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Idrees S, Paudel KR, Sadaf T, Hansbro PM. Uncovering domain motif interactions using high-throughput protein-protein interaction detection methods. FEBS Lett 2024; 598:725-742. [PMID: 38439692 DOI: 10.1002/1873-3468.14841] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 01/09/2024] [Accepted: 02/18/2024] [Indexed: 03/06/2024]
Abstract
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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Affiliation(s)
- Sobia Idrees
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, Australia
- Centre for Inflammation, Centenary Institute and Faculty of Science, School of Life Sciences, University of Technology Sydney, Australia
| | - Keshav Raj Paudel
- Centre for Inflammation, Centenary Institute and Faculty of Science, School of Life Sciences, University of Technology Sydney, Australia
| | - Tayyaba Sadaf
- Centre for Inflammation, Centenary Institute and Faculty of Science, School of Life Sciences, University of Technology Sydney, Australia
| | - Philip M Hansbro
- Centre for Inflammation, Centenary Institute and Faculty of Science, School of Life Sciences, University of Technology Sydney, Australia
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Idrees S, Paudel KR, Sadaf T, Hansbro PM. How different viruses perturb host cellular machinery via short linear motifs. EXCLI JOURNAL 2023; 22:1113-1128. [PMID: 38054205 PMCID: PMC10694346 DOI: 10.17179/excli2023-6328] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Figures] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 10/18/2023] [Indexed: 12/07/2023]
Abstract
The virus interacts with its hosts by developing protein-protein interactions. Most viruses employ protein interactions to imitate the host protein: A viral protein with the same amino acid sequence or structure as the host protein attaches to the host protein's binding partner and interferes with the host protein's pathways. Being opportunistic, viruses have evolved to manipulate host cellular mechanisms by mimicking short linear motifs. In this review, we shed light on the current understanding of mimicry via short linear motifs and focus on viral mimicry by genetically different viral subtypes by providing recent examples of mimicry evidence and how high-throughput methods can be a reliable source to study SLiM-mediated viral mimicry.
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Affiliation(s)
- Sobia Idrees
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW, Australia
- Centre for Inflammation, Centenary Institute and the University of Technology Sydney, School of Life Sciences, Faculty of Science, Sydney, New South Wales, Australia
| | - Keshav Raj Paudel
- Centre for Inflammation, Centenary Institute and the University of Technology Sydney, School of Life Sciences, Faculty of Science, Sydney, New South Wales, Australia
| | - Tayyaba Sadaf
- Centre for Inflammation, Centenary Institute and the University of Technology Sydney, School of Life Sciences, Faculty of Science, Sydney, New South Wales, Australia
| | - Philip M. Hansbro
- Centre for Inflammation, Centenary Institute and the University of Technology Sydney, School of Life Sciences, Faculty of Science, Sydney, New South Wales, Australia
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Wang L, Li FL, Ma XY, Cang Y, Bai F. PPI-Miner: A Structure and Sequence Motif Co-Driven Protein-Protein Interaction Mining and Modeling Computational Method. J Chem Inf Model 2022; 62:6160-6171. [PMID: 36448715 DOI: 10.1021/acs.jcim.2c01033] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2022]
Abstract
Protein-protein interactions (PPIs) play important roles in biological processes of life, and predicting PPIs becomes a critical scientific issue of concern. Most PPIs occur through small domains or motifs (fragments), which are challenging and laborious to map by standard biochemical approaches because they generally require the cloning of several truncation mutants. Here, we present a computational method, named as PPI-Miner, to fish potential protein interacting partners utilizing protein motifs as queries. In brief, this work first developed a motif-matching algorithm designed to identify the proteins that contain sequential or structural similar motifs with the given query motif. Being aligned to the query motif, the binding mode of the discovered motif and its receptor protein will be initially determined to be used to build PPI complexes accordingly. Eventually, a PPI complex structure could be built and optimized with a designed automatic protocol. Besides discovering PPIs, PPI-Miner can also be applied to other areas, i.e., the rational design of molecular glues and protein vaccines. In this work, PPI-Miner was employed to mine the potential cereblon (CRBN) substrates from human proteome. As a result, 1,739 candidates were predicted, and 16 of them have been experimentally validated in previous studies. The source code of PPI-Miner can be obtained from the GitHub repository (https://github.com/Wang-Lin-boop/PPI-Miner), the webserver is freely available for users (https://bailab.siais.shanghaitech.edu.cn/services/ppi-miner), and the database of predicted CRBN substrates is accessible at https://bailab.siais.shanghaitech.edu.cn/services/crbn-subslib.
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Affiliation(s)
| | | | | | | | - Fang Bai
- Shanghai Clinical Research and Trial Center, Shanghai201210, China
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Short Linear Motifs in Colorectal Cancer Interactome and Tumorigenesis. Cells 2022; 11:cells11233739. [PMID: 36496998 PMCID: PMC9737320 DOI: 10.3390/cells11233739] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 11/16/2022] [Accepted: 11/21/2022] [Indexed: 11/25/2022] Open
Abstract
Colorectal tumorigenesis is driven by alterations in genes and proteins responsible for cancer initiation, progression, and invasion. This multistage process is based on a dense network of protein-protein interactions (PPIs) that become dysregulated as a result of changes in various cell signaling effectors. PPIs in signaling and regulatory networks are known to be mediated by short linear motifs (SLiMs), which are conserved contiguous regions of 3-10 amino acids within interacting protein domains. SLiMs are the minimum sequences required for modulating cellular PPI networks. Thus, several in silico approaches have been developed to predict and analyze SLiM-mediated PPIs. In this review, we focus on emerging evidence supporting a crucial role for SLiMs in driver pathways that are disrupted in colorectal cancer (CRC) tumorigenesis and related PPI network alterations. As a result, SLiMs, along with short peptides, are attracting the interest of researchers to devise small molecules amenable to be used as novel anti-CRC targeted therapies. Overall, the characterization of SLiMs mediating crucial PPIs in CRC may foster the development of more specific combined pharmacological approaches.
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Shuler G, Hagai T. Rapidly evolving viral motifs mostly target biophysically constrained binding pockets of host proteins. Cell Rep 2022; 40:111212. [PMID: 35977510 DOI: 10.1016/j.celrep.2022.111212] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 06/11/2022] [Accepted: 07/22/2022] [Indexed: 11/28/2022] Open
Abstract
Evolutionary changes in host-virus interactions can alter the course of infection, but the biophysical and regulatory constraints that shape interface evolution remain largely unexplored. Here, we focus on viral mimicry of host-like motifs that allow binding to host domains and modulation of cellular pathways. We observe that motifs from unrelated viruses preferentially target conserved, widely expressed, and highly connected host proteins, enriched with regulatory and essential functions. The interface residues within these host domains are more conserved and bind a larger number of cellular proteins than similar motif-binding domains that are not known to interact with viruses. In contrast, rapidly evolving viral-binding human proteins form few interactions with other cellular proteins and display high tissue specificity, and their interfaces have few inter-residue contacts. Our results distinguish between conserved and rapidly evolving host-virus interfaces and show how various factors limit host capacity to evolve, allowing for efficient viral subversion of host machineries.
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Affiliation(s)
- Gal Shuler
- Shmunis School of Biomedicine and Cancer Research, George S Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv 69978, Israel
| | - Tzachi Hagai
- Shmunis School of Biomedicine and Cancer Research, George S Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv 69978, Israel.
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Khazen G, Gyulkhandanian A, Issa T, Maroun RC. Getting to know each other: PPIMem, a novel approach for predicting transmembrane protein-protein complexes. Comput Struct Biotechnol J 2021; 19:5184-5197. [PMID: 34630938 PMCID: PMC8476896 DOI: 10.1016/j.csbj.2021.09.013] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Revised: 08/23/2021] [Accepted: 09/12/2021] [Indexed: 02/03/2023] Open
Abstract
Because of their considerable number and diversity, membrane proteins and their macromolecular complexes represent the functional units of cells. Their quaternary structure may be stabilized by interactions between the α-helices of different proteins in the hydrophobic region of the cell membrane. Membrane proteins equally represent potential pharmacological targets par excellence for various diseases. Unfortunately, their experimental 3D structure and that of their complexes with other intramembrane protein partners are scarce due to technical difficulties. To overcome this key problem, we devised PPIMem, a computational approach for the specific prediction of higher-order structures of α-helical transmembrane proteins. The novel approach involves proper identification of the amino acid residues at the interface of molecular complexes with a 3D structure. The identified residues compose then nonlinear interaction motifs that are conveniently expressed as mathematical regular expressions. These are efficiently implemented for motif search in amino acid sequence databases, and for the accurate prediction of intramembrane protein-protein complexes. Our template interface-based approach predicted 21,544 binary complexes between 1,504 eukaryotic plasma membrane proteins across 39 species. We compare our predictions to experimental datasets of protein-protein interactions as a first validation method. The online database that results from the PPIMem algorithm with the annotated predicted interactions are implemented as a web server and can be accessed directly at https://transint.univ-evry.fr.
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Affiliation(s)
- Georges Khazen
- Computer Science and Mathematics Department, Lebanese American University, Byblos, Lebanon
| | - Aram Gyulkhandanian
- Inserm U1204/Université d'Evry/Université Paris-Saclay, Structure-Activité des Biomolécules Normales et Pathologiques, 91025 Evry, France
| | - Tina Issa
- Computer Science and Mathematics Department, Lebanese American University, Byblos, Lebanon
| | - Rachid C Maroun
- Inserm U1204/Université d'Evry/Université Paris-Saclay, Structure-Activité des Biomolécules Normales et Pathologiques, 91025 Evry, France
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Sarkar D, Saha S. Machine-learning techniques for the prediction of protein-protein interactions. J Biosci 2019; 44:104. [PMID: 31502581] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Protein-protein interactions (PPIs) are important for the study of protein functions and pathways involved in different biological processes, as well as for understanding the cause and progression of diseases. Several high-throughput experimental techniques have been employed for the identification of PPIs in a few model organisms, but still, there is a huge gap in identifying all possible binary PPIs in an organism. Therefore, PPI prediction using machine-learning algorithms has been used in conjunction with experimental methods for discovery of novel protein interactions. The two most popular supervised machine-learning techniques used in the prediction of PPIs are support vector machines and random forest classifiers. Bayesian-probabilistic inference has also been used but mainly for the scoring of high-throughput PPI dataset confidence measures. Recently, deep-learning algorithms have been used for sequence-based prediction of PPIs. Several clustering methods such as hierarchical and k-means are useful as unsupervised machine-learning algorithms for the prediction of interacting protein pairs without explicit data labelling. In summary, machine-learning techniques have been widely used for the prediction of PPIs thus allowing experimental researchers to study cellular PPI networks.
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Sarkar D, Saha S. Machine-learning techniques for the prediction of protein–protein interactions. J Biosci 2019. [DOI: 10.1007/s12038-019-9909-z] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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Idrees S, Pérez-Bercoff Å, Edwards RJ. SLiM-Enrich: computational assessment of protein-protein interaction data as a source of domain-motif interactions. PeerJ 2018; 6:e5858. [PMID: 30402352 PMCID: PMC6215436 DOI: 10.7717/peerj.5858] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2018] [Accepted: 10/02/2018] [Indexed: 01/21/2023] Open
Abstract
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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Affiliation(s)
- Sobia Idrees
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW, Australia
| | - Åsa Pérez-Bercoff
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW, Australia
| | - Richard J Edwards
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW, Australia
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Sarkar D, Jana T, Saha S. LMDIPred: A web-server for prediction of linear peptide sequences binding to SH3, WW and PDZ domains. PLoS One 2018; 13:e0200430. [PMID: 30001346 PMCID: PMC6042728 DOI: 10.1371/journal.pone.0200430] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2017] [Accepted: 06/26/2018] [Indexed: 12/29/2022] Open
Abstract
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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Affiliation(s)
| | - Tanmoy Jana
- Bioinformatics Centre, Bose Institute, Kolkata, India
| | - Sudipto Saha
- Bioinformatics Centre, Bose Institute, Kolkata, India
- * E-mail: ,
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The present and the future of motif-mediated protein-protein interactions. Curr Opin Struct Biol 2018; 50:162-170. [PMID: 29730529 DOI: 10.1016/j.sbi.2018.04.005] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2017] [Revised: 02/07/2018] [Accepted: 04/11/2018] [Indexed: 01/14/2023]
Abstract
Protein-protein interactions (PPIs) are essential to governing virtually all cellular processes. Of particular importance are the versatile motif-mediated interactions (MMIs), which are thus far underrepresented in available interaction data. This is largely due to technical difficulties inherent in the properties of MMIs, but due to the increasing recognition of the vital roles of MMIs in biology, several systematic approaches have recently been developed to detect novel MMIs. Consequently, rapidly growing numbers of motifs are being identified and pursued further for therapeutic applications. In this review, we discuss the current understanding on the diverse functions and disease-relevance of MMIs, the key methodologies for detection of MMIs, and the potential of MMIs for drug development.
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Becerra A, Bucheli VA, Moreno PA. Prediction of virus-host protein-protein interactions mediated by short linear motifs. BMC Bioinformatics 2017; 18:163. [PMID: 28279163 PMCID: PMC5345135 DOI: 10.1186/s12859-017-1570-7] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2016] [Accepted: 02/24/2017] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Short linear motifs in host organisms proteins can be mimicked by viruses to create protein-protein interactions that disable or control metabolic pathways. Given that viral linear motif instances of host motif regular expressions can be found by chance, it is necessary to develop filtering methods of functional linear motifs. We conduct a systematic comparison of linear motifs filtering methods to develop a computational approach for predicting motif-mediated protein-protein interactions between human and the human immunodeficiency virus 1 (HIV-1). RESULTS We implemented three filtering methods to obtain linear motif sets: 1) conserved in viral proteins (C), 2) located in disordered regions (D) and 3) rare or scarce in a set of randomized viral sequences (R). The sets C,D,R are united and intersected. The resulting sets are compared by the number of protein-protein interactions correctly inferred with them - with experimental validation. The comparison is done with HIV-1 sequences and interactions from the National Institute of Allergy and Infectious Diseases (NIAID). The number of correctly inferred interactions allows to rank the interactions by the sets used to deduce them: D∪R and C. The ordering of the sets is descending on the probability of capturing functional interactions. With respect to HIV-1, the sets C∪R, D∪R, C∪D∪R infer all known interactions between HIV1 and human proteins mediated by linear motifs. We found that the majority of conserved linear motifs in the virus are located in disordered regions. CONCLUSION We have developed a method for predicting protein-protein interactions mediated by linear motifs between HIV-1 and human proteins. The method only use protein sequences as inputs. We can extend the software developed to any other eukaryotic virus and host in order to find and rank candidate interactions. In future works we will use it to explore possible viral attack mechanisms based on linear motif mimicry.
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Affiliation(s)
- Andrés Becerra
- Escuela de ingeniería de sistemas y computación, Universidad del Valle, Calle 13 # 100-00, A. A. 25360, Cali, Colombia
| | - Victor A Bucheli
- Escuela de ingeniería de sistemas y computación, Universidad del Valle, Calle 13 # 100-00, A. A. 25360, Cali, Colombia
| | - Pedro A Moreno
- Escuela de ingeniería de sistemas y computación, Universidad del Valle, Calle 13 # 100-00, A. A. 25360, Cali, Colombia.
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Sarkar D, Patra P, Ghosh A, Saha S. Computational Framework for Prediction of Peptide Sequences That May Mediate Multiple Protein Interactions in Cancer-Associated Hub Proteins. PLoS One 2016; 11:e0155911. [PMID: 27218803 PMCID: PMC4878775 DOI: 10.1371/journal.pone.0155911] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2015] [Accepted: 05/08/2016] [Indexed: 01/26/2023] Open
Abstract
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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Affiliation(s)
| | - Piya Patra
- Maulana Abdul Kalam Azad University of Technology, Kolkata, India
| | - Abhirupa Ghosh
- Maulana Abdul Kalam Azad University of Technology, Kolkata, India
| | - Sudipto Saha
- Bioinformatics Centre, Bose Institute, Kolkata, India
- * E-mail: ;
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Blikstad C, Ivarsson Y. High-throughput methods for identification of protein-protein interactions involving short linear motifs. Cell Commun Signal 2015; 13:38. [PMID: 26297553 PMCID: PMC4546347 DOI: 10.1186/s12964-015-0116-8] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2015] [Accepted: 08/11/2015] [Indexed: 02/07/2023] Open
Abstract
Interactions between modular domains and short linear motifs (3–10 amino acids peptide stretches) are crucial for cell signaling. The motifs typically reside in the disordered regions of the proteome and the interactions are often transient, allowing for rapid changes in response to changing stimuli. The properties that make domain-motif interactions suitable for cell signaling also make them difficult to capture experimentally and they are therefore largely underrepresented in the known protein-protein interaction networks. Most of the knowledge on domain-motif interactions is derived from low-throughput studies, although there exist dedicated high-throughput methods for the identification of domain-motif interactions. The methods include arrays of peptides or proteins, display of peptides on phage or yeast, and yeast-two-hybrid experiments. We here provide a survey of scalable methods for domain-motif interaction profiling. These methods have frequently been applied to a limited number of ubiquitous domain families. It is now time to apply them to a broader set of peptide binding proteins, to provide a comprehensive picture of the linear motifs in the human proteome and to link them to their potential binding partners. Despite the plethora of methods, it is still a challenge for most approaches to identify interactions that rely on post-translational modification or context dependent or conditional interactions, suggesting directions for further method development.
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Affiliation(s)
- Cecilia Blikstad
- Department of Chemistry - BMC, Husargatan 3, 751 23, Uppsala, Sweden
| | - Ylva Ivarsson
- Department of Chemistry - BMC, Husargatan 3, 751 23, Uppsala, Sweden.
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