Protein secondary structure prediction of PDB 4HU7 using Genetic Algorithm (GA)

Rout, Subhendu Bhusan; Kisan, Sumitra; Mishra, Sasmita

doi:10.1109/iccci.2017.8117710

Cited by 1 publication

(2 citation statements)

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“…In comparison to SSpro5 technique achieves an accuracy of 2.1%, and the corresponding QH, QE, QC accuracy of 9.9%, 2.0%, and 6.2% respectively. The prediction performed upon the 1240 non redundant fragments with a k-point crossover with the modified datasets [20]. Application of Genetic Algorithm for the secondary structure prediction achieves consistently good result for QH, QE, QC values.…”

Section: Ch Ce and CC Indicate Correlation Coefficient Of α-Helix (H) β-Sheet (E) And Coil (C)mentioning

confidence: 98%

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Q3 Accuracy and SOV Measure Analysis of Application of GA in Protein Secondary Structure Prediction

Rout¹,

Mishra²,

Sahoo³

2021

RIA

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The protein secondary structure prediction (PSP) of the large biological molecule protein is an important task of bioinformatics and in the last decades many machines learning and soft computing methodologies play vital roles in achieving satisfactory results. The protein structural class determination is an important topic in protein science because an idea about protein structural class is quite useful to know about the changes and reaction of a living body in order to design new drugs and medicines. Though several hard computing techniques may be helpful in these areas but focusing upon the steady development and big data size in protein sequences that are entering into databanks, it is a challenge to do experiments with the hard computing techniques. Soft computing techniques like Artificial Neural Network, Fuzzy logic, Genetic Algorithm play a vital role for these types of genomic researches. To face these complex challenges, this article presents a novel method to predict the protein structure by using Genetic Algorithm. The Q3 accuracy and SOV measure analysis with SOVH, SOVE, SOVC value of respective α-helix (H), β-sheet (E) and coil/loop(C) structures are also discussed. The application of Genetic algorithm i.e. the proposed technique GApred provides better result than that of SPIDER2, JPred4, FSVM and SSpro5 for all the three datasets in the experiment. This method is helpful for distinct protein secondary structure prediction and a significant success rate was observed, which indicates that it can be used as a powerful tool in drug design and medicine research.

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Section: Ch Ce and CC Indicate Correlation Coefficient Of α-Helix (H) β-Sheet (E) And Coil (C)mentioning

confidence: 98%

“…The Secondary structure is a non-linier arrangement with different regularities of different amino acids with respect to each other. The basic secondary structure has 3 regular forms: α-helix (H), β-sheet (E) and loops or coils (C) [2]. From these secondary structures, the tertiary structures are derived [3].…”

Section: Introductionmentioning

confidence: 99%