Literature DB >> 32775434

Use Chou's 5-Step Rule to Predict DNA-Binding Proteins with Evolutionary Information.

Weizhong Lu1,2, Zhengwei Song1, Yijie Ding1,2, Hongjie Wu1,2, Yan Cao1, Yu Zhang3, Haiou Li1.   

Abstract

The knowledge of DNA-binding proteins would help to understand the functions of proteins better in cellular biological processes. Research on the prediction of DNA-binding proteins can promote the research of drug proteins and computer acidified drugs. In recent years, methods based on machine learning are usually used to predict proteins. Although great predicted performance can be achieved via current methods, researchers still need to invest more research in terms of the improvement of predicted performance. In this study, the prediction of DNA-binding proteins is studied from the perspective of evolutionary information and the support vector machine method. One machine learning model for predicting DNA-binding proteins based on evolutionary features by using Chou's 5-step rule is put forward. The results show that great predicted performance is obtained on benchmark dataset PDB1075 and independent dataset PDB186, achieving the accuracy of 86.05% and 75.30%, respectively. Thus, the method proposed is comparable to a certain degree, and it may work even better than other methods to some extent.
Copyright © 2020 Weizhong Lu et al.

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Year:  2020        PMID: 32775434      PMCID: PMC7407024          DOI: 10.1155/2020/6984045

Source DB:  PubMed          Journal:  Biomed Res Int            Impact factor:   3.411


  28 in total

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3.  On position-specific scoring matrix for protein function prediction.

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5.  Protein secondary structure. Studies on the limits of prediction accuracy.

Authors:  J Palau; P Argos; P Puigdomenech
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6.  Predicting anticancer peptides with Chou's pseudo amino acid composition and investigating their mutagenicity via Ames test.

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7.  Identifying DNA-binding proteins by combining support vector machine and PSSM distance transformation.

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8.  iDNA-Prot|dis: identifying DNA-binding proteins by incorporating amino acid distance-pairs and reduced alphabet profile into the general pseudo amino acid composition.

Authors:  Bin Liu; Jinghao Xu; Xun Lan; Ruifeng Xu; Jiyun Zhou; Xiaolong Wang; Kuo-Chen Chou
Journal:  PLoS One       Date:  2014-09-03       Impact factor: 3.240

9.  DNA binding protein identification by combining pseudo amino acid composition and profile-based protein representation.

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Journal:  Sci Rep       Date:  2015-10-20       Impact factor: 4.379

10.  DFLpred: High-throughput prediction of disordered flexible linker regions in protein sequences.

Authors:  Fanchi Meng; Lukasz Kurgan
Journal:  Bioinformatics       Date:  2016-06-15       Impact factor: 6.937

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  3 in total

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2.  KK-DBP: A Multi-Feature Fusion Method for DNA-Binding Protein Identification Based on Random Forest.

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Journal:  Front Genet       Date:  2021-11-29       Impact factor: 4.599

3.  Identifying Membrane Protein Types Based on Lifelong Learning With Dynamically Scalable Networks.

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