66 Citations
Structure-based protein design with deep learning.
- BiologyCurrent opinion in chemical biology
- 2021
Deep Learning-Based Advances in Protein Structure Prediction
- Biology, Computer ScienceInternational journal of molecular sciences
- 2021
Important milestones and progresses in the field of protein structure prediction due to DL-based methods as observed in CASP experiments are highlighted and advances in various steps ofprotein structure prediction pipeline viz. protein contact map prediction, protein distogram prediction,protein real-valued distance prediction, and Quality Assessment/refinement are described.
Protein Design with Deep Learning
- Computer Science, BiologyInternational journal of molecular sciences
- 2021
The representations used so far are described, their strengths and weaknesses are discussed, and their associated DL architecture for design and related tasks are details.
Protein loop modeling and refinement using deep learning models
- Computer Science
- 2021
Two novel deep learning architectures for loop modeling are proposed: one uses a combined convolutional neural network-recursive neural network (RNN) structure and the other is based on refinement of histograms using a 2D CNN architecture (DeepHisto).
Current directions in combining simulation-based macromolecular modeling approaches with deep learning
- Computer ScienceExpert opinion on drug discovery
- 2021
This review introduces biologists to deep neural network architecture, surveys recent successes of deep learning in structure prediction, and discusses emerging deep learning-based approaches for structure-function analysis and design, with particular focus on the interplay between simulation-based and neural network- based approaches.
Antibody structure prediction using interpretable deep learning
- Biology, Computer SciencebioRxiv
- 2021
DeepAb, a deep learning method for predicting accurate antibody FV structures from sequence, is presented and improved accuracy is demonstrated and interpretable outputs about specific amino acids and residue interactions are revealed that should facilitate design of novel therapeutic antibodies.
How Deep Learning Tools Can Help Protein Engineers Find Good Sequences.
- Computer ScienceThe journal of physical chemistry. B
- 2021
The use of deep learning tools to find good sequences for protein engineering are reviewed, including developing oracles/predictors of a property of the proteins and methods that sample from a distribution of protein-like sequences to optimize the desired property.
Deep learning of Protein Sequence Design of Protein-protein Interactions
- Biology, Computer Science
- 2022
An attention-based deep learning model inspired by algorithms used for image-caption assignments for sequence design of peptides or protein fragments is developed which allows the one-sided design of a given protein fragment which can be applicable for the redesign of protein-interfaces or the de novo design of new interactions fragments.
One-sided design of protein-protein interaction motifs using deep learning
- Biology, Computer SciencebioRxiv
- 2022
An extensive set of NN-based models, referred to as iNNterfaceDesign, is developed, which is believed to be the first deep learning model for one-sided design of protein-protein interactions.
References
SHOWING 1-10 OF 302 REFERENCES
Deep learning methods in protein structure prediction
- Computer ScienceComputational and structural biotechnology journal
- 2020
Accurate Protein Structure Prediction by Embeddings and Deep Learning Representations
- Computer ScienceArXiv
- 2019
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
- Computer Science, BiologyProceedings of the National Academy of Sciences
- 2019
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
- Biology, Computer ScienceNature Methods
- 2019
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
- BiologyDGS@ICLR
- 2019
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
- Biology, Computer SciencebioRxiv
- 2019
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
- Computer Science, BiologyNeurIPS
- 2018
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
- Computer ScienceProteins
- 2019
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
- Computer ScienceScientific Reports
- 2018
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.