  The inapplicability of amino acid covariation methods to small protein
families has limited their use for structural annotation of whole genomes.
Recently, deep learning has shown promise in allowing accurate residue-residue
contact prediction even for shallow sequence alignments. Here we introduce
DMPfold, which uses deep learning to predict inter-atomic distance bounds, the
main chain hydrogen bond network, and torsion angles, which it uses to build
models in an iterative fashion. DMPfold produces more accurate models than two
popular methods for a test set of CASP12 domains, and works just as well for
transmembrane proteins. Applied to all Pfam domains without known structures,
confident models for 25% of these so-called dark families were produced in
under a week on a small 200 core cluster. DMPfold provides models for 16% of
human proteome UniProt entries without structures, generates accurate models
with fewer than 100 sequences in some cases, and is freely available.
