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Updated: Jul 26, 2025

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions

Published on: December 1, 2020

Integrating temporal and spatial variabilities for identifying ion binding proteins in phage.

Hongliang Zou¹, Zizheng Yu¹, Zhijian Yin¹

¹School of Communications and Electronics, Jiangxi Science and Technology Normal University, Nanchang 330003, P. R. China.

Journal of Bioinformatics and Computational Biology

|June 16, 2023

Summary

A new computational model accurately identifies ion binding proteins (IBPs) in phages, crucial for developing drugs against drug-resistant bacteria. This method enhances understanding of IBPs and aids in novel therapeutic strategies.

Keywords:

F-score Ion binding proteins Pearson’s correlation coefficient physicochemical property support vector machine

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Phage Phenomics: Physiological Approaches to Characterize Novel Viral Proteins

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Phage Phenomics: Physiological Approaches to Characterize Novel Viral Proteins

Phage Phenomics: Physiological Approaches to Characterize Novel Viral Proteins

Published on: June 11, 2015

Area of Science:

Computational biology
Bioinformatics
Drug discovery

Background:

Ion binding proteins (IBPs) in phages are critical for developing new drugs against drug-resistant bacteria.
Accurate identification of IBPs is essential for understanding their biological roles and therapeutic potential.

Purpose of the Study:

To develop a novel computational model for the accurate identification of ion binding proteins (IBPs).
To improve the classification performance of IBPs compared to existing methods.

Main Methods:

Utilized physicochemical properties and Pearson's correlation coefficient to represent protein sequences.
Employed temporal and spatial variabilities for feature extraction.
Applied similarity network fusion and F-score feature selection.
Used Support Vector Machine (SVM) for classification of IBPs.

Main Results:

The proposed computational model demonstrated significant improvements in classification performance.
The method effectively identified IBPs from non-IBPs.
Achieved superior performance compared to state-of-the-art approaches.

Conclusions:

The developed computational model offers a robust and accurate method for identifying IBPs.
This advancement facilitates the study of IBPs and the development of novel antibacterial drugs.
The methodology provides a valuable tool for bioinformatics and drug discovery research.