Deep Learning in Protein Structural Modeling and Design

@article{Gao2020DeepLI,
  title={Deep Learning in Protein Structural Modeling and Design},
  author={Wenhao Gao and Sai Pooja Mahajan and Jeremias Sulam and Jeffrey J. Gray},
  journal={Patterns},
  year={2020},
  volume={1}
}

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References

SHOWING 1-10 OF 302 REFERENCES
Deep learning methods in protein structure prediction
Accurate Protein Structure Prediction by Embeddings and Deep Learning Representations
TLDR
This work demonstrates state-of-the-art protein structure prediction (PSP) results using embeddings and deep learning models for prediction of backbone atom distance matrices and torsion angles, and creates a new gold standard dataset of proteins which is comprehensive and easy to use.
Distance-based protein folding powered by deep learning
  • Jinbo Xu
  • Computer Science, Biology
    Proceedings of the National Academy of Sciences
  • 2019
TLDR
It is shown that by using a powerful deep learning technique, even with only a personal computer the authors can predict new folds much more accurately than ever before and accurately predict interresidue distance distribution of a protein by deep learning, even for proteins with ∼60 sequence homologs.
Unified rational protein engineering with sequence-based deep representation learning
TLDR
Deep learning is applied to unlabeled amino-acid sequences to distill the fundamental features of a protein into a statistical representation that is semantically rich and structurally, evolutionarily and biophysically grounded and broadly applicable to unseen regions of sequence space.
Generative Models for Graph-Based Protein Design
TLDR
This framework significantly improves in both speed and robustness over conventional and deep-learning-based methods for structure-based protein sequence design, and takes a step toward rapid and targeted biomolecular design with the aid of deep generative models.
Unified rational protein engineering with sequence-only deep representation learning
TLDR
This work applies deep learning to unlabelled amino acid sequences to distill the fundamental features of a protein into a statistical representation that is semantically rich and structurally, evolutionarily, and biophysically grounded.
Generative modeling for protein structures
TLDR
This work applies Generative Adversarial Networks (GANs) to the task of generating protein structures, and encodes protein structures in terms of pairwise distances between alpha-carbons on the protein backbone, which eliminates the need for the generative model to learn translational and rotational symmetries.
Recent developments in deep learning applied to protein structure prediction
TLDR
A brief introduction to some of the key principles and properties of DNN models is offered and why they are naturally suited to certain problems in structural bioinformatics is discussed, as well as a discussion on potential pitfalls.
Computational Protein Design with Deep Learning Neural Networks
TLDR
Using the network output as residue type restraints improves the average sequence identity in designing three natural proteins using Rosetta, and the predictions from the network show ~3% higher sequence identity than a previous method.
End-to-End Differentiable Learning of Protein Structure.
...
1
2
3
4
5
...