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Single-Strand DNA Binding Proteins

Single-Strand DNA Binding Proteins

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...

Protein Networks

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...

Conserved Binding Sites

Conserved Binding Sites

Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...

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Related Experiment Video

Updated: Nov 3, 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

A sequence-based multiple kernel model for identifying DNA-binding proteins.

Yuqing Qian¹, Limin Jiang², Yijie Ding³

¹School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, People's Republic of China.

BMC Bioinformatics

|June 1, 2021

Summary

This summary is machine-generated.

This study introduces a new machine learning model for detecting DNA-binding proteins (DBP). By integrating protein features using Multiple Kernel Learning, the model accurately identifies DBP, improving upon existing methods.

Keywords:

Centered kernel alignment DNA-binding proteins Feature extraction Multiple kernel learning Support vector machine

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Last Updated: Nov 3, 2025

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Area of Science:

Bioinformatics
Computational Biology
Machine Learning

Background:

DNA-binding proteins (DBP) are crucial in biological systems.
Traditional experimental DBP detection is laborious and time-intensive.
Machine learning offers an alternative but faces challenges in protein feature representation.

Purpose of the Study:

To develop an accurate and efficient method for DNA-binding protein detection.
To integrate diverse protein sequence features for improved prediction.
To establish a robust predictive model for identifying novel DBP.

Main Methods:

Extraction of six distinct features from protein sequences.
Application of Multiple Kernel Learning with Centered Kernel Alignment for feature integration.
Utilizing a Support Vector Machine classifier for the predictive model.

Main Results:

The developed model demonstrated superior accuracy on benchmark datasets (PDB1075 and PDB186).
Performance evaluation showed significant improvements over existing DBP detection methods.
The integrated feature approach effectively captured protein information for prediction.

Conclusions:

Multiple Kernel Learning effectively fuses complementary information from different protein features.
The proposed method achieves state-of-the-art results on established DBP datasets.
This approach offers a powerful tool for DBP identification in bioinformatics.