scholarly journals The TissueNet v.2 database: A quantitative view of protein-protein interactions across human tissues

2016 ◽  
Vol 45 (D1) ◽  
pp. D427-D431 ◽  
Author(s):  
Omer Basha ◽  
Ruth Barshir ◽  
Moran Sharon ◽  
Eugene Lerman ◽  
Binyamin F. Kirson ◽  
...  
2021 ◽  
Author(s):  
Dongjie Guo ◽  
Ruifang Guo ◽  
Zhaoyang Li ◽  
Yuyang Zhang ◽  
Wei Zheng ◽  
...  

Since December 2019, the COVID-19 caused by SARS-CoV-2 has been widely spread all over the world. It is reported that SARS-CoV-2 infection affects a series of human tissues, including lung, gastrointestinal tract, kidney, etc. ACE2 has been identified as the primary receptor of the SARS-CoV-2 Spike (S) protein. The relatively low expression level of this known receptor in the lungs, which is the predominantly infected organ in COVID-19, indicates that there may be some other co-receptors or alternative receptors of SARS-CoV-2 to work in coordination with ACE2. Here, we identified twenty-one candidate receptors of SARS-CoV-2, including ACE2-interactor proteins and SARS-CoV receptors. Then we investigated the protein expression levels of these twenty-one candidate receptors in different human tissues and found that five of which CAT, MME, L-SIGN, DC-SIGN, and AGTR2 were specifically expressed in SARS-CoV-2 affected tissues. Next, we performed molecular simulations of the above five candidate receptors with SARS-CoV-2 S protein, and found that the binding affinities of CAT, AGTR2, L-SIGN and DC-SIGN to S protein were even higher than ACE2. Interestingly, we also observed that CAT and AGTR2 bound to S protein in different regions with ACE2 conformationally, suggesting that these two proteins are likely capable of the co-receptors of ACE2. Conclusively, we considered that CAT, AGTR2, L-SIGN and DC-SIGN were the potential receptors of SARS-CoV-2. Moreover, AGTR2 and DC-SIGN tend to be highly expressed in the lungs of smokers, which is consistent with clinical phenomena of COVID-19, and further confirmed our conclusion. Besides, we also predicted the binding hot spots for these putative protein-protein interactions, which would help develop drugs against SARS-CoV-2.


2017 ◽  
Vol 46 (D1) ◽  
pp. D522-D526 ◽  
Author(s):  
Omer Basha ◽  
Rotem Shpringer ◽  
Chanan M Argov ◽  
Esti Yeger-Lotem

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>


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>


2020 ◽  
Author(s):  
James Frederich ◽  
Ananya Sengupta ◽  
Josue Liriano ◽  
Ewa A. Bienkiewicz ◽  
Brian G. Miller

Fusicoccin A (FC) is a fungal phytotoxin that stabilizes protein–protein interactions (PPIs) between 14-3-3 adapter proteins and their phosphoprotein interaction partners. In recent years, FC has emerged as an important chemical probe of human 14-3-3 PPIs implicated in cancer and neurological diseases. These previous studies have established the structural requirements for FC-induced stabilization of 14-3-3·client phosphoprotein complexes; however, the effect of different 14-3-3 isoforms on FC activity has not been systematically explored. This is a relevant question for the continued development of FC variants because there are seven distinct isoforms of 14-3-3 in humans. Despite their remarkable sequence and structural similarities, a growing body of experimental evidence supports both tissue-specific expression of 14-3-3 isoforms and isoform-specific functions <i>in vivo</i>. Herein, we report the isoform-specificity profile of FC <i>in vitro</i>using recombinant human 14-3-3 isoforms and a focused library of fluorescein-labeled hexaphosphopeptides mimicking the C-terminal 14-3-3 recognition domains of client phosphoproteins targeted by FC in cell culture. Our results reveal modest isoform preferences for individual client phospholigands and demonstrate that FC differentially stabilizes PPIs involving 14-3-3s. Together, these data provide strong motivation for the development of non-natural FC variants with enhanced selectivity for individual 14-3-3 isoforms.


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