LCR-eXXXplorer: a web platform to search, visualize and share data for low complexity regions in protein sequences

Optimizing strategy for the discovery of compositionally-biased or low-complexity regions in proteins

Proteins can contain tracts dominated by a subset of amino acids and that have a functional significance. These are often termed 'low-complexity regions' (LCRs) or 'compositionally-biased regions' (CBRs). However, a wide spectrum of compositional bias is possible, and program parameters used to annotate these regions are often arbitrarily chosen. Also, investigators are sometimes interested in longer regions, or sometimes very short ones. Here, two programs for annotating LCRs/CBRs, namely SEG and fLPS, are investigated in detail across the whole expanse of their parameter spaces. In doing so, boundary behaviours are resolved that are used to derive an optimized systematic strategy for annotating LCRs/CBRs. Sets of parameters that progressively annotate or 'cover' more of protein sequence space and are optimized for a given target length have been derived. This progressive annotation can be applied to discern the biological relevance of CBRs, e.g., in parsing domains for experimental constructs and in generating hypotheses. It is also useful for picking out candidate regions of interest of a given target length and bias signature, and for assessing the parameter dependence of annotations. This latter application is demonstrated for a set of human intrinsically-disordered proteins associated with cancer.

Bioinformatics tools for the sequence complexity estimates

We review current methods and bioinformatics tools for the text complexity estimates (information and entropy measures). The search DNA regions with extreme statistical characteristics such as low complexity regions are important for biophysical models of chromosome function and gene transcription regulation in genome scale. We discuss the complexity profiling for segmentation and delineation of genome sequences, search for genome repeats and transposable elements, and applications to next-generation sequencing reads. We review the complexity methods and new applications fields: analysis of mutation hotspots loci, analysis of short sequencing reads with quality control, and alignment-free genome comparisons. The algorithms implementing various numerical measures of text complexity estimates including combinatorial and linguistic measures have been developed before genome sequencing era. The series of tools to estimate sequence complexity use compression approaches, mainly by modification of Lempel-Ziv compression. Most of the tools are available online providing large-scale service for whole genome analysis. Novel machine learning applications for classification of complete genome sequences also include sequence compression and complexity algorithms. We present comparison of the complexity methods on the different sequence sets, the applications for gene transcription regulatory regions analysis. Furthermore, we discuss approaches and application of sequence complexity for proteins. The complexity measures for amino acid sequences could be calculated by the same entropy and compression-based algorithms. But the functional and evolutionary roles of low complexity regions in protein have specific features differing from DNA. The tools for protein sequence complexity aimed for protein structural constraints. It was shown that low complexity regions in protein sequences are conservative in evolution and have important biological and structural functions. Finally, we summarize recent findings in large scale genome complexity comparison and applications for coronavirus genome analysis.

Compensatory relationship between low-complexity regions and gene paralogy in the evolution of prokaryotes

The evolution of genomes in all life forms involves two distinct, dynamic types of genomic changes: gene duplication (and loss) that shape families of paralogous genes and extension (and contraction) of low-complexity regions (LCR), which occurs through dynamics of short repeats in protein-coding genes. Although the roles of each of these types of events in genome evolution have been studied, their co-evolutionary dynamics is not thoroughly understood. Here, by analyzing a wide range of genomes from diverse bacteria and archaea, we show that LCR and paralogy represent two distinct routes of evolution that are inversely correlated. The emergence of LCR is a prominent evolutionary mechanism in fast evolving, young protein families, whereas paralogy dominates the comparatively slow evolution of old protein families. The analysis of multiple prokaryotic genomes shows that the formation of LCR is likely a widespread, transient evolutionary mechanism that temporally and locally affects also ancestral functions, but apparently, fades away with time, under mutational and selective pressures, yielding to gene paralogy. We propose that compensatory relationships between short-term and longer-term evolutionary mechanisms are universal in the evolution of life.

The LCD-Composer webserver: high-specificity identification and functional analysis of low-complexity domains in proteins

SUMMARY

Low-complexity domains (LCDs) in proteins are regions enriched in a small subset of amino acids. LCDs exist in all domains of life, often have unusual biophysical behavior, and function in both normal and pathological processes. We recently developed an algorithm to identify LCDs based predominantly on amino acid composition thresholds. Here, we have integrated this algorithm with a webserver and augmented it with additional analysis options. Specifically, users can (i) search for LCDs in whole proteomes by setting minimum composition thresholds for individual or grouped amino acids, (ii) submit a known LCD sequence to search for similar LCDs, (iii) search for and plot LCDs within a single protein, (iv) statistically test for enrichment of LCDs within a user-provided protein set and (v) specifically identify proteins with multiple types of LCDs.

AVAILABILITY AND IMPLEMENTATION

The LCD-Composer server can be accessed at http://lcd-composer.bmb.colostate.edu. The corresponding command-line scripts can be accessed at https://github.com/RossLabCSU/LCD-Composer/tree/master/WebserverScripts.

De novo determination of mosquitocidal Cry11Aa and Cry11Ba structures from naturally-occurring nanocrystals

Cry11Aa and Cry11Ba are the two most potent toxins produced by mosquitocidal Bacillus thuringiensis subsp. israelensis and jegathesan, respectively. The toxins naturally crystallize within the host; however, the crystals are too small for structure determination at synchrotron sources. Therefore, we applied serial femtosecond crystallography at X-ray free electron lasers to in vivo-grown nanocrystals of these toxins. The structure of Cry11Aa was determined de novo using the single-wavelength anomalous dispersion method, which in turn enabled the determination of the Cry11Ba structure by molecular replacement. The two structures reveal a new pattern for in vivo crystallization of Cry toxins, whereby each of their three domains packs with a symmetrically identical domain, and a cleavable crystal packing motif is located within the protoxin rather than at the termini. The diversity of in vivo crystallization patterns suggests explanations for their varied levels of toxicity and rational approaches to improve these toxins for mosquito control.

fLPS 2.0: rapid annotation of compositionally-biased regions in biological sequences

Compositionally-biased (CB) regions in biological sequences are enriched for a subset of sequence residue types. These can be shorter regions with a concentrated bias (i.e., those termed ‘low-complexity’), or longer regions that have a compositional skew. These regions comprise a prominent class of the uncharacterized ‘dark matter’ of the protein universe. Here, I report the latest version of the fLPS package for the annotation of CB regions, which includes added consideration of DNA sequences, to label the eight possible biased regions of DNA. In this version, the user is now able to restrict analysis to a specified subset of residue types, and also to filter for previously annotated domains to enable detection of discontinuous CB regions. A ‘thorough’ option has been added which enables the labelling of subtler biases, typically made from a skew for several residue types. In the output, protein CB regions are now labelled with bias classes reflecting the physico-chemical character of the biasing residues. The fLPS 2.0 package is available from: https://github.com/pmharrison/flps2 or in a Supplemental File of this paper.

Increased BUB1B/BUBR1 expression contributes to aberrant DNA repair activity leading to resistance to DNA-damaging agents

There has been accumulating evidence for the clinical benefit of chemoradiation therapy (CRT), whereas mechanisms in CRT-recurrent clones derived from the primary tumor are still elusive. Herein, we identified an aberrant BUB1B/BUBR1 expression in CRT-recurrent clones in bladder cancer (BC) by comprehensive proteomic analysis. CRT-recurrent BC cells exhibited a cell-cycle-independent upregulation of BUB1B/BUBR1 expression rendering an enhanced DNA repair activity in response to DNA double-strand breaks (DSBs). With DNA repair analyses employing the CRISPR/cas9 system, we revealed that cells with aberrant BUB1B/BUBR1 expression dominantly exploit mutagenic nonhomologous end joining (NHEJ). We further found that phosphorylated ATM interacts with BUB1B/BUBR1 after ionizing radiation (IR) treatment, and the resistance to DSBs by increased BUB1B/BUBR1 depends on the functional ATM. In vivo, tumor growth of CRT-resistant T24R cells was abrogated by ATM inhibition using AZD0156. A dataset analysis identified FOXM1 as a putative BUB1B/BUBR1-targeting transcription factor causing its increased expression. These data collectively suggest a redundant role of BUB1B/BUBR1 underlying mutagenic NHEJ in an ATM-dependent manner, aside from the canonical activity of BUB1B/BUBR1 on the G2/M checkpoint, and offer novel clues to overcome CRT resistance.

Defining the RBPome of primary T helper cells to elucidate higher-order Roquin-mediated mRNA regulation

Post-transcriptional gene regulation in T cells is dynamic and complex as targeted transcripts respond to various factors. This is evident for the Icos mRNA encoding an essential costimulatory receptor that is regulated by several RNA-binding proteins (RBP), including Roquin-1 and Roquin-2. Here, we identify a core RBPome of 798 mouse and 801 human T cell proteins by utilizing global RNA interactome capture (RNA-IC) and orthogonal organic phase separation (OOPS). The RBPome includes Stat1, Stat4 and Vav1 proteins suggesting unexpected functions for these transcription factors and signal transducers. Based on proximity to Roquin-1, we select ~50 RBPs for testing coregulation of Roquin-1/2 targets by induced expression in wild-type or Roquin-1/2-deficient T cells. Besides Roquin-independent contributions from Rbms1 and Cpeb4 we also show Roquin-1/2-dependent and target-specific coregulation of Icos by Celf1 and Igf2bp3. Connecting the cellular RBPome in a post-transcriptional context, we find contributions from multiple RBPs to the prototypic regulation of mRNA targets by individual trans-acting factors.

An extensive RNA binding protein atlas (RBPome) for primary T cells would be a useful resource. Here the authors use two different methods to characterise the mouse and human T cell RBPome and show regulation of Roquin-1/2 dependent and independent pathways.

Disentangling the complexity of low complexity proteins

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.

Assessing the low complexity of protein sequences via the low complexity triangle

Background

Proteins with low complexity regions (LCRs) have atypical sequence and structural features. Their amino acid composition varies from the expected, determined proteome-wise, and they do not follow the rules of structural folding that prevail in globular regions. One way to characterize these regions is by assessing the repeatability of a sequence, that is, calculating the local propensity of a region to be part of a repeat.

Results

We combine two local measures of low complexity, repeatability (using the RES algorithm) and fraction of the most frequent amino acid, to evaluate different proteomes, datasets of protein regions with specific features, and individual cases of proteins with extreme compositions. We apply a representation called ‘low complexity triangle’ as a proof-of-concept to represent the low complexity measured values. Results show that proteomes have distinct signatures in the low complexity triangle, and that these signatures are associated to complexity features of the sequences. We developed a web tool called LCT (http://cbdm-01.zdv.uni-mainz.de/~munoz/lct/) to allow users to calculate the low complexity triangle of a given protein or region of interest.

Conclusions

The low complexity triangle proves to be a suitable procedure to represent the general low complexity of a sequence or protein dataset. Homorepeats, direpeats, compositionally biased regions and globular regions occupy characteristic positions in the triangle. The described pipeline can be used to characterize LCRs and may help in quantifying the content of degenerated tandem repeats in proteins and proteomes.

A Selective Autophagy Pathway for Phase-Separated Endocytic Protein Deposits

Autophagy eliminates cytoplasmic content selected by autophagy receptors, which link cargo to the membrane-bound autophagosomal ubiquitin-like protein Atg8/LC3. Here, we report a selective autophagy pathway for protein condensates formed by endocytic proteins in yeast. In this pathway, the endocytic protein Ede1 functions as a selective autophagy receptor. Distinct domains within Ede1 bind Atg8 and mediate phase separation into condensates. Both properties are necessary for an Ede1-dependent autophagy pathway for endocytic proteins, which differs from regular endocytosis and does not involve other known selective autophagy receptors but requires the core autophagy machinery. Cryo-electron tomography of Ede1-containing condensates, at the plasma membrane and in autophagic bodies, shows a phase-separated compartment at the beginning and end of the Ede1-mediated selective autophagy route. Our data suggest a model for autophagic degradation of macromolecular protein complexes by the action of intrinsic autophagy receptors.

PlaToLoCo: the first web meta-server for visualization and annotation of low complexity regions in proteins

Low complexity regions (LCRs) in protein sequences are characterized by a less diverse amino acid composition compared to typically observed sequence diversity. Recent studies have shown that LCRs may co-occur with intrinsically disordered regions, are highly conserved in many organisms, and often play important roles in protein functions and in diseases. In previous decades, several methods have been developed to identify regions with LCRs or amino acid bias, but most of them as stand-alone applications and currently there is no web-based tool which allows users to explore LCRs in protein sequences with additional functional annotations. We aim to fill this gap by providing PlaToLoCo - PLAtform of TOols for LOw COmplexity-a meta-server that integrates and collects the output of five different state-of-the-art tools for discovering LCRs and provides functional annotations such as domain detection, transmembrane segment prediction, and calculation of amino acid frequencies. In addition, the union or intersection of the results of the search on a query sequence can be obtained. By developing the PlaToLoCo meta-server, we provide the community with a fast and easily accessible tool for the analysis of LCRs with additional information included to aid the interpretation of the results. The PlaToLoCo platform is available at: http://platoloco.aei.polsl.pl/.

Dual ARID1A/ARID1B loss leads to rapid carcinogenesis and disruptive redistribution of BAF complexes

SWI/SNF chromatin remodelers play critical roles in development and cancer. The causal links between SWI/SNF complex disassembly and carcinogenesis are obscured by redundancy between paralogous components. Canonical cBAF-specific paralogs ARID1A and ARID1B are synthetic lethal in some contexts, but simultaneous mutations in both ARID1s are prevalent in cancer. To understand if and how cBAF abrogation causes cancer, we examined the physiologic and biochemical consequences of ARID1A/ARID1B loss. In double knockout liver and skin, aggressive carcinogenesis followed de-differentiation and hyperproliferation. In double mutant endometrial cancer, add-back of either induced senescence. Biochemically, residual cBAF subcomplexes resulting from loss of ARID1 scaffolding were unexpectedly found to disrupt polybromo containing pBAF function. 37 of 69 mutations in the conserved scaffolding domains of ARID1 proteins observed in human cancer caused complex disassembly, partially explaining their mutation spectra. ARID1-less, cBAF-less states promote carcinogenesis across tissues, and suggest caution against paralog-directed therapies for ARID1-mutant cancer.

Colorimetric detection of class A soybean saponins by coupling DNAzyme with the gap ligase chain reaction

Class A saponins are responsible for the taste of soybean products, and the rapid identification of class A saponins from soybean food is essential for both food safety and cultivar screening. In this study, we propose a colorimetric assay based on the coupling of gap ligase chain reaction (Gap-LCR) with DNAzyme to detect the target GmSg-1 genes of class A soybean saponins with the naked eye, without the involvement of expensive instruments. The limits of detection (LODs) for the GmSg-1a and GmSg-1b genes were determined to be 0.1618 and 0.1625 μM, respectively, with a linear range of 0.2-1.2 μM. The DNAzyme-based Gap LCR assay was successfully employed to identify the target genes from different soybean cultivars, providing a simple means for monitoring the quality of soybean products.

Pseudomonas aeruginosa core metabolism exerts a widespread growth-independent control on virulence

To assess the role of core metabolism genes in bacterial virulence - independently of their effect on growth - we correlated the genome, the transcriptome and the pathogenicity in flies and mice of 30 fully sequenced Pseudomonas strains. Gene presence correlates robustly with pathogenicity differences among all Pseudomonas species, but not among the P. aeruginosa strains. However, gene expression differences are evident between highly and lowly pathogenic P. aeruginosa strains in multiple virulence factors and a few metabolism genes. Moreover, 16.5%, a noticeable fraction of the core metabolism genes of P. aeruginosa strain PA14 (compared to 8.5% of the non-metabolic genes tested), appear necessary for full virulence when mutated. Most of these virulence-defective core metabolism mutants are compromised in at least one key virulence mechanism independently of auxotrophy. A pathway level analysis of PA14 core metabolism, uncovers beta-oxidation and the biosynthesis of amino-acids, succinate, citramalate, and chorismate to be important for full virulence. Strikingly, the relative expression among P. aeruginosa strains of genes belonging in these metabolic pathways is indicative of their pathogenicity. Thus, P. aeruginosa strain-to-strain virulence variation, remains largely obscure at the genome level, but can be dissected at the pathway level via functional transcriptomics of core metabolism.

Low complexity regions in the proteins of prokaryotes perform important functional roles and are highly conserved

We provide the first high-throughput analysis of the properties and functional role of Low Complexity Regions (LCRs) in more than 1500 prokaryotic and phage proteomes. We observe that, contrary to a widespread belief based on older and sparse data, LCRs actually have a significant, persistent and highly conserved presence and role in many and diverse prokaryotes. Their specific amino acid content is linked to proteins with certain molecular functions, such as the binding of RNA, DNA, metal-ions and polysaccharides. In addition, LCRs have been repeatedly identified in very ancient, and usually highly expressed proteins of the translation machinery. At last, based on the amino acid content enriched in certain categories, we have developed a neural network web server to identify LCRs and accurately predict whether they can bind nucleic acids, metal-ions or are involved in chaperone functions. An evaluation of the tool showed that it is highly accurate for eukaryotic proteins as well.

Compositionally Biased Dark Matter in the Protein Universe

Compositionally biased regions (BRs) occur when a few amino-acid types are enriched in a protein segment. There are possibly BR types in the known protein universe that have not been characterized experimentally. The UniProt protein database has been surveyed for evidence of such compositionally ''dark matter''. A ''dark biased region'' (DBR) is defined as a biased region with low probability of being an individual structural domain or intrinsically disordered region. The bias annotation program fLPS is used to generate a list of >13 million BRs, which is then thoroughly filtered for structure and intrinsic disorder. About a third of BRs (31%) has both substantial intrinsic disorder and structure. After filtering, there are ≈0.9 million DBRs (≈7% of the original BRs in ≈1.4% of proteins). These DBRs are hugely enriched in eukaryotes and hugely depleted in bacteria. They tend to be more hydrophobic than other protein regions, but are made of less extreme combinations of hydrophobic/hydrophilic residues. Given varying assumptions, It has been estimated that how many DBRs there might be for the high bias levels examined (with p-values < 1 × 10^-06 ), deriving a reasonable range of 0.7-7.2% of proteins having such DBRs. Hypotheses are examined about what such DBRs might be, that is, that they are from un- or undersampled domain/region categories or are unappreciated categories somewhat like existing ones.

A bioinformatics pipeline to search functional motifs within whole-proteome data: a case study of poxviruses

Proteins harbor domains or short linear motifs, which facilitate their functions and interactions. Finding functional motifs in protein sequences could predict the putative cellular roles or characteristics of hypothetical proteins. In this study, we present Shetti-Motif, which is an interactive tool to (i) map UniProt and PROSITE flat files, (ii) search for multiple pre-defined consensus patterns or experimentally validated functional motifs in large datasets protein sequences (proteome-wide), (iii) search for motifs containing repeated residues (low-complexity regions, e.g., Leu-, SR-, PEST-rich motifs, etc.). As proof of principle, using this comparative proteomics pipeline, eleven proteomes encoded by member of Poxviridae family were searched against about 100 experimentally validated functional motifs. The closely related viruses and viruses infect the same host cells (e.g. vaccinia and variola viruses) show similar motif-containing proteins profile. The motifs encoded by these viruses are correlated, which explains why poxviruses are able to interact with wide range of host cells. In conclusion, this in silico analysis is useful to establish a dataset(s) or potential proteins for further investigation or compare between species.

Profiles of low complexity regions in Apicomplexa

BACKGROUND

Low complexity regions (LCRs) are a ubiquitous feature in genomes and yet their evolutionary history and functional roles are unclear. Previous studies have shown contrasting evidence in favor of both neutral and selective mechanisms of evolution for different sets of LCRs suggesting that modes of identification of these regions may play a role in our ability to discern their evolutionary history. To further investigate this issue, we used a multiple threshold approach to identify species-specific profiles of proteome complexity and, by comparing properties of these sets, determine the influence that starting parameters have on evolutionary inferences.

RESULTS

We find that, although qualitatively similar, quantitatively each species has a unique LCR profile which represents the frequency of these regions within each genome. Inferences based on these profiles are more accurate in comparative analyses of genome complexity as they allow to determine the relative complexity of multiple genomes as well as the type of repetitiveness that is most common in each. Based on the multiple threshold LCR sets obtained, we identified predominant evolutionary mechanisms at different complexity levels, which show neutral mechanisms acting on highly repetitive LCRs (e.g., homopolymers) and selective forces becoming more important as heterogeneity of the LCRs increases.

CONCLUSIONS

Our results show how inferences based on LCRs are influenced by the parameters used to identify these regions. Sets of LCRs are heterogeneous aggregates of regions that include homo- and heteropolymers and, as such, evolve according to different mechanisms. LCR profiles provide a new way to investigate genome complexity across species and to determine the driving mechanism of their evolution.