Functional Switches in Transcription Regulation; Molecular Mimicry and Plasticity in Protein−Protein Interactions†

Biochemistry ◽  
2004 ◽  
Vol 43 (25) ◽  
pp. 7983-7991 ◽  
Author(s):  
Dorothy Beckett
Keyword(s):  
Transcription Regulation ◽  
Protein Interactions ◽  
Molecular Mimicry ◽  
Protein Protein Interactions

scholarly journals Blocking peptides and molecular mimicry as treatment for kidney disease

2017 ◽  
Vol 312 (6) ◽  
pp. F1016-F1025 ◽  
Author(s):  
Andrea Havasi ◽  
Weining Lu ◽  
Herbert T. Cohen ◽  
Laurence Beck ◽  
Zhiyong Wang ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Molecular Mimicry ◽  
Renal Diseases ◽  
Protein Protein Interactions ◽  
Therapeutic Trials ◽  
Bulk Absorption ◽  
Therapeutic Peptides ◽  
Novel Drug

Protein mimotopes, or blocking peptides, are small therapeutic peptides that prevent protein-protein interactions by selectively mimicking a native binding domain. Inexpensive technology facilitates straightforward design and production of blocking peptides in sufficient quantities to allow preventive and therapeutic trials in both in vitro and in vivo experimental disease models. The kidney is an ideal peptide target, since small molecules undergo rapid filtration and efficient bulk absorption by tubular epithelial cells. Because the half-life of peptides is markedly prolonged in the kidneys compared with the bloodstream, blocking peptides are an attractive tool for treating diverse renal diseases, including ischemia, proteinuric states, such as membranous nephropathy and focal and segmental glomerulosclerosis, and renal cell carcinoma. Therapeutic peptides represent one of the fastest-growing reagent classes for novel drug development in human disease, partly because of their ease of administration, high binding affinity, and minimal off-target effects. This review introduces the concepts of blocking peptide design, production, and administration and highlights the potential use of therapeutic peptides to prevent or treat specific renal diseases.

scholarly journals SLiMScape 3.x: a Cytoscape 3 app for discovery of Short Linear Motifs in protein interaction networks

F1000Research ◽  
2015 ◽  
Vol 4 ◽  
pp. 477 ◽  
Author(s):  
Emily Olorin ◽  
Kevin T. O'Brien ◽  
Nicolas Palopoli ◽  
Åsa Pérez-Bercoff ◽  
Denis C. Shields ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Evolutionary Dynamics ◽  
De Novo ◽  
Molecular Mimicry ◽  
Interaction Networks ◽  
Protein Protein Interactions ◽  
Homologous Proteins ◽  
Short Linear Motifs ◽  
Sequence Patterns ◽  
Linear Motifs

Short linear motifs (SLiMs) are small protein sequence patterns that mediate a large number of critical protein-protein interactions, involved in processes such as complex formation, signal transduction, localisation and stabilisation. SLiMs show rapid evolutionary dynamics and are frequently the targets of molecular mimicry by pathogens. Identifying enriched sequence patterns due to convergent evolution in non-homologous proteins has proven to be a successful strategy for computational SLiM prediction. Tools of the SLiMSuite package use this strategy, using a statistical model to identify SLiM enrichment based on the evolutionary relationships, amino acid composition and predicted disorder of the input proteins. The quality of input data is critical for successful SLiM prediction. Cytoscape provides a user-friendly, interactive environment to explore interaction networks and select proteins based on common features, such as shared interaction partners. SLiMScape embeds tools of the SLiMSuite package for de novo SLiM discovery (SLiMFinder and QSLiMFinder) and identifying occurrences/enrichment of known SLiMs (SLiMProb) within this interactive framework. SLiMScape makes it easier to (1) generate high quality hypothesis-driven datasets for these tools, and (2) visualise predicted SLiM occurrences within the context of the network. To generate new predictions, users can select nodes from a protein network or provide a set of Uniprot identifiers. SLiMProb also requires additional query motif input. Jobs are then run remotely on the SLiMSuite server (http://rest.slimsuite.unsw.edu.au) for subsequent retrieval and visualisation. SLiMScape can also be used to retrieve and visualise results from jobs run directly on the server. SLiMScape and SLiMSuite are open source and freely available via GitHub under GNU licenses.

scholarly journals ImitateDB: A Resource for Domain and Motif Mimicry in Host and Pathogen Proteins

2021 ◽  
Author(s):  
Sonali Tayal ◽  
Venugopal Bhatia ◽  
Tanya Mehrotra ◽  
Sonika Bhatnagar
Keyword(s):  
Protein Interactions ◽  
Molecular Mimicry ◽  
Sequence Motif ◽  
Structural Domain ◽  
Protein Protein Interactions ◽  
Web Based ◽  
Interaction Detection ◽  
Local Sequence ◽  
Host Pathogen ◽  
User Friendly

The host pathogen interactome can be visualized as a vast continuous network in which molecular mimicry of host proteins by pathogens constitutes a strategy to hijack the host pathways. Despite extensive work in this field, there is no dedicated resource for mimicked domains and motifs in host pathogen interactome. In this work, we collated all the data regarding the experimental host pathogen (HP) and host-host (HH) protein-protein interactions (PPIs). The domains and sequence linear motifs of the proteins were annotated using CD Search and ScanProsite. Host and pathogen proteins with a shared host interactor and similar domain/motif constitute a linear pair. A linear pair that exhibits global structural domain similarity (Domain linear pair; DLP) or local sequence motif similarity (Motif Linear Pair; MLP) has a high probability of being co-expressed and co-localized. 2,06,449 DLPs and 38,45,643 MLPs were identified in 50,812 experimental HP-PPIs and organized in a web- based resource, ImitateDB, accessible at http://imitatedb.sblab-nsit.net. ImitateDB provides user-friendly access to the mimicry data. It can be queried by protein UniProt ID, pathogen, domain, motif, or interaction detection method. The results are externally integrated using hyperlinked domain PSSM ID, motif ID and protein ID. Kinase, UL36, Smc and DEXDc were frequent DLP domains whereas Protein Kinase C, Casein Kinase 2, glycosylation and myristoylation sites were frequent MLP motifs. Novel DLP domains SANT, Tudor, PhoX and MLP motifs Microbodies C-terminal targeting signal, Ubiquitin-interacting motif and Lipocalin signature were proposed. ImitateDB constitutes a resource for researchers in the field of infectious diseases and microbiology.

Protein-protein interactions of the p53 family with the Bcl-2 family in hepatocellular carcinoma

2011 ◽  
Vol 49 (08) ◽  
Author(s):  
LC König ◽  
M Meinhard ◽  
C Sandig ◽  
MH Bender ◽  
A Lovas ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Protein Interactions ◽  
Protein Protein Interactions ◽  
P53 Family

Partial Purification of a Specific Plasminogen Activator from Porcine Parotid Glands

1974 ◽  
Vol 31 (03) ◽  
pp. 403-414 ◽  
Author(s):  
Terence Cartwright
Keyword(s):  
Nucleic Acid ◽  
Salivary Glands ◽  
Plasminogen Activator ◽  
Protein Interactions ◽  
Partial Purification ◽  
Protein Protein Interactions ◽  
Parotid Glands ◽  
Two Stage ◽  
Preliminary Characterization ◽  
Specific Inhibitors

SummaryA method is described for the extraction with buffers of near physiological pH of a plasminogen activator from porcine salivary glands. Substantial purification of the activator was achieved although this was to some extent complicated by concomitant extraction of nucleic acid from the glands. Preliminary characterization experiments using specific inhibitors suggested that the activator functioned by a similar mechanism to that proposed for urokinase, but with some important kinetic differences in two-stage assay systems. The lack of reactivity of the pig gland enzyme in these systems might be related to the tendency to protein-protein interactions observed with this material.

Target-Templated de novo Design of Macrocyclic D-/L-Peptides: Inhibitors of the PD-1/PD-L1 Interaction

2020 ◽  
Author(s):  
Salvador Guardiola ◽  
Monica Varese ◽  
Xavier Roig ◽  
Jesús Garcia ◽  
Ernest Giralt
Keyword(s):  
Protein Interactions ◽  
Cyclic Peptides ◽  
General Framework ◽  
Large Scale ◽  
De Novo ◽  
Inhibitory Effect ◽  
Original Text ◽  
Protein Protein Interactions ◽  
Retraction Notice ◽  
Pharmaceutical Properties

<p>NOTE: This preprint has been retracted by consensus from all authors. See the retraction notice in place above; the original text can be found under "Version 1", accessible from the version selector above.</p><p><br></p><p>------------------------------------------------------------------------</p><p><br></p><p>Peptides, together with antibodies, are among the most potent biochemical tools to modulate challenging protein-protein interactions. However, current structure-based methods are largely limited to natural peptides and are not suitable for designing target-specific binders with improved pharmaceutical properties, such as macrocyclic peptides. Here we report a general framework that leverages the computational power of Rosetta for large-scale backbone sampling and energy scoring, followed by side-chain composition, to design heterochiral cyclic peptides that bind to a protein surface of interest. To showcase the applicability of our approach, we identified two peptides (PD-<i>i</i>3 and PD-<i>i</i>6) that target PD-1, a key immune checkpoint, and work as protein ligand decoys. A comprehensive biophysical evaluation confirmed their binding mechanism to PD-1 and their inhibitory effect on the PD-1/PD-L1 interaction. Finally, elucidation of their solution structures by NMR served as validation of our <i>de novo </i>design approach. We anticipate that our results will provide a general framework for designing target-specific drug-like peptides.<i></i></p>

Target-Templated de novo Design of Macrocyclic D-/L-Peptides: Inhibitors of the PD-1/PD-L1 Interaction

2020 ◽  
Author(s):  
Salvador Guardiola ◽  
Monica Varese ◽  
Xavier Roig ◽  
Jesús Garcia ◽  
Ernest Giralt
Keyword(s):  
Protein Interactions ◽  
Cyclic Peptides ◽  
General Framework ◽  
Large Scale ◽  
De Novo ◽  
Inhibitory Effect ◽  
Original Text ◽  
Protein Protein Interactions ◽  
Retraction Notice ◽  
Pharmaceutical Properties

<p>NOTE: This preprint has been retracted by consensus from all authors. See the retraction notice in place above; the original text can be found under "Version 1", accessible from the version selector above.</p><p><br></p><p>------------------------------------------------------------------------</p><p><br></p><p>Peptides, together with antibodies, are among the most potent biochemical tools to modulate challenging protein-protein interactions. However, current structure-based methods are largely limited to natural peptides and are not suitable for designing target-specific binders with improved pharmaceutical properties, such as macrocyclic peptides. Here we report a general framework that leverages the computational power of Rosetta for large-scale backbone sampling and energy scoring, followed by side-chain composition, to design heterochiral cyclic peptides that bind to a protein surface of interest. To showcase the applicability of our approach, we identified two peptides (PD-<i>i</i>3 and PD-<i>i</i>6) that target PD-1, a key immune checkpoint, and work as protein ligand decoys. A comprehensive biophysical evaluation confirmed their binding mechanism to PD-1 and their inhibitory effect on the PD-1/PD-L1 interaction. Finally, elucidation of their solution structures by NMR served as validation of our <i>de novo </i>design approach. We anticipate that our results will provide a general framework for designing target-specific drug-like peptides.<i></i></p>

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