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Adaptation of Hybridization Capture of Chromatin-associated Proteins for Proteomics to Mammalian Cells

Adaptation of Hybridization Capture of Chromatin-associated Proteins for Proteomics to Mammalian Cells

Published on: June 1, 2018

HKAM-MKM: A hybrid kernel alignment maximization-based multiple kernel model for identifying DNA-binding proteins.

Shulin Zhao¹, Yijie Ding², Xiaobin Liu³

¹Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China; Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, Zhejiang, China.

Computers in Biology and Medicine

|March 25, 2022

Summary

This summary is machine-generated.

This study introduces a new computational model, the hybrid kernel alignment maximization-based multiple kernel model (HKAM-MKM), for predicting DNA-binding proteins (DBPs). The model achieves high accuracy, offering an efficient alternative to experimental identification methods.

Keywords:

DNA-Binding proteins Kernel alignment Kernel weight Multiple kernel learning

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Experimental identification of DNA-binding proteins (DBPs) is resource-intensive.
Developing computational models for DBP prediction is crucial for efficient sequence classification.

Purpose of the Study:

To develop and evaluate a novel hybrid kernel alignment maximization-based multiple kernel model (HKAM-MKM) for predicting DNA-binding proteins (DBPs).

Main Methods:

Collected two datasets and extracted six feature groups from sequences.
Constructed kernels and proposed local kernel alignment to capture sample-neighbor relationships.
Developed a hybrid kernel alignment model, optimizing kernel weights via maximization.
Integrated the fused kernel into a support vector machine for DBP prediction.

Main Results:

The HKAM-MKM model achieved the highest Matthew's correlation coefficient (MCC) of 0.768 and 0.5962 on PDB186 and PDB2272 datasets, respectively.
The model attained the highest accuracy of 87.1% and 78.43% on the independent test sets.
Performance metrics surpassed those of existing DBP prediction tools.

Conclusions:

The HKAM-MKM demonstrates superior efficiency and accuracy in predicting DNA-binding proteins.
This computational tool offers a valuable alternative to resource-intensive experimental methods for DBP identification.