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Cited by 17Protein Structure and DynamicsMachine Learning in BioinformaticsRNA and protein synthesis mechanisms

The DOI matches its Crossref record; 45 of the 45 references it cites were found in a registry or an index.

Protein intrinsically disordered regions have a non-random, modular architecture

Open access
McConnell, Brendan S, Parker, Matthew W·2023·Bioinformatics
DOI 10.1093/bioinformatics/btad732PMC PMC10719218

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Abstract

Motivation: Protein sequences can be broadly categorized into two classes: those which adopt stable secondary structure and fold into a domain (i.e. globular proteins), and those that do not. The sequences belonging to this latter class are conformationally heterogeneous and are described as being intrinsically disordered. Decades of investigation into the structure and function of globular proteins has resulted in a suite of computational tools that enable their sub-classification by domain type, an approach that has revolutionized how we understand and predict protein functionality. Conversely, it is unknown if sequences of disordered protein regions are subject to broadly generalizable organizational principles that would enable their sub-classification. Results: Here, we report the development of a statistical approach that quantifies linear variance in amino acid composition across a sequence. With multiple examples, we provide evidence that intrinsically disordered regions are organized into statistically non-random modules of unique compositional bias. Modularity is observed for both low and high-complexity sequences and, in some cases, we find that modules are organized in repetitive patterns. These data demonstrate that disordered sequences are non-randomly organized into modular architectures and motivate future experiments to comprehensively classify module types and to determine the degree to which modules constitute functionally separable units analogous to the domains of globular proteins. Availability and implementation: The source code, documentation, and data to reproduce all figures are freely available at https://github.com/MWPlabUTSW/Chi-Score-Analysis.git. The analysis is also available as a Google Colab Notebook (https://colab.research.google.com/github/MWPlabUTSW/Chi-Score-Analysis/blob/main/ChiScore_Analysis.ipynb).

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Registry

Crossref · 10.1093/bioinformatics/btad732

Cites

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Statistics

Figures

Figure 1. A bioinformatic algorithm to measure compositional dispersion across a sequence. (A) For a given sequence, (a) pairwise chi-scores are calculated for all window-defined subsequences and plotted as a matrix. Window sizes include every even size from 6–22. Then, for each window size, (b) chi-scores are converted to Pearson’s correlation coefficients, (c) insulation scores are calculated to define local minima, which represent compositional boundaries, and (d) the minima identified for all window sizes are grouped according to proximity. From these groups, (e) each boundary placement is optimized to maximize chi-score and (f) z-scores are calculated for each boundary element and are used to iteratively remove non-significant boundaries. (B) The Chi-Score Analysis method can distinguish between human languages on the basis of alphabet usage. A quote from Peter Medawar’s Romane Lecture was translated into five languages and each translation was strung end-to-end for analysis. “Eng” = English, “Jpn” = Japanese, “Yrb” = Yoruba, “Spn” = Spanish, and “Pol” = Polish. (C) The Chi-Score Analysis method can distinguish between regions of sequence bias in the human protein FUS. Boundaries are shown for 95% confidence level.
Figure 1. A bioinformatic algorithm to measure compositional dispersion across a sequence. (A) For a given sequence, (a) pairwise chi-scores are calculated for all window-defined subsequences and plotted as a matrix. Window sizes include every even size from 6–22. Then, for each window size, (b) chi-scores are converted to Pearson’s correlation coefficients, (c) insulation scores are calculated to define local minima, which represent compositional boundaries, and (d) the minima identified for all window sizes are grouped according to proximity. From these groups, (e) each boundary placement is optimized to maximize chi-score and (f) z-scores are calculated for each boundary element and are used to iteratively remove non-significant boundaries. (B) The Chi-Score Analysis method can distinguish between human languages on the basis of alphabet usage. A quote from Peter Medawar’s Romane Lecture was translated into five languages and each translation was strung end-to-end for analysis. “Eng” = English, “Jpn” = Japanese, “Yrb” = Yoruba, “Spn” = Spanish, and “Pol” = Polish. (C) The Chi-Score Analysis method can distinguish between regions of sequence bias in the human protein FUS. Boundaries are shown for 95% confidence level.CC BY · PubMed Central
Figure 2. High-complexity disordered regions are non-randomly organized. (A) The C.elegans Orc1 protein has a long N-terminal IDR (Orc1-IDR, residues 1–245) as predicted by Metapredict (Emenecker et al. 2021) but no obvious sequence patterning. (B) Chi-Score Analysis reveals a strikingly modular architecture for Orc1-IDR with alternately repeating blocks of like-type sequences. Boundaries are shown for 95% confidence level and a sequence entropy plot is shown above the matrix. (C) Modularity is lost when the sequence of Orc1-IDR is randomly scrambled.
Figure 2. High-complexity disordered regions are non-randomly organized. (A) The C.elegans Orc1 protein has a long N-terminal IDR (Orc1-IDR, residues 1–245) as predicted by Metapredict (Emenecker et al. 2021) but no obvious sequence patterning. (B) Chi-Score Analysis reveals a strikingly modular architecture for Orc1-IDR with alternately repeating blocks of like-type sequences. Boundaries are shown for 95% confidence level and a sequence entropy plot is shown above the matrix. (C) Modularity is lost when the sequence of Orc1-IDR is randomly scrambled.CC BY · PubMed Central
Figure 3. Many IDRs have a modular architecture. Chi-Score Analysis for (A) human TDP-43 C-IDR (residues 259–414), (B) human Spt6 N-IDR (residues 1–310), (C) human NCL, (D) human KMT2B N-IDR (residues 1–962), and (E) C.crescentus RNaseE C-IDR (residues 543–907). Boundaries are shown for 95% confidence level and a sequence entropy (“E”) plot is shown above each matrix. NCL contains four tandem RNA binding domains, which are contained within the largest module in the matrix and appear compositionally uniform. (F) IDRs should not be thought of as indivisible units but as modular sequences with region-specific physicochemical features and biological function.
Figure 3. Many IDRs have a modular architecture. Chi-Score Analysis for (A) human TDP-43 C-IDR (residues 259–414), (B) human Spt6 N-IDR (residues 1–310), (C) human NCL, (D) human KMT2B N-IDR (residues 1–962), and (E) C.crescentus RNaseE C-IDR (residues 543–907). Boundaries are shown for 95% confidence level and a sequence entropy (“E”) plot is shown above each matrix. NCL contains four tandem RNA binding domains, which are contained within the largest module in the matrix and appear compositionally uniform. (F) IDRs should not be thought of as indivisible units but as modular sequences with region-specific physicochemical features and biological function.CC BY · PubMed Central

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Citations

Cites

Research works printed but not linked: 2

  1. Patil, 2012, Pac Symp Biocomput, 164
  2. Wetlaufer, 1973, Proc Natl Acad Sci USA, 697

Metadata

Authors
McConnell, B. S., & Parker, M. W.
Affiliations
The University of Texas Southwestern Medical Center (US)
Corresponding author
Parker, Matthew W
ORCID
Parker, Matthew W
Venue
Bioinformatics
Publisher
Oxford University Press (OUP)
Published
2023-12-01
Volume
39
Issue
12
ISSN
  • 1367-4803 (ISSN-L)
  • 1367-4811
DOI
10.1093/bioinformatics/btad732
PMC
PMC10719218
OpenAlex
W4389279319
Cited by
17
References
45
Citations by year
2026: 5 · 2025: 9 · 2024: 2 · 2023: 1
FWCI
1.51
Type
journal article
Language
en
Open access
Licence
Locations
  • Bioinformatics (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Funders
Welch Foundation (I-2074–20210327)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
Keywords
intrinsically disordered regions, amino acid composition, compositional bias, sequence organization, protein domain classification, conformational heterogeneity
MeSH terms
Protein Domains, Amino Acid Sequence, Amino Acids, Software, Intrinsically Disordered Proteins

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