scholarly journals Google matrix analysis of bi-functional SIGNOR network of protein–protein interactions

2020 ◽  
Vol 559 ◽  
pp. 125019
Author(s):  
Klaus M. Frahm ◽  
Dima L. Shepelyansky
Keyword(s):  
Protein Interactions ◽  
Matrix Analysis ◽  
Protein Protein Interactions ◽  
Google Matrix

scholarly journals Fibrosis Protein-Protein Interactions from Google Matrix Analysis of MetaCore Network

2021 ◽  
Vol 23 (1) ◽  
pp. 67
Author(s):  
Ekaterina Kotelnikova ◽  
Klaus M. Frahm ◽  
Dima L. Shepelyansky ◽  
Oksana Kunduzova
Keyword(s):  
Heart Failure ◽  
Protein Interactions ◽  
Cardiac Fibrosis ◽  
Matrix Analysis ◽  
Great Promise ◽  
Protein Protein Interactions ◽  
Matrix Algorithms ◽  
Google Matrix ◽  
End Stage Heart Failure ◽  
End Stage

Protein–protein interactions is a longstanding challenge in cardiac remodeling processes and heart failure. Here, we use the MetaCore network and the Google matrix algorithms for prediction of protein–protein interactions dictating cardiac fibrosis, a primary cause of end-stage heart failure. The developed algorithms allow identification of interactions between key proteins and predict new actors orchestrating fibroblast activation linked to fibrosis in mouse and human tissues. These data hold great promise for uncovering new therapeutic targets to limit myocardial fibrosis.

scholarly journals Google matrix analysis of bi-functional SIGNOR network of protein-protein interactions

2019 ◽  
Author(s):  
Klaus M. Frahm ◽  
Dima L. Shepelyansky
Keyword(s):  
Protein Interactions ◽  
Biological Networks ◽  
Linear Response Theory ◽  
Matrix Analysis ◽  
Global Network ◽  
Protein Protein Interactions ◽  
Effective Interactions ◽  
Ising Spin ◽  
Google Matrix ◽  
Frame Work

AbstractMotivationDirected protein networks with only a few thousand of nodes are rather complex and do not allow to extract easily the effective influence of one protein to another taking into account all indirect pathways via the global network. Furthermore, the different types of activation and inhibition actions between proteins provide a considerable challenge in the frame work of network analysis. At the same time these protein interactions are of crucial importance and at the heart of cellular functioning.ResultsWe develop the Google matrix analysis of the protein-protein network from the open public database SIGNOR. The developed approach takes into account the bi-functional activation or inhibition nature of interactions between each pair of proteins describing it in the frame work of Ising-spin matrix transitions. We also apply a recently developed linear response theory for the Google matrix which highlights a pathway of proteins whose PageRank probabilities are most sensitive with respect to two proteins selected for the analysis. This group of proteins is analyzed by the reduced Google matrix algorithm which allows to determine the effective interactions between them due to direct and indirect pathways in the global network. We show that the dominating activation or inhibition function of each protein can be characterized by its magnetization. The results of this Google matrix analysis are presented for three examples of selected pairs of proteins. The developed methods work rapidly and efficiently even for networks with several million of nodes and can be applied to various biological networks.AvailabilityThe Google matrix data and executive code of described algorithms are available at http://www.quantware.ups-tlse.fr/QWLIB/google4signornet/

scholarly journals Fibrosis protein-protein interactions from Google matrix analysis of MetaCore network

2021 ◽  
Author(s):  
Ekaterina Kotelnikova ◽  
Klaus M. Frahm ◽  
Dima L. Shepelyansky ◽  
Oksana Kunduzova
Keyword(s):  
Heart Failure ◽  
Protein Interactions ◽  
Cardiac Fibrosis ◽  
Matrix Analysis ◽  
Great Promise ◽  
Protein Protein Interactions ◽  
Matrix Algorithms ◽  
Google Matrix ◽  
End Stage Heart Failure ◽  
End Stage

Protein-protein interactions is a longstanding challenge in cardiac remodeling processes and heart failure. Here we use the MetaCore network and the Google matrix algorithms for prediction of protein-protein interactions dictating cardiac fibrosis, a primary causes of end-stage heart failure. The developed algorithms allow to identify interactions between key proteins and predict new actors orchestrating fibroblast activation linked to fibrosis in mouse and human tissues. These data hold great promise for uncovering new therapeutic targets to limit myocardial fibrosis.

scholarly journals Statistical properties of the MetaCore network of protein-protein interactions

2021 ◽  
Author(s):  
Ekaterina Kotelnikova ◽  
Klaus Michael Frahm ◽  
José Lages ◽  
Dima L Shepelyansky
Keyword(s):  
Protein Interactions ◽  
Statistical Properties ◽  
Matrix Analysis ◽  
Directed Network ◽  
Protein Protein Interactions ◽  
Google Matrix ◽  
The World ◽  
Functional Nature ◽  
Average Action ◽  
Effective Network

The MetaCore commercial database describes interactions of proteins and other chemical molecules and clusters in the form of directed network between these elements, viewed as nodes. The number of nodes goes beyond 40 thousands with almost 300 thousands links between them. The links have essentially bi-functional nature describing either activation or inhibition actions between proteins. We present here the analysis of statistical properties of this complex network applying the methods of the Google matrix, PageRank and CheiRank algorithms broadly used in the frame of the World Wide Web, Wikipedia, the world trade and other directed networks. We specifically describe the Ising PageRank approach which allows to treat the bi-functional type of protein-protein interactions. We also show that the developed reduced Google matrix algorithm allows to obtain an effective network of interactions inside a specific group of selected proteins. This method takes into account not only direct protein-protein interactions but also recover their indirect nontrivial couplings appearing due to summation over all the pathways passing via the global bi-functional network. The developed analysis allows to espablish an average action of each protein being more oriented to activation or inhibition. We argue that the described Google matrix analysis represents an efficient tool for investigation of influence of specific groups of proteins related to specific diseases.

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>

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