After decades of effort, 17% of the total residues in human protein sequences are covered by an experimentally-determined structure1. Here we dramatically expand structural coverage by applying the state-of-the-art machine learning method, AlphaFold2, at scale to 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.
The metric they use (residues) is a bit unusual (I would have used number of proteins instead), but I assume they wanted to account for ambiguity (such as proteins with partial structures).
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After decades of effort, 17% of the total residues in human protein sequences are covered by an experimentally-determined structure1. Here we dramatically expand structural coverage by applying the state-of-the-art machine learning method, AlphaFold2, at scale to 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.
https://www.nature.com/articles/s41586-021-03828-1
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The metric they use (residues) is a bit unusual (I would have used number of proteins instead), but I assume they wanted to account for ambiguity (such as proteins with partial structures).