Recent developments in deep learning applied to protein structure prediction
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Title
Recent developments in deep learning applied to protein structure prediction
Authors
Keywords
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Journal
PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS
Volume 87, Issue 12, Pages 1179-1189
Publisher
Wiley
Online
2019-10-08
DOI
10.1002/prot.25824
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- (2018) David T Jones et al. BIOINFORMATICS
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- (2017) Evan Shelhamer et al. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
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- (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
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- CCMpred—fast and precise prediction of protein residue–residue contacts from correlated mutations
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- MetaPSICOV: combining coevolution methods for accurate prediction of contacts and long range hydrogen bonding in proteins
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- (2014) Magnus Ekeberg et al. JOURNAL OF COMPUTATIONAL PHYSICS
- De Novo Structure Prediction of Globular Proteins Aided by Sequence Variation-Derived Contacts
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- ECOD: An Evolutionary Classification of Protein Domains
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- Accurate de novo structure prediction of large transmembrane protein domains using fragment-assembly and correlated mutation analysis
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- PSICOV: precise structural contact prediction using sparse inverse covariance estimation on large multiple sequence alignments
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- 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
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- 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
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