Higher Expression Level and Lower Toxicity of Genetically Spliced Rotavirus NSP4 in Comparison to the Full-Length Protein in E. coli

Sahmani, Mehdi; Azari, Siavash; Tebianian, Majid; Gheibi, Nematollah; Pourasgari, Farzaneh

doi:10.15171/ijb.1233

Cited by 1 publication

(1 citation statement)

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“…The most important thing is that it is related to life phenomena. Protein is the material basis of life, is an organic macromolecule, is the basic organic substance of the cell, and is the main undertaker of life activities (13)(14). There is no life without protein.…”

Section: Introductionmentioning

confidence: 99%

Protein molecular defect detection method based on a neural network algorithm

Zheng

Kahrizi²

2020

Cell Mol Biol (Noisy-le-grand)

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Proteins, as the largest macromolecules in the body, are among the most important components of the body and play very vital and important roles.These substances are made up of a series of amino acid chains that, depending on the type of protein, the number of these amino acids can reach several thousand.Proteins function differently depending on the type and location of their presence, including enzymatic activity to catalyze the process, identify microbes and cancer cells, transport substances such as respiratory gases, and signalize. In the biochemical experiment, the problem of optimizing the detection of protein molecular defects, because of the randomness of the information, parameters, selection and setting, limits the detection accuracy of protein molecular defects. Based on the characteristics of fast learning speed and a robust network of neural network algorithm, a protein molecular defect detection method based on a neural network algorithm was proposed. Firstly, the protein secondary structure was predicted by the method of protein secondary structure prediction based on the generalized regression neural network to obtain the protein structural features; secondly, the protein defective molecular sequence classification model based on the neural network was used to classify the protein defective molecular sequence to achieve the protein molecular defect detection. The results showed that the detection accuracy of the proposed method was very high, which meets the needs of protein molecular defect detection, and has some application advantages compared with similar detection methods.

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Section: Introductionmentioning

confidence: 99%