Amino Acid Patterns that Determine Antihyperuricemic Activity of Peptides: Identification and Predictive Analysis

Peptides offer a promising analogue to synthetic drugs in treating hyperuricemia. This article introduces reliable amino acid patterns that cause the inhibitory xanthine oxidase (CSR) activity of peptides. The research objective was to propose new antihyperuricemic peptides and prove their effectiveness by predictive analytics in silico. The study featured peptides with inhibitory xanthine oxidase activity. The authors developed a protocol for searching, identifying, and quantifying patterns of amino acid residues in target peptide sequences. The identified peptides were tested for physicochemical properties, pharmacokinetic profile, inhibitory xanthine oxidase activity, general and target biological activity, and toxicity. The research revealed amino acid patterns responsible for inhibiting the xanthine oxidase enzyme, as well as generated new peptide sequences. Forty-nine non-toxic peptides with different lengths of amino acid sequences demonstrated high antimicrobial and inhibitory potential against the targeted drugs used to treat hyperuricemia and type 2 diabetes mellitus. The peptides were low-molecular compounds of predominantly hydrophilic and hydrophobic nature, 4-7 amino acids long. They contained negatively charged amino acid residues of proline, tryptophan, and phenylalanine with an average molecular weight of 723 Da. The study offers an important insight into the molecular mechanisms of xanthine oxidase inhibition and opens up new prospects for developing novel antihyperuricemic peptide drugs.

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Publication Details

Journal
Food Processing Techniques and Technology
Published
2024-12-24
DOI
https://doi.org/10.21603/2074-9414-2024-4-2536
Citations
1
Primary Topic
Gout, Hyperuricemia, Uric Acid
Type
article
Field-Weighted Citation Impact
0.34
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article

Amino Acid Patterns that Determine Antihyperuricemic Activity of Peptides: Identification and Predictive Analysis

А. В. Смирнова, С.Л. Тихонов
1 citations
Food Processing Techniques and Technology
Gout, Hyperuricemia, Uric Acid
0.34
article

Amino Acid Patterns that Determine Antihyperuricemic Activity of Peptides: Identification and Predictive Analysis

А. В. Смирнова, С.Л. Тихонов
article en
1 citations

Abstract

Peptides offer a promising analogue to synthetic drugs in treating hyperuricemia. This article introduces reliable amino acid patterns that cause the inhibitory xanthine oxidase (CSR) activity of peptides. The research objective was to propose new antihyperuricemic peptides and prove their effectiveness by predictive analytics in silico. The study featured peptides with inhibitory xanthine oxidase activity. The authors developed a protocol for searching, identifying, and quantifying patterns of amino acid residues in target peptide sequences. The identified peptides were tested for physicochemical properties, pharmacokinetic profile, inhibitory xanthine oxidase activity, general and target biological activity, and toxicity. The research revealed amino acid patterns responsible for inhibiting the xanthine oxidase enzyme, as well as generated new peptide sequences. Forty-nine non-toxic peptides with different lengths of amino acid sequences demonstrated high antimicrobial and inhibitory potential against the targeted drugs used to treat hyperuricemia and type 2 diabetes mellitus. The peptides were low-molecular compounds of predominantly hydrophilic and hydrophobic nature, 4-7 amino acids long. They contained negatively charged amino acid residues of proline, tryptophan, and phenylalanine with an average molecular weight of 723 Da. The study offers an important insight into the molecular mechanisms of xanthine oxidase inhibition and opens up new prospects for developing novel antihyperuricemic peptide drugs.

Food Processing Techniques and Technology
Ural State University of Economics (RU)
Openalex Percentile: Top 32%
Gout, Hyperuricemia, Uric Acid
0.34
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Amino Acid Patterns that Determine Antihyperuricemic Activity of Peptides: Identification and Predictive Analysis — А. В. Смирнова, С.Л. Тихонов · Food Processing Techniques and Technology (2024) | TGRS Research Map | TGRS