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Improved protein structure prediction using predicted interresidue orientations
TLDR
We develop a deep residual network for predicting interresidue orientations, in addition to distances, and a Rosetta-constrained energy-minimization protocol for rapidly and accurately generating structure models guided by these restraints. Expand
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Origins of coevolution between residues distant in protein 3D structures
Significance Coevolution-derived contact predictions are enabling accurate protein structure modeling. However, coevolving residues are not always in contact, and this is a potential source of errorExpand
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Protein interaction networks revealed by proteome coevolution
Predicting protein pairs Biological function is driven by interaction between proteins. High-throughput experimental techniques have provided large datasets of protein interactions in severalExpand
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Dockground: A comprehensive data resource for modeling of protein complexes
TLDR
We present a comprehensive description of the Dockground resource (http://dockground.ku.edu) for structural modeling of protein interactions, including previously unpublished unbound docking benchmark set 4, and the X‐ray docking decoy set 2. Expand
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ProteinGCN: Protein model quality assessment using Graph Convolutional Networks
TLDR
We train a graph convolutional network with nodes representing protein atoms and edges connecting spatially adjacent atom pairs on the dataset Rosetta-300k which contains a set of 300k conformations from 2,897 proteins. Expand
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Structural templates for comparative protein docking
TLDR
Structural characterization of protein‐protein interactions is important for understanding life processes. Expand
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Protein contact prediction using metagenome sequence data and residual neural networks
TLDR
We developed MapPred, a new deep learning-based contact prediction method based on the rich sequence data from the metagenome sequencing projects. Expand
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Improved protein structure prediction using predicted inter-residue orientations
TLDR
We develop a deep residual network for predicting inter-residue orientations in addition to distances, and a Rosetta constrained energy minimization protocol for rapidly and accurately generating structure models guided by these restraints. Expand
  • 16
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Protein sequence design by explicit energy landscape optimization
TLDR
We show that by backpropagating gradients through the trRosetta structure prediction network from the desired structure to the input amino acid sequence, we can directly optimize over all possible amino Acid sequences and all possible structures, and in one calculation explicitly design amino acid sequences predicted to fold into a desired structure. Expand
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De novo protein design by deep network hallucination
There has been considerable recent progress in protein structure prediction using deep neural networks to infer distance constraints from amino acid residue co-evolution1–3. We investigated whetherExpand
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