Machine learning in protein structure prediction

TorchMD: A Deep Learning Framework for Molecular Simulations

(2021) Stefan Doerr et al.   Journal of Chemical Theory and Computation

Machine Learning for Molecular Simulation

(2020) Frank Noé et al.   Annual Review of Physical Chemistry

Improved protein structure prediction using potentials from deep learning

(2020) Andrew W. Senior et al.   NATURE

Improved protein structure prediction using predicted interresidue orientations

(2020) Jianyi Yang et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Macromolecular modeling and design in Rosetta: recent methods and frameworks

(2020) Julia Koehler Leman et al.   NATURE METHODS

AlphaFold at CASP13

(2019) Mohammed AlQuraishi   BIOINFORMATICS

Machine Learning of Coarse-Grained Molecular Dynamics Force Fields

(2019) Jiang Wang et al.   ACS Central Science

HH-suite3 for fast remote homology detection and deep protein annotation

(2019) Martin Steinegger et al.   BMC 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

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

DeepMSA: constructing deep multiple sequence alignment to improve contact prediction and fold-recognition for distant-homology proteins

(2019) Chengxin Zhang et al.   BIOINFORMATICS

Unified rational protein engineering with sequence-based deep representation learning

(2019) Ethan C. Alley et al.   NATURE METHODS

Transferable Neural Networks for Enhanced Sampling of Protein Dynamics

(2018) Mohammad M. Sultan et al.   Journal of Chemical Theory and Computation

Trajectory-based training enables protein simulations with accurate folding and Boltzmann ensembles in cpu-hours

(2018) John M. Jumper et al.   PLoS Computational Biology

ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost

(2017) J. S. Smith et al.   Chemical Science

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

(2017) Sheng Wang et al.   PLoS Computational Biology

The I-TASSER Suite: protein structure and function prediction

(2015) Jianyi Yang et al.   NATURE METHODS

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

(2014) David T. Jones et al.   BIOINFORMATICS

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

(2014) Tomasz Kosciolek et al.   PLoS One

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

Protein structure prediction from sequence variation

(2012) Debora S Marks et al.   NATURE BIOTECHNOLOGY

Genomics-aided structure prediction

(2012) J. I. Sulkowska 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

Direct correlation analysis improves fold recognition

(2011) Michael I. Sadowski et al.   COMPUTATIONAL BIOLOGY AND CHEMISTRY

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

Protein topology from predicted residue contacts

(2011) William R. Taylor et al.   PROTEIN SCIENCE

Hidden Markov model speed heuristic and iterative HMM search procedure

(2010) L Steven Johnson et al.   BMC BIOINFORMATICS

Learning generative models for protein fold families

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

Graphical Models of Residue Coupling in Protein Families

(2008) J. Thomas et al.   IEEE-ACM Transactions on Computational Biology 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

Using scores derived from statistical coupling analysis to distinguish correct and incorrect folds in de-novo protein structure prediction

(2007) Gail J. Bartlett et al.   PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS