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Cited by 104

The DOI matches its Crossref record; 96 of the 96 references it cites were found in a registry or an index.

Disentangling the complexity of low complexity proteins

Mier, Pablo, Paladin, Lisanna, Tamana, Stella, Petrosian, Sophia, Hajdu-Soltész, Borbála, Urbanek, Annika, Gruca, Aleksandra, Plewczynski, Dariusz, Grynberg, Marcin, Bernadó, Pau, Gáspári, Zoltán, Ouzounis, Christos A, Promponas, Vasilis J, Kajava, Andrey V, Hancock, John M, Tosatto, Silvio C E, Dosztanyi, Zsuzsanna, Andrade-Navarro, Miguel A·2020·Briefings in Bioinformatics
DOI 10.1093/bib/bbz007PMC PMC7299295
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Abstract

There are multiple definitions for low complexity regions (LCRs) in protein sequences, with all of them broadly considering LCRs as regions with fewer amino acid types compared to an average composition. Following this view, LCRs can also be defined as regions showing composition bias. In this critical review, we focus on the definition of sequence complexity of LCRs and their connection with structure. We present statistics and methodological approaches that measure low complexity (LC) and related sequence properties. Composition bias is often associated with LC and disorder, but repeats, while compositionally biased, might also induce ordered structures. We illustrate this dichotomy, and more generally the overlaps between different properties related to LCRs, using examples. We argue that statistical measures alone cannot capture all structural aspects of LCRs and recommend the combined usage of a variety of predictive tools and measurements. While the methodologies available to study LCRs are already very advanced, we foresee that a more comprehensive annotation of sequences in the databases will enable the improvement of predictions and a better understanding of the evolution and the connection between structure and function of LCRs. This will require the use of standards for the generation and exchange of data describing all aspects of LCRs. Short abstract: There are multiple definitions for low complexity regions (LCRs) in protein sequences. In this critical review, we focus on the definition of sequence complexity of LCRs and their connection with structure. We present statistics and methodological approaches that measure low complexity (LC) and related sequence properties. Composition bias is often associated with LC and disorder, but repeats, while compositionally biased, might also induce ordered structures. We illustrate this dichotomy, plus overlaps between different properties related to LCRs, using examples.

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Figures

Figure 1 The LC diagram: sequence complexity composition versus periodicity. The diagram illustrates where several types of sequences would be placed in relation to two measures related to sequence complexity.
Figure 1 The LC diagram: sequence complexity composition versus periodicity. The diagram illustrates where several types of sequences would be placed in relation to two measures related to sequence complexity.CC BY-NC · PubMed Central
Figure 2 Shannon entropy value for each detected CBR against the CAST score normalized by the sequence length.
Figure 2 Shannon entropy value for each detected CBR against the CAST score normalized by the sequence length.CC BY-NC · PubMed Central
Figure 3 Motif graph based on SIMPLE analysis of CO1A1_HUMAN.
Figure 3 Motif graph based on SIMPLE analysis of CO1A1_HUMAN.CC BY-NC · PubMed Central
Figure 4 Comparison of positions detected to be of LC in the 21 proteins of our dataset. Methods SEG (in orange), CAST (in red), SIMPLE (in brown) and IUPred (in purple) were used. ANCHOR (in light blue), which includes structural aspects, is also compared.
Figure 4 Comparison of positions detected to be of LC in the 21 proteins of our dataset. Methods SEG (in orange), CAST (in red), SIMPLE (in brown) and IUPred (in purple) were used. ANCHOR (in light blue), which includes structural aspects, is also compared.CC BY-NC · PubMed Central
Figure 5 LC diagram for various sequence datasets. The percentage of the top amino acid as a function of the percentage of mutations to perfect repeats calculated for a dataset of globular (GLOB), disordered (IUP) sequences as well as fragments of our protein dataset with LC character according to the SEG, CAST and SIMPLE methods.
Figure 5 LC diagram for various sequence datasets. The percentage of the top amino acid as a function of the percentage of mutations to perfect repeats calculated for a dataset of globular (GLOB), disordered (IUP) sequences as well as fragments of our protein dataset with LC character according to the SEG, CAST and SIMPLE methods.CC BY-NC · PubMed Central
Figure 6 Structural features of LC proteins. Venn diagram representing the FELLS prediction of dataset proteins, in four categories: secondary structure (SS), LCRs, disorder and aggregation. Each protein is assigned to a category if more than 30% of the residues in its sequence are predicted in that state.
Figure 6 Structural features of LC proteins. Venn diagram representing the FELLS prediction of dataset proteins, in four categories: secondary structure (SS), LCRs, disorder and aggregation. Each protein is assigned to a category if more than 30% of the residues in its sequence are predicted in that state.CC BY-NC · PubMed Central

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Metadata

Authors
Mier, P., Paladin, L., Tamana, S., Petrosian, S., Hajdu-Soltész, B., Urbanek, A., Gruca, A., Plewczynski, D., Grynberg, M., Bernadó, P., Gáspári, Z., Ouzounis, C. A., Promponas, V. J., Kajava, A. V., Hancock, J. M., Tosatto, S. C. E., Dosztanyi, Z., & Andrade-Navarro, M. A.
Affiliations
  • Institute of Organismic and Molecular Evolution, Johannes Gutenberg University of Mainz, Mainz, Germany
  • Department of Biomedical Science, University of Padova, Padova, Italy
  • Bioinformatics Research Laboratory, Department of Biological Sciences, University of Cyprus, Nicosia, Cyprus
  • Biological Computation and Process Laboratory, Chemical Process & Energy Resources Institute, Centre for Research & Technology Hellas, Thessalonica, Greece
  • MTA-ELTE Lendület Bioinformatics Research Group, Department of Biochemistry, Eötvös Loránd University, Budapest, Hungary
  • Centre de Biochimie Structurale, INSERM, CNRS, Université de Montpellier, Montpellier, France
  • Institute of Informatics, Silesian University of Technology, Gliwice, Poland
  • Center of New Technologies, University of Warsaw, Warsaw, Poland
  • Institute of Biochemistry and Biophysics, Warsaw, Poland
  • Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
  • Centre de Recherche en Biologie Cellulaire de Montpellier, CNRS-UMR, Institut de Biologie Computationnelle, Universite de Montpellier, Montpellier, France
  • Earlham Institute, Norwich, UK
ORCID
Venue
Briefings in Bioinformatics
Publisher
Oxford University Press (OUP)
Published
2019-01-30
Volume
21
Issue
2
Pages
458-472
ISSN
  • 1467-5463
  • 1477-4054
DOI
10.1093/bib/bbz007
PMC
PMC7299295
Cited by
104
References
96
Language
en
Licence
creativecommons.org/licenses/by-nc/4.0/ (vor)
Funders
  • Institute of Informatics (BK-213/RAU2/2018)
  • National Research Development and Innovation Office (NN124363)
  • Hungarian Academy of Sciences (LP2014-18)
  • European Research Council (648030)
  • European Union (778247)
  • COST Association (BM1405)
  • János Bolyai Research Scholar

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