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Chemical Structure| 16297-94-2 Chemical Structure| 16297-94-2

Structure of 16297-94-2

Chemical Structure| 16297-94-2

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Product Citations

Product Citations

Zhang, Mengru ;

Abstract: Proteins, one of the most fundamental biomolecules, adopt unique higher order structures (HOS) to enable diverse biological functions. Deciphering protein HOS is crucial to gain deeper insights of their working mechanisms and to develop biotherapeutics. Mass spectrometry (MS)-based approaches evolved rapidly in the past 30 years and are now playing critical roles in protein HOS characterization. One of those approaches is MS-based footprinting whose principle is to map the solvent accessible surface area (SASA) to deliver structural information. Protein footprinting can be achieved by reversible labeling, e.g., hydrogen-deuterium exchange (HDX), and by irreversible labeling using radical-based reagents or other targeted labeling reagents. Irreversible labeling such as fast photochemical oxidation of protein (FPOP) and cross-linking (XL) delivers the information of the reactive amino acid side chains, whereas HDX allows the analysis of the backbone amides. Information from the two aspects are different yet complementing to each other. In Chapter 1, the two MS-based footprinting methodologies are reviewed and discussed in detail including the fundamental, history, and recent applications.  In the second section, development of the irreversible radical footprinting is the primary focus, specifically the elaboration of FPOP platform. Chapter 2 describes the generation and evaluation of a new radical reagent, the carbonate radical anion, on the FPOP platform; a radical that can selectively label methionine and aromatic residues, complementing others radical reagents in the footprinter “toolbox”. Chapter 3 demonstrates a novel way of elaborating FPOP platform that is to follow protein unfolding by coupling two lasers together: one to induce protein conformational changes by pH jump and the other to label protein with radical reagents. A time delay between the two lasers enables the characterization of the dominant protein conformations at different stages of unfolding.  The third section emphasizes the integration of several footprinting approaches as well as computational methods for comprehensive analysis of protein HOS. In Chapter 4 and 5, HDX, XL-MS and molecular docking are combined to determine protein-protein binding interfaces and to map epitope/paratope of an antigen-antibody complex, respectively. In particular, Chapter 4 discusses the potential of using HDX to adjudicate candidate docking models for quaternary structure elucidation. Besides molecular docking, homology modeling in combination with XLMS is also a successful marriage to decipher protein structures, an example is demonstrated in Chapter 6. Restraints derived from cross-links help modify and validate a predicted structure of phycolisome, contributing to the first proposed architecture of the protein complex in cyanobacteria.  The six chapters combine to demonstrate the development and application of MS-based footprinting in protein HOS characterization. Given the effectiveness and powerfulness of these methods, significant contributions by MS-related approaches are well to be expected in the field of structural proteomics.

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Product Details of [ 16297-94-2 ]

CAS No. :16297-94-2
Formula : C5H10N2O
M.W : 114.15
SMILES Code : OCCCC1(C)N=N1
MDL No. :MFCD27995612
InChI Key :DJJQQKQBBXUDCM-UHFFFAOYSA-N
Pubchem ID :14248196

Safety of [ 16297-94-2 ]

GHS Pictogram:
Signal Word:Danger
Hazard Statements:H226-H314
Precautionary Statements:P210-P240-P242-P243-P264-P271-P280-P301+P330+P331-P303+P361+P353-P304+P340-P305+P351+P338-P310-P363-P370+P378-P403+P233-P501
Class:3(8)
UN#:2924
Packing Group:

Computational Chemistry of [ 16297-94-2 ] Show Less

Physicochemical Properties

Num. heavy atoms 8
Num. arom. heavy atoms 0
Fraction Csp3 1.0
Num. rotatable bonds 3
Num. H-bond acceptors 3.0
Num. H-bond donors 1.0
Molar Refractivity 36.74
TPSA ?

Topological Polar Surface Area: Calculated from
Ertl P. et al. 2000 J. Med. Chem.

44.95 Ų

Lipophilicity

Log Po/w (iLOGP)?

iLOGP: in-house physics-based method implemented from
Daina A et al. 2014 J. Chem. Inf. Model.

1.6
Log Po/w (XLOGP3)?

XLOGP3: Atomistic and knowledge-based method calculated by
XLOGP program, version 3.2.2, courtesy of CCBG, Shanghai Institute of Organic Chemistry

0.54
Log Po/w (WLOGP)?

WLOGP: Atomistic method implemented from
Wildman SA and Crippen GM. 1999 J. Chem. Inf. Model.

-0.48
Log Po/w (MLOGP)?

MLOGP: Topological method implemented from
Moriguchi I. et al. 1992 Chem. Pharm. Bull.
Moriguchi I. et al. 1994 Chem. Pharm. Bull.
Lipinski PA. et al. 2001 Adv. Drug. Deliv. Rev.

0.43
Log Po/w (SILICOS-IT)?

SILICOS-IT: Hybrid fragmental/topological method calculated by
FILTER-IT program, version 1.0.2, courtesy of SILICOS-IT, http://www.silicos-it.com

1.02
Consensus Log Po/w?

Consensus Log Po/w: Average of all five predictions

0.62

Water Solubility

Log S (ESOL):?

ESOL: Topological method implemented from
Delaney JS. 2004 J. Chem. Inf. Model.

-0.69
Solubility 23.3 mg/ml ; 0.204 mol/l
Class?

Solubility class: Log S scale
Insoluble < -10 < Poorly < -6 < Moderately < -4 < Soluble < -2 Very < 0 < Highly

Very soluble
Log S (Ali)?

Ali: Topological method implemented from
Ali J. et al. 2012 J. Chem. Inf. Model.

-1.06
Solubility 10.0 mg/ml ; 0.088 mol/l
Class?

Solubility class: Log S scale
Insoluble < -10 < Poorly < -6 < Moderately < -4 < Soluble < -2 Very < 0 < Highly

Very soluble
Log S (SILICOS-IT)?

SILICOS-IT: Fragmental method calculated by
FILTER-IT program, version 1.0.2, courtesy of SILICOS-IT, http://www.silicos-it.com

-1.21
Solubility 7.09 mg/ml ; 0.0621 mol/l
Class?

Solubility class: Log S scale
Insoluble < -10 < Poorly < -6 < Moderately < -4 < Soluble < -2 Very < 0 < Highly

Soluble

Pharmacokinetics

GI absorption?

Gatrointestinal absorption: according to the white of the BOILED-Egg

High
BBB permeant?

BBB permeation: according to the yolk of the BOILED-Egg

No
P-gp substrate?

P-glycoprotein substrate: SVM model built on 1033 molecules (training set)
and tested on 415 molecules (test set)
10-fold CV: ACC=0.72 / AUC=0.77
External: ACC=0.88 / AUC=0.94

No
CYP1A2 inhibitor?

Cytochrome P450 1A2 inhibitor: SVM model built on 9145 molecules (training set)
and tested on 3000 molecules (test set)
10-fold CV: ACC=0.83 / AUC=0.90
External: ACC=0.84 / AUC=0.91

No
CYP2C19 inhibitor?

Cytochrome P450 2C19 inhibitor: SVM model built on 9272 molecules (training set)
and tested on 3000 molecules (test set)
10-fold CV: ACC=0.80 / AUC=0.86
External: ACC=0.80 / AUC=0.87

No
CYP2C9 inhibitor?

Cytochrome P450 2C9 inhibitor: SVM model built on 5940 molecules (training set)
and tested on 2075 molecules (test set)
10-fold CV: ACC=0.78 / AUC=0.85
External: ACC=0.71 / AUC=0.81

No
CYP2D6 inhibitor?

Cytochrome P450 2D6 inhibitor: SVM model built on 3664 molecules (training set)
and tested on 1068 molecules (test set)
10-fold CV: ACC=0.79 / AUC=0.85
External: ACC=0.81 / AUC=0.87

No
CYP3A4 inhibitor?

Cytochrome P450 3A4 inhibitor: SVM model built on 7518 molecules (training set)
and tested on 2579 molecules (test set)
10-fold CV: ACC=0.77 / AUC=0.85
External: ACC=0.78 / AUC=0.86

No
Log Kp (skin permeation)?

Skin permeation: QSPR model implemented from
Potts RO and Guy RH. 1992 Pharm. Res.

-6.61 cm/s

Druglikeness

Lipinski?

Lipinski (Pfizer) filter: implemented from
Lipinski CA. et al. 2001 Adv. Drug Deliv. Rev.
MW ≤ 500
MLOGP ≤ 4.15
N or O ≤ 10
NH or OH ≤ 5

0.0
Ghose?

Ghose filter: implemented from
Ghose AK. et al. 1999 J. Comb. Chem.
160 ≤ MW ≤ 480
-0.4 ≤ WLOGP ≤ 5.6
40 ≤ MR ≤ 130
20 ≤ atoms ≤ 70

None
Veber?

Veber (GSK) filter: implemented from
Veber DF. et al. 2002 J. Med. Chem.
Rotatable bonds ≤ 10
TPSA ≤ 140

0.0
Egan?

Egan (Pharmacia) filter: implemented from
Egan WJ. et al. 2000 J. Med. Chem.
WLOGP ≤ 5.88
TPSA ≤ 131.6

0.0
Muegge?

Muegge (Bayer) filter: implemented from
Muegge I. et al. 2001 J. Med. Chem.
200 ≤ MW ≤ 600
-2 ≤ XLOGP ≤ 5
TPSA ≤ 150
Num. rings ≤ 7
Num. carbon > 4
Num. heteroatoms > 1
Num. rotatable bonds ≤ 15
H-bond acc. ≤ 10
H-bond don. ≤ 5

1.0
Bioavailability Score?

Abbott Bioavailability Score: Probability of F > 10% in rat
implemented from
Martin YC. 2005 J. Med. Chem.

0.55

Medicinal Chemistry

PAINS?

Pan Assay Interference Structures: implemented from
Baell JB. & Holloway GA. 2010 J. Med. Chem.

0.0 alert
Brenk?

Structural Alert: implemented from
Brenk R. et al. 2008 ChemMedChem

0.0 alert: heavy_metal
Leadlikeness?

Leadlikeness: implemented from
Teague SJ. 1999 Angew. Chem. Int. Ed.
250 ≤ MW ≤ 350
XLOGP ≤ 3.5
Num. rotatable bonds ≤ 7

No; 1 violation:MW<1.0
Synthetic accessibility?

Synthetic accessibility score: from 1 (very easy) to 10 (very difficult)
based on 1024 fragmental contributions (FP2) modulated by size and complexity penaties,
trained on 12'782'590 molecules and tested on 40 external molecules (r2 = 0.94)

2.49
 

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