scholarly journals HMMBinder: DNA-Binding Protein Prediction Using HMM Profile Based Features

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Rianon Zaman ◽  
Shahana Yasmin Chowdhury ◽  
Mahmood A. Rashid ◽  
Alok Sharma ◽  
Abdollah Dehzangi ◽  
...  
Keyword(s):  
Dna Binding ◽  
Binding Protein ◽  
Prediction Method ◽  
Dna Binding Protein ◽  
Support Vector ◽  
Classification Technique ◽  
Protein Prediction ◽  
Wide Range ◽  
Vector Machines ◽  
Benchmark Datasets

DNA-binding proteins often play important role in various processes within the cell. Over the last decade, a wide range of classification algorithms and feature extraction techniques have been used to solve this problem. In this paper, we propose a novel DNA-binding protein prediction method called HMMBinder. HMMBinder uses monogram and bigram features extracted from the HMM profiles of the protein sequences. To the best of our knowledge, this is the first application of HMM profile based features for the DNA-binding protein prediction problem. We applied Support Vector Machines (SVM) as a classification technique in HMMBinder. Our method was tested on standard benchmark datasets. We experimentally show that our method outperforms the state-of-the-art methods found in the literature.

scholarly journals Sequence Based DNA-Binding Protein Prediction

2021 ◽  
Vol 9 (6) ◽  
pp. 44-48
Author(s):  
Farisa T S ◽  
Elizabeth Isaac
Keyword(s):  
Dna Binding ◽  
Protein Sequence ◽  
Solvent Accessibility ◽  
Binding Protein ◽  
Differential Evolution Algorithm ◽  
Prediction Method ◽  
Dna Binding Protein ◽  
Support Vector ◽  
Sequence Information ◽  
Data Set

Protein and DNA have vital role in our biological processes. For accurately predicting DNA binding protein, develop a new sequence based prediction method from the protein sequence. Sequence based method only considers the protein sequence information as input. For accurately predicting DBP, first develop a reliable benchmark data set from the protein data bank. Second, using Amino Acid Composition (AAC), Position Specific Scoring Matrix (PSSM), Predicted Solvent Accessibility (PSA), and Predicted Probabilities of DNA-Binding Sites (PDBS) to produce four specific protein sequence baselines. Using a differential evolution algorithm, weights of the properties are taught. Based on those attained properties, merge the characteristics with weights to create an original super feature. And tensor-flow is used to paralyze the weights. A suitable feature selection algorithm of tensor flow’s binary classifier is used to extract the excellent subset from weighted feature vector. The training sample set is obtained in the training process, after generating final features. The classification is learned through the support vector machine and the tensor flow. And the output is measured using a tensor surface. The choice is done on the basis of threshold of likelihood and protein with above-threshold chance is considered to be DBP and others are non-DBP.

Damage-specific DNA binding protein 1 (DDB1): a protein with a wide range of functions

2011 ◽  
Vol 43 (12) ◽  
pp. 1664-1667 ◽  
Author(s):  
Barbara Iovine ◽  
Maria Luigia Iannella ◽  
Maria Assunta Bevilacqua
Keyword(s):  
Dna Binding ◽  
Binding Protein ◽  
Dna Binding Protein ◽  
Wide Range ◽  
Range Of Functions

DPP-PseAAC: A DNA-binding protein prediction model using Chou’s general PseAAC

2018 ◽  
Vol 452 ◽  
pp. 22-34 ◽  
Author(s):  
M. Saifur Rahman ◽  
Swakkhar Shatabda ◽  
Sanjay Saha ◽  
M. Kaykobad ◽  
M. Sohel Rahman
Keyword(s):  
Dna Binding ◽  
Prediction Model ◽  
Binding Protein ◽  
Dna Binding Protein ◽  
Protein Prediction

Single-stranded and double-stranded DNA-binding protein prediction using HMM profiles

2021 ◽  
Vol 612 ◽  
pp. 113954
Author(s):  
Ronesh Sharma ◽  
Shiu Kumar ◽  
Tatsuhiko Tsunoda ◽  
Thirumananseri Kumarevel ◽  
Alok Sharma
Keyword(s):  
Dna Binding ◽  
Binding Protein ◽  
Dna Binding Protein ◽  
Double Stranded Dna ◽  
Protein Prediction

scholarly journals Molecular dynamics of DNA-binding protein and its 2D-crystals

2021 ◽  
Vol 2056 (1) ◽  
pp. 012016
Author(s):  
E V Tereshkin ◽  
K B Tereshkina ◽  
Y F Krupyanskii
Keyword(s):  
Molecular Dynamics ◽  
Dna Binding ◽  
Binding Protein ◽  
Dna Binding Protein ◽  
Dynamics Simulation ◽  
Amino Acid Residues ◽  
Wide Range ◽  
Living Nature ◽  
Dps Protein ◽  
2D Crystals

Abstract In this work the dodecamers and the two-dimensional crystals of DNA-binding protein from starved cells (DPS) of Escherichia coli bacteria were investigated. The DPS monomer contains 167 amino acids residues. It can form dimers, trimers, and dodecamers. The versatility of the DPS protein structure can be used to design nanomaterials with structures and functions not found in living nature. The ability of this protein to self-assemble into complex shapes and structures defined on the nanometer scale can make them highly demanded for various technological applications. It was used all-atom classical molecular dynamics simulation on 0.1 microsecond scale to obtain the spatial and energy characteristics of the proteins and the components of the simulation box. The fluctuation mobility of DPS protein at various temperatures was discussed. The diffusion of ions in the presence of dodecamers and 2D crystals was compared. It has been shown that this protein retains its ability to accumulate ions in a wide range of biological temperatures from 277 to 369K. It also retains the mobility of key amino acid residues involved in the formation of nanocrystals and the transport of ions into the cavity, even at low physiological temperatures.

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