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Chemical Structure| 110990-08-4 Chemical Structure| 110990-08-4

Structure of 110990-08-4

Chemical Structure| 110990-08-4

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Synonyms: Fmoc-D-Lys-OH

4.5 *For Research Use Only !

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Product Citations

Product Citations      Show More

Andrew J. Borchert ; Alissa C. Bleem ; Hyun Gyu Lim ; Kevin Rychel ; Keven D. Dooley ; Zoe A. Kellermyer , et al.

Abstract: There is growing interest in engineering Pseudomonas putida KT2440 as a microbial chassis for the conversion of renewable and waste-based feedstocks, and metabolic engineering of P. putida relies on the understanding of the functional relationships between genes. In this work, independent component analysis (ICA) was applied to a compendium of existing fitness data from randomly barcoded transposon insertion sequencing (RB-TnSeq) of P. putida KT2440 grown in 179 unique experimental conditions. ICA identified 84 independent groups of genes, which we call fModules (“functional modules”), where gene members displayed shared functional influence in a specific cellular process. This machine learning-based approach both successfully recapitulated previously characterized functional relationships and established hitherto unknown associations between genes. Selected gene members from fModules for hydroxycinnamate metabolism and stress resistance, assimilation, and nitrogen metabolism were validated with engineered mutants of P. putida. Additionally, functional gene clusters from ICA of RB-TnSeq data sets were compared with regulatory gene clusters from prior ICA of RNAseq data sets to draw connections between gene regulation and function. Because ICA profiles the functional role of several distinct gene networks simultaneously, it can reduce the time required to annotate gene function relative to manual curation of RB-TnSeq data sets.

Purchased from AmBeed:

Agrawal, Anushka ; Euliano, Erin M ; Pogostin, Brett H ; Yu, Marina H ; Swain, Joseph WR ; Hartgerink, Jeffrey D , et al.

Abstract: Introduction Multidomain peptides (MDPs) are amino acid sequences that self-assemble to form supramolecular hydrogels under physiological conditions that have shown promise for a number of biomedical applications. K2(SL)6K2 (“K2”), a widely studied MDP, has demonstrated the ability to enhance the humoral immune response to co-delivered antigen. Herein, we sought to explore the in vitro and in vivo properties of a peptide with the same sequence but opposite chirality (D-K2) since peptides composed of D-amino acids are resistant to protease degradation and potentially more immunostimulatory than their canonical counterparts. Methods K2 and D-K2 hydrogels were characterized and evaluated in vitro using circular dichroism, rheology, cryo-electron microscopy, and fuorescence recovery after photobleaching studies. In vivo experiments in SKH-1 mice were conducted to evaluate both ovalbumin release from the hydrogels and hydrogel degradation. The injection site of the hydrogels was analyzed using histology and humoral immunity was assessed by ELISA. Results In vitro, the enantiomeric hydrogels exhibited similar rheological properties, and fuorescence recovery after pho_x005f_x0002_tobleaching experiments demonstrated that the difusion of ovalbumin (OVA), a model antigen, was similar within both hydrogels. In vivo, K2 and D-K2 peptide hydrogels had similar OVA release rates, both releasing 89% of the antigen within 8 days. Both hydrogels elicited a similar antigen-specifc humoral immune response. However, the in vivo degradation of the D-K2 hydrogel progressed signifcantly slower than K2. After 4 weeks in vivo, only 23±7% of the K2 hydrogel remained at the injection site compared to 94±7% of the D-K2 hydrogel, likely due to their diferent protease susceptibilities. Conclusion Taken together, these data suggest that peptide chirality can be a useful tool for increasing hydrogel residence time for biomedical applications that would beneft from long persistence times and that, if an antigen releases over a suf_x005f_x0002_fciently short period, release can be largely independent of degradation rate, though slower-difusing payloads may exhibit degradation rate dependence.

Keywords: Hydrogel ; Chirality ; Degradation ; Drug delivery ; Peptides ; Adjuvants

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Alternative Products

Product Details of [ 110990-08-4 ]

CAS No. :110990-08-4
Formula : C21H24N2O4
M.W : 368.43
SMILES Code : NCCCC[C@H](C(O)=O)NC(OCC1C2=C(C3=C1C=CC=C3)C=CC=C2)=O
Synonyms :
Fmoc-D-Lys-OH
MDL No. :MFCD04113545
InChI Key :YRKFMPDOFHQWPI-LJQANCHMSA-N
Pubchem ID :6992519

Safety of [ 110990-08-4 ]

GHS Pictogram:
Signal Word:Warning
Hazard Statements:H302-H315-H319-H335
Precautionary Statements:P261-P305+P351+P338

Computational Chemistry of [ 110990-08-4 ] Show Less

Physicochemical Properties

Num. heavy atoms 27
Num. arom. heavy atoms 12
Fraction Csp3 0.33
Num. rotatable bonds 10
Num. H-bond acceptors 5.0
Num. H-bond donors 3.0
Molar Refractivity 102.3
TPSA ?

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

101.65 Ų

Lipophilicity

Log Po/w (iLOGP)?

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

2.54
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.

3.11
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.

2.19
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

2.89
Consensus Log Po/w?

Consensus Log Po/w: Average of all five predictions

2.25

Water Solubility

Log S (ESOL):?

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

-2.13
Solubility 2.71 mg/ml ; 0.00736 mol/l
Class?

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

Soluble
Log S (Ali)?

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

-2.25
Solubility 2.09 mg/ml ; 0.00567 mol/l
Class?

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

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

-5.73
Solubility 0.000684 mg/ml ; 0.00000186 mol/l
Class?

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

Moderately 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

Yes
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

Yes
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.

-8.16 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

0.0
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<2.0
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)

3.93

Application In Synthesis of [ 110990-08-4 ]

* All experimental methods are cited from the reference, please refer to the original source for details. We do not guarantee the accuracy of the content in the reference.

  • Downstream synthetic route of [ 110990-08-4 ]

[ 110990-08-4 ] Synthesis Path-Downstream   1~2

  • 1
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  • 2
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  • [ 35661-38-2 ]
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  • [ 104091-08-9 ]
  • Fmoc-D-Cys(Trt)-OH [ No CAS ]
  • N-α-[(9H-fluoren-9-ylmethoxy)carbonyl]-NG-(2,2,4,6,7-pentamethyldihydrobenzofuran-5-sulfonyl)-D-arginine [ No CAS ]
  • C72H95N16O18PS [ No CAS ]
 

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