Extraordinary metabolic stability of peptides containing α-aminoxy acids

Chen, Fei; Ma, Bin; Yang, Zhenwei; Lin, Ge; Yang, Dan

doi:10.1007/s00726-011-1095-8

Cited by 26 publications

(26 citation statements)

References 25 publications

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“…However, toxicity, immunogenicity and stability remain the main concerns in the development of peptide-based drugs. Stability of peptides can be enhanced by various ways [8], including incorporation of D-amino acids (making peptides protease resistant), changing the backbone chemistry, cyclization, and incorporation of α-aminoxy amino acids [9]. Similarly, there are numerous in silico tools, which can predict the immunogenicity of the peptides [10], [11], [12], [13], [14], but there is hardly any way/method to predict the toxicity of peptides.…”

Section: Introductionmentioning

confidence: 99%

In Silico Approach for Predicting Toxicity of Peptides and Proteins

et al. 2013

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BackgroundOver the past few decades, scientific research has been focused on developing peptide/protein-based therapies to treat various diseases. With the several advantages over small molecules, including high specificity, high penetration, ease of manufacturing, peptides have emerged as promising therapeutic molecules against many diseases. However, one of the bottlenecks in peptide/protein-based therapy is their toxicity. Therefore, in the present study, we developed in silico models for predicting toxicity of peptides and proteins.DescriptionWe obtained toxic peptides having 35 or fewer residues from various databases for developing prediction models. Non-toxic or random peptides were obtained from SwissProt and TrEMBL. It was observed that certain residues like Cys, His, Asn, and Pro are abundant as well as preferred at various positions in toxic peptides. We developed models based on machine learning technique and quantitative matrix using various properties of peptides for predicting toxicity of peptides. The performance of dipeptide-based model in terms of accuracy was 94.50% with MCC 0.88. In addition, various motifs were extracted from the toxic peptides and this information was combined with dipeptide-based model for developing a hybrid model. In order to evaluate the over-optimization of the best model based on dipeptide composition, we evaluated its performance on independent datasets and achieved accuracy around 90%. Based on above study, a web server, ToxinPred has been developed, which would be helpful in predicting (i) toxicity or non-toxicity of peptides, (ii) minimum mutations in peptides for increasing or decreasing their toxicity, and (iii) toxic regions in proteins.ConclusionToxinPred is a unique in silico method of its kind, which will be useful in predicting toxicity of peptides/proteins. In addition, it will be useful in designing least toxic peptides and discovering toxic regions in proteins. We hope that the development of ToxinPred will provide momentum to peptide/protein-based drug discovery (http://crdd.osdd.net/raghava/toxinpred/).

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Section: Introductionmentioning

confidence: 99%

In Silico Approach for Predicting Toxicity of Peptides and Proteins

et al. 2013

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“…α ‐Aminoxy peptides are analogs of β ‐peptides where the β ‐carbon is replaced by an oxygen atom and α ‐NH‐ O structures form eight‐membered intramolecular hydrogen bonds . Aminoxy peptides (APs) can serve as building blocks for cell‐penetrating peptides and also enhance the known properties of naturally occurring peptides . Aminoxy bonds also significantly increase metabolic stability of peptides .…”

Section: Resultsmentioning

confidence: 99%

“…Aminoxy peptides (APs) can serve as building blocks for cell‐penetrating peptides and also enhance the known properties of naturally occurring peptides . Aminoxy bonds also significantly increase metabolic stability of peptides . In order to synthesize a stable Goralatide analog, α ‐aminoxy peptidomimetic moiety was chosen for modification.…”

Section: Resultsmentioning

confidence: 99%

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Synthesis of L‐Lys‐Aminoxy‐Goralatide

Lebedyeva

Zhao

et al. 2014

Journal of Peptide Science

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“…Nanorod formation through intermolecular H-bonding has also been observed on a symmetric cyclotetrapeptide prepared from a 2-( C -furanosyl) β-amino acid and an α-aminooxy acid [19]. Besides, the N -oxyamide-linkage is resistant to chemical and enzymatic hydrolysis [20], and N -oxyamide bonds could be readily formed using classical amide formation methods.…”

Section: Sugar Aminooxy Acids For Oligosaccharide Mimicsmentioning

confidence: 99%

Synthesis of O-Amino Sugars and Nucleosides

Chen

Xie

2018

Molecules

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Nucleic acids and carbohydrates are essential biomolecules involved in numerous biological and pathological processes. Development of multifunctional building blocks based on nucleosides and sugars is in high demand for the generation of novel oligonucleotide mimics and glycoconjugates for biomedical applications. Recently, aminooxyl-functionalized compounds have attracted increasing research interest because of their easy derivatization through oxime ligation or N-oxyamide formation reactions. Various biological applications have been reported for O-amino carbohydrate- and nucleoside-derived compounds. Here, we report our efforts in the design and synthesis of glyco-, glycosyl, nucleoside- and nucleo-aminooxy acid derivatives from readily available sugars and amino acids, and their use for the generation of N-oxyamide-linked oligosaccharides, glycopeptides, glycolipids, oligonucleosides and nucleopeptides as novel glycoconjugates or oligonucleotide mimics. Delicate and key points in the synthesis will be emphasized.

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Extraordinary metabolic stability of peptides containing α-aminoxy acids

Cited by 26 publications

References 25 publications

In Silico Approach for Predicting Toxicity of Peptides and Proteins

In Silico Approach for Predicting Toxicity of Peptides and Proteins

Synthesis of L‐Lys‐Aminoxy‐Goralatide

Synthesis of O-Amino Sugars and Nucleosides

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