QSARs and activity predicting models for competitive inhibitors of adenosine deaminase

Rosmarinic acid in Perilla frutescens L. as a potential adenosine deaminase inhibitor: Preparation, machine learning validation and binding mechanism study

Gout, a prevalent arthritic disease, can be mitigated by adenosine deaminase (ADA) inhibitors that reduce uric acid production. In this study, the extraction process of rosmarinic acid (RA) from Perilla frutescens L. (P. frutescens) was optimized, and the best yield was 2.22 mg/g. Quantitative structure-activity relationship (QSAR) model was used to predict ADA inhibitors (ADAIs) in potential foods. The random forest model constructed by ChemoPy descriptor was the best (AUC = 0.9648), and five candidate compounds (including RA) were screened. The inhibitory activity of RA on ADA was confirmed in vitro (IC50 = 55.11 μM). Molecular docking showed that ADAIs and ADA were stably bound through hydrogen bonding and hydrophobic interaction. Molecular dynamics (MD) simulation verified the dynamic stability of ADAIs and ADA complexes. This study provides a theoretical basis for RA from P. frutescens extract as a potential compound for the treatment of gout and for the rapid screening of foodborne ADAIs.

Deamination of 6-aminodeoxyfutalosine in menaquinone biosynthesis by distantly related enzymes

Proteins of unknown function belonging to cog1816 and cog0402 were characterized. Sav2595 from Steptomyces avermitilis MA-4680, Acel0264 from Acidothermus cellulolyticus 11B, Nis0429 from Nitratiruptor sp. SB155-2 and Dr0824 from Deinococcus radiodurans R1 were cloned, purified, and their substrate profiles determined. These enzymes were previously incorrectly annotated as adenosine deaminases or chlorohydrolases. It was shown here that these enzymes actually deaminate 6-aminodeoxyfutalosine. The deamination of 6-aminodeoxyfutalosine is part of an alternative menaquinone biosynthetic pathway that involves the formation of futalosine. 6-Aminodeoxyfutalosine is deaminated by these enzymes with catalytic efficiencies greater than 10(5) M(-1) s(-1), Km values of 0.9-6.0 μM, and kcat values of 1.2-8.6 s(-1). Adenosine, 2'-deoxyadenosine, thiomethyladenosine, and S-adenosylhomocysteine are deaminated at least an order of magnitude slower than 6-aminodeoxyfutalosine. The crystal structure of Nis0429 was determined and the substrate, 6-aminodeoxyfutalosine, was positioned in the active site on the basis of the presence of adventitiously bound benzoic acid. In this model, Ser-145 interacts with the carboxylate moiety of the substrate. The structure of Dr0824 was also determined, but a collapsed active site pocket prevented docking of substrates. A computational model of Sav2595 was built on the basis of the crystal structure of adenosine deaminase and substrates were docked. The model predicted a conserved arginine after β-strand 1 to be partially responsible for the substrate specificity of Sav2595.

Pa0148 from Pseudomonas aeruginosa catalyzes the deamination of adenine

Four proteins from NCBI cog1816, previously annotated as adenosine deaminases, have been subjected to structural and functional characterization. Pa0148 (Pseudomonas aeruginosa PAO1), AAur1117 (Arthrobacter aurescens TC1), Sgx9403e, and Sgx9403g have been purified and their substrate profiles determined. Adenosine is not a substrate for any of these enzymes. All of these proteins will deaminate adenine to produce hypoxanthine with k(cat)/K(m) values that exceed 10(5) M(-1) s(-1). These enzymes will also accept 6-chloropurine, 6-methoxypurine, N-6-methyladenine, and 2,6-diaminopurine as alternate substrates. X-ray structures of Pa0148 and AAur1117 have been determined and reveal nearly identical distorted (β/α)(8) barrels with a single zinc ion that is characteristic of members of the amidohydrolase superfamily. Structures of Pa0148 with adenine, 6-chloropurine, and hypoxanthine were also determined, thereby permitting identification of the residues responsible for coordinating the substrate and product.

Investigations into the origin of the molecular recognition of several adenosine deaminase inhibitors

Inhibitors of adenosine deaminase (ADA, EC 3.5.4.4) are potential therapeutic agents for the treatment of various health disorders. Several highly potent inhibitors were previously identified, yet they exhibit unacceptable toxicities. We performed a SAR study involving a series of C2 or C8 substituted purine-riboside analogues with a view to discover less potent inhibitors with a lesser toxicity. We found that any substitution at C8 position of nebularine resulted in total loss of activity toward calf intestinal ADA. However, several 2-substituted-adenosine, 8-aza-adenosine, and nebularine analogues exhibited inhibitory activity. Specifically, 2-Cl-purine riboside, 8-aza-2-thiohexyl adenosine, 2-thiohexyl adenosine, and 2-MeS-purine riboside were found to be competitive inhibitors of ADA with K(i) values of 25, 22, 6, and 3 μM, respectively. We concluded that electronic parameters are not major recognition determinants of ADA but rather steric parameters. A C2 substituent which fits ADA hydrophobic pocket and improves H-bonding with the enzyme makes a good inhibitor. In addition, a gg rotamer about C4'-C5' bond is apparently an important recognition determinant.

QSAR models to predict mutagenicity of acrylates, methacrylates and alpha,beta-unsaturated carbonyl compounds

OBJECTIVE

The purpose of this study is to develop a quantitative structure-activity relationship (QSAR) model that can distinguish mutagenic from non-mutagenic species with alpha,beta-unsaturated carbonyl moiety using two endpoints for this activity - Ames test and mammalian cell gene mutation test - and also to gather information about the molecular features that most contribute to eliminate the mutagenic effects of these chemicals.

METHODS

Two data sets were used for modeling the two mutagenicity endpoints: (1) Ames test and (2) mammalian cells mutagenesis. The first one comprised 220 molecules, while the second one 48 substances, ranging from acrylates, methacrylates to alpha,beta-unsaturated carbonyl compounds. The QSAR models were developed by applying linear discriminant analysis (LDA) along with different sets of descriptors computed using the DRAGON software.

RESULTS

For both endpoints, there was a concordance of 89% in the prediction and 97% confidentiality by combining the three models for the Ames test mutagenicity. We have also identified several structural alerts to assist the design of new monomers.

SIGNIFICANCE

These individual models and especially their combination are attractive from the point of view of molecular modeling and could be used for the prediction and design of new monomers that do not pose a human health risk.