PubMed Health⌕ Search
Home/Archive/PubMed

PubMed · 11259061

Improved solid-phase peptide synthesis method utilizing alpha-azide-protected amino acids.

J T Lundquist·J C Pelletier
View original source ↗Export metadata ↓

Abstract

[structure: see text]. Pure alpha-azido acids were prepared using an efficient diazo transfer method followed by buffered workup. These building blocks were used to prepare small peptides on Wang resin by two approaches. Peptides prone to diketopiperazine formation were prepared in good yields by coupling acids to resin bound iminophosphoranes during Fmoc-Wang synthesis. The iminophosphoranes can also be hydrolyzed under neutral conditions to provide unprotected amines ready for further coupling.

Explore related subjects

Amino AcidsAzidesPeptidesJournal ArticleResearch Support, Non-U.S. Gov't

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

J T Lundquist, J C Pelletier. 2001-03-08. Improved solid-phase peptide synthesis method utilizing alpha-azide-protected amino acids.. https://doi.org/10.1021/ol0155485

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2026

Chimeric structural isomer fragments as cost-efficient internal standards for amino acid quantification by mass spectrometry.

Amino acid (AA) profiles from body fluids such as blood and urine are clinical indicators for diagnosing metabolic and hepatic diseases. Current quantitative methods, such as liquid chromatography-mass spectrometry (LC-MS) with isotopically labelled internal standards (ISs), are costly and technically demanding. This study proposes a cost-efficient alternative using structural isomers as ISs in a direct liquid infusion (DLI) tandem mass spectrometry (MS/MS) approach. The method leverages chimeric spectra and fragment intensity ratios to quantify AAs, demonstrating high linearity and precision even with a 3D ion trap mass analyser. This approach offers a viable strategy for AA quantification in preventive medicine, particularly for screening metabolic diseases such as phenylketonuria, diabetes, and liver dysfunction.

Amino Acids↗
PubMed2005

PPRODO: prediction of protein domain boundaries using neural networks.

Successful prediction of protein domain boundaries provides valuable information not only for the computational structure prediction of multidomain proteins but also for the experimental structure determination. Since protein sequences of multiple domains may contain much information regarding evolutionary processes such as gene-exon shuffling, this information can be detected by analyzing the position-specific scoring matrix (PSSM) generated by PSI-BLAST. We have presented a method, PPRODO (Prediction of PROtein DOmain boundaries) that predicts domain boundaries of proteins from sequence information by a neural network. The network is trained and tested using the values obtained from the PSSM generated by PSI-BLAST. A 10-fold cross-validation technique is performed to obtain the parameters of neural networks using a nonredundant set of 522 proteins containing 2 contiguous domains. PPRODO provides good and consistent results for the prediction of domain boundaries, with accuracy of about 66% using the +/-20 residue criterion. The PPRODO source code, as well as all data sets used in this work, are available from http://gene.kias.re.kr/ approximately jlee/pprodo/.

Amino Acids↗
PubMed2005

Identification of domains and domain interface residues in multidomain proteins from graph spectral method.

We present a novel method for the identification of structural domains and domain interface residues in proteins by graph spectral method. This method converts the three-dimensional structure of the protein into a graph by using atomic coordinates from the PDB file. Domain definitions are obtained by constructing either a protein backbone graph or a protein side-chain graph. The graph is constructed based on the interactions between amino acid residues in the three-dimensional structure of the proteins. The spectral parameters of such a graph contain information regarding the domains and subdomains in the protein structure. This is based on the fact that the interactions among amino acids are higher within a domain than across domains. This is evident in the spectra of the protein backbone and the side-chain graphs, thus differentiating the structural domains from one another. Further, residues that occur at the interface of two domains can also be easily identified from the spectra. This method is simple, elegant, and robust. Moreover, a single numeric computation yields both the domain definitions and the interface residues.

Amino Acids↗