N,S-di-Z-L-cysteine
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N,S-di-Z-L-cysteine

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Category
CBZ-Amino Acids
Catalog number
BAT-003198
CAS number
57912-35-3
Molecular Formula
C19H19NO6S
Molecular Weight
389.40
N,S-di-Z-L-cysteine
IUPAC Name
(2R)-2-(phenylmethoxycarbonylamino)-3-phenylmethoxycarbonylsulfanylpropanoic acid
Synonyms
Z-L-Cys(Z)-OH; N,S-DI-Z-L-CYSTEINE; Cbz-Cys(Cbz)-OH; N,S-Bis-benzyloxycarbonyl-L-cystein; N-[(phenylmethoxy)carbonyl]-L-cysteine; N-(Phenylmethoxycarbonyl)-S-(phenylmethyloxycarbonyl)-L-cysteine
Appearance
White to off-white powder
Purity
≥ 98% (HPLC)
Density
1.341±0.06 g/cm3
Melting Point
93-102 °C
Storage
Store at 2-8 °C
InChI
InChI=1S/C19H19NO6S/c21-17(22)16(20-18(23)25-11-14-7-3-1-4-8-14)13-27-19(24)26-12-15-9-5-2-6-10-15/h1-10,16H,11-13H2,(H,20,23)(H,21,22)/t16-/m0/s1
InChI Key
PXKPRICKEUGRRR-INIZCTEOSA-N
Canonical SMILES
C1=CC=C(C=C1)COC(=O)NC(CSC(=O)OCC2=CC=CC=C2)C(=O)O

NS-di-Z-L-cysteine, a synthetic derivative of the amino acid cysteine, finds diverse applications in bioscience and chemistry. Here are four key applications of NS-di-Z-L-cysteine:

Peptide Synthesis: Embedded within the realm of peptide synthesis, NS-di-Z-L-cysteine serves as a fundamental building block. Its shielded thiol group enables selective reactions, thwarting undesired side reactions during the intricate assembly of peptide chains. This meticulous process ensures optimal yield and purity in the production of custom peptides utilized in cutting-edge research and therapeutic endeavors.

Bioconjugation: Stepping into the domain of bioconjugation techniques, NS-di-Z-L-cysteine acts as a pivotal player in attaching molecules to proteins or other biomolecules. Serving as a liaison, it facilitates the establishment of enduring linkages between diverse components. This capability proves invaluable in crafting sophisticated constructs like antibody-drug conjugates and targeted therapeutic modalities, pushing the boundaries of precision medicine.

Enzyme Studies: Enter the captivating realm of enzyme studies, where researchers harness NS-di-Z-L-cysteine to delve into the activity and inhibition of cysteine-dependent enzymes. By creating a controlled arena to scrutinize enzyme-substrate interactions, it aids in unraveling the intricacies of enzyme kinetics and mechanisms. This critical insight underpins the development of enzyme inhibitors as potential pharmaceutical agents, driving forward the frontier of drug discovery.

Chemical Safety Studies: Transitioning to the fascinating realm of chemical safety studies, NS-di-Z-L-cysteine emerges as a crucial tool in assessing oxidative stress and antioxidant responses within cells. By introducing this compound, researchers gain insights into how cells react to oxidative agents and assess the effectiveness of antioxidant interventions. This knowledge is instrumental in deciphering cellular defense strategies and crafting interventions to alleviate oxidative damage, reshaping the landscape of cellular biology and therapeutics.

1. Advancing Pan-cancer Gene Expression Survial Analysis by Inclusion of Non-coding RNA
Bo Ye, et al. RNA Biol. 2020 Nov;17(11):1666-1673. doi: 10.1080/15476286.2019.1679585. Epub 2019 Oct 18.
Non-coding RNAs occupy a significant fraction of the human genome. Their biological significance is backed up by a plethora of emerging evidence. One of the most robust approaches to demonstrate non-coding RNA's biological relevance is through their prognostic value. Using the rich gene expression data from The Cancer Genome Altas (TCGA), we designed Advanced Expression Survival Analysis (AESA), a web tool which provides several novel survival analysis approaches not offered by previous tools. In addition to the common single-gene approach, AESA computes the gene expression composite score of a set of genes for survival analysis and utilizes permutation test or cross-validation to assess the significance of log-rank statistic and the degree of over-fitting. AESA offers survival feature selection with post-selection inference and utilizes expanded TCGA clinical data including overall, disease-specific, disease-free, and progression-free survival information. Users can analyse either protein-coding or non-coding regions of the transcriptome. We demonstrated the effectiveness of AESA using several empirical examples. Our analyses showed that non-coding RNAs perform as well as messenger RNAs in predicting survival of cancer patients. These results reinforce the potential prognostic value of non-coding RNAs. AESA is developed as a module in the freely accessible analysis suite MutEx.
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