LMPID: A manually curated database of linear motifs mediating protein–protein interactions

Sarkar, Debasree; Jana, Tanmoy; Saha, Sudipto

doi:10.1093/database/bav014

Cited by 17 publications

(13 citation statements)

References 15 publications

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“…Knowledge of DMIs can provide molecular details of cellular processes and thus it is important to discover SLiMs and link them to their domain partners ( Davey et al, 2012 ; Neduva & Russell, 2005 ). Despite this, only a small fraction of the likely range of SLiMs, and the DMIs they mediate, have been identified ( Tompa et al, 2014 ) and curated in resources such as the Eukaryotic Linear Motif (ELM) resource ( Gouw et al, 2018 ), Linear Motif mediated Protein Interaction Database (LMPID) ( Sarkar, Jana & Saha, 2015 ), interActions of moDular domAiNs (ADAN) ( Encinar et al, 2009 ), and the database of three-dimensional interacting domains (3did) ( Mosca et al, 2014 ). SLiM-mediated interactions are typically low affinity ( Davey et al, 2012 ) and are thus vulnerable to being overlooked by classical PPI detection methods, such as affinity pulldown mass spectrometry (AP-MS) and yeast two-hybrid (Y2H), where high stringencies are typically employed to reduce false positive interactions.…”

Section: Introductionmentioning

confidence: 99%

SLiM-Enrich: computational assessment of protein–protein interaction data as a source of domain-motif interactions

Idrees

Pérez-Bercoff

Edwards

2018

PeerJ

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Many important cellular processes involve protein–protein interactions (PPIs) mediated by a Short Linear Motif (SLiM) in one protein interacting with a globular domain in another. Despite their significance, these domain-motif interactions (DMIs) are typically low affinity, which makes them challenging to identify by classical experimental approaches, such as affinity pulldown mass spectrometry (AP-MS) and yeast two-hybrid (Y2H). DMIs are generally underrepresented in PPI networks as a result. A number of computational methods now exist to predict SLiMs and/or DMIs from experimental interaction data but it is yet to be established how effective different PPI detection methods are for capturing these low affinity SLiM-mediated interactions. Here, we introduce a new computational pipeline (SLiM-Enrich) to assess how well a given source of PPI data captures DMIs and thus, by inference, how useful that data should be for SLiM discovery. SLiM-Enrich interrogates a PPI network for pairs of interacting proteins in which the first protein is known or predicted to interact with the second protein via a DMI. Permutation tests compare the number of known/predicted DMIs to the expected distribution if the two sets of proteins are randomly associated. This provides an estimate of DMI enrichment within the data and the false positive rate for individual DMIs. As a case study, we detect significant DMI enrichment in a high-throughput Y2H human PPI study. SLiM-Enrich analysis supports Y2H data as a source of DMIs and highlights the high false positive rates associated with naïve DMI prediction. SLiM-Enrich is available as an R Shiny app. The code is open source and available via a GNU GPL v3 license at: https://github.com/slimsuite/SLiMEnrich. A web server is available at: http://shiny.slimsuite.unsw.edu.au/SLiMEnrich/.

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Section: Introductionmentioning

confidence: 99%

SLiM-Enrich: computational assessment of protein–protein interaction data as a source of domain-motif interactions

Idrees

Pérez-Bercoff

Edwards

2018

PeerJ

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“…However, if the same sequence came either from a different position of the protein or from another protein or protein isoform, then all such entries were inserted in the positive dataset. Thus, a non-redundant and non-overlapping dataset consisting of 115, 140 and 165 peptide instances binding to SH3, WW and PDZ domains respectively, were extracted from the LMPID database [ 17 ], and used as positive training examples for the respective class of peptides. We wanted to use the same dataset for comparing the four methods and observed that 6-residue long peptides were the most abundant in all the three classes of peptide ligands ( Fig A in S1 File ).…”

Section: Methodsmentioning

confidence: 99%

“…We have formulated four different prediction strategies for LM peptides binding to SH3, WW and PDZ domains and assembled them all into a web-based bioinformatics resource named L inear M otif D omain I nteraction Pred iction ( LMDIPred ). We had previously compiled experimentally validated LM instances from published data into a manually curated database called LMPID (Linear Motif mediated Protein-Protein Interaction Database) [ 17 ]. Herein, we observed that the highest number of ligand peptides reported were for SH3, WW and PDZ domains.…”

Section: Introductionmentioning

confidence: 99%

LMDIPred: A web-server for prediction of linear peptide sequences binding to SH3, WW and PDZ domains

2018

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Protein-peptide interactions form an important subset of the total protein interaction network in the cell and play key roles in signaling and regulatory networks, and in major biological processes like cellular localization, protein degradation, and immune response. In this work, we have described the LMDIPred web server, an online resource for generalized prediction of linear peptide sequences that may bind to three most prevalent and well-studied peptide recognition modules (PRMs)—SH3, WW and PDZ. We have developed support vector machine (SVM)-based prediction models that achieved maximum Matthews Correlation Coefficient (MCC) of 0.85 with an accuracy of 94.55% for SH3, MCC of 0.90 with an accuracy of 95.82% for WW, and MCC of 0.83 with an accuracy of 92.29% for PDZ binding peptides. LMDIPred output combines predictions from these SVM models with predictions using Position-Specific Scoring Matrices (PSSMs) and string-matching methods using known domain-binding motif instances and regular expressions. All of these methods were evaluated using a five-fold cross-validation technique on both balanced and unbalanced datasets, and also validated on independent datasets. LMDIPred aims to provide a preliminary bioinformatics platform for sequence-based prediction of probable binding sites for SH3, WW or PDZ domains.

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“…Disordered region of the CP were searched in DisProt [ 34 ], IUpred [ 35 ] and ESpritz [ 36 ] and the locations of the identified OLPs were mapped accordingly. In addition, the protein sequences of the CPs (MYC, APC and MDM2) were scanned using the ELM [ 37 ] resource and searched in the LMPID [ 38 ] database, to look for already reported linear motif instances that coincide with those identified in this study. Surface accessibility of the OLP sequences was verified by submitting the CP sequences to SCRATCH prediction server [ 39 ].…”

Section: Methodsmentioning

confidence: 99%

Computational Framework for Prediction of Peptide Sequences That May Mediate Multiple Protein Interactions in Cancer-Associated Hub Proteins

et al. 2016

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A considerable proportion of protein-protein interactions (PPIs) in the cell are estimated to be mediated by very short peptide segments that approximately conform to specific sequence patterns known as linear motifs (LMs), often present in the disordered regions in the eukaryotic proteins. These peptides have been found to interact with low affinity and are able bind to multiple interactors, thus playing an important role in the PPI networks involving date hubs. In this work, PPI data and de novo motif identification based method (MEME) were used to identify such peptides in three cancer-associated hub proteins—MYC, APC and MDM2. The peptides corresponding to the significant LMs identified for each hub protein were aligned, the overlapping regions across these peptides being termed as overlapping linear peptides (OLPs). These OLPs were thus predicted to be responsible for multiple PPIs of the corresponding hub proteins and a scoring system was developed to rank them. We predicted six OLPs in MYC and five OLPs in MDM2 that scored higher than OLP predictions from randomly generated protein sets. Two OLP sequences from the C-terminal of MYC were predicted to bind with FBXW7, component of an E3 ubiquitin-protein ligase complex involved in proteasomal degradation of MYC. Similarly, we identified peptides in the C-terminal of MDM2 interacting with FKBP3, which has a specific role in auto-ubiquitinylation of MDM2. The peptide sequences predicted in MYC and MDM2 look promising for designing orthosteric inhibitors against possible disease-associated PPIs. Since these OLPs can interact with other proteins as well, these inhibitors should be specific to the targeted interactor to prevent undesired side-effects. This computational framework has been designed to predict and rank the peptide regions that may mediate multiple PPIs and can be applied to other disease-associated date hub proteins for prediction of novel therapeutic targets of small molecule PPI modulators.

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LMPID: A manually curated database of linear motifs mediating protein–protein interactions

Cited by 17 publications

References 15 publications

SLiM-Enrich: computational assessment of protein–protein interaction data as a source of domain-motif interactions

SLiM-Enrich: computational assessment of protein–protein interaction data as a source of domain-motif interactions

LMDIPred: A web-server for prediction of linear peptide sequences binding to SH3, WW and PDZ domains

Computational Framework for Prediction of Peptide Sequences That May Mediate Multiple Protein Interactions in Cancer-Associated Hub Proteins

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