Set of approaches based on 3D structure and position specific-scoring matrix for predicting DNA-binding proteins

2018 ◽  
Vol 35 (11) ◽  
pp. 1844-1851 ◽  
Author(s):  
Loris Nanni ◽  
Sheryl Brahnam
Keyword(s):  
Dna Binding ◽  
Binding Proteins ◽  
3D Structure ◽  
Dna Binding Proteins ◽  
Position Specific Scoring Matrix ◽  
Scoring Matrix

Identification of DNA binding proteins using evolutionary profiles position specific scoring matrix

2016 ◽  
Vol 199 ◽  
pp. 154-162 ◽  
Author(s):  
Muhammad Waris ◽  
Khurshid Ahmad ◽  
Muhammad Kabir ◽  
Maqsood Hayat
Keyword(s):  
Dna Binding ◽  
Binding Proteins ◽  
Dna Binding Proteins ◽  
Position Specific Scoring Matrix ◽  
Scoring Matrix

scholarly journals StackPDB: predicting DNA-binding proteins based on XGB-RFE feature optimization and stacked ensemble classifier

2020 ◽  
Author(s):  
Qingmei Zhang ◽  
Peishun Liu ◽  
Yu Han ◽  
Yaqun Zhang ◽  
Xue Wang ◽  
...  
Keyword(s):  
Dna Binding ◽  
Binding Proteins ◽  
Ensemble Classifier ◽  
Dna Binding Proteins ◽  
Position Specific Scoring Matrix ◽  
Gradient Boosting ◽  
Feature Subset ◽  
Extreme Gradient Boosting ◽  
Independent Test ◽  
Scoring Matrix

ABSTRACTDNA binding proteins (DBPs) not only play an important role in all aspects of genetic activities such as DNA replication, recombination, repair, and modification but also are used as key components of antibiotics, steroids, and anticancer drugs in the field of drug discovery. Identifying DBPs becomes one of the most challenging problems in the domain of proteomics research. Considering the high-priced and inefficient of the experimental method, constructing a detailed DBPs prediction model becomes an urgent problem for researchers. In this paper, we propose a stacked ensemble classifier based method for predicting DBPs called StackPDB. Firstly, pseudo amino acid composition (PseAAC), pseudo position-specific scoring matrix (PsePSSM), position-specific scoring matrix-transition probability composition (PSSM-TPC), evolutionary distance transformation (EDT), and residue probing transformation (RPT) are applied to extract protein sequence features. Secondly, extreme gradient boosting-recursive feature elimination (XGB-RFE) is employed to gain an excellent feature subset. Finally, the best features are applied to the stacked ensemble classifier composed of XGBoost, LightGBM, and SVM to construct StackPDB. After applying leave-one-out cross-validation (LOOCV), StackPDB obtains high ACC and MCC on PDB1075, 93.44% and 0.8687, respectively. Besides, the ACC of the independent test datasets PDB186 and PDB180 are 84.41% and 90.00%, respectively. The MCC of the independent test datasets PDB186 and PDB180 are 0.6882 and 0.7997, respectively. The results on the training dataset and the independent test dataset show that StackPDB has a great predictive ability to predict DBPs.

scholarly journals Genomic Marks Associated with Chromatin Compartments in the CTCF, RNAPII Loop and Genomic Windows

2021 ◽  
Vol 22 (21) ◽  
pp. 11591
Author(s):  
Teresa Szczepińska ◽  
Ayatullah Faruk Mollah ◽  
Dariusz Plewczynski
Keyword(s):  
Dna Binding ◽  
Histone Modifications ◽  
Binding Proteins ◽  
Gene Expression Regulation ◽  
3D Structure ◽  
Gc Content ◽  
Dna Binding Proteins ◽  
Open Chromatin ◽  
The Individual ◽  
Monte Carlo Feature Selection

The nature of genome organization into two basic structural compartments is as yet undiscovered. However, it has been indicated to be a mechanism of gene expression regulation. Using the classification approach, we ranked genomic marks that hint at compartmentalization. We considered a broad range of marks, including GC content, histone modifications, DNA binding proteins, open chromatin, transcription and genome regulatory segmentation in GM12878 cells. Genomic marks were defined over CTCF or RNAPII loops, which are basic elements of genome 3D structure, and over 100 kb genomic windows. Experiments were carried out to empirically assess the whole set of features, as well as the individual features in classification of loops/windows, into compartment A or B. Using Monte Carlo Feature Selection and Analysis of Variance, we constructed a ranking of feature importance for classification. The best simple indicator of compartmentalization is DNase-seq open chromatin measurement for CTCF loops, H3K4me1 for RNAPII loops and H3K79me2 for genomic windows. Among DNA binding proteins, this is RUNX3 transcription factor for loops and RNAPII for genomic windows. Chromatin state prediction methods that indicate active elements like promoters, enhancers or heterochromatin enhance the prediction of loop segregation into compartments. However, H3K9me3, H4K20me1, H3K27me3 histone modifications and GC content poorly indicate compartments.

DBP-PSSM: Combination of evolutionary profiles with the XGBoost algorithm to improve the identification of DNA-binding proteins

2020 ◽  
Vol 23 ◽  
Author(s):  
Yanping Zhang ◽  
Pengcheng Chen ◽  
Ya Gao ◽  
Jianwei Ni ◽  
Xiaosheng Wang
Keyword(s):  
Logistic Regression ◽  
Protein Structure ◽  
Dna Binding ◽  
Molecular Biology ◽  
Binding Proteins ◽  
Protein Sequences ◽  
Low Complexity ◽  
Dna Binding Proteins ◽  
Position Information ◽  
Position Representation

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.

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

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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