The use of deep learning for protein residue-residue contact prediction has not extended to template-free model generation, with current methods using predicted contacts to enhance existing approaches. The process of model generation provides information on the viability of predicted contacts, indicating there is potential to combine the two steps . Here we introduce DMPfold, a development of the DeepMetaPSICOV contact predictor, that predicts inter-atomic distances and uses these to build models . Unsatisfied constraints are removed in an iterative process . DMPfold produces more accurate models than two popular methods for the CASP12 free modelling domains, and is able to generate high quality models without any modifications for a set of transmembrane proteins. It shows particularly good performance when generating a single model, making it a computationally tractable way of generating models from availablesequence data .
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 angles and 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 .
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The use of deep learning for protein
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
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contact prediction has not extended to template-free model generation, with current methods using predicted contacts to enhance existing approaches. The process of model generation provides information on the viability of predicted contacts, indicating there is potential to combine the two steps
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It shows particularly good performance when generating a single model, making it a computationally tractable way of generating models from availablesequence data
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