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

Jarnot, Patryk; Ziemska-Legięcka, Joanna; Dobson, László; Merski, M.; Mier, Pablo; Andrade‐Navarro, Miguel A.; Hancock, John M.; Dosztányi, Zsuzsanna; Paladin, Lisanna; Necci, Marco; Piovesan, Damiano; Tosatto, Silvio C. E.; Promponas, Vasilis J.; Grynberg, Marcin; Gruca, Aleksandra

doi:10.1093/nar/gkaa339

Cited by 82 publications

(37 citation statements)

References 52 publications

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“…The results obtained with the LCT server for this protein are comparable to those obtained when executing PlaToLoCo [ 22 ] with default parameters ( S2 Fig ). It calculates the low complexity regions from the input protein using several tools (SEG, CAST, fLPS, SIMPLE and GBSC).…”

Section: Resultssupporting

confidence: 67%

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

Mier

Andrade‐Navarro

2020

PLoS ONE

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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.

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Section: Resultssupporting

confidence: 67%

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

Mier

Andrade‐Navarro

2020

PLoS ONE

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“…All protein sequences were analyzed locally with InterProScan version 5.47-82.0 (default parameters) [ 20 ], sQanner (requiring a minimum of four identical consecutive amino acids) [ 21 ], and PlaToLoCo (running CAST with default parameters) [ 22 ], to positionally annotate their domains, polyX, and CBRs, respectively. In the case of CAST, each sequence is compared against a database of 20 different homopolymers, one per amino acid, using BLAST.…”

Section: Methodsmentioning

confidence: 99%

The Conservation of Low Complexity Regions in Bacterial Proteins Depends on the Pathogenicity of the Strain and Subcellular Location of the Protein

Mier

Andrade‐Navarro

2021

Genes

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Low complexity regions (LCRs) in proteins are characterized by amino acid frequencies that differ from the average. These regions evolve faster and tend to be less conserved between homologs than globular domains. They are not common in bacteria, as compared to their prevalence in eukaryotes. Studying their conservation could help provide hypotheses about their function. To obtain the appropriate evolutionary focus for this rapidly evolving feature, here we study the conservation of LCRs in bacterial strains and compare their high variability to the closeness of the strains. For this, we selected 20 taxonomically diverse bacterial species and obtained the completely sequenced proteomes of two strains per species. We calculated all orthologous pairs for each of the 20 strain pairs. Per orthologous pair, we computed the conservation of two types of LCRs: compositionally biased regions (CBRs) and homorepeats (polyX). Our results show that, in bacteria, Q-rich CBRs are the most conserved, while A-rich CBRs and polyA are the most variable. LCRs have generally higher conservation when comparing pathogenic strains. However, this result depends on protein subcellular location: LCRs accumulate in extracellular and outer membrane proteins, with conservation increased in the extracellular proteins of pathogens, and decreased for polyX in the outer membrane proteins of pathogens. We conclude that these dependencies support the functional importance of LCRs in host–pathogen interactions.

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“…Each experiment is manually curated to a high standard and mapped to the most recent assembly of the rice reference genome. The tools employed for genome alignment, quantification and differential expression include Tophat2/Star, HTSeq-count (Papatheodorou et al, 2020). The normalized gene expression values are for all samples in each dataset are in Transcripts Per Million (TPM) (see Table S3).…”

Section: Gene Expression Analysis and Visualizationmentioning

confidence: 99%

Beyond gene ontology (GO): using biocuration approach to improve the gene nomenclature and functional annotation of rice S-domain kinase subfamily

Naithani¹,

Dikeman²,

Garg³

et al. 2021

PeerJ

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The S-domain subfamily of receptor-like kinases (SDRLKs) in plants is poorly characterized. Most members of this subfamily are currently assigned gene function based on the S-locus Receptor Kinase from Brassica that acts as the female determinant of self-incompatibility (SI). However, Brassica like SI mechanisms does not exist in most plants. Thus, automated Gene Ontology (GO) pipelines are not sufficient for functional annotation of SDRLK subfamily members and lead to erroneous association with the GO biological process of SI. Here, we show that manual bio-curation can help to correct and improve the gene annotations and association with relevant biological processes. Using publicly available genomic and transcriptome datasets, we conducted a detailed analysis of the expansion of the rice (Oryza sativa) SDRLK subfamily, the structure of individual genes and proteins, and their expression.The 144-member SDRLK family in rice consists of 82 receptor-like kinases (RLKs) (67 full-length, 15 truncated),12 receptor-like proteins, 14 SD kinases, 26 kinase-like and 10 GnK2 domain-containing kinases and RLKs. Except for nine genes, all other SDRLK family members are transcribed in rice, but they vary in their tissue-specific and stress-response expression profiles. Furthermore, 98 genes show differential expression under biotic stress and 98 genes show differential expression under abiotic stress conditions, but share 81 genes in common.Our analysis led to the identification of candidate genes likely to play important roles in plant development, pathogen resistance, and abiotic stress tolerance. We propose a nomenclature for 144 SDRLK gene family members based on gene/protein conserved structural features, gene expression profiles, and literature review. Our biocuration approach, rooted in the principles of findability, accessibility, interoperability and reusability, sets forth an example of how manual annotation of large-gene families can fill in the knowledge gap that exists due to the implementation of automated GO projections, thereby helping to improve the quality and contents of public databases.

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

Cited by 82 publications

References 52 publications

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

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

The Conservation of Low Complexity Regions in Bacterial Proteins Depends on the Pathogenicity of the Strain and Subcellular Location of the Protein

Beyond gene ontology (GO): using biocuration approach to improve the gene nomenclature and functional annotation of rice S-domain kinase subfamily

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