Article

Extraordinary metabolic stability of peptides containing α-aminoxy acids.

Department of Chemistry, The University of Hong Kong, Pokfulam Road, Hong Kong, People's Republic of China.
Amino Acids (Impact Factor: 3.91). 10/2011; 43(1):499-503. DOI: 10.1007/s00726-011-1095-8
Source: PubMed

ABSTRACT The metabolic stability of peptides containing a mixed sequence of α-aminoxy acids and α-amino acids is significantly improved compared to peptides composed of only natural α-amino acids. The introduction of an α-aminoxy acid into peptide chain dramatically improves the stability of the amide bonds immediately before and after it. These peptides containing α-aminoxy acids represent excellent structural scaffold for the design of metabolically stable and biologically active peptides.

0 Bookmarks
 · 
98 Views
  • [Show abstract] [Hide abstract]
    ABSTRACT: Peptidomimetics represent an important field in chemistry, pharmacology and material science as they circumvent the limitations of traditional peptides used in therapy. Self-structural organizations such as turns, helices, sheets and loops can be accessed by chemical modifications of amino acids or peptides. In-depth structural and conformational analysis and structure-activity relationships (SAR) offer a way to establish peptidomimetic libraries. Herein, we review recent developments in peptidomimetics that are formed via heteroatom replacement within the native amino acid backbone. Each sub-section describes structural features, utility and preparative methods.
    Chemical Society Reviews 03/2014; · 24.89 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: BACKGROUND: Over 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. DESCRIPTION: We 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. CONCLUSION: ToxinPred 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/).
    PLoS ONE 01/2013; 8(9):e73957. · 3.53 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Long acting luteinizing hormone-releasing hormone (LHRH) antagonists designed to be protease-resistant were a series of novel decapeptides structurally similar to LHRH. In the present work, a high-throughput method based on a LC-MS/MS has been developed for the simultaneous determination of pharmacokinetics of five LHRH antagonists in rat via cassette dosing. The method was performed under selected reaction monitoring (SRM) in positive ion mode. The analytes were extracted from rat plasma by liquid-liquid extraction with acetonitrile. Chromatographic separation of the analytes was successfully achieved on a Hypersil Gold (100mm×2.1mm, 3μm) using a mobile phase composed of acetonitrile-water (30:70) containing 0.05% (v/v) formic acid. The result showed good linearity and selectivity were obtained for all antagonists. The limits of quantification of the five LHRH antagonists were from 5 to 10ng/mL. The average extract recoveries in the rat plasma were all over 72%. The intra-day and inter-day precisions (R.S.D. %) were all within 10% and the accuracy was ranged from 92.54 to 109.05%. This method has been successfully applied to the pharmacokinetic studies of the five LHRH antagonists. The results indicated that the plasma drug concentrations versus time curves after intravenous injection of five antagonists via cassette dosing were all fitted to a two-compartment model. The pharmacokinetic parameters of five LHRH antagonists suggested that LY616 could be the more stable candidate drugs and optimized as the candidate drug for further study. Our studies enabled high-throughput rapid screening for pharmacokinetic assessment of new peptide candidates, and provided abundant information on the metabolic properties of these LHRH antagonists.
    Journal of chromatography. B, Analytical technologies in the biomedical and life sciences 05/2014; 962C:94-101. · 2.78 Impact Factor