Use of P22 challenge phage to identify protein-nucleic acid binding sites

1998 ◽  
Vol 3 (1) ◽  
pp. 111-119 ◽  
Author(s):  
Stanley Maloy ◽  
Jeffrey Gardner
Keyword(s):  
Nucleic Acid ◽  
Binding Sites ◽  
Nucleic Acid Binding ◽  
Protein Nucleic Acid

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 mmCSM-NA: accurately predicting effects of single and multiple mutations on protein–nucleic acid binding affinity

2021 ◽  
Vol 3 (4) ◽  
Author(s):  
Thanh Binh Nguyen ◽  
Yoochan Myung ◽  
Alex G C de Sá ◽  
Douglas E V Pires ◽  
David B Ascher
Keyword(s):  
Nucleic Acid ◽  
Binding Affinity ◽  
Single Point ◽  
Point Mutations ◽  
Multiple Point ◽  
Missense Mutations ◽  
Nucleic Acid Binding ◽  
Single Point Mutations ◽  
Protein Nucleic Acid ◽  
Nucleic Acid Interactions

Abstract While protein–nucleic acid interactions are pivotal for many crucial biological processes, limited experimental data has made the development of computational approaches to characterise these interactions a challenge. Consequently, most approaches to understand the effects of missense mutations on protein-nucleic acid affinity have focused on single-point mutations and have presented a limited performance on independent data sets. To overcome this, we have curated the largest dataset of experimentally measured effects of mutations on nucleic acid binding affinity to date, encompassing 856 single-point mutations and 141 multiple-point mutations across 155 experimentally solved complexes. This was used in combination with an optimized version of our graph-based signatures to develop mmCSM-NA (http://biosig.unimelb.edu.au/mmcsm_na), the first scalable method capable of quantitatively and accurately predicting the effects of multiple-point mutations on nucleic acid binding affinities. mmCSM-NA obtained a Pearson's correlation of up to 0.67 (RMSE of 1.06 Kcal/mol) on single-point mutations under cross-validation, and up to 0.65 on independent non-redundant datasets of multiple-point mutations (RMSE of 1.12 kcal/mol), outperforming similar tools. mmCSM-NA is freely available as an easy-to-use web-server and API. We believe it will be an invaluable tool to shed light on the role of mutations affecting protein–nucleic acid interactions in diseases.

scholarly journals Dissecting and predicting different types of binding sites in nucleic acids based on structural information

2021 ◽  
Author(s):  
Zheng Jiang ◽  
Si-Rui Xiao ◽  
Rong Liu
Keyword(s):  
Nucleic Acid ◽  
Nucleic Acids ◽  
Binding Sites ◽  
Structural Information ◽  
Machine Learning Algorithms ◽  
Nucleic Acid Binding ◽  
Integrative Framework ◽  
Dna And Rna ◽  
Feature Spaces ◽  
Critical Binding

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.

scholarly journals An in silico approach to identification, categorization and prediction of nucleic acid binding proteins

2020 ◽  
Author(s):  
Lei Xu ◽  
Shanshan Jiang ◽  
Jin Wu ◽  
Quan Zou
Keyword(s):  
Nucleic Acid ◽  
Drug Targets ◽  
Nucleic Acid Binding ◽  
Structural Protein ◽  
Interaction Sites ◽  
Trained Classifier ◽  
Nucleic Acid Binding Proteins ◽  
Protein Nucleic Acid ◽  
And Function ◽  
Nucleic Acid Interactions

Abstract The interaction between proteins and nucleic acid plays an important role in many processes, such as transcription, translation and DNA repair. The mechanisms of related biological events can be understood by exploring the function of proteins in these interactions. The number of known protein sequences has increased rapidly in recent years, but the databases for describing the structure and function of protein have unfortunately grown quite slowly. Thus, improving such databases is meaningful for predicting protein–nucleic acid interactions. Furthermore, the mechanism of related biological events, such as viral infection or designing novel drug targets, can be further understood by understanding the function of proteins in these interactions. The information for each sequence, including its function and interaction sites, were collected and identified, and a database called PNIDB was built. The proteins in PNIDB were grouped into 27 classes, such as transcription, immune system, and structural protein, etc. The function of each protein was then predicted using a machine learning method. Using our method, the predictor was trained on labeled sequences, and then the function of a protein was predicted based on the trained classifier. The prediction accuracy achieved a score of 77.43% by 10-fold cross validation.

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