The next wave of interactomics: Mapping the SLiM-based interactions of the intrinsically disordered proteome

Davey, Norman E.; Simonetti, Leandro; Ivarsson, Ylva

doi:10.1016/j.sbi.2023.102593

Cited by 20 publications

(13 citation statements)

References 68 publications

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“…generate extensive PPI datasets across various domain families [110][111][112]. In recent years, there has been a collective effort within the SLiM field to devise largescale approaches tailored for the comprehensive characterisation of SLiMs (Table 3) [109]. In a recent study, researchers compared the efficacy of biotinylated peptide pulldown and the protein interaction screen on a peptide matrix (PRISMA) coupled with mass spectrometry.…”

Section: High-throughput Ppi Detection Methods and Dmismentioning

confidence: 99%

“…Furthermore, Y2H might have limitations in capturing interactions mediated by PTMs, a crucial aspect for some SLiMs. Additionally, it is essential to recognise that Y2H, taking place in yeast cells, might not have fully recapitulated the complexity of interactions observed in higher eukaryotic cells [108,109]. Recently, a variation of Y2H has been developed known as protein domain mapping using Yeast 2 Hybrid-Next-Generation Sequencing (DoMY-Seq) to address the limitations associated with conventional methods.…”

Section: Yeast Two-hybridmentioning

confidence: 99%

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Uncovering domain motif interactions using high‐throughput protein–protein interaction detection methods

Idrees,

Paudel,

Sadaf

et al. 2024

FEBS Letters

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Protein–protein interactions (PPIs) are often mediated by short linear motifs (SLiMs) in one protein and domain in another, known as domain–motif interactions (DMIs). During the past decade, SLiMs have been studied to find their role in cellular functions such as post‐translational modifications, regulatory processes, protein scaffolding, cell cycle progression, cell adhesion, cell signalling and substrate selection for proteasomal degradation. This review provides a comprehensive overview of the current PPI detection techniques and resources, focusing on their relevance to capturing interactions mediated by SLiMs. We also address the challenges associated with capturing DMIs. Moreover, a case study analysing the BioGrid database as a source of DMI prediction revealed significant known DMI enrichment in different PPI detection methods. Overall, it can be said that current high‐throughput PPI detection methods can be a reliable source for predicting DMIs.

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Section: High-throughput Ppi Detection Methods and Dmismentioning

confidence: 99%

Section: Yeast Two-hybridmentioning

confidence: 99%

Uncovering domain motif interactions using high‐throughput protein–protein interaction detection methods

Idrees,

Paudel,

Sadaf

et al. 2024

FEBS Letters

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“…Focusing on the segments of IDR sequences that show strong similarity in sequence alignments (which we refer to as "positional conservation") has provided rich insights into the functions of so-called short-linear motifs (SLiMs) and Molecular Recognition Features (MoRFs) (Davey et al 2023;Kumar et al 2022;Malhis & Gsponer 2015;Mohan et al 2006;Tompa et al 2014). However, positionally conserved elements typically constitute only a minor fraction of an IDR sequence, and many of the experimentally characterized SLiMs are not positionally conserved (Davey et al 2012;Kumar et al 2022;Nguyen Ba et al 2012;Van Roey et al 2014).…”

Section: Introductionmentioning

confidence: 99%

“…While the presence of IDRs in proteins can generally be predicted with high accuracy from their amino-acid sequences (Emenecker et al 2021; Necci et al 2021), identifying the relationship between the sequences and functions of IDRs remains a difficult task (Basu et al 2023; Chow et al 2023; Hu et al 2021; Lu et al 2022; Pang & Liu 2022; Pritišanac et al 2019; Zarin et al 2019, 2021; Zhao et al 2021). Focusing on the segments of IDR sequences that show strong similarity in sequence alignments (which we refer to as “positional conservation”) has provided rich insights into the functions of so-called short-linear motifs (SLiMs) and Molecular Recognition Features (MoRFs) (Davey et al 2023; Kumar et al 2022; Malhis & Gsponer 2015; Mohan et al 2006; Tompa et al 2014). However, positionally conserved elements typically constitute only a minor fraction of an IDR sequence, and many of the experimentally characterized SLiMs are not positionally conserved (Davey et al 2012; Kumar et al 2022; Nguyen Ba et al 2012; Van Roey et al 2014).…”

Section: Introductionmentioning

confidence: 99%

A Functional Map of the Human Intrinsically Disordered Proteome

Pritišanac,

Alderson,

Kolarić

et al. 2024

Preprint

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Intrinsically disordered regions (IDRs) represent at least one-third of the human proteome and defy the established structure-function paradigm. Because IDRs often have limited positional sequence conservation, the functional classification of IDRs using standard bioinformatics is generally not possible. Here, we show that evolutionarily conserved molecular features of the intrinsically disordered human proteome (IDR-ome), termed evolutionary signatures, enable classification and prediction of IDR functions. Hierarchical clustering of the human IDR-ome based on evolutionary signatures reveals strong enrichments for frequently studied functions of IDRs in transcription and RNA processing, as well as diverse, rarely studied functions, ranging from sub-cellular localization and biomolecular condensates to cellular signaling, transmembrane transport, and the constitution of the cytoskeleton. We exploit the information that is encoded within evolutionary conservation of molecular features to propose functional annotations for every IDR in the human proteome, inspect the conserved molecular features that correlate with different functions, and discover frequently co-occurring IDR functions on the proteome scale. Further, we identify patterns of evolutionary conserved molecular features of IDRs within proteins of unknown function and disease-risk genes for conditions such as cancer and developmental disorders. Our map of the human IDR-ome should be a valuable resource that aids in the discovery of new IDR biology.

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“…Significant experimental efforts are being invested in finding SLiM-containing peptides from given proteomes. The experimental methods include peptide arrays coupled to mass-spectrometry, phage display of peptides from given proteomes, as well as functional assays such as transactivation assays and degradation assays (Davey, Simonetti and Ivarsson 2023). These methods typically generate a list of peptides that directly or indirectly bind to a given protein or exert a given function.…”

Section: Introductionmentioning

confidence: 99%

Benchmarking computational tools for de novo motif discovery

Simonetti,

Ivarsson,

Davey

2024

Preprint

Self Cite

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STRUCTURED ABSTRACTMotivationHigh-throughput experimental methods aimed at uncovering domain-peptide interactions have created numerous use cases for bioinformatic tools capable of detecting short linear motifs (SLiMs). Over the past twenty years, countless motif discovery tools have been introduced. However, the evaluation of these tools generally focus on a single tool and use artificially generated test-sets that fail to capture the authentic context of motifs within proteomic data. Consequently, these evaluations fall short in addressing real-world use cases.ResultsIn the current work, five motif discovery tools and seven general sequence alignment tools were benchmarked on their capacity to find SLiMs in datasets of peptides of varying complexity built from curated SLiM instances from the Eukaryotic Linear Motif database. We explored the effect of dataset size, peptide lengths, background noise levels and motif complexity on the motif discovery of these tools. The main metric used to compare all tools outputs was the percentage of correctly aligned SLiM containing peptides. Two motif discovery tools (DEME and SLiMFinder) and a sequence alignment tool (OPAL) outperformed the rest of the tools when benchmarked with this metric, averaging over 70% correctly aligned motif-containing peptides. The performance of the motif discovery tools and OPAL were not affected by the sizes of the test-sets, while increasing peptide length and noise levels lowered the performances of all tools. For N-/C-terminal motifs all tools performed well, while for those motifs that had low motif complexity only DEME and SLiMFinder returned correctly aligned motifs for ∼50% or more of the datasets.Availabilityhttps://doi.org/10.5281/zenodo.10467208.

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The next wave of interactomics: Mapping the SLiM-based interactions of the intrinsically disordered proteome

Cited by 20 publications

References 68 publications

Uncovering domain motif interactions using high‐throughput protein–protein interaction detection methods

Uncovering domain motif interactions using high‐throughput protein–protein interaction detection methods

A Functional Map of the Human Intrinsically Disordered Proteome

Benchmarking computational tools for de novo motif discovery

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