The Fractionation and Isolation of DNA-Binding Proteins from Human Serum

Aim and Objective:: Given the rapidly increasing number of molecular biology data available, computational methods of low complexity are necessary to infer protein structure, function, and evolution. Method:: In the work, we proposed a novel mthod, FermatS, which based on the global position information and local position representation from the curve and normalized moments of inertia, respectively, to extract features information of protein sequences. Furthermore, we use the generated features by FermatS method to analyze the similarity/dissimilarity of nine ND5 proteins and establish the prediction model of DNA-binding proteins based on logistic regression with 5-fold crossvalidation. Results:: In the similarity/dissimilarity analysis of nine ND5 proteins, the results are consistent with evolutionary theory. Moreover, this method can effectively predict the DNA-binding proteins in realistic situations. Conclusion:: The findings demonstrate that the proposed method is effective for comparing, recognizing and predicting protein sequences. The main code and datasets can download from https://github.com/GaoYa1122/FermatS.

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MK-FSVM-SVDD: A Multiple Kernel-based Fuzzy SVM Model for Predicting DNA-binding Proteins via Support Vector Data Description

Current Bioinformatics ◽

10.2174/1574893615999200607173829 ◽

2020 ◽

Vol 15 ◽

Author(s):

Yi Zou ◽

Hongjie Wu ◽

Xiaoyi Guo ◽

Li Peng ◽

Yijie Ding ◽

...

Keyword(s):

Dna Binding ◽

Binding Proteins ◽

Detection Efficiency ◽

Dna Binding Proteins ◽

Support Vector ◽

Support Vector Data Description ◽

Vector Data ◽

Data Description ◽

Multiple Kernel ◽

Svm Model

Background: Detecting DNA-binding proetins (DBPs) based on biological and chemical methods is time consuming and expensive. Objective: In recent years, the rise of computational biology methods based on Machine Learning (ML) has greatly improved the detection efficiency of DBPs. Method: In this study, Multiple Kernel-based Fuzzy SVM Model with Support Vector Data Description (MK-FSVM-SVDD) is proposed to predict DBPs. Firstly, sex features are extracted from protein sequence. Secondly, multiple kernels are constructed via these sequence feature. Than, multiple kernels are integrated by Centered Kernel Alignment-based Multiple Kernel Learning (CKA-MKL). Next, fuzzy membership scores of training samples are calculated with Support Vector Data Description (SVDD). FSVM is trained and employed to detect new DBPs. Results: Our model is test on several benchmark datasets. Compared with other methods, MK-FSVM-SVDD achieves best Matthew's Correlation Coefficient (MCC) on PDB186 (0.7250) and PDB2272 (0.5476). Conclusion: We can conclude that MK-FSVM-SVDD is more suitable than common SVM, as the classifier for DNA-binding proteins identification.

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Bacteriophage-specific DNA-binding proteins in P22-lysogenic and in P22-infected Salmonella typhimurium.

Journal of Virology ◽

10.1128/jvi.20.1.334-338.1976 ◽

1976 ◽

Vol 20 (1) ◽

pp. 334-338 ◽

Cited By ~ 6

Author(s):

W Schumann ◽

E Lindenblatt ◽

E G Bade

Keyword(s):

Dna Binding ◽

Salmonella Typhimurium ◽

Binding Proteins ◽

Dna Binding Proteins

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