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Single-Strand DNA Binding Proteins

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For successful DNA replication, the unwinding of double-stranded DNA must be accompanied by stabilization and protection of the separated single strands of the DNA. This crucial task is performed by single-strand DNA-binding (SSB) proteins. They bind to the DNA in a sequence-independent manner, which means that the nitrogenous bases of the DNA need not be present in a specific order for binding of SSB proteins to it. The binding of SSB proteins straightens single-stranded DNA (ssDNA) and makes...

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Related Experiment Video

Updated: Dec 26, 2025

DNA Sequence Recognition by DNA Primase Using High-Throughput Primase Profiling

DNA Sequence Recognition by DNA Primase Using High-Throughput Primase Profiling

Published on: October 8, 2019

Sequence-based Detection of DNA-binding Proteins using Multiple-view Features Allied with Feature Selection.

Liling Zhou¹, Xiaoning Song¹, Dong-Jun Yu²

¹School of Internet of Things Engineering, Jiangnan University, Wuxi, China.

Molecular Informatics

|March 8, 2020

Summary

This summary is machine-generated.

This study introduces iDBP-DEP, a novel computational method for identifying DNA-binding proteins. The new approach significantly improves prediction accuracy and robustness compared to existing methods.

Keywords:

DNA-binding protein Dipeptide composition Evolutionary profile Feature selection Physicochemical properties

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Last Updated: Dec 26, 2025

DNA Sequence Recognition by DNA Primase Using High-Throughput Primase Profiling

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Published on: October 8, 2019

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Published on: October 11, 2018

Area of Science:

Computational biology
Genomics
Protein science

Background:

DNA-binding proteins are crucial for gene regulation and molecular functions.
Accurate computational detection of DNA-binding proteins is highly desirable.
Existing methods require improvement for robust DNA-binding prediction.

Purpose of the Study:

To develop and evaluate a novel computational method, iDBP-DEP, for DNA-binding protein prediction.
To enhance the accuracy and reliability of detecting DNA-binding proteins using multi-view features.
To provide an effective tool for researchers in molecular biology and genomics.

Main Methods:

Proposed iDBP-DEP method utilizing discriminative features from evolutionary profiles, dipeptide composition, and physicochemical properties.
Implemented feature selection to optimize the input data for prediction.
Evaluated performance using rigorous Jackknife and independent validation tests on benchmark datasets (PDB1075, PDB594, PDB186).

Main Results:

iDBP-DEP demonstrated significant accuracy improvements over state-of-the-art methods.
Achieved 1.8% and 3.0% higher accuracy (Acc) and Mathew's Correlation Coefficient (MCC) on PDB1075.
Showcased 7.4% and 14.8% improvements in Acc and MCC on PDB594, with 80.1% Acc and 0.684 MCC on PDB186.

Conclusions:

iDBP-DEP offers a robust and accurate approach for DNA-binding protein detection.
The multi-view feature strategy effectively enhances prediction performance.
The developed method and available code contribute to advancing computational tools in molecular biology.