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Prediction method of HIV-1 integrase mutant strain on EVG resistance index change value

An HIV-1IN, fold change technology, applied in the field of bioinformatics, can solve problems such as reducing drug efficacy and hindering the treatment of AIDs

Inactive Publication Date: 2016-01-27
BEIJING UNIV OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] At present, the IN drugs that have been marketed have been highly resistant, and the emergence of drug resistance has significantly reduced the efficacy of the drugs, hindering the treatment of AIDs

Method used

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  • Prediction method of HIV-1 integrase mutant strain on EVG resistance index change value
  • Prediction method of HIV-1 integrase mutant strain on EVG resistance index change value
  • Prediction method of HIV-1 integrase mutant strain on EVG resistance index change value

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0041] Take the factor integration genotype of whether the volume change of the side chain of the IN residue is consistent as an example to specifically illustrate the implementation process of the present invention:

[0042] (1) Encoded datasets. The FC data set of the EVG drug-resistant mutants of IN was obtained, and the data set was binary coded. The mutated residues were represented by 1, and the non-mutated residues were represented by 0. The FC values ​​were uniformly log-transformed. In this way, a data set corresponding to the logFC value of a mutant genotype and its phenotype is established. As shown in table 2.

[0043] Table 2 Main mutant genotypes and their corresponding phenotype data sets

[0044]

[0045]

[0046] Table 2 continued

[0047]

[0048] Table 2 continued

[0049]

[0050] Table 2 continued

[0051]

[0052] Table 2 continued

[0053] (2) Genotype integration. According to the volume change factor of the mutation of the same res...

Embodiment 2

[0067] Taking the integrated genotype according to whether the change of the side chain charge of the IN residue is consistent as an example, the implementation process of the present invention is specifically described:

[0068] (1) Encoded datasets. The FC data set of the EVG drug-resistant mutants of IN was obtained, and the data set was binary coded. The mutated residues were represented by 1, and the non-mutated residues were represented by 0. The FC values ​​were uniformly log-transformed. In this way, a data set corresponding to the logFC value of a mutant genotype and its phenotype is established. As shown in table 2.

[0069] (2) Genotype integration. According to the charge change factor of the mutation of the same residue, items with the same charge change after the mutation of the same residue are combined. As shown in Table 5. Due to the large amount of data, Table 5 only shows a part of the data.

[0070] Table 5 The same residue mutation is integrated acco...

Embodiment 3

[0083] Taking the integrated genotype according to the factor of whether the volume of the side chain of the IN residue and the change of charge are considered as an example, the implementation process of the present invention is specifically described:

[0084] (1) Encoded datasets. The FC data set of the EVG drug-resistant mutants of IN was obtained, and the data set was binary coded. The mutated residues were represented by 1, and the non-mutated residues were represented by 0. The FC values ​​were uniformly log-transformed. In this way, a data set corresponding to the logFC value of a mutant genotype and its phenotype is established. As shown in table 2.

[0085] (2) Genotype integration. According to the volume change factor of the mutation of the same residue, the items with the same volume and charge changes after the mutation of the same residue are combined. As shown in Table 7. Due to the large amount of data, Table 7 only shows a part of the data.

[0086] Tab...

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Abstract

The invention provides a prediction method of an HIV-1 integrase mutant strain on an EVG (Elvitegravir) resistance index change value, and belongs to the field of bioinformatics. The method comprises the following steps of (1) coding a data set: building a main mutational genotype and a corresponding expression type log FC value of the main mutational genotype to obtain the data set; (2) performing three kinds of different integration on the genotype in the data set according to the result that whether the residue side chain physicochemical property change caused by mutation is consistent or not, wherein the bases of the three kinds of integration are respectively the volume change, the charge change and the integral volume and charge change; (3) eliminating an abnormal value by a clustering analysis method; (4) screening the three kinds of data sets obtained in the step (3) by a genetic algorithm to obtain an excellent population individual; (5) respectively building multivariate stepwise regression models for the three kinds of data sets to obtain a regression equation and parameters of a multiple correlation coefficient and the like; and (6) substituting data to be predicted into the regression equation to obtain a corresponding prediction value.

Description

technical field [0001] The invention relates to a method for predicting the fold change (FC) value of an integrase (IN) mutant strain to Elvitegravir (EVG) drug resistance based on a genetic algorithm (GA) and a multiple stepwise regression algorithm. in the field of bioinformatics. Background technique [0002] In recent years, HIV-1 IN has become the most attractive target for the development of anti-AIDS drugs, and the development of drugs targeting IN has become a hot spot. IN inhibitors are mainly divided into: peptide inhibitors, nucleoside inhibitors, polyhydroxylated aromatic compounds and diketoacids (DKAs) inhibitors. Among them, only DKAs-like IN inhibitors and their derivatives have shown good selectivity and inhibitory activity in in vitro enzyme inhibitory activity tests and animal model experiments, so DKAs and their derivatives have become the most promising IN inhibitors . [0003] The IN inhibitors currently in clinical research and the three anti-IN dru...

Claims

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Application Information

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IPC IPC(8): G06F19/18
Inventor 张小轶刘洋张乐刘明李晓琴李春华谭建军
Owner BEIJING UNIV OF TECH