Abstract P135: Systemic targeting of a CNS tumor (medulloblastoma) using a novel cell-penetrating, nucleic acid binding, monoclonal antibody

2021 ◽  
Author(s):  
Elias Quijano ◽  
Minsoo Khang ◽  
Bruce C. Turner ◽  
Stephen Squinto ◽  
Ranjit Bindra ◽  
...  
Keyword(s):  
Monoclonal Antibody ◽  
Nucleic Acid ◽  
Nucleic Acid Binding ◽  
Cns Tumor ◽  
Cell Penetrating

Snapshot blotting: The transfer of nucleic acids and nucleoprotein complexes from electrophoresis gels to EM grids

1992 ◽  
Vol 50 (1) ◽  
pp. 762-763
Author(s):  
Stephen D. Jett
Keyword(s):  
Nucleic Acid ◽  
Nucleic Acids ◽  
Nucleic Acid Binding ◽  
Mobility Shift ◽  
Carbon Coated ◽  
Nucleoprotein Complexes ◽  
Mobility Shift Assay ◽  
Protein Nucleic Acid ◽  
Gel Mobility Shift ◽  
Nucleic Acid Interactions

The electrophoresis gel mobility shift assay is a popular method for the study of protein-nucleic acid interactions. The binding of proteins to DNA is characterized by a reduction in the electrophoretic mobility of the nucleic acid. Binding affinity, stoichiometry, and kinetics can be obtained from such assays; however, it is often desirable to image the various species in the gel bands using TEM. Present methods for isolation of nucleoproteins from gel bands are inefficient and often destroy the native structure of the complexes. We have developed a technique, called “snapshot blotting,” by which nucleic acids and nucleoprotein complexes in electrophoresis gels can be electrophoretically transferred directly onto carbon-coated grids for TEM imaging.

scholarly journals Bioinformatic Analysis of Structure and Function of LIM Domains of Human Zyxin Family Proteins

2021 ◽  
Vol 22 (5) ◽  
pp. 2647
Author(s):  
M. Quadir Siddiqui ◽  
Maulik D. Badmalia ◽  
Trushar R. Patel
Keyword(s):  
Nucleic Acid ◽  
Nuclear Export ◽  
Consensus Sequence ◽  
Nuclear Export Signal ◽  
Bioinformatic Analysis ◽  
Nucleic Acid Binding ◽  
Protein Protein Interaction ◽  
Lim Domains ◽  
Protein Nucleic Acid ◽  
And Function

Members of the human Zyxin family are LIM domain-containing proteins that perform critical cellular functions and are indispensable for cellular integrity. Despite their importance, not much is known about their structure, functions, interactions and dynamics. To provide insights into these, we used a set of in-silico tools and databases and analyzed their amino acid sequence, phylogeny, post-translational modifications, structure-dynamics, molecular interactions, and functions. Our analysis revealed that zyxin members are ohnologs. Presence of a conserved nuclear export signal composed of LxxLxL/LxxxLxL consensus sequence, as well as a possible nuclear localization signal, suggesting that Zyxin family members may have nuclear and cytoplasmic roles. The molecular modeling and structural analysis indicated that Zyxin family LIM domains share similarities with transcriptional regulators and have positively charged electrostatic patches, which may indicate that they have previously unanticipated nucleic acid binding properties. Intrinsic dynamics analysis of Lim domains suggest that only Lim1 has similar internal dynamics properties, unlike Lim2/3. Furthermore, we analyzed protein expression and mutational frequency in various malignancies, as well as mapped protein-protein interaction networks they are involved in. Overall, our comprehensive bioinformatic analysis suggests that these proteins may play important roles in mediating protein-protein and protein-nucleic acid interactions.

scholarly journals GraphBind: protein structural context embedded rules learned by hierarchical graph neural networks for recognizing nucleic-acid-binding residues

2021 ◽  
Author(s):  
Ying Xia ◽  
Chun-Qiu Xia ◽  
Xiaoyong Pan ◽  
Hong-Bin Shen
Keyword(s):  
Neural Networks ◽  
Nucleic Acid ◽  
Biological Activities ◽  
New Drugs ◽  
Superior Performance ◽  
Nucleic Acid Binding ◽  
Hierarchical Graph ◽  
Binding Residue ◽  
Binding Residues ◽  
Graph Neural Networks

Abstract Knowledge of the interactions between proteins and nucleic acids is the basis of understanding various biological activities and designing new drugs. How to accurately identify the nucleic-acid-binding residues remains a challenging task. In this paper, we propose an accurate predictor, GraphBind, for identifying nucleic-acid-binding residues on proteins based on an end-to-end graph neural network. Considering that binding sites often behave in highly conservative patterns on local tertiary structures, we first construct graphs based on the structural contexts of target residues and their spatial neighborhood. Then, hierarchical graph neural networks (HGNNs) are used to embed the latent local patterns of structural and bio-physicochemical characteristics for binding residue recognition. We comprehensively evaluate GraphBind on DNA/RNA benchmark datasets. The results demonstrate the superior performance of GraphBind than state-of-the-art methods. Moreover, GraphBind is extended to other ligand-binding residue prediction to verify its generalization capability. Web server of GraphBind is freely available at http://www.csbio.sjtu.edu.cn/bioinf/GraphBind/.

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