Highly accurate protein structure prediction for the human proteome

Nature. 2021 Aug;596(7873):590-596. doi: 10.1038/s41586-021-03828-1. Epub 2021 Jul 22.


Protein structures can provide invaluable information, both for reasoning about biological processes and for enabling interventions such as structure-based drug development or targeted mutagenesis. After decades of effort, 17% of the total residues in human protein sequences are covered by an experimentally determined structure1. Here we markedly expand the structural coverage of the proteome by applying the state-of-the-art machine learning method, AlphaFold2, at a scale that covers almost the entire human proteome (98.5% of human proteins). The resulting dataset covers 58% of residues with a confident prediction, of which a subset (36% of all residues) have very high confidence. We introduce several metrics developed by building on the AlphaFold model and use them to interpret the dataset, identifying strong multi-domain predictions as well as regions that are likely to be disordered. Finally, we provide some case studies to illustrate how high-quality predictions could be used to generate biological hypotheses. We are making our predictions freely available to the community and anticipate that routine large-scale and high-accuracy structure prediction will become an important tool that will allow new questions to be addressed from a structural perspective.

MeSH terms

  • Computational Biology / standards*
  • Datasets as Topic / standards
  • Deep Learning / standards*
  • Diacylglycerol O-Acyltransferase / chemistry
  • Glucose-6-Phosphatase / chemistry
  • Humans
  • Membrane Proteins / chemistry
  • Models, Molecular*
  • Protein Conformation*
  • Protein Folding
  • Proteome / chemistry*
  • Reproducibility of Results


  • Membrane Proteins
  • Proteome
  • wolframin protein
  • Diacylglycerol O-Acyltransferase
  • Glucose-6-Phosphatase