scholarly journals The AT-rich tract of the SV40 oh core: negative synergism and specific recognition by single stranded and duplex DNA binding proteins

1992 ◽  
Vol 20 (13) ◽  
pp. 3333-3339 ◽  
Author(s):  
Ivo Galli ◽  
Sanae M.M. Iguchi-Ariga ◽  
Hiroyoshi Ariga
Keyword(s):  
Dna Binding ◽  
Binding Proteins ◽  
Dna Binding Proteins ◽  
Duplex Dna ◽  
Specific Recognition

A duplex DNA–gold nanoparticle probe composed as a colorimetric biosensor for sequence-specific DNA-binding proteins

The Analyst ◽  
2016 ◽  
Vol 141 (6) ◽  
pp. 2040-2045 ◽  
Author(s):  
Junho Ahn ◽  
Yeonweon Choi ◽  
Ae-Ree Lee ◽  
Joon-Hwa Lee ◽  
Jong Hwa Jung
Keyword(s):  
Dna Binding ◽  
Gold Nanoparticle ◽  
Binding Proteins ◽  
Dna Binding Proteins ◽  
Duplex Dna ◽  
Colorimetric Sensing

The SPL-12-mediated change of Au aggregation via duplex DNA–SPL-12 interactions could be utilized for colorimetric sensing of SPL-12 using duplex DNA–Au.

scholarly journals Substrate conformational dynamics drive structure-specific recognition of gapped DNA by DNA polymerase

2018 ◽  
Author(s):  
Timothy D. Craggs ◽  
Marko Sustarsic ◽  
Anne Plochowietz ◽  
Majid Mosayebi ◽  
Hendrik Kaju ◽  
...  
Keyword(s):  
Dna Binding ◽  
Dna Polymerase ◽  
Single Molecule ◽  
Solution Structure ◽  
Binding Proteins ◽  
Conformational Dynamics ◽  
Dna Binding Proteins ◽  
Binary Complex ◽  
Specific Recognition ◽  
Gapped Dna

AbstractDNA-binding proteins utilise different recognition mechanisms to locate their DNA targets. Some proteins recognise specific nucleotide sequences, while many DNA repair proteins interact with specific (often bent) DNA structures. While sequence-specific DNA binding mechanisms have been studied extensively, structure-specific mechanisms remain unclear. Here, we study structure-specific DNA recognition by examining the structure and dynamics of DNA polymerase I (Pol) substrates both alone and in Pol-DNA complexes. Using a rigid-body docking approach based on a network of 73 distance restraints collected using single-molecule FRET, we determined a novel solution structure of the singlenucleotide-gapped DNA-Pol binary complex. The structure was highly consistent with previous crystal structures with regards to the downstream primer-template DNA substrate; further, our structure showed a previously unobserved sharp bend (~120°) in the DNA substrate; we also showed that this pronounced bending of the substrate is present in living bacteria. All-atom molecular dynamics simulations and single-molecule quenching assays revealed that 4-5 nt of downstream gap-proximal DNA are unwound in the binary complex. Coarsegrained simulations on free gapped substrates reproduced our experimental FRET values with remarkable accuracy (<ΔFRET> = -0.0025 across 34 independent distances) and revealed that the one-nucleotide-gapped DNA frequently adopted highly bent conformations similar to those in the Pol-bound state (ΔG < 4 kT); such conformations were much less accessible to nicked (> 7 kT) or duplex (>> 10 kT) DNA. Our results suggest a mechanism by which Pol and other structure-specific DNA-binding proteins locate their DNA targets through sensing of the conformational dynamics of DNA substrates.Significance StatementMost genetic processes, including DNA replication, repair and transcription, rely on DNA-binding proteins locating specific sites on DNA; some sites contain a specific sequence, whereas others present a specific structure. While sequence-specific recognition has a clear physical basis, structure-specific recognition mechanisms remain obscure. Here, we use single-molecule FRET and computer simulations to show that the conformational dynamics of an important repair intermediate (1nt-gapped DNA) act as central recognition signals for structure-specific binding by DNA polymerase I (Pol). Our conclusion is strongly supported by a novel solution structure of the Pol-DNA complex wherein the gapped-DNA is significantly bent. Our iterative approach combining precise single-molecule measurements with molecular modelling is general and can elucidate the structure and dynamics for many large biomachines.

DBP-PSSM: Combination of evolutionary profiles with the XGBoost algorithm to improve the identification of DNA-binding proteins

2020 ◽  
Vol 23 ◽  
Author(s):  
Yanping Zhang ◽  
Pengcheng Chen ◽  
Ya Gao ◽  
Jianwei Ni ◽  
Xiaosheng Wang
Keyword(s):  
Logistic Regression ◽  
Protein Structure ◽  
Dna Binding ◽  
Molecular Biology ◽  
Binding Proteins ◽  
Protein Sequences ◽  
Low Complexity ◽  
Dna Binding Proteins ◽  
Position Information ◽  
Position Representation

Aim and Objective:: Given the rapidly increasing number of molecular biology data available, computational methods of low complexity are necessary to infer protein structure, function, and evolution. Method:: In the work, we proposed a novel mthod, FermatS, which based on the global position information and local position representation from the curve and normalized moments of inertia, respectively, to extract features information of protein sequences. Furthermore, we use the generated features by FermatS method to analyze the similarity/dissimilarity of nine ND5 proteins and establish the prediction model of DNA-binding proteins based on logistic regression with 5-fold crossvalidation. Results:: In the similarity/dissimilarity analysis of nine ND5 proteins, the results are consistent with evolutionary theory. Moreover, this method can effectively predict the DNA-binding proteins in realistic situations. Conclusion:: The findings demonstrate that the proposed method is effective for comparing, recognizing and predicting protein sequences. The main code and datasets can download from https://github.com/GaoYa1122/FermatS.

MK-FSVM-SVDD: A Multiple Kernel-based Fuzzy SVM Model for Predicting DNA-binding Proteins via Support Vector Data Description

2020 ◽  
Vol 15 ◽  
Author(s):  
Yi Zou ◽  
Hongjie Wu ◽  
Xiaoyi Guo ◽  
Li Peng ◽  
Yijie Ding ◽  
...  
Keyword(s):  
Dna Binding ◽  
Binding Proteins ◽  
Detection Efficiency ◽  
Dna Binding Proteins ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data ◽  
Data Description ◽  
Multiple Kernel ◽  
Svm Model

Background: Detecting DNA-binding proetins (DBPs) based on biological and chemical methods is time consuming and expensive. Objective: In recent years, the rise of computational biology methods based on Machine Learning (ML) has greatly improved the detection efficiency of DBPs. Method: In this study, Multiple Kernel-based Fuzzy SVM Model with Support Vector Data Description (MK-FSVM-SVDD) is proposed to predict DBPs. Firstly, sex features are extracted from protein sequence. Secondly, multiple kernels are constructed via these sequence feature. Than, multiple kernels are integrated by Centered Kernel Alignment-based Multiple Kernel Learning (CKA-MKL). Next, fuzzy membership scores of training samples are calculated with Support Vector Data Description (SVDD). FSVM is trained and employed to detect new DBPs. Results: Our model is test on several benchmark datasets. Compared with other methods, MK-FSVM-SVDD achieves best Matthew's Correlation Coefficient (MCC) on PDB186 (0.7250) and PDB2272 (0.5476). Conclusion: We can conclude that MK-FSVM-SVDD is more suitable than common SVM, as the classifier for DNA-binding proteins identification.

Sign in / Sign up

Export Citation Format

Share Document