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Items: 1 to 50 of 140

1.

Codon selection reduces GC content bias in nucleic acids encoding for intrinsically disordered proteins.

Oldfield CJ, Peng Z, Uversky VN, Kurgan L.

Cell Mol Life Sci. 2019 Jun 7. doi: 10.1007/s00018-019-03166-6. [Epub ahead of print]

PMID:
31175370
2.

Endoplasmic reticulum and the microRNA environment in the cardiovascular system 1.

Groenendyk J, Fan X, Peng Z, Kurgan L, Michalak M.

Can J Physiol Pharmacol. 2019 Jun;97(6):515-527. doi: 10.1139/cjpp-2018-0720. Epub 2019 May 7.

PMID:
31063413
3.

Parallel experimental evolution reveals a novel repressive control of GalP on xylose fermentation in Escherichia coli.

Kurgan G, Sievert C, Flores A, Schneider A, Billings T, Panyon L, Morris C, Taylor E, Kurgan L, Cartwright R, Wang X.

Biotechnol Bioeng. 2019 Apr 30. doi: 10.1002/bit.27004. [Epub ahead of print]

PMID:
31038200
4.

Computational Prediction of MoRFs, Short Disorder-to-order Transitioning Protein Binding Regions.

Katuwawala A, Peng Z, Yang J, Kurgan L.

Comput Struct Biotechnol J. 2019 Mar 26;17:454-462. doi: 10.1016/j.csbj.2019.03.013. eCollection 2019. Review.

5.

Computational Prediction of Secondary and Supersecondary Structures from Protein Sequences.

Oldfield CJ, Chen K, Kurgan L.

Methods Mol Biol. 2019;1958:73-100. doi: 10.1007/978-1-4939-9161-7_4.

PMID:
30945214
6.

DeepFunc: A Deep Learning Framework for Accurate Prediction of Protein Functions from Protein Sequences and Interactions.

Zhang F, Song H, Zeng M, Li Y, Kurgan L, Li M.

Proteomics. 2019 Jun;19(12):e1900019. doi: 10.1002/pmic.201900019. Epub 2019 May 27.

PMID:
30941889
7.

Prediction of DNA-binding residues in local segments of protein sequences with Fuzzy Cognitive Maps.

Amirkhani A, Kolahdoozi M, Wang C, Kurgan L.

IEEE/ACM Trans Comput Biol Bioinform. 2018 Dec 28. doi: 10.1109/TCBB.2018.2890261. [Epub ahead of print]

PMID:
30602422
8.

Cyclosporine A binding to COX-2 reveals a novel signaling pathway that activates the IRE1α unfolded protein response sensor.

Groenendyk J, Paskevicius T, Urra H, Viricel C, Wang K, Barakat K, Hetz C, Kurgan L, Agellon LB, Michalak M.

Sci Rep. 2018 Nov 12;8(1):16678. doi: 10.1038/s41598-018-34891-w.

9.

Survey of Similarity-based Prediction of Drug-protein Interactions.

Kurgan L, Wang C.

Curr Med Chem. 2018 Nov 1. doi: 10.2174/0929867325666181101115314. [Epub ahead of print]

PMID:
30381061
10.

Prediction of Ion Channels and their Types from Protein Sequences: Comprehensive Review and Comparative Assessment.

Gao J, Miao Z, Zhang Z, Wei H, Kurgan L.

Curr Drug Targets. 2019;20(5):579-592. doi: 10.2174/1389450119666181022153942.

PMID:
30360734
11.

Quality assessment for the putative intrinsic disorder in proteins.

Hu G, Wu Z, Oldfield CJ, Wang C, Kurgan L.

Bioinformatics. 2019 May 15;35(10):1692-1700. doi: 10.1093/bioinformatics/bty881.

PMID:
30329008
12.

Predicting Functions of Disordered Proteins with MoRFpred.

Oldfield CJ, Uversky VN, Kurgan L.

Methods Mol Biol. 2019;1851:337-352. doi: 10.1007/978-1-4939-8736-8_19.

PMID:
30298407
13.

Taxonomic Landscape of the Dark Proteomes: Whole-Proteome Scale Interplay Between Structural Darkness, Intrinsic Disorder, and Crystallization Propensity.

Hu G, Wang K, Song J, Uversky VN, Kurgan L.

Proteomics. 2018 Nov;18(21-22):e1800243. doi: 10.1002/pmic.201800243. Epub 2018 Oct 8.

PMID:
30198635
14.

Sequence Similarity Searching.

Hu G, Kurgan L.

Curr Protoc Protein Sci. 2019 Feb;95(1):e71. doi: 10.1002/cpps.71. Epub 2018 Aug 13. Review.

PMID:
30102464
15.

Review and comparative assessment of similarity-based methods for prediction of drug-protein interactions in the druggable human proteome.

Wang C, Kurgan L.

Brief Bioinform. 2018 Aug 8. doi: 10.1093/bib/bby069. [Epub ahead of print]

PMID:
30102367
16.

High-throughput prediction of disordered moonlighting regions in protein sequences.

Meng F, Kurgan L.

Proteins. 2018 Oct;86(10):1097-1110. doi: 10.1002/prot.25590. Epub 2018 Sep 23.

PMID:
30099775
17.

In Silico Prediction and Validation of Novel RNA Binding Proteins and Residues in the Human Proteome.

Chowdhury S, Zhang J, Kurgan L.

Proteomics. 2018 Nov;18(21-22):e1800064. doi: 10.1002/pmic.201800064. Epub 2018 Jun 20.

PMID:
29806170
18.
19.

fDETECT webserver: fast predictor of propensity for protein production, purification, and crystallization.

Meng F, Wang C, Kurgan L.

BMC Bioinformatics. 2018 Jan 3;18(1):580. doi: 10.1186/s12859-017-1995-z.

20.

Functional Analysis of Human Hub Proteins and Their Interactors Involved in the Intrinsic Disorder-Enriched Interactions.

Hu G, Wu Z, Uversky VN, Kurgan L.

Int J Mol Sci. 2017 Dec 19;18(12). pii: E2761. doi: 10.3390/ijms18122761.

21.

Comprehensive review and empirical analysis of hallmarks of DNA-, RNA- and protein-binding residues in protein chains.

Zhang J, Ma Z, Kurgan L.

Brief Bioinform. 2017 Dec 15. doi: 10.1093/bib/bbx168. [Epub ahead of print]

PMID:
29253082
22.

Survey of Predictors of Propensity for Protein Production and Crystallization with Application to Predict Resolution of Crystal Structures.

Gao J, Wu Z, Hu G, Wang K, Song J, Joachimiak A, Kurgan L.

Curr Protein Pept Sci. 2018;19(2):200-210. doi: 10.2174/1389203718666170921114437.

PMID:
28933304
23.

Critical evaluation of bioinformatics tools for the prediction of protein crystallization propensity.

Wang H, Feng L, Webb GI, Kurgan L, Song J, Lin D.

Brief Bioinform. 2017 Nov 1;18(6):1092. doi: 10.1093/bib/bbx076. No abstract available.

PMID:
28651335
24.

Comprehensive review of methods for prediction of intrinsic disorder and its molecular functions.

Meng F, Uversky VN, Kurgan L.

Cell Mol Life Sci. 2017 Sep;74(17):3069-3090. doi: 10.1007/s00018-017-2555-4. Epub 2017 Jun 6. Review.

PMID:
28589442
25.

Computational Prediction of Intrinsic Disorder in Proteins.

Meng F, Uversky V, Kurgan L.

Curr Protoc Protein Sci. 2017 Apr 3;88:2.16.1-2.16.14. doi: 10.1002/cpps.28.

PMID:
28369666
26.

Review and comparative assessment of sequence-based predictors of protein-binding residues.

Zhang J, Kurgan L.

Brief Bioinform. 2018 Sep 28;19(5):821-837. doi: 10.1093/bib/bbx022. Review.

PMID:
28334258
27.

Critical evaluation of bioinformatics tools for the prediction of protein crystallization propensity.

Wang H, Feng L, Webb GI, Kurgan L, Song J, Lin D.

Brief Bioinform. 2018 Sep 28;19(5):838-852. doi: 10.1093/bib/bbx018. Review. Erratum in: Brief Bioinform. 2017 Nov 1;18(6):1092.

28.

Genes encoding intrinsic disorder in Eukaryota have high GC content.

Peng Z, Uversky VN, Kurgan L.

Intrinsically Disord Proteins. 2016 Dec 15;4(1):e1262225. doi: 10.1080/21690707.2016.1262225. eCollection 2016.

29.

How disordered is my protein and what is its disorder for? A guide through the "dark side" of the protein universe.

Lieutaud P, Ferron F, Uversky AV, Kurgan L, Uversky VN, Longhi S.

Intrinsically Disord Proteins. 2016 Dec 21;4(1):e1259708. doi: 10.1080/21690707.2016.1259708. eCollection 2016.

30.

DRNApred, fast sequence-based method that accurately predicts and discriminates DNA- and RNA-binding residues.

Yan J, Kurgan L.

Nucleic Acids Res. 2017 Jun 2;45(10):e84. doi: 10.1093/nar/gkx059.

31.

Computational Prediction of Protein Secondary Structure from Sequence.

Meng F, Kurgan L.

Curr Protoc Protein Sci. 2016 Nov 1;86:2.3.1-2.3.10. doi: 10.1002/cpps.19.

PMID:
27801519
32.

Prediction of Disordered RNA, DNA, and Protein Binding Regions Using DisoRDPbind.

Peng Z, Wang C, Uversky VN, Kurgan L.

Methods Mol Biol. 2017;1484:187-203. doi: 10.1007/978-1-4939-6406-2_14.

PMID:
27787828
33.

What are the structural features that drive partitioning of proteins in aqueous two-phase systems?

Wu Z, Hu G, Wang K, Zaslavsky BY, Kurgan L, Uversky VN.

Biochim Biophys Acta Proteins Proteom. 2017 Jan;1865(1):113-120. doi: 10.1016/j.bbapap.2016.09.010. Epub 2016 Sep 24.

PMID:
27663889
34.

Autophagy-related intrinsically disordered proteins in intra-nuclear compartments.

Na I, Meng F, Kurgan L, Uversky VN.

Mol Biosyst. 2016 Aug 16;12(9):2798-817. doi: 10.1039/c6mb00069j.

PMID:
27377881
35.

DFLpred: High-throughput prediction of disordered flexible linker regions in protein sequences.

Meng F, Kurgan L.

Bioinformatics. 2016 Jun 15;32(12):i341-i350. doi: 10.1093/bioinformatics/btw280.

36.

PSIONplus: Accurate Sequence-Based Predictor of Ion Channels and Their Types.

Gao J, Cui W, Sheng Y, Ruan J, Kurgan L.

PLoS One. 2016 Apr 4;11(4):e0152964. doi: 10.1371/journal.pone.0152964. eCollection 2016.

37.

Disordered nucleiome: Abundance of intrinsic disorder in the DNA- and RNA-binding proteins in 1121 species from Eukaryota, Bacteria and Archaea.

Wang C, Uversky VN, Kurgan L.

Proteomics. 2016 May;16(10):1486-98. doi: 10.1002/pmic.201500177. Epub 2016 Apr 27.

PMID:
27037624
38.

Compartmentalization and Functionality of Nuclear Disorder: Intrinsic Disorder and Protein-Protein Interactions in Intra-Nuclear Compartments.

Meng F, Na I, Kurgan L, Uversky VN.

Int J Mol Sci. 2015 Dec 25;17(1). pii: E24. doi: 10.3390/ijms17010024.

39.

Molecular recognition features (MoRFs) in three domains of life.

Yan J, Dunker AK, Uversky VN, Kurgan L.

Mol Biosyst. 2016 Mar;12(3):697-710. doi: 10.1039/c5mb00640f.

40.

PDID: database of molecular-level putative protein-drug interactions in the structural human proteome.

Wang C, Hu G, Wang K, Brylinski M, Xie L, Kurgan L.

Bioinformatics. 2016 Feb 15;32(4):579-86. doi: 10.1093/bioinformatics/btv597. Epub 2015 Oct 26.

41.

Genome-wide analysis of thapsigargin-induced microRNAs and their targets in NIH3T3 cells.

Groenendyk J, Fan X, Peng Z, Ilnytskyy Y, Kurgan L, Michalak M.

Genom Data. 2014 Oct 7;2:325-7. doi: 10.1016/j.gdata.2014.10.002. eCollection 2014 Dec.

42.

In various protein complexes, disordered protomers have large per-residue surface areas and area of protein-, DNA- and RNA-binding interfaces.

Wu Z, Hu G, Yang J, Peng Z, Uversky VN, Kurgan L.

FEBS Lett. 2015 Sep 14;589(19 Pt A):2561-9. doi: 10.1016/j.febslet.2015.08.014. Epub 2015 Aug 20.

43.

Untapped Potential of Disordered Proteins in Current Druggable Human Proteome.

Hu G, Wu Z, Wang K, Uversky VN, Kurgan L.

Curr Drug Targets. 2016;17(10):1198-205. Review.

PMID:
26201486
44.

High-throughput prediction of RNA, DNA and protein binding regions mediated by intrinsic disorder.

Peng Z, Kurgan L.

Nucleic Acids Res. 2015 Oct 15;43(18):e121. doi: 10.1093/nar/gkv585. Epub 2015 Jun 24.

45.

Unstructural biology of the Dengue virus proteins.

Meng F, Badierah RA, Almehdar HA, Redwan EM, Kurgan L, Uversky VN.

FEBS J. 2015 Sep;282(17):3368-94. doi: 10.1111/febs.13349. Epub 2015 Jul 15.

46.

A comprehensive comparative review of sequence-based predictors of DNA- and RNA-binding residues.

Yan J, Friedrich S, Kurgan L.

Brief Bioinform. 2016 Jan;17(1):88-105. doi: 10.1093/bib/bbv023. Epub 2015 May 1. Review.

PMID:
25935161
47.

Systematic investigation of sequence and structural motifs that recognize ATP.

Chen K, Wang D, Kurgan L.

Comput Biol Chem. 2015 Jun;56:131-41. doi: 10.1016/j.compbiolchem.2015.04.008. Epub 2015 Apr 20.

PMID:
25935117
48.

Correction to disordered proteinaceous machines.

Fuxreiter M, Tóth-Petróczy Á, Kraut DA, Matouschek A, Lim RY, Xue B, Kurgan L, Uversky VN.

Chem Rev. 2015 Apr 8;115(7):2780. doi: 10.1021/acs.chemrev.5b00150. Epub 2015 Mar 26. No abstract available.

49.

Comprehensive overview and assessment of computational prediction of microRNA targets in animals.

Fan X, Kurgan L.

Brief Bioinform. 2015 Sep;16(5):780-94. doi: 10.1093/bib/bbu044. Epub 2014 Dec 2. Review.

PMID:
25471818
50.

Covering complete proteomes with X-ray structures: a current snapshot.

Mizianty MJ, Fan X, Yan J, Chalmers E, Woloschuk C, Joachimiak A, Kurgan L.

Acta Crystallogr D Biol Crystallogr. 2014 Nov;70(Pt 11):2781-93. doi: 10.1107/S1399004714019427. Epub 2014 Oct 23.

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