scholarly journals Novel biochemical, structural and systems insights into inflammatory signaling revealed by contextual interaction proteomics

2021 ◽  
Author(s):  
Rodolfo Ciuffa ◽  
Federico Uliana ◽  
Martin Mehnert ◽  
Cathy Marulli ◽  
Ari Satanowski ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Large Scale ◽  
Functional Organization ◽  
Temporal Organization ◽  
Protein Protein Interactions ◽  
Main Mode ◽  
Signaling Complex ◽  
Ppi Networks ◽  
Level Information ◽  
Spatio Temporal

Protein-protein interactions (PPI) represent the main mode of the proteome organization in the cell. In the last decade, several large-scale representations of PPI networks have captured generic aspects of the functional organization of network components, but mostly lack the context of cellular states. However, the generation of contextual representations of PPI networks is essential for structural and systems-level modeling of biological processes and remains an unsolved challenge. In this study we describe an integrated experimental/computational strategy to achieve a contextualized modeling of PPI. This strategy defines the composition, stoichiometry, spatio-temporal organization and cellular requirements for the formation of target assemblies. We used this approach to generate an integrated model of the formation principles and architecture of a large signalosome, the TNF-receptor signaling complex (TNF-RSC). Overall, we show that the integration of systems- and structure-level information provides a generic, largely unexplored link between the modular proteome and cellular function.

scholarly journals Short loop functional commonality identified in leukaemia proteome highlights crucial protein sub-networks

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Sun Sook Chung ◽  
Joseph C F Ng ◽  
Anna Laddach ◽  
N Shaun B Thomas ◽  
Franca Fraternali
Keyword(s):  
Protein Interactions ◽  
Large Scale ◽  
Interaction Network ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Short Loop ◽  
New Strategy ◽  
Loop Network ◽  
Protein Protein Interaction Network

Abstract Direct drug targeting of mutated proteins in cancer is not always possible and efficacy can be nullified by compensating protein–protein interactions (PPIs). Here, we establish an in silico pipeline to identify specific PPI sub-networks containing mutated proteins as potential targets, which we apply to mutation data of four different leukaemias. Our method is based on extracting cyclic interactions of a small number of proteins topologically and functionally linked in the Protein–Protein Interaction Network (PPIN), which we call short loop network motifs (SLM). We uncover a new property of PPINs named ‘short loop commonality’ to measure indirect PPIs occurring via common SLM interactions. This detects ‘modules’ of PPI networks enriched with annotated biological functions of proteins containing mutation hotspots, exemplified by FLT3 and other receptor tyrosine kinase proteins. We further identify functional dependency or mutual exclusivity of short loop commonality pairs in large-scale cellular CRISPR–Cas9 knockout screening data. Our pipeline provides a new strategy for identifying new therapeutic targets for drug discovery.

scholarly journals From Experimental Approaches to Computational Techniques: A Review on the Prediction of Protein-Protein Interactions

2010 ◽  
Vol 2010 ◽  
pp. 1-15 ◽  
Author(s):  
Fiona Browne ◽  
Huiru Zheng ◽  
Haiying Wang ◽  
Francisco Azuaje
Keyword(s):  
Protein Interactions ◽  
Large Scale ◽  
Cellular Systems ◽  
Computational Techniques ◽  
Protein Protein Interactions ◽  
Therapeutic Approaches ◽  
Ppi Networks ◽  
Wide Scale ◽  
Experimental Approaches ◽  
High Throughput Experiments

A crucial step towards understanding the properties of cellular systems in organisms is to map their network of protein-protein interactions (PPIs) on a proteomic-wide scale completely and as accurately as possible. Uncovering the diverse function of proteins and their interactions within the cell may improve our understanding of disease and provide a basis for the development of novel therapeutic approaches. The development of large-scale high-throughput experiments has resulted in the production of a large volume of data which has aided in the uncovering of PPIs. However, these data are often erroneous and limited in interactome coverage. Therefore, additional experimental and computational methods are required to accelerate the discovery of PPIs. This paper provides a review on the prediction of PPIs addressing key prediction principles and highlighting the common experimental and computational techniques currently employed to infer PPI networks along with relevant studies in the area.

scholarly journals Protein-fragment complementation assays for large-scale analysis of protein–protein interactions

2021 ◽  
Author(s):  
Ewa Blaszczak ◽  
Natalia Lazarewicz ◽  
Aswani Sudevan ◽  
Robert Wysocki ◽  
Gwenaël Rabut
Keyword(s):  
Protein Interactions ◽  
Drug Targets ◽  
Large Scale ◽  
Protein Protein Interactions ◽  
Protein Fragment ◽  
Ppi Networks ◽  
Large Scale Analysis ◽  
Cellular Environment ◽  
Protein Fragment Complementation ◽  
Fragment Complementation

Protein–protein interactions (PPIs) orchestrate nearly all biological processes. They are also considered attractive drug targets for treating many human diseases, including cancers and neurodegenerative disorders. Protein-fragment complementation assays (PCAs) provide a direct and straightforward way to study PPIs in living cells or multicellular organisms. Importantly, PCAs can be used to detect the interaction of proteins expressed at endogenous levels in their native cellular environment. In this review, we present the principle of PCAs and discuss some of their advantages and limitations. We describe their application in large-scale experiments to investigate PPI networks and to screen or profile PPI targeting compounds.

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>

scholarly journals Multiple Interactions within the AKAP220 Signaling Complex Contribute to Protein Phosphatase 1 Regulation

2001 ◽  
Vol 276 (15) ◽  
pp. 12128-12134 ◽  
Author(s):  
Robynn V. Schillace ◽  
James W. Voltz ◽  
Alistair T. R. Sim ◽  
Shirish Shenolikar ◽  
John D. Scott
Keyword(s):  
Intermolecular Interactions ◽  
Protein Interactions ◽  
Protein Phosphatase ◽  
Competitive Inhibitor ◽  
Regulatory Subunit ◽  
Protein Protein Interactions ◽  
Signaling Complex ◽  
Activity Measurements ◽  
Anchoring Protein ◽  
Pka Regulatory Subunit

The phosphorylation status of cellular proteins is controlled by the opposing actions of protein kinases and phosphatases. Compartmentalization of these enzymes is critical for spatial and temporal control of these phosphorylation/dephosphorylation events. We previously reported that a 220-kDa A-kinase anchoring protein (AKAP220) coordinates the location of the cAMP-dependent protein kinase (PKA) and the type 1 protein phosphatase catalytic subunit (PP1c) (Schillace, R. V., and Scott, J. D. (1999)Curr. Biol.9, 321–324). We now demonstrate that an AKAP220 fragment is a competitive inhibitor of PP1c activity (Ki= 2.9 ± 0.7 μm). Mapping studies and activity measurements indicate that several protein-protein interactions act synergistically to inhibit PP1. A consensus targeting motif, between residues 1195 and 1198 (Lys-Val-Gln-Phe), binds but does not affect enzyme activity, whereas determinants between residues 1711 and 1901 inhibit the phosphatase. Analysis of truncated PP1c and chimeric PP1/2A catalytic subunits suggests that AKAP220 inhibits the phosphatase in a manner distinct from all known PP1 inhibitors and toxins. Intermolecular interactions within the AKAP220 signaling complex further contribute to PP1 inhibition as addition of the PKA regulatory subunit (RII) enhances phosphatase inhibition. These experiments indicate that regulation of PP1 activity by AKAP220 involves a complex network of intra- and intermolecular interactions.

scholarly journals Investigating the Role of Large-Scale Domain Dynamics in Protein-Protein Interactions

2016 ◽  
Vol 3 ◽  
Author(s):  
Elise Delaforge ◽  
Sigrid Milles ◽  
Jie-rong Huang ◽  
Denis Bouvier ◽  
Malene Ringkjøbing Jensen ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Large Scale ◽  
Protein Protein Interactions ◽  
Domain Dynamics

scholarly journals Protein–protein interactions in bacteria: a promising and challenging avenue towards the discovery of new antibiotics

2018 ◽  
Vol 14 ◽  
pp. 2881-2896 ◽  
Author(s):  
Laura Carro
Keyword(s):  
Protein Interactions ◽  
Bacterial Infections ◽  
Multidrug Resistant ◽  
Resistant Bacteria ◽  
Protein Protein Interactions ◽  
Lead Discovery ◽  
Antibiotic Development ◽  
Cellular Processes ◽  
Ppi Networks ◽  
New Antibiotics

Antibiotics are potent pharmacological weapons against bacterial infections; however, the growing antibiotic resistance of microorganisms is compromising the efficacy of the currently available pharmacotherapies. Even though antimicrobial resistance is not a new problem, antibiotic development has failed to match the growth of resistant pathogens and hence, it is highly critical to discover new anti-infective drugs with novel mechanisms of action which will help reducing the burden of multidrug-resistant microorganisms. Protein–protein interactions (PPIs) are involved in a myriad of vital cellular processes and have become an attractive target to treat diseases. Therefore, targeting PPI networks in bacteria may offer a new and unconventional point of intervention to develop novel anti-infective drugs which can combat the ever-increasing rate of multidrug-resistant bacteria. This review describes the progress achieved towards the discovery of molecules that disrupt PPI systems in bacteria for which inhibitors have been identified and whose targets could represent an alternative lead discovery strategy to obtain new anti-infective molecules.

Sign in / Sign up

Export Citation Format

Share Document