scholarly journals Nucleic acid binding by SAMHD1 contributes to the antiretroviral activity and is enhanced by the GpsN modification

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Corey H. Yu ◽  
Akash Bhattacharya ◽  
Mirjana Persaud ◽  
Alexander B. Taylor ◽  
Zhonghua Wang ◽  
...  

AbstractSAMHD1 impedes infection of myeloid cells and resting T lymphocytes by retroviruses, and the enzymatic activity of the protein—dephosphorylation of deoxynucleotide triphosphates (dNTPs)—implicates enzymatic dNTP depletion in innate antiviral immunity. Here we show that the allosteric binding sites of the enzyme are plastic and can accommodate oligonucleotides in place of the allosteric activators, GTP and dNTP. SAMHD1 displays a preference for oligonucleotides containing phosphorothioate bonds in the Rp configuration located 3’ to G nucleotides (GpsN), the modification pattern that occurs in a mechanism of antiviral defense in prokaryotes. In the presence of GTP and dNTPs, binding of GpsN-containing oligonucleotides promotes formation of a distinct tetramer with mixed occupancy of the allosteric sites. Mutations that impair formation of the mixed-occupancy complex abolish the antiretroviral activity of SAMHD1, but not its ability to deplete dNTPs. The findings link nucleic acid binding to the antiretroviral activity of SAMHD1, shed light on the immunomodulatory effects of synthetic phosphorothioated oligonucleotides and raise questions about the role of nucleic acid phosphorothioation in human innate immunity.

2020 ◽  
Author(s):  
Mei Dang ◽  
Yifan Li ◽  
Jianxing Song

AbstractTDP-43 and hnRNPA1 contain tandemly-tethered RRM domains, which not only functionally bind an array of nucleic acids, but also participate in aggregation/fibrillation, a pathological hallmark of various human diseases including ALS, FTD, AD and MSP. Here, by DSF, NMR and MD simulations we systematically characterized stability, ATP-binding and conformational dynamics of TDP-43 and hnRNPA1 RRM domains in both tethered and isolated forms. The results reveal three key findings: 1) very unexpectedly, upon tethering TDP-43 RRM domains become dramatically coupled and destabilized with Tm reduced to only 49 °C. 2) ATP specifically binds TDP-43 and hnRNPA1 RRM domains, in which ATP occupies the similar pockets within the conserved nucleic-acid-binding surfaces, with the affinity higher to the tethered than isolated forms. 3) MD simulations indicate that the tethered RRM domains of TDP-43 and hnRNPA1 have higher conformational dynamics than the isolated forms. Two RRM domains become coupled as shown by NMR characterization and analysis of inter-domain correlation motions. The study explains the long-standing puzzle that the tethered TDP-43 RRM1-RRM2 is particularly prone to aggregation/fibrillation, and underscores the general role of ATP in inhibiting aggregation/fibrillation of RRM-containing proteins. The results also rationalize the observation that the risk of aggregation-causing diseases increases with aging.


Author(s):  
Zheng Jiang ◽  
Si-Rui Xiao ◽  
Rong Liu

Abstract The biological functions of DNA and RNA generally depend on their interactions with other molecules, such as small ligands, proteins and nucleic acids. However, our knowledge of the nucleic acid binding sites for different interaction partners is very limited, and identification of these critical binding regions is not a trivial work. Herein, we performed a comprehensive comparison between binding and nonbinding sites and among different categories of binding sites in these two nucleic acid classes. From the structural perspective, RNA may interact with ligands through forming binding pockets and contact proteins and nucleic acids using protruding surfaces, while DNA may adopt regions closer to the middle of the chain to make contacts with other molecules. Based on structural information, we established a feature-based ensemble learning classifier to identify the binding sites by fully using the interplay among different machine learning algorithms, feature spaces and sample spaces. Meanwhile, we designed a template-based classifier by exploiting structural conservation. The complementarity between the two classifiers motivated us to build an integrative framework for improving prediction performance. Moreover, we utilized a post-processing procedure based on the random walk algorithm to further correct the integrative predictions. Our unified prediction framework yielded promising results for different binding sites and outperformed existing methods.


2020 ◽  
pp. 217-242
Author(s):  
Dhanusha Yesudhas ◽  
Ambuj Srivastava ◽  
Nisha Muralidharan ◽  
A. Kulandaisamy ◽  
R. Nagarajan ◽  
...  

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