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iDNA-Prot specifications

Information


Unique identifier OMICS_18563
Name iDNA-Prot
Interface Web user interface
Restrictions to use None
Input data Some sequences of proteins (maximum 50 proteins).
Input format FASTA
Computer skills Basic
Stability Stable
Maintained Yes

Documentation


Maintainer


  • person_outline Xuan Xiao <>

Publication for iDNA-Prot

iDNA-Prot citations

 (7)
library_books

Protein Sequence Comparison and DNA binding Protein Identification with Generalized PseAAC and Graphical Representation

2018
PMCID: 5930480
PMID: 29380690
DOI: 10.2174/1386207321666180130100838

[…] a generalized pseaac based svm (support vector machine) model was developed to identify dna-binding proteins. experiment results showed that our method performed better than dnabinder, dna-prot, idna-prot and endna-prot by 3.29-10.44% in terms of acc, 0.056-0.206 in terms of mcc, and 1.45-15.76% in terms of f1m. when the benchmark dataset was expanded with negative samples, the presented […]

library_books

Investigation of the inhibition effect and mechanism of myricetin to Suilysin by molecular modeling

2017
PMCID: 5603505
PMID: 28924148
DOI: 10.1038/s41598-017-12168-y

[…] matrix c of the atomic coordinates. some other similar approaches also can be used to predict the motion of protein, such as the so-called profile-based protein representation, ipro54-pseknc and idna-prot–., according to previous reports, the conformational change of sly should be complete to achieve haemolytic activity through monomeric oligomerization–. in the present study, the haemolytic […]

library_books

Identification of DNA binding proteins using multi features fusion and binary firefly optimization algorithm

2016
PMCID: 5002159
PMID: 27565741
DOI: 10.1186/s12859-016-1201-8

[…] problem. kumar et al. [] utilized various svm modules and evolutionary information to forge the dna-binder method. kumar et al. [] employed random forest to predict dbps. lin et al. [] proposed the idna-prot predictor by incorporating the features into the general form of pseudo amino acid composition that were extracted from protein sequence via the grey model and adopting the random forest […]

library_books

HSP70 binding protein 1 (HspBP1) suppresses HIV 1 replication by inhibiting NF κB mediated activation of viral gene expression

2015
PMCID: 4770212
PMID: 26538602
DOI: 10.1093/nar/gkv1151

[…] the mechanistic details of the inhibitory effect of hspbp1 on ltr-mediated gene-expression, we used bioinformatic analysis to predict the dna binding activity of hspbp1. both dnabinder () and idna-prot () suggested that hspbp1 might have dna binding activity. therefore, it was plausible to hypothesize that hspbp1 might have a direct influence on the ltr promoter. the direct role of hspbp1 […]

library_books

Sequence Based Prediction of DNA Binding Proteins Based on Hybrid Feature Selection Using Random Forest and Gaussian Naïve Bayes

2014
PMCID: 3901691
PMID: 24475169
DOI: 10.1371/journal.pone.0086703

[…] benchmark dataset pdb594. subsequently, blind tests on the independent dataset pdb186 by the proposed model trained on the entire pdb594 dataset and by other five existing methods (including idna-prot, dna-prot, dnabinder, dnabind and dbd-threader) were performed, resulting in that the proposed dbppred yielded the highest accuracy of 0.769, mcc of 0.538, and auc of 0.790. the independent […]

library_books

Identification of DNA Binding Proteins Using Support Vector Machine with Sequence Information

2013
PMCID: 3787635
PMID: 24151525
DOI: 10.1155/2013/524502

[…] using a set of 62 sequence features []. kumar et al. reported a random forest method, dna-prot, to identify dna-binding proteins from protein sequence []. lin et al. proposed a new predictor, called idna-prot, for predicting uncharacterized proteins as dna-binding proteins or non-dna-binding proteins based on their amino acid sequences information alone []., in this study, we attempt to predict […]


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iDNA-Prot institution(s)
Information Science and Technology School, Donghua University, Shanghai, China; Computer Department, Jing-De-Zhen Ceramic Institute, Jing-De-Zhen, China; Gordon Life Science Institute, San Diego, California, USA
iDNA-Prot funding source(s)
Supported by the grants from the National Natural Science Foundation of China (60961003), the Key Project of Chinese Ministry of Education (210116), the Province National Natural Science Foundation of JiangXi (2009GZS0064 and 2010GZS0122), and the department of education of Jiang-Xi Province (GJJ09271).

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