N,N'-Di-BOC-L-cystine
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N,N'-Di-BOC-L-cystine

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A reagent used in the synthesis of Toll-Like Receptor-2 agonists lipopeptides.

Category
BOC-Amino Acids
Catalog number
BAT-000780
CAS number
10389-65-8
Molecular Formula
C16H28N2O8S2
Molecular Weight
440.53
N,N'-Di-BOC-L-cystine
IUPAC Name
(2R)-3-[[(2R)-2-carboxy-2-[(2-methylpropan-2-yl)oxycarbonylamino]ethyl]disulfanyl]-2-[(2-methylpropan-2-yl)oxycarbonylamino]propanoic acid
Synonyms
NSC 164046; (Boc-Cys-OH)2; N,N'-bis[(1,1-Dimethylethoxy)carbonyl]-L-cystine; N,N'-Dicarboxycystine N,N'-Di-tert-butyl Ester; N,N'-Bis(tert-butoxycarbonyl)-L-cysteine
Appearance
White powder
Purity
95%
Density
1.313 g/cm3
Melting Point
140-145 °C
Boiling Point
627.4°C at 760 mmHg
Storage
Store at -20°C
InChI
InChI=1S/C16H28N2O8S2/c1-15(2,3)25-13(23)17-9(11(19)20)7-27-28-8-10(12(21)22)18-14(24)26-16(4,5)6/h9-10H,7-8H2,1-6H3,(H,17,23)(H,18,24)(H,19,20)(H,21,22)/t9-,10-/m0/s1
InChI Key
MHDQAZHYHAOTKR-UWVGGRQHSA-N
Canonical SMILES
CC(C)(C)OC(=O)NC(CSSCC(C(=O)O)NC(=O)OC(C)(C)C)C(=O)O

N,N’-Di-BOC-L-cystine, a derivative of the amino acid cystine, finds widespread use in diverse scientific realms. Here are four key applications of N,N’-Di-BOC-L-cystine:

Peptide Synthesis: A cornerstone in peptide and protein synthesis, N,N’-Di-BOC-L-cystine acts as a shielded variant of cystine, safeguarding thiol groups from undesired reactions during peptide chain construction. Following synthesis, acidic conditions strip away the protective BOC groups, unveiling the cystine-containing peptide.

Pharmaceutical Development: In the pharmaceutical arena, N,N’-Di-BOC-L-cystine plays a pivotal role in crafting drug molecules that target specific proteins or enzymes. Its shielded nature enables precise integration into drug candidates, thwarting premature disulfide bond formation and fostering the creation of potent and stable therapeutic agents.

Bioconjugation: Employed in bioconjugation strategies, N,N’-Di-BOC-L-cystine serves as a conduit for linking biomolecules through disulfide bonds. Selective deprotection of the shielded cystine facilitates the formation of disulfide crosslinks with other molecules, a boon for crafting conjugated antibodies, protein-drug hybrids, and diverse bioconjugates for research and therapeutic exploits.

Structural Biology: In the realm of structural biology, N,N’-Di-BOC-L-cystine emerges as a vital tool for introducing disulfide bonds into proteins and peptides, bolstering the stability of their intricate three-dimensional architectures. These bonds play a pivotal role in upholding the structural integrity and biological efficacy of proteins, aiding researchers in exploring protein folding, stability, and functionality to refine the design of robust protein-based 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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