scholarly journals Detection of regulatory circuits by integrating the cellular networks of protein-protein interactions and transcription regulation

2003 ◽  
Vol 31 (20) ◽  
pp. 6053-6061 ◽  
Author(s):  
E. Yeger-Lotem
2020 ◽  
Vol 401 (12) ◽  
pp. 1323-1334
Author(s):  
Sandra Kunz ◽  
Peter L. Graumann

AbstractThe second messenger cyclic di-GMP regulates a variety of processes in bacteria, many of which are centered around the decision whether to adopt a sessile or a motile life style. Regulatory circuits include pathogenicity, biofilm formation, and motility in a wide variety of bacteria, and play a key role in cell cycle progression in Caulobacter crescentus. Interestingly, multiple, seemingly independent c-di-GMP pathways have been found in several species, where deletions of individual c-di-GMP synthetases (DGCs) or hydrolases (PDEs) have resulted in distinct phenotypes that would not be expected based on a freely diffusible second messenger. Several recent studies have shown that individual signaling nodes exist, and additionally, that protein/protein interactions between DGCs, PDEs and c-di-GMP receptors play an important role in signaling specificity. Additionally, subcellular clustering has been shown to be employed by bacteria to likely generate local signaling of second messenger, and/or to increase signaling specificity. This review highlights recent findings that reveal how bacteria employ spatial cues to increase the versatility of second messenger signaling.


2019 ◽  
Author(s):  
Yi Guo ◽  
Xiang Chen

AbstractMotivationAlmost all critical functions and processes in cells are sustained by the cellular networks of protein-protein interactions (PPIs), understanding these is therefore crucial in the investigation of biological systems. Despite all past efforts, we still lack high-quality PPI data for constructing the networks, which makes it challenging to study the functions of association of proteins. High-throughput experimental techniques have produced abundant data for systematically studying the cellular networks of a biological system and the development of computational method for PPI identification.ResultsWe have developed a deep learning-based framework, named iPPI, for accurately predicting PPI on a proteome-wide scale depended only on sequence information. iPPI integrates the amino acid properties and compositions of protein sequence into a unified prediction framework using a hybrid deep neural network. Extensive tests demonstrated that iPPI can greatly outperform the state-of-the-art prediction methods in identifying PPIs. In addition, the iPPI prediction score can be related to the strength of protein-protein binding affinity and further showed the biological relevance of our deep learning framework to identify PPIs.Availability and ImplementationiPPI is available as an open-source software and can be downloaded from https://github.com/model-lab/[email protected]


2018 ◽  
Vol 9 (1) ◽  
pp. 216-226 ◽  
Author(s):  
Alicia Bravo ◽  
Sofia Ruiz-Cruz ◽  
Itziar Alkorta ◽  
Manuel Espinosa

AbstractBacterial resistance to antibiotics poses enormous health and economic burdens to our society, and it is of the essence to explore old and new ways to deal with these problems. Here we review the current status of multi-resistance genes and how they spread among bacteria. We discuss strategies to deal with resistant bacteria, namely the search for new targets and the use of inhibitors of protein-protein interactions, fragment-based methods, or modified antisense RNAs. Finally, we discuss integrated approaches that consider bacterial populations and their niches, as well as the role of global regulators that activate and/or repress the expression of multiple genes in fluctuating environments and, therefore, enable resistant bacteria to colonize new niches. Understanding how the global regulatory circuits work is, probably, the best way to tackle bacterial resistance.


2008 ◽  
pp. 391-404
Author(s):  
Snehal Naik ◽  
Britney L. Moss ◽  
David Piwnica-Worms ◽  
Andrea Pichler-Wallace

2011 ◽  
Vol 49 (08) ◽  
Author(s):  
LC König ◽  
M Meinhard ◽  
C Sandig ◽  
MH Bender ◽  
A Lovas ◽  
...  

1974 ◽  
Vol 31 (03) ◽  
pp. 403-414 ◽  
Author(s):  
Terence Cartwright

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.


2020 ◽  
Author(s):  
Salvador Guardiola ◽  
Monica Varese ◽  
Xavier Roig ◽  
Jesús Garcia ◽  
Ernest Giralt

<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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