Assessment of contact predictions in CASP12: Co-evolution and deep learning coming of age

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

(2017) Sheng Wang et al.   PLoS Computational Biology

R2C: improvingab initioresidue contact map prediction using dynamic fusion strategy and Gaussian noise filter

(2016) Jing Yang et al.   BIOINFORMATICS

conSSert: Consensus SVM Model for Accurate Prediction of Ordered Secondary Structure

(2016) Chris A. Kieslich et al.   Journal of Chemical Information and Modeling

HMMER web server: 2015 update

(2015) Robert D. Finn et al.   NUCLEIC ACIDS RESEARCH

Improving accuracy of protein contact prediction using balanced network deconvolution

(2015) Hai-Ping Sun et al.   PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS

New encouraging developments in contact prediction: Assessment of the CASP11 results

(2015) Bohdan Monastyrskyy et al.   PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS

PconsFold: improved contact predictions improve protein models

(2014) Mirco Michel et al.   BIOINFORMATICS

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

(2014) Stefan Seemayer et al.   BIOINFORMATICS

SSpro/ACCpro 5: almost perfect prediction of protein secondary structure and relative solvent accessibility using profiles, machine learning and structural similarity

(2014) C. N. Magnan 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

FreeContact: fast and free software for protein contact prediction from residue co-evolution

(2014) László Kaján et al.   BMC 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

Combining Physicochemical and Evolutionary Information for Protein Contact Prediction

(2014) Michael Schneider et al.   PLoS One

Improved Contact Predictions Using the Recognition of Protein Like Contact Patterns

(2014) Marcin J. Skwark et al.   PLoS Computational Biology

Improving Contact Prediction along Three Dimensions

(2014) Christoph Feinauer et al.   PLoS Computational Biology

Robust and accurate prediction of residue–residue interactions across protein interfaces using evolutionary information

(2014) Sergey Ovchinnikov et al.   eLife

Sequence co-evolution gives 3D contacts and structures of protein complexes

(2014) Thomas A Hopf et al.   eLife

PconsC: combination of direct information methods and alignments improves contact prediction

(2013) Marcin J. Skwark et al.   BIOINFORMATICS

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

Assessment of CASP10 contact-assisted predictions

(2013) Todd J. Taylor et al.   PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS

One contact for every twelve residues allows robust and accurate topology-level protein structure modeling

(2013) David E. Kim et al.   PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS

Evaluation of residue-residue contact prediction in CASP10

(2013) Bohdan Monastyrskyy et al.   PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS

Collaboration gets the most out of software

(2013) Andrew Morin et al.   eLife

Predicting protein residue–residue contacts using deep networks and boosting

(2012) Jesse Eickholt et al.   BIOINFORMATICS

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

Improving protein fold recognition and template-based modeling by employing probabilistic-based matching between predicted one-dimensional structural properties of query and corresponding native properties of templates

(2011) Y. Yang et al.   BIOINFORMATICS

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

(2011) Michael Remmert et al.   NATURE METHODS

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

Evaluation of residue-residue contact predictions in CASP9

(2011) Bohdan Monastyrskyy et al.   PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS

Incorporation of evolutionary information into Rosetta comparative modeling

(2011) James Thompson et al.   PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS

Hidden Markov model speed heuristic and iterative HMM search procedure

(2010) L Steven Johnson et al.   BMC BIOINFORMATICS

Disentangling Direct from Indirect Co-Evolution of Residues in Protein Alignments

(2010) Lukas Burger et al.   PLoS Computational Biology

Assessment of domain boundary predictions and the prediction of intramolecular contacts in CASP8

(2009) Iakes Ezkurdia et al.   PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS

FT-COMAR: fault tolerant three-dimensional structure reconstruction from protein contact maps

(2008) Marco Vassura et al.   BIOINFORMATICS

A comprehensive assessment of sequence-based and template-based methods for protein contact prediction

(2008) Sitao Wu et al.   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

MAMMOTH (Matching molecular models obtained from theory): An automated method for model comparison

(2002) Angel R. Ortiz et al.   PROTEIN SCIENCE