New algorithms for exploiting amino acid covariation have had a large impact on de novo protein modelling. The limited applicability of these methods to smaller protein families has limited their use for structural annotation of whole genomes , however. Most recently , deep learning techniques have shown promise in allowing  residue-residue contacts to be predicted accurately even for shallow sequence alignments. Here we introduce DMPfold, a development of the DeepMetaPSICOV contact predictor, which uses deep learning throughout to predict inter-atomic distance bounds, the main chain hydrogen bond network, and main chain torsion anglesand uses these to build models in an iterative procedure . DMPfold produces more accurate models than two popular methods for a test set of CASP12 free modelling domains, and is able to generate high quality models without any modifications for a set of transmembrane proteins. We apply it to all Pfam domains without a known structure and provide high confidence models for 25\% of these so-called "dark " families , a calculation that took less than a week on a small cluster with 200 available cores . DMPfold provides models for 16\% of human proteome UniProt entries without structures, can generate accurate models with alignments of fewer than 100 sequences in some cases, and is freely available.
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.
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