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Leaner, simpler, cheaper

Burkhard Rost, a computational biologist at the Technical University of Munich in Germany, is impressed with the combination of speed and accuracy of Meta’s model. But he questions whether it really offers an advantage over AlphaFold’s precision, when it comes to predicting proteins from metagenomic databases. Language model-based prediction methods — including one developed by his team3 — are better suited to quickly determine how mutations alter protein structure, which is not possible with AlphaFold. “We will see structure prediction become leaner, simpler cheaper and that will open the door for new things,” he says.

DeepMind doesn’t currently have plans to include metagenomic structure predictions in its database, but hasn’t ruled this out for future releases, according to a company representative. But Steinegger and his collaborators have used a version of AlphaFold to predict the structures of some 30 million metagenomic proteins. They are hoping to find new kinds of RNA viruses by looking for novel forms of their genome-copying enzymes.

Steinegger sees trawling biology’s dark matter as obvious next step for such tools. “I do think we will quite soon have an explosion in the analysis of these metagenomic structures.”