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

RaptorX-Angle: real-value prediction of protein backbone dihedral angles through a hybrid method of clustering and deep learning

(2018) Yujuan Gao et al.   BMC BIOINFORMATICS

MUFOLD-SS: New deep inception-inside-inception networks for protein secondary structure prediction

(2018) Chao Fang et al.   PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS

Clustering huge protein sequence sets in linear time

(2018) Martin Steinegger et al.   Nature Communications

Proteus: a random forest classifier to predict disorder-to-order transitioning binding regions in intrinsically disordered proteins

(2017) Sankar Basu et al.   JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN

Protein secondary structure prediction by using deep learning method

(2017) Yangxu Wang et al.   KNOWLEDGE-BASED SYSTEMS

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

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

(2017) Joerg Schaarschmidt 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

RaptorX-Property: a web server for protein structure property prediction

(2016) Sheng Wang et al.   NUCLEIC ACIDS RESEARCH

Deep learning

(2015) Yann LeCun et al.   NATURE

JPred4: a protein secondary structure prediction server

(2015) Alexey Drozdetskiy et al.   NUCLEIC ACIDS RESEARCH

Continuous Distributed Representation of Biological Sequences for Deep Proteomics and Genomics

(2015) Ehsaneddin Asgari et al.   PLoS One

DISOPRED3: precise disordered region predictions with annotated protein-binding activity

(2014) David T. Jones et al.   BIOINFORMATICS

Toward an accurate prediction of inter-residue distances in proteins using 2D recursive neural networks

(2014) Predrag Kukic 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

ECOD: An Evolutionary Classification of Protein Domains

(2014) Hua Cheng et al.   PLoS Computational Biology

SCOPe: Structural Classification of Proteins—extended, integrating SCOP and ASTRAL data and classification of new structures

(2013) Naomi K. Fox et al.   NUCLEIC ACIDS RESEARCH

Maximum Allowed Solvent Accessibilites of Residues in Proteins

(2013) Matthew Z. Tien et al.   PLoS One

Improved model quality assessment using ProQ2

(2012) Arjun Ray et al.   BMC BIOINFORMATICS

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

(2011) David T. Jones et al.   BIOINFORMATICS

Protein sequence comparison and fold recognition: progress and good-practice benchmarking

(2011) Johannes Söding et al.   CURRENT OPINION IN STRUCTURAL BIOLOGY

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

Hidden Markov model speed heuristic and iterative HMM search procedure

(2010) L Steven Johnson et al.   BMC BIOINFORMATICS

I-TASSER: a unified platform for automated protein structure and function prediction

(2010) Ambrish Roy et al.   Nature Protocols

Atomic-Level Characterization of the Structural Dynamics of Proteins

(2010) D. E. Shaw et al.   SCIENCE

Blind Test of Physics-Based Prediction of Protein Structures

(2009) M. Scott Shell et al.   BIOPHYSICAL JOURNAL

NNcon: improved protein contact map prediction using 2D-recursive neural networks

(2009) A. N. Tegge et al.   NUCLEIC ACIDS RESEARCH