A quantum biochemistry investigation of the protein–protein interactions for the description of allosteric modulation on biomass-degrading chimera

2020 ◽  
Vol 22 (44) ◽  
pp. 25936-25948
Author(s):  
Sérgio Ruschi Bergamachi Silva ◽  
José Xavier de Lima Neto ◽  
Carlos Alessandro Fuzo ◽  
Umberto Laino Fulco ◽  
Davi Serradella Vieira

Detailed binding energies features of interdomain allosteric modulation caused by xylose binding for enhanced xylanolytic activity on xylanase-XBP chimera.

Molecules ◽  
2020 ◽  
Vol 25 (12) ◽  
pp. 2807
Author(s):  
Xue Zhi Zhao ◽  
Fa Liu ◽  
Terrence R. Burke

Protein–protein interactions (PPIs) represent an extremely attractive class of potential new targets for therapeutic intervention; however, the shallow extended character of many PPIs can render developing inhibitors against them as exceptionally difficult. Yet this problem can be made tractable by taking advantage of the fact that large interacting surfaces are often characterized by confined “hot spot” regions, where interactions contribute disproportionately to overall binding energies. Peptides afford valuable starting points for developing PPI inhibitors because of their high degrees of functional diversity and conformational adaptability. Unfortunately, contacts afforded by the 20 natural amino acids may be suboptimal and inefficient for accessing both canonical binding interactions and transient “cryptic” binding pockets. Oxime ligation represents a class of biocompatible “click” chemistry that allows the structural diversity of libraries of aldehydes to be rapidly evaluated within the context of a parent oxime-containing peptide platform. Importantly, oxime ligation represents a form of post solid-phase diversification, which provides a facile and empirical means of identifying unanticipated protein–peptide interactions that may substantially increase binding affinities and selectivity. The current review will focus on the authors’ use of peptide ligation to optimize PPI antagonists directed against several targets, including tumor susceptibility gene 101 (Tsg101), protein tyrosine phosphatases (PTPases) and the polo-like kinase 1 (Plk1). This should provide insights that can be broadly directed against an almost unlimited range of physiologically important PPIs.


2017 ◽  
Vol 8 (1) ◽  
Author(s):  
Xiao Cong ◽  
Yang Liu ◽  
Wen Liu ◽  
Xiaowen Liang ◽  
Arthur Laganowsky

2021 ◽  
Vol 16 (10) ◽  
pp. 1934578X2110460
Author(s):  
Ying Zhang ◽  
Li Lu ◽  
YiWen Liu ◽  
AiXia Yang ◽  
Yanfang Yang

Objective: Shenling Baizhu San (SBS) was selected as the regimen for the treatment of COVID-19 in Guangdong Province. It is mainly used for the convalescent treatment of COVID-19 patients with deficiency of both lung and spleen. In this study, we aimed to explore the mechanism of SBS in the treatment of COVID-19 through network pharmacology combined with molecular docking. Methods: The targets of active components of SBS were collected through Traditional Chinese Medicine Systems Pharmacology (TCMSP) and ETCM databases. Using the Genecards, TTD, OMIM and other databases, the targets of COVID-19 were determined. The next step was to use a string database to build a protein–protein interactions (PPI) network between proteins, and use David database to perform gene ontology (GO) function enrichment analysis, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis on core targets. Then we used Cytoscape software to construct the active ingredients-core target-signaling pathway network, and finally the active ingredients of SBS were molecularly docked with the core targets to predict the mechanism of SBS in the treatment of COVID-19. Results: A total of 177 active compounds, 43 core targets and 58 signaling pathways were selected. Molecular docking results showed that the binding energies of the top six active components and the targets were all less than −5 kcal/MOL. Conclusion: The potential mechanism of action of SBS in the treatment of COVID-19 may be associated with the regulation of genes co-expressed with IL6, DPP4, PTGS2, PTGS1 and TNF.


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>


Sign in / Sign up

Export Citation Format

Share Document