The glutathione S-transferases (GST) represent a major group of detoxification enzymes. All eukaryotic species possess multiple cytosolic and membrane-bound GST isoenzymes, each of which displays distinct catalytic as well as noncatalytic binding properties: the cytosolic enzymes are encoded by at least five distantly related gene families (designated class alpha, mu, pi, sigma, and theta GST), whereas the membrane-bound enzymes, microsomal GST and leukotriene C, synthetase, are encoded by single genes and both have arisen separately from the soluble GST. Evidence suggests that the level of expression of GST is a crucial factor in determining the sensitivity of cells to a broad spectrum of toxic chemicals. In this article the biochemical functions of GST are described to show how individual isoenzymes contribute to resistance to carcinogens, antitumor drugs, environmental pollutants, and products of oxidative stress.A description of the mechanisms of transcriptional and posttranscriptional regulat...

The glutathione S-transferase supergene family: regulation of GST and the contribution of the isoenzymes to cancer chemoprotection and drug resistance.

Functional characterization of a protein sequence is a common goal in biology, and is usually facilitated by having an accurate three-dimensional (3-D) structure of the studied protein. In the absence of an experimentally determined structure, comparative or homology modeling can sometimes provide a useful 3-D model for a protein that is related to at least one known protein structure. Comparative modeling predicts the 3-D structure of a given protein sequence (target) based primarily on its alignment to one or more proteins of known structure (templates). The prediction process consists of fold assignment, target-template alignment, model building, and model evaluation. This unit describes how to calculate comparative models using the program MODELLER and discusses all four steps of comparative modeling, frequently observed errors, and some applications. Modeling lactate dehydrogenase from Trichomonas vaginalis (TvLDH) is described as an example. The download and installation of the MODELLER software is also described.

/pdf/comparative-protein-structure-modeling-using-modeller-262v4shee8.pdf

Comparative Protein Structure Modeling Using MODELLER

■ Abstract Comparative modeling predicts the three-dimensional structure of a given protein sequence (target) based primarily on its alignment to one or more pro- teins of known structure (templates). The prediction process consists of fold assign- ment, target-template alignment, model building, and model evaluation. The number of protein sequences that can be modeled and the accuracy of the predictions are in- creasing steadily because of the growth in the number of known protein structures and because of the improvements in the modeling software. Further advances are nec- essary in recognizing weak sequence-structure similarities, aligning sequences with structures, modeling of rigid body shifts, distortions, loops and side chains, as well as detecting errors in a model. Despite these problems, it is currently possible to model with useful accuracy significant parts of approximately one third of all known protein sequences. The use of individual comparative models in biology is already rewarding and increasingly widespread. A major new challenge for comparative modeling is the integration of it with the torrents of data from genome sequencing projects as well as from functional and structural genomics. In particular, there is a need to develop an automated, rapid, robust, sensitive, and accurate comparative modeling pipeline applicable to whole genomes. Such large-scale modeling is likely to encourage new kinds of applications for the many resulting models, based on their large number and completeness at the level of the family, organism, or functional network.

Comparative protein structure modeling of genes and genomes

Functional characterization of a protein sequence is one of the most frequent problems in biology. This task is usually facilitated by accurate three-dimensional (3-D) structure of the studied protein. In the absence of an experimentally determined structure, comparative or homology modeling can sometimes provide a useful 3-D model for a protein that is related to at least one known protein structure. Comparative modeling predicts the 3-D structure of a given protein sequence (target) based primarily on its alignment to one or more proteins of known structure (templates). The prediction process consists of fold assignment, target-template alignment, model building, and model evaluation. This unit describes how to calculate comparative models using the program MODELLER and discusses all four steps of comparative modeling, frequently observed errors, and some applications. Modeling lactate dehydrogenase from Trichomonas vaginalis (TvLDH) is described as an example. The download and installation of the MODELLER software is also described.

/pdf/comparative-protein-structure-modeling-using-modeller-1ye86wsiv7.pdf

Comparative protein structure modeling using Modeller.

https://cyberleninka.org/article/n/148766.pdf

Cytochrome P450 enzymes in drug metabolism: Regulation of gene expression, enzyme activities, and impact of genetic variation

Nomenclature for human glutathione transferases

Mammalian cytosolic glutathione S-transferases (GSTs; EC 2.5.1.18) form a supergene family consisting of four distinct families, named alpha, mu, pi and theta. In humans one member of the mu class gene family (GSTM1) has been shown to be polymorphic and is only expressed in 55-60% of individuals. Previous studies have shown a possible link with the null phenotype and susceptibility to cancer, in particular to lung cancer. In this study we genotyped individuals with breast, bladder and colorectal cancer. A total of 490 individuals with cancer were studied, and consisted of 97 bladder, 197 breast and 196 colorectal cancers. No significant differences were observed in the frequency of nulled individuals in bladder or breast cancer patients when compared with a control population of 225 individuals. However, a significant excess of nulled individuals were seen in colorectal cancer: 56.1% compared with the control group value of 41.8%. This was shown to be highly significant depending on the site of the tumours and > 70% of individuals with a tumour in the proximal colon were GSTM1 nulled. This is an approximately 2-fold increase in colon cancer risk in these individuals.

Relationship between the GSTM1 genetic polymorphism and susceptibility to bladder, breast and colon cancer

Debrisoquine hydroxylase gene polymorphism and susceptibility to Parkinson's disease

To standardize CYP2D6 allele nomenclature, and to conform with international human gene nomenclature guidelines, an alternative to the current arbitrary system is described. Based on recommendations for human genome nomenclature, we propose that alleles be designated by CYP2D6 followed by an asteris

Nomenclature for human CYP2D6 alleles.

Cytochrome P450 CYP2C9 metabolizes a wide variety of clinically important drugs, including phenytoin, tolbutamide, warfarin and a large number of non-steroidal anti-inflammatory drugs. Previous studies have shown that even relatively conservative changes in the amino acid composition of this enzyme can affect both its activity and substrate specificity. To date six different human CYP2C9 cDNA sequences, as well as the highly homologous CYP2C10 sequence have been reported suggesting that the CYP2C9 gene is polymorphic. Only nine single base substitutions in the coding region of CYP2C9 account for the differences seen between the CYP2C9 proteins. In this report we have developed polymerase chain reaction (PCR)-based assays to distinguish all seven sequences, and have determined their allele frequencies in the Caucasian population. Of the seven sequences studied in one hundred individuals only three appeared to be CYP2C9 alleles. These alleles termed CYP2C9*1, CYP2C9*2 and CYP2C9*3 had allele frequencies of 0.79, 0.125 and 0.085 respectively. The CYP2C10 gene could not be found in any of the samples studied. The assays developed here will allow the prediction of CYP2C9 phenotype, thus identifying those individuals who may exhibit different drug pharmacokinetics for CYP2C9 substrates.

C R Wolf

Papers

Nomenclature for human glutathione transferases

Relationship between the GSTM1 genetic polymorphism and susceptibility to bladder, breast and colon cancer

Debrisoquine hydroxylase gene polymorphism and susceptibility to Parkinson's disease

Nomenclature for human CYP2D6 alleles.

Genetic analysis of the human cytochrome P450 CYP2C9 locus.