Literature DB >> 30198635

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

Gang Hu1, Kui Wang1, Jiangning Song2,3, Vladimir N Uversky4,5, Lukasz Kurgan6.   

Abstract

Growth rate of the protein sequence universe dramatically exceeds the speed of expansion for the protein structure universe, generating an immense dark proteome that includes proteins with unknown structure. A whole-proteome scale analysis of 5.4 million proteins from 987 proteomes in the three domains of life and viruses to systematically dissect an interplay between structural coverage, degree of putative intrinsic disorder, and predicted propensity for structure determination is performed. It has been found that Archaean and Bacterial proteomes have relatively high structural coverage and low amounts of disorder, whereas Eukaryotic and Viral proteomes are characterized by a broad spread of structural coverage and higher disorder levels. The analysis reveals that dark proteomes (i.e., proteomes containing high fractions of proteins with unknown structure) have significantly elevated amounts of intrinsic disorder and are predicted to be difficult to solve structurally. Although the majority of dark proteomes are of viral origin, many dark viral proteomes have at least modest crystallization propensity and only a handful of them are enriched in the intrinsic disorder. The disorder, structural coverage, and propensity are mapped for structural determination onto a novel proteome-level sequence similarity network to analyze the interplay of these characteristics in the taxonomic landscape.
© 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Entities:  

Keywords:  X-ray crystallography; dark proteomes; intrinsic disorder; protein universe; structural darkness

Mesh:

Substances:

Year:  2018        PMID: 30198635     DOI: 10.1002/pmic.201800243

Source DB:  PubMed          Journal:  Proteomics        ISSN: 1615-9853            Impact factor:   3.984


  9 in total

Review 1.  Multi-functionality of proteins involved in GPCR and G protein signaling: making sense of structure-function continuum with intrinsic disorder-based proteoforms.

Authors:  Alexander V Fonin; April L Darling; Irina M Kuznetsova; Konstantin K Turoverov; Vladimir N Uversky
Journal:  Cell Mol Life Sci       Date:  2019-08-19       Impact factor: 9.261

2.  IDPology of the living cell: intrinsic disorder in the subcellular compartments of the human cell.

Authors:  Bi Zhao; Akila Katuwawala; Vladimir N Uversky; Lukasz Kurgan
Journal:  Cell Mol Life Sci       Date:  2020-09-30       Impact factor: 9.261

3.  DISOselect: Disorder predictor selection at the protein level.

Authors:  Akila Katuwawala; Christopher J Oldfield; Lukasz Kurgan
Journal:  Protein Sci       Date:  2019-11-07       Impact factor: 6.725

4.  Sequence-Derived Markers of Drug Targets and Potentially Druggable Human Proteins.

Authors:  Sina Ghadermarzi; Xingyi Li; Min Li; Lukasz Kurgan
Journal:  Front Genet       Date:  2019-11-15       Impact factor: 4.599

Review 5.  Comparative Assessment of Intrinsic Disorder Predictions with a Focus on Protein and Nucleic Acid-Binding Proteins.

Authors:  Akila Katuwawala; Lukasz Kurgan
Journal:  Biomolecules       Date:  2020-12-04

Review 6.  Deep learning in prediction of intrinsic disorder in proteins.

Authors:  Bi Zhao; Lukasz Kurgan
Journal:  Comput Struct Biotechnol J       Date:  2022-03-08       Impact factor: 7.271

7.  BMI1 Silencing Induces Mitochondrial Dysfunction in Lung Epithelial Cells Exposed to Hyperoxia.

Authors:  Helena Hernández-Cuervo; Ramani Soundararajan; Sahebgowda Sidramagowda Patil; Mason Breitzig; Matthew Alleyn; Lakshmi Galam; Richard Lockey; Vladimir N Uversky; Narasaiah Kolliputi
Journal:  Front Physiol       Date:  2022-03-30       Impact factor: 4.566

8.  flDPnn: Accurate intrinsic disorder prediction with putative propensities of disorder functions.

Authors:  Gang Hu; Akila Katuwawala; Kui Wang; Zhonghua Wu; Sina Ghadermarzi; Jianzhao Gao; Lukasz Kurgan
Journal:  Nat Commun       Date:  2021-07-21       Impact factor: 14.919

9.  SPOT-Disorder2: Improved Protein Intrinsic Disorder Prediction by Ensembled Deep Learning.

Authors:  Jack Hanson; Kuldip K Paliwal; Thomas Litfin; Yaoqi Zhou
Journal:  Genomics Proteomics Bioinformatics       Date:  2020-03-13       Impact factor: 7.691

  9 in total

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