Deep-learning based approach to identify substrates of human E3 ubiquitin ligases and deubiquitinases

[1]  Tingting Li,et al.  Systematic prediction of degrons and E3 ubiquitin ligase binding via deep learning , 2022, BMC Biology.

[2]  F. Gao,et al.  The role of ubiquitination and deubiquitination in tumor invasion and metastasis , 2022, International journal of biological sciences.

[3]  F. He,et al.  UbiBrowser 2.0: a comprehensive resource for proteome-wide known and predicted ubiquitin ligase/deubiquitinase–substrate interactions in eukaryotic species , 2021, Nucleic Acids Res..

[4]  Dongbo Min,et al.  Comprehensive Survey of Recent Drug Discovery Using Deep Learning , 2021, International journal of molecular sciences.

[5]  Xiangzheng Fu,et al.  Pretraining model for biological sequence data , 2021, Briefings in functional genomics.

[6]  Geoffrey I. Webb,et al.  iLearnPlus: a comprehensive and automated machine-learning platform for nucleic acid and protein sequence analysis, prediction and visualization , 2021, Nucleic acids research.

[7]  S. Elledge,et al.  Systematic characterization of mutations altering protein degradation in human cancers. , 2021, Molecular cell.

[8]  Kai-Yao Huang,et al.  UbiNet 2.0: a verified, classified, annotated and updated database of E3 ubiquitin ligase–substrate interactions , 2021, Database J. Biol. Databases Curation.

[9]  Jimeng Sun,et al.  DeepPurpose: a deep learning library for drug–target interaction prediction , 2020, Bioinform..

[10]  Myle Ott,et al.  Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences , 2019, Proceedings of the National Academy of Sciences.

[11]  Joseph C Ahn,et al.  Application of Artificial Intelligence for the Diagnosis and Treatment of Liver Diseases , 2020, Hepatology.

[12]  Qing Yang,et al.  The role of ubiquitination and deubiquitination in cancer metabolism , 2020, Molecular cancer.

[13]  D. Guan,et al.  UFMylation maintains tumour suppressor p53 stability by antagonizing its ubiquitination , 2020, Nature Cell Biology.

[14]  Victor Guallar,et al.  Mapping enzyme-substrate interactions: its potential to study the mechanism of enzymes. , 2020, Advances in protein chemistry and structural biology.

[15]  K. Wong.  DNMT1 as a therapeutic target in pancreatic cancer: mechanisms and clinical implications , 2020, Cellular Oncology.

[16]  Keng Wong Kah.  DNMT1: A Key Drug Target in Triple-Negative Breast Cancer. , 2020, Seminars in cancer biology.

[17]  Icgc,et al.  Pan-cancer analysis of whole genomes , 2017, bioRxiv.

[18]  Natalia Gimelshein,et al.  PyTorch: An Imperative Style, High-Performance Deep Learning Library , 2019, NeurIPS.

[19]  Y. Assaraf,et al.  Targeting the ubiquitin-proteasome pathway to overcome anti-cancer drug resistance. , 2019, Drug resistance updates : reviews and commentaries in antimicrobial and anticancer chemotherapy.

[20]  Miguel Pérez-Enciso,et al.  A Guide on Deep Learning for Complex Trait Genomic Prediction , 2019, Genes.

[21]  K. Baek,et al.  Regulatory interplay between deubiquitinating enzymes and cytokines , 2019, Cytokine & Growth Factor Reviews.

[22]  Jing Liu,et al.  A Multidimensional Characterization of E3 Ubiquitin Ligase and Substrate Interaction Network , 2019, iScience.

[23]  Ying Zhang,et al.  iUUCD 2.0: an update with rich annotations for ubiquitin and ubiquitin-like conjugations , 2017, Nucleic Acids Res..

[24]  F. He,et al.  An integrated bioinformatics platform for investigating the human E3 ubiquitin ligase-substrate interaction network , 2017, Nature Communications.

[25]  Anne E Carpenter,et al.  Opportunities and obstacles for deep learning in biology and medicine , 2017, bioRxiv.

[26]  J. Huibregtse,et al.  Enzyme–substrate relationships in the ubiquitin system: approaches for identifying substrates of ubiquitin ligases , 2017, Cellular and Molecular Life Sciences.

[27]  Shanshan Liu,et al.  DNMT1, DNMT3A and DNMT3B Polymorphisms Associated With Gastric Cancer Risk: A Systematic Review and Meta-analysis , 2016, EBioMedicine.

[28]  Tsippi Iny Stein,et al.  The GeneCards Suite: From Gene Data Mining to Disease Genome Sequence Analyses , 2016, Current protocols in bioinformatics.

[29]  H. Walden,et al.  Types of Ubiquitin Ligases , 2016, Cell.

[30]  P. Tompa,et al.  Tripartite degrons confer diversity and specificity on regulated protein degradation in the ubiquitin-proteasome system , 2016, Nature Communications.

[31]  Ellen T. Gelfand,et al.  The Genotype-Tissue Expression (GTEx) project , 2013, Nature Genetics.

[32]  Hodong Lee,et al.  E3Net: A System for Exploring E3-mediated Regulatory Networks of Cellular Functions* , 2011, Molecular & Cellular Proteomics.

[33]  Chuan-Yun Li,et al.  KOBAS 2.0: a web server for annotation and identification of enriched pathways and diseases , 2011, Nucleic Acids Res..

[34]  Mike Tyers,et al.  An Allosteric Inhibitor of the Human Cdc34 Ubiquitin-Conjugating Enzyme , 2011, Cell.

[35]  M. Unoki,et al.  Drug discovery targeting epigenetic codes: the great potential of UHRF1, which links DNA methylation and histone modifications, as a drug target in cancers and toxoplasmosis. , 2009, Biochemical pharmacology.

[36]  Mike Tyers,et al.  BioGRID: a general repository for interaction datasets , 2005, Nucleic Acids Res..

[37]  David S. Wishart,et al.  DrugBank: a comprehensive resource for in silico drug discovery and exploration , 2005, Nucleic Acids Res..

[38]  Cathy H. Wu,et al.  UniProt: the Universal Protein knowledgebase , 2004, Nucleic Acids Res..

[39]  Pierre Baldi,et al.  Assessing the accuracy of prediction algorithms for classification: an overview , 2000, Bioinform..

[40]  Johannes Gerdes,et al.  The Ki‐67 protein: From the known and the unknown , 2000, Journal of cellular physiology.