COUSCOus: improved protein contact prediction using an empirical Bayes covariance estimator
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Ehsan Ullah | Raghvendra Mall | Michaël Aupetit | Khalid Kunji | Halima Bensmail | Mohammed El Anbari | Reda Rawi
[1] D T Jones,et al. Protein secondary structure prediction based on position-specific scoring matrices. , 1999, Journal of molecular biology.
[2] Marcin J. Skwark,et al. PconsC: combination of direct information methods and alignments improves contact prediction , 2013, Bioinform..
[3] T. N. Bhat,et al. The Protein Data Bank , 2000, Nucleic Acids Res..
[4] Jianlin Cheng,et al. Predicting protein residue-residue contacts using deep networks and boosting , 2012, Bioinform..
[5] Erik van Nimwegen,et al. Disentangling Direct from Indirect Co-Evolution of Residues in Protein Alignments , 2010, PLoS Comput. Biol..
[6] David T. Jones,et al. De Novo Structure Prediction of Globular Proteins Aided by Sequence Variation-Derived Contacts , 2014, PloS one.
[7] Thomas A. Hopf,et al. Protein structure prediction from sequence variation , 2012, Nature Biotechnology.
[8] L. C. Martin,et al. Using information theory to search for co-evolving residues in proteins , 2005, Bioinform..
[9] R. Tibshirani,et al. Sparse inverse covariance estimation with the graphical lasso. , 2008, Biostatistics.
[10] Massimiliano Pontil,et al. PSICOV: precise structural contact prediction using sparse inverse covariance estimation on large multiple sequence alignments , 2012, Bioinform..
[11] Dominik Heider,et al. Interpol: An R package for preprocessing of protein sequences , 2011, BioData Mining.
[12] L. R. Haff. Empirical Bayes Estimation of the Multivariate Normal Covariance Matrix , 1980 .
[13] Ying Liu,et al. Evol and ProDy for bridging protein sequence evolution and structural dynamics , 2014, Bioinform..
[14] C. Yanofsky,et al. Protein Structure Relationships Revealed by Mutational Analysis , 1964, Science.
[15] Frank DiMaio,et al. CASP11 refinement experiments with ROSETTA , 2016, Proteins.
[16] R Core Team,et al. R: A language and environment for statistical computing. , 2014 .
[17] David T. Jones,et al. MetaPSICOV: combining coevolution methods for accurate prediction of contacts and long range hydrogen bonding in proteins , 2014, Bioinform..
[18] Alfonso Valencia,et al. Assessment of intramolecular contact predictions for CASP7 , 2007, Proteins.
[19] D. Baker,et al. Assessing the utility of coevolution-based residue–residue contact predictions in a sequence- and structure-rich era , 2013, Proceedings of the National Academy of Sciences.
[20] Zhiyong Wang,et al. Protein contact prediction by integrating joint evolutionary coupling analysis and supervised learning , 2013, Bioinform..
[21] Alfonso Valencia,et al. Emerging methods in protein co-evolution , 2013 .
[22] S. Eddy,et al. Pfam: the protein families database , 2013, Nucleic Acids Res..
[23] T. Hwa,et al. Identification of direct residue contacts in protein–protein interaction by message passing , 2009, Proceedings of the National Academy of Sciences.
[24] Peter Dalgaard,et al. R Development Core Team (2010): R: A language and environment for statistical computing , 2010 .
[25] Olivier Ledoit,et al. Improved estimation of the covariance matrix of stock returns with an application to portfolio selection , 2003 .
[26] B. Matthews. Comparison of the predicted and observed secondary structure of T4 phage lysozyme. , 1975, Biochimica et biophysica acta.
[27] I. Johnstone. On the distribution of the largest eigenvalue in principal components analysis , 2001 .
[28] Michael I. Jordan. Graphical Models , 2003 .
[29] E. Aurell,et al. Improved contact prediction in proteins: using pseudolikelihoods to infer Potts models. , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.
[30] Kevin M. Knight,et al. NMR identification of the binding surfaces involved in the Salmonella and Shigella Type III secretion tip‐translocon protein–protein interactions , 2016, Proteins.
[31] A. Horovitz,et al. Mapping pathways of allosteric communication in GroEL by analysis of correlated mutations , 2002, Proteins.
[32] Thomas A. Hopf,et al. Three-Dimensional Structures of Membrane Proteins from Genomic Sequencing , 2012, Cell.
[33] Burkhard Rost,et al. FreeContact: fast and free software for protein contact prediction from residue co-evolution , 2014, BMC Bioinformatics.
[34] N. Meinshausen,et al. High-dimensional graphs and variable selection with the Lasso , 2006, math/0608017.
[35] C. Sander,et al. Direct-coupling analysis of residue coevolution captures native contacts across many protein families , 2011, Proceedings of the National Academy of Sciences.
[36] W. Fitch,et al. An improved method for determining codon variability in a gene and its application to the rate of fixation of mutations in evolution , 1970, Biochemical Genetics.
[37] D. Baker,et al. Robust and accurate prediction of residue–residue interactions across protein interfaces using evolutionary information , 2014, eLife.
[38] Thomas A. Hopf,et al. Protein 3D Structure Computed from Evolutionary Sequence Variation , 2011, PloS one.
[39] Gregory B. Gloor,et al. Mutual information without the influence of phylogeny or entropy dramatically improves residue contact prediction , 2008, Bioinform..
[40] G. Gloor,et al. Mutual information in protein multiple sequence alignments reveals two classes of coevolving positions. , 2005, Biochemistry.
[41] C. Stein,et al. Estimation with Quadratic Loss , 1992 .
[42] Marcin J. Skwark,et al. PconsFold: improved contact predictions improve protein models , 2014, Bioinform..
[43] Thomas A. Hopf,et al. Sequence co-evolution gives 3D contacts and structures of protein complexes , 2014, eLife.
[44] Leo S. D. Caves,et al. Bio3d: An R Package , 2022 .