N,N'-Di-BOC-L-cystine
Need Assistance?
  • US & Canada:
    +
  • UK: +

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

* Please kindly note that our products are not to be used for therapeutic purposes and cannot be sold to patients.

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
627.4ºC 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
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.
Online Inquiry
Verification code
Inquiry Basket