Opinion Prediction of protein Post-Translational Modification sites: An overview
暂无分享,去创建一个
[1] Liam J. McGuffin,et al. The PSIPRED protein structure prediction server , 2000, Bioinform..
[2] Xianlin Han,et al. Multi-dimensional mass spectrometry-based shotgun lipidomics and novel strategies for lipidomic analyses. , 2012, Mass spectrometry reviews.
[3] Jianding Qiu,et al. Systematic Analysis and Prediction of Pupylation Sites in Prokaryotic Proteins , 2013, PloS one.
[4] M. Mann,et al. Lysine Acetylation Targets Protein Complexes and Co-Regulates Major Cellular Functions , 2009, Science.
[5] Y. Ishihama,et al. Large-scale identification of phosphorylation sites for profiling protein kinase selectivity. , 2014, Journal of proteome research.
[6] Lennart Martens,et al. Protein structure as a means to triage proposed PTM sites , 2013, Proteomics.
[7] M. Mann,et al. Uncovering Global SUMOylation Signaling Networks in a Site-Specific Manner , 2014, Nature Structural &Molecular Biology.
[8] Corinna Cortes,et al. Support-Vector Networks , 1995, Machine Learning.
[9] Wei Liu,et al. First succinyl-proteome profiling of extensively drug-resistant Mycobacterium tuberculosis revealed involvement of succinylation in cellular physiology. , 2015, Journal of proteome research.
[10] Md. Nurul Haque Mollah,et al. SuccinSite: a computational tool for the prediction of protein succinylation sites by exploiting the amino acid patterns and properties. , 2016, Molecular bioSystems.
[11] S.-W. Zhang,et al. Prediction of protein homo-oligomer types by pseudo amino acid composition: Approached with an improved feature extraction and Naive Bayes Feature Fusion , 2006, Amino Acids.
[12] R. Ranganathan,et al. Evolutionarily conserved pathways of energetic connectivity in protein families. , 1999, Science.
[13] Mike P. Liang,et al. Structural characterization of proteins using residue environments , 2005, Proteins.
[14] Edward L. Huttlin,et al. Systematic and quantitative assessment of the ubiquitin-modified proteome. , 2011, Molecular cell.
[15] Chun-Yuan Chen,et al. Covalent Small Ubiquitin-like Modifier (SUMO) Modification of Maf1 Protein Controls RNA Polymerase III-dependent Transcription Repression* , 2013, The Journal of Biological Chemistry.
[16] Derek J. Bailey,et al. One-hour proteome analysis in yeast , 2015, Nature Protocols.
[17] A. Burlingame,et al. Electron transfer dissociation (ETD): The mass spectrometric breakthrough essential for O‐GlcNAc protein site assignments—a study of the O‐GlcNAcylated protein Host Cell Factor C1 , 2013, Proteomics.
[18] Hu Chen,et al. A novel method for protein secondary structure prediction using dual‐layer SVM and profiles , 2004, Proteins.
[19] G. Nelsestuen,et al. Amino-terminal alanine functions in a calcium-specific process essential for membrane binding by prothrombin fragment 1. , 1988, Biochemistry.
[20] Dong Xu,et al. Computational Identification of Protein Methylation Sites through Bi-Profile Bayes Feature Extraction , 2009, PloS one.
[21] P. Radivojac,et al. Evaluation of features for catalytic residue prediction in novel folds , 2007 .
[22] G. Demartino. PUPylation: something old, something new, something borrowed, something Glu. , 2009, Trends in biochemical sciences.
[23] Paul Tempst,et al. Protein S-nitrosylation: a physiological signal for neuronal nitric oxide , 2001, Nature Cell Biology.
[24] Kunihiko Fukushima,et al. Cognitron: A self-organizing multilayered neural network , 1975, Biological Cybernetics.
[25] C. Sander,et al. Correlated Mutations and Residue Contacts , 1994 .
[26] Eran Segal,et al. Proteome-wide prediction of acetylation substrates , 2009, Proceedings of the National Academy of Sciences.
[27] Kelley W. Moremen,et al. Vertebrate protein glycosylation: diversity, synthesis and function , 2012, Nature Reviews Molecular Cell Biology.
[28] N. Blom,et al. Prediction of post‐translational glycosylation and phosphorylation of proteins from the amino acid sequence , 2004, Proteomics.
[29] D. Umlauf,et al. Site-specific analysis of histone methylation and acetylation. , 2004, Methods in molecular biology.
[30] Ashok Sharma,et al. Type 2 diabetes mellitus: phylogenetic motifs for predicting protein functional sites , 2007, Journal of Biosciences.
[31] Yang Zhang,et al. A comprehensive assessment of sequence-based and template-based methods for protein contact prediction , 2008, Bioinform..
[32] Derek J. Bailey,et al. The One Hour Yeast Proteome* , 2013, Molecular & Cellular Proteomics.
[33] Dariya S. Glazer,et al. The FEATURE framework for protein function annotation: modeling new functions, improving performance, and extending to novel applications , 2008, BMC Genomics.
[34] Ronenn Roubenoff,et al. Convergent Random Forest predictor: methodology for predicting drug response from genome-scale data applied to anti-TNF response. , 2009, Genomics.
[35] Changjiang Jin,et al. CSS-Palm 2.0: an updated software for palmitoylation sites prediction. , 2008, Protein engineering, design & selection : PEDS.
[36] Zexian Liu,et al. GPS-YNO2: computational prediction of tyrosine nitration sites in proteins. , 2011, Molecular bioSystems.
[37] J. Shabanowitz,et al. Peptide and protein sequence analysis by electron transfer dissociation mass spectrometry. , 2004, Proceedings of the National Academy of Sciences of the United States of America.
[38] P. Simon. Too Big to Ignore: The Business Case for Big Data , 2013 .
[39] Chuan Wang,et al. DescFold: A web server for protein fold recognition , 2009, BMC Bioinformatics.
[40] C. Sander,et al. Correlated mutations and residue contacts in proteins , 1994, Proteins.
[41] Marvin Minsky,et al. An introduction to computational geometry , 1969 .
[42] Zhi-Ping Liu,et al. Prediction of protein-RNA binding sites by a random forest method with combined features , 2010, Bioinform..
[43] Hiroyuki Kurata,et al. Computational identification of protein S-sulfenylation sites by incorporating the multiple sequence features information. , 2017, Molecular bioSystems.
[44] D. Hand,et al. Idiot's Bayes—Not So Stupid After All? , 2001 .
[45] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[46] Dianjing Guo,et al. A systematic identification of species-specific protein succinylation sites using joint element features information , 2017, International journal of nanomedicine.
[47] Yong-Zi Chen,et al. GANNPhos: a new phosphorylation site predictor based on a genetic algorithm integrated neural network. , 2007, Protein engineering, design & selection : PEDS.
[48] Masaru Tomita,et al. Microscale phosphoproteome analysis of 10,000 cells from human cancer cell lines. , 2011, Analytical chemistry.
[49] R. Sheppard,et al. Feline gastrin. An example of peptide sequence analysis by mass spectrometry. , 1969, Journal of the American Chemical Society.
[50] Vikram Pudi,et al. RBNBC: Repeat Based Naive Bayes Classifier for Biological Sequences , 2008, 2008 Eighth IEEE International Conference on Data Mining.
[51] Gil Amitai,et al. Network analysis of protein structures identifies functional residues. , 2004, Journal of molecular biology.
[52] Yanjun Qi,et al. Random Forest Similarity for Protein-Protein Interaction Prediction from Multiple Sources , 2004, Pacific Symposium on Biocomputing.
[53] Jinyan Li,et al. Computational Identification of Protein Pupylation Sites by Using Profile-Based Composition of k-Spaced Amino Acid Pairs , 2015, PloS one.
[54] S. Brunak,et al. Quantitative Phosphoproteomics Reveals Widespread Full Phosphorylation Site Occupancy During Mitosis , 2010, Science Signaling.
[55] Katalin F Medzihradszky,et al. Peptide sequence analysis. , 2005, Methods in enzymology.
[56] Xin Yao,et al. Diversity creation methods: a survey and categorisation , 2004, Inf. Fusion.
[57] A. Burlingame,et al. Global Identification and Characterization of Both O-GlcNAcylation and Phosphorylation at the Murine Synapse* , 2012, Molecular & Cellular Proteomics.
[58] Rafael A. Calvo,et al. Accuracy and Diversity in Ensembles of Text Categorisers , 2005, CLEI Electron. J..
[59] M. Sutter,et al. Bacterial ubiquitin-like modifier Pup is deamidated and conjugated to substrates by distinct but homologous enzymes , 2009, Nature Structural &Molecular Biology.
[60] C. Tung. Prediction of pupylation sites using the composition of k-spaced amino acid pairs. , 2013, Journal of theoretical biology.
[61] David E James,et al. Re-fraction: a machine learning approach for deterministic identification of protein homologues and splice variants in large-scale MS-based proteomics. , 2012, Journal of proteome research.
[62] Y. Zhang,et al. Influence of succinylation on physicochemical property of yak casein micelles. , 2016, Food chemistry.
[63] R. Polikar,et al. Ensemble based systems in decision making , 2006, IEEE Circuits and Systems Magazine.
[64] D. Opitz,et al. Popular Ensemble Methods: An Empirical Study , 1999, J. Artif. Intell. Res..
[65] Ian H. Witten,et al. Data mining in bioinformatics using Weka , 2004, Bioinform..
[66] Richard W. Aldrich,et al. A perturbation-based method for calculating explicit likelihood of evolutionary co-variance in multiple sequence alignments , 2004, Bioinform..
[67] B. Rost,et al. Identifying cysteines and histidines in transition‐metal‐binding sites using support vector machines and neural networks , 2006, Proteins.
[68] Chih-Jen Lin,et al. LIBSVM: A library for support vector machines , 2011, TIST.
[69] Philippe Bogaerts,et al. Fast and accurate predictions of protein stability changes upon mutations using statistical potentials and neural networks: PoPMuSiC-2.0 , 2009, Bioinform..
[70] Yingming Zhao,et al. Lysine glutarylation is a protein posttranslational modification regulated by SIRT5. , 2014, Cell metabolism.