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

End-to-End Differentiable Learning of Protein Structure

(2019) Mohammed AlQuraishi   Cell Systems

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

(2019) Jinbo Xu 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

Mastering the game of Go without human knowledge

(2017) David Silver et al.   NATURE

Template-based and free modeling of I-TASSER and QUARK pipelines using predicted contact maps in CASP12

(2017) Chengxin Zhang et al.   PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS

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

(2017) Sheng Wang et al.   PLoS Computational Biology

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

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

Combined Covalent-Electrostatic Model of Hydrogen Bonding Improves Structure Prediction with Rosetta

(2015) Matthew J. O’Meara et al.   Journal of Chemical Theory and Computation

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

(2014) David T. Jones et al.   BIOINFORMATICS

Improving fragment quality for de novo structure prediction

(2014) Rojan Shrestha et al.   PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS

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

(2013) Magnus Ekeberg et al.   PHYSICAL REVIEW E

Relaxation of backbone bond geometry improves protein energy landscape modeling

(2013) Patrick Conway et al.   PROTEIN SCIENCE

A Position-Specific Distance-Dependent Statistical Potential for Protein Structure and Functional Study

(2012) Feng Zhao et al.   STRUCTURE

HHblits: lightning-fast iterative protein sequence searching by HMM-HMM alignment

(2011) Michael Remmert et al.   NATURE METHODS

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

Fragment-free approach to protein folding using conditional neural fields

(2010) F. Zhao et al.   BIOINFORMATICS

Macromolecular Modeling with Rosetta

(2008) Rhiju Das et al.   Annual Review of Biochemistry

A generative, probabilistic model of local protein structure

(2008) Wouter Boomsma et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA