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For: Katuwawala A, Oldfield CJ, Kurgan L. Accuracy of protein-level disorder predictions. Briefings in Bioinformatics 2020;21:1509-22. [DOI: 10.1093/bib/bbz100] [Cited by in Crossref: 13] [Cited by in F6Publishing: 12] [Article Influence: 4.3] [Reference Citation Analysis]
Number Citing Articles
1 Kurgan L. Resources for computational prediction of intrinsic disorder in proteins. Methods 2022. [DOI: 10.1016/j.ymeth.2022.03.018] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
2 Almog G, Olabode AS, Poon AFY. Tuning intrinsic disorder predictors for virus proteins. Virus Evol 2021;7:veaa106. [PMID: 33614158 DOI: 10.1093/ve/veaa106] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
3 Fan BL, Jiang Z, Sun J, Liu R. Systematic characterization and prediction of coenzyme A-associated proteins using sequence and network information. Brief Bioinform 2021;22:bbaa308. [PMID: 33253385 DOI: 10.1093/bib/bbaa308] [Reference Citation Analysis]
4 Katuwawala A, Ghadermarzi S, Hu G, Wu Z, Kurgan L. QUARTERplus: Accurate disorder predictions integrated with interpretable residue-level quality assessment scores. Comput Struct Biotechnol J 2021;19:2597-606. [PMID: 34025946 DOI: 10.1016/j.csbj.2021.04.066] [Reference Citation Analysis]
5 Katuwawala A, Zhao B, Kurgan L. DisoLipPred: Accurate prediction of disordered lipid binding residues in protein sequences with deep recurrent networks and transfer learning. Bioinformatics 2021:btab640. [PMID: 34487138 DOI: 10.1093/bioinformatics/btab640] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]
6 Zhang F, Shi W, Zhang J, Zeng M, Li M, Kurgan L. PROBselect: accurate prediction of protein-binding residues from proteins sequences via dynamic predictor selection. Bioinformatics 2020;36:i735-44. [DOI: 10.1093/bioinformatics/btaa806] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
7 Zhao B, Katuwawala A, Oldfield CJ, Dunker AK, Faraggi E, Gsponer J, Kloczkowski A, Malhis N, Mirdita M, Obradovic Z, Söding J, Steinegger M, Zhou Y, Kurgan L. DescribePROT: database of amino acid-level protein structure and function predictions. Nucleic Acids Res 2021;49:D298-308. [PMID: 33119734 DOI: 10.1093/nar/gkaa931] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
8 Micsonai A, Moussong É, Murvai N, Tantos Á, Tőke O, Réfrégiers M, Wien F, Kardos J. Disordered–Ordered Protein Binary Classification by Circular Dichroism Spectroscopy. Front Mol Biosci 2022;9:863141. [DOI: 10.3389/fmolb.2022.863141] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
9 Poboinev VV, Khrustalev VV, Khrustaleva TA, Kasko TE, Popkov VD. The PentUnFOLD algorithm as a tool to distinguish the dark and the light sides of the structural instability of proteins. Amino Acids 2022. [PMID: 35294674 DOI: 10.1007/s00726-022-03153-5] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
10 Biro B, Zhao B, Kurgan L. Complementarity of the residue-level protein function and structure predictions in human proteins. Computational and Structural Biotechnology Journal 2022. [DOI: 10.1016/j.csbj.2022.05.003] [Reference Citation Analysis]
11 Wang K, Hu G, Wu Z, Su H, Yang J, Kurgan L. Comprehensive Survey and Comparative Assessment of RNA-Binding Residue Predictions with Analysis by RNA Type. Int J Mol Sci 2020;21:E6879. [PMID: 32961749 DOI: 10.3390/ijms21186879] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 2.5] [Reference Citation Analysis]
12 Emenecker RJ, Griffith D, Holehouse AS. Metapredict: a fast, accurate, and easy-to-use predictor of consensus disorder and structure. Biophys J 2021;120:4312-9. [PMID: 34480923 DOI: 10.1016/j.bpj.2021.08.039] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
13 Katuwawala A, Oldfield CJ, Kurgan L. DISOselect: Disorder predictor selection at the protein level. Protein Sci 2020;29:184-200. [PMID: 31642118 DOI: 10.1002/pro.3756] [Cited by in Crossref: 6] [Cited by in F6Publishing: 5] [Article Influence: 2.0] [Reference Citation Analysis]
14 Peng Z, Xing Q, Kurgan L. APOD: accurate sequence-based predictor of disordered flexible linkers. Bioinformatics 2020;36:i754-61. [PMID: 33381830 DOI: 10.1093/bioinformatics/btaa808] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
15 Shamilov R, Vinogradova O, Aneskievich BJ. The Anti-Inflammatory Protein TNIP1 Is Intrinsically Disordered with Structural Flexibility Contributed by Its AHD1-UBAN Domain. Biomolecules 2020;10:E1531. [PMID: 33182596 DOI: 10.3390/biom10111531] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
16 Deom CM, Brewer MT, Severns PM. Positive selection and intrinsic disorder are associated with multifunctional C4(AC4) proteins and geminivirus diversification. Sci Rep 2021;11:11150. [PMID: 34045539 DOI: 10.1038/s41598-021-90557-0] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
17 Bondos SE, Dunker AK, Uversky VN. Intrinsically disordered proteins play diverse roles in cell signaling. Cell Commun Signal 2022;20:20. [PMID: 35177069 DOI: 10.1186/s12964-022-00821-7] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
18 Chakraborty R, Hasija Y. Predicting protein intrinsically disordered regions by applying natural language processing practices. Soft Comput. [DOI: 10.1007/s00500-022-07085-w] [Reference Citation Analysis]
19 Roy S, Ghosh P, Bandyopadhyay A, Basu S. Capturing a Crucial ‘Disorder-to-Order Transition’ at the Heart of the Coronavirus Molecular Pathology—Triggered by Highly Persistent, Interchangeable Salt-Bridges. Vaccines 2022;10:301. [DOI: 10.3390/vaccines10020301] [Reference Citation Analysis]
20 Zhao B, Kurgan L. Surveying over 100 predictors of intrinsic disorder in proteins. Expert Rev Proteomics 2021;:1-11. [PMID: 34894985 DOI: 10.1080/14789450.2021.2018304] [Reference Citation Analysis]
21 Zhao B, Katuwawala A, Uversky VN, Kurgan L. IDPology of the living cell: intrinsic disorder in the subcellular compartments of the human cell. Cell Mol Life Sci 2021;78:2371-85. [PMID: 32997198 DOI: 10.1007/s00018-020-03654-0] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
22 Katuwawala A, Kurgan L. Comparative Assessment of Intrinsic Disorder Predictions with a Focus on Protein and Nucleic Acid-Binding Proteins. Biomolecules 2020;10:E1636. [PMID: 33291838 DOI: 10.3390/biom10121636] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
23 Zhao B, Kurgan L. Deep Learning in Prediction of Intrinsic Disorder in Proteins. Computational and Structural Biotechnology Journal 2022. [DOI: 10.1016/j.csbj.2022.03.003] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
24 Zhao B, Katuwawala A, Oldfield CJ, Hu G, Wu Z, Uversky VN, Kurgan L. Intrinsic Disorder in Human RNA-Binding Proteins. J Mol Biol 2021;433:167229. [PMID: 34487791 DOI: 10.1016/j.jmb.2021.167229] [Reference Citation Analysis]