Recent developments in deep learning applied to protein structure prediction

End-to-End Differentiable Learning of Protein Structure

(2019) Mohammed AlQuraishi   Cell Systems

Prediction of interresidue contacts with DeepMetaPSICOV in CASP13

(2019) Shaun M. Kandathil et al.   PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS

rawMSA: End-to-end Deep Learning using raw Multiple Sequence Alignments

(2019) Claudio Mirabello et al.   PLoS One

Distance-based protein folding powered by deep learning

(2019) Jinbo Xu   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Analysis of distance‐based protein structure prediction by deep learning in CASP13

(2019) Jinbo Xu et al.   PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS

Ensembling multiple raw coevolutionary features with deep residual neural networks for contact‐map prediction in CASP13

(2019) Yang Li et al.   PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS

Protein structure prediction using multiple deep neural networks in the 13th Critical Assessment of Protein Structure Prediction (CASP13)

(2019) Andrew W. Senior et al.   PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS

Deep learning extends de novo protein modelling coverage of genomes using iteratively predicted structural constraints

(2019) Joe G. Greener et al.   Nature Communications

High precision in protein contact prediction using fully convolutional neural networks and minimal sequence features

(2018) David T Jones et al.   BIOINFORMATICS

Deep convolutional networks for quality assessment of protein folds

(2018) Georgy Derevyanko et al.   BIOINFORMATICS

Protein threading using residue co-variation and deep learning

(2018) Jianwei Zhu et al.   BIOINFORMATICS

Enhancing Evolutionary Couplings with Deep Convolutional Neural Networks

(2018) Yang Liu et al.   Cell Systems

Design of metalloproteins and novel protein folds using variational autoencoders

(2018) Joe G. Greener et al.   Scientific Reports

DeepBipolar: Identifying genomic mutations for bipolar disorder via deep learning

(2017) Sundaram Laksshman et al.   HUMAN MUTATION

Fully Convolutional Networks for Semantic Segmentation

(2017) Evan Shelhamer et al.   IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE

Improved protein contact predictions with the MetaPSICOV2 server in CASP12

(2017) Daniel W A Buchan et al.   PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS

Analysis of deep learning methods for blind protein contact prediction in CASP12

(2017) Sheng Wang et al.   PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS

Protein structure determination using metagenome sequence data

(2017) Sergey Ovchinnikov et al.   SCIENCE

Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model

(2017) Sheng Wang et al.   PLoS Computational Biology

ECOD: new developments in the evolutionary classification of domains

(2016) R. Dustin Schaeffer et al.   NUCLEIC ACIDS RESEARCH

CATH: an expanded resource to predict protein function through structure and sequence

(2016) Natalie L. Dawson et al.   NUCLEIC ACIDS RESEARCH

Improved de novo structure prediction in CASP11 by incorporating coevolution information into Rosetta

(2016) Sergey Ovchinnikov et al.   PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS

Deep learning

(2015) Yann LeCun et al.   NATURE

Deep learning in neural networks: An overview

(2015) Jürgen Schmidhuber   NEURAL NETWORKS

Manual classification strategies in the ECOD database

(2015) Hua Cheng et al.   PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS

CCMpred—fast and precise prediction of protein residue–residue contacts from correlated mutations

(2014) Stefan Seemayer et al.   BIOINFORMATICS

MetaPSICOV: combining coevolution methods for accurate prediction of contacts and long range hydrogen bonding in proteins

(2014) David T. Jones et al.   BIOINFORMATICS

Fast pseudolikelihood maximization for direct-coupling analysis of protein structure from many homologous amino-acid sequences

(2014) Magnus Ekeberg et al.   JOURNAL OF COMPUTATIONAL PHYSICS

De Novo Structure Prediction of Globular Proteins Aided by Sequence Variation-Derived Contacts

(2014) Tomasz Kosciolek et al.   PLoS One

ECOD: An Evolutionary Classification of Protein Domains

(2014) Hua Cheng et al.   PLoS Computational Biology

Improved contact prediction in proteins: Using pseudolikelihoods to infer Potts models

(2013) Magnus Ekeberg et al.   PHYSICAL REVIEW E

Assessing the utility of coevolution-based residue-residue contact predictions in a sequence- and structure-rich era

(2013) H. Kamisetty et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Accurate de novo structure prediction of large transmembrane protein domains using fragment-assembly and correlated mutation analysis

(2012) T. Nugent et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

PSICOV: precise structural contact prediction using sparse inverse covariance estimation on large multiple sequence alignments

(2011) David T. Jones et al.   BIOINFORMATICS

Protein 3D Structure Computed from Evolutionary Sequence Variation

(2011) Debora S. Marks et al.   PLoS One

Direct-coupling analysis of residue coevolution captures native contacts across many protein families

(2011) F. Morcos et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Learning generative models for protein fold families

(2010) Sivaraman Balakrishnan et al.   PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS

Identification of direct residue contacts in protein-protein interaction by message passing

(2008) M. Weigt et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA