical reconstructions, as the books by Stephen Hall (1987) and by Sally Hughes (2011), on interviews with many protagonists available from the Berkeley Library Digital Collections and on conversations with former employees, including Roberto Crea, who was one of the first five employees and even before that a key author in the papers describing the initial work done at City of Hope with Genentech support (Crea et al, 1978; Itakura et al, 1977; Hirose et al 1978; Goeddel et al, 1979).

The comparative analysis is ongoing, but the following points are…

Mohammed AlQuraishi is an Assistant Professor in the Department of Systems Biology at Columbia University. He is one of the leaders of the OpenFold consortium (https://openfold.io).

Thanks for reaching out about this.

With regards to additions, one piece from my own work is the RGN paper (https://www.sciencedirect.com/science/article/pii/S2405471219300766),

which was the first paper to do end-to-end differentiable learning of protein structure, and the first to show that a protein can be folded implicitly using a neural network. This ended up being the approach that AlphaFold2 ultimately took (with many more additions and elaborations on top of course). 

Jake Feala is a cofounder at Lila Sciences, a company unveiled in March 2025 aiming to use AI and autonomous labs to accelerate scientific discovery.

We invited him to contribute to our historical timeline, from PDB to AlphaFold. Specifically, we asked him about the reasons why the protein folding problem was solved by a VC-backed company, DeepMind, and not by an academic group, and also if he thinks that this achievement provides a general solution for the future of AI in science.

Thanks for the opportunity to contribute!

I think it's not exactly the right question to ask why the protein structure prediction problem was solved by a company and not an academic group. A more fitting question is why DeepMind solved it and not some other entity, academic or not. Back then DeepMind was a totally unique company and not only beat out academia but also the entire…