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Chemical Structure| 6485-79-6 Chemical Structure| 6485-79-6

Structure of Triisopropylsilane
CAS No.: 6485-79-6

Chemical Structure| 6485-79-6

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Yingjie Wu ; Samuel J. Rozans ; Abolfazl Salehi Moghaddam ; E. Thomas Pashuck ;

Abstract: Cells dynamically modify their local extracellular matrix by expressing proteases that degrade matrix proteins. This enables cells to spread and migrate within tissues, and this process is often mimicked in hydrogels through the incorporation of peptide crosslinks that can be degraded by cell-secreted proteases. However, the cleavage of hydrogel crosslinks will also reduce the local matrix mechanical properties, and most crosslinking peptides, such as the widely used GPQGIWGQ “PanMMP” sequence, lead to bulk degradation of the hydrogel. A subset of proteases are localized to the cell membrane and are only active in the pericellular region in the immediate vicinity of the cell surface. These membrane-type proteases have important physiological roles and enable cells to migrate within tissues. In this work we developed an approach to identify and optimize peptide sequences that are specifically degraded by membrane-type proteases. We utilized a proteomic screen to identify peptide targets, and coupled this with a functional assay that both quantifies peptide degradation by individual cell types and can elucidate whether the peptides are primarily cleaved by soluble proteases or membrane-type proteases. We then used a split-and-pool synthesis approach to generate more than 300 variants of the target peptide to improve the degradation behavior. We identified an optimized peptide sequence, KLVADLMASAE, which is primarily degraded by membrane-type proteases, but enables both endothelial cells and stem cells grown in KLVADLMASAE-crosslinked hydrogels to spread and have viabilities similar to the gels crosslinked by the PanMMP peptide. Notably, the biological performance of the KLVADLMASAE peptide-cross linked gels was significantly improved from the initial peptide target found in the proteomic screen. This work introduces a functional approach to identifying and refining protease-substrate peptides as a way to enhance the properties of hydrogel matrices.

Purchased from AmBeed: ; ;

Samuel J. Rozans ; Abolfazl Salehi Moghaddam ; Yingjie Wu ; Kayleigh Atanasoff ; Liliana Nino ; Katelyn Dunne , et al.

Abstract: Peptides are widely used within biomaterials to improve cell adhesion, incorporate bioactive ligands, and enable cell-mediated degradation of the matrix. While many of the peptides incorporated into biomaterials are intended to be present throughout the life of the material, their stability is not typically quantified during culture. In this work we designed a series of peptide libraries containing four different N-terminal peptide functionalizations and three C-terminal functionalization to better understand how simple modifications can be used to reduce non-specific degradation of peptides. We tested these libraries with three cell types commonly used in biomaterials research, including mesenchymal stem/stromal cells (hMSCs), endothelial cells, and macrophages, and quantified how these cell types non-specifically degraded peptide as a function of terminal amino acid and chemistry. We found that peptides in solution which contained N-terminal amines were almost entirely degraded by 48 hours, irrespective of the terminal amino acid, and that degradation occurred even at high peptide concentrations. Peptides with C-terminal carboxylic acids also had significant degradation when cultured with cells. We found that simple modifications to the termini could significantly reduce or completely abolish non-specific degradation when soluble peptides were added to cells cultured on tissue culture plastic or within hydrogel matrices, and that functionalizations which mimicked peptide conjugations to hydrogel matrices significantly slowed non-specific degradation. We also found that there were minimal differences across cell donors, and that sequences mimicking different peptides commonly-used to functionalized biomaterials all had significant non-specific degradation. Finally, we saw that there was a positive trend between RGD stability and hMSC spreading within hydrogels, indicating that improving the stability of peptides within biomaterial matrices may improve the performance of engineered matrices.

Purchased from AmBeed: ; ;

Rozans, Samuel J ; Moghaddam, Abolfazl S ; Pashuck, E Thomas ;

Abstract: Peptides are widely used in biomaterials due to their easy of synthesis, ability to signal cells, and modify the properties of biomaterials. A key benefit of using peptides is that they are natural substrates for cell-secreted enzymes, which creates the possibility of utilizing cell-secreted enzymes for tuning cell-material interactions. However, these enzymes can also induce unwanted degradation of bioactive peptides in biomaterials, or in peptide therapies. Liquid chromatography-mass spectrometry (LC-MS) is a widely used, powerful methodology that can separate complex mixtures of molecules and quantify numerous analytes within a single run. There are several challenges in using LC-MS for the multiplexed quantification of cell-induced peptide degradation, including the need for non-degradable internal standards and the identification of optimal sample storage conditions. Another problem is that cell culture media and biological samples typically contain both proteins and lipids that can accumulate on chromatography columns and degrade their performance. However, removing these constituents can be expensive, time consuming, and increases sample variability. Here we show that directly injecting samples onto the LC-MS without any purification enables rapid and accurate quantification of peptide concentration, and that hundreds of LC-MS runs can be done on a single column without a significantly diminish the ability to quantify the degradation of peptide libraries. We also show that column failure is evident when hydrophilic peptides fail to be retained on the column, and this can be easily identified using standard peptide mixtures for column benchmarking. In total, this work introduces a simple and effective method for simultaneously quantifying the degradation of dozens of peptides in cell culture. By providing a streamlined and cost-effective method for the direct quantification of peptide degradation in complex biological samples, this work enables more efficient assessment of peptide stability and functionality, facilitating the development of advanced biomaterials and peptide-based therapies.

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Suresh, Vallabh ; Sheik, Daniel A. ; Detomasi, Tyler C. ; Zhao, Tianqi ; Zepeda, Theresa ; Saladi, Shyam , et al.

Abstract: With the current state of COVID-19 changing from a pandemic to being more endemic, the priorities of diagnostics will likely vary from rapid detection to stratification for the treatment of the most vulnerable patients. Such patient stratification can be facilitated using multiple markers, including SARS-CoV-2-specific viral enzymes, like the 3CL protease, and viral-life-cycle-associated host proteins, such as ACE2. To enable future explorations, we have developed a fluorescent and Raman spectroscopic SARS-CoV-2 3CL protease assay that can be run sequentially with a fluorescent ACE2 activity measurement within the same sample. Our prototype assay functions well in saliva, enabling non-invasive sampling. ACE2 and 3CL protease activity can be run with minimal sample volumes in 30 min. To test the prototype, a small initial cohort of eight clin. samples was used to check if the assay could differentiate COVID-19-pos. and -neg. samples. Though these small clin. cohort samples did not reach statistical significance, results trended as expected. The high sensitivity of the assay also allowed the detection of a low-activity 3CL protease mutant.

Keywords: COVID-19 ; proteases ; enzymatic assays

Purchased from AmBeed:

Jonathan G. Kwok ;

Abstract: A long-standing goal in the field of chemical biology is coupling molecular recognition with the prowess of synthetic chemistry to produce novel compounds serving as chemical probes and therapeutic agents. One class of biomolecules that has gained considerable research focus and advancement for targeting is ribonucleic acid, RNA. Transcriptomic data have shown that both coding and non-coding RNA have critical roles in regulating every aspect of the central dogma of molecular biology. Although the research field continues to thrive in the development of RNA-binding ligands, current modalities are limited to targeting single-stranded and structurally complex RNA. The double-stranded RNA remains one of the most challenging structures to target. Double-stranded RNA is found in many functional tertiary and quaternary structured RNA and offers opportunities to modulate its biological activities. Herein, I describe my efforts for the design, synthesis, and biochemical and biophysical evaluations of a novel proteomimetic scaffold, referred to as the Crosslinked Helical Fork for the structure-and sequence-specific recognition of double-stranded RNA. Chapter 1 introduces the current advancements in targeting primary, secondary, and tertiary structured RNA. The topics for discussion will focus on modalities recognizing RNA in a sequence- or structured-specific manner and how current complex structured RNA are liganded. Chapter 2 describes the design and binding assessments of two ahelical RNA-binding peptides that mimic the groove-binding proteins Rnt1p from Saccharomyces cerevisiae and Tat from the equine infectious anemia virus. Chapter 3 addresses the issues from the results of Chapter 2 by introducing an encodable proteomimetic scaffold that mimics a dimeric a-helical RNA-binding viral protein binding in the major groove of double-stranded RNA. The synthetic scaffold was inspired by the Tomato Aspermy Virus 2b (TAV2b) protein. The chapter concludes with the firstgeneration design principles of targeting double-stranded RNA in a structure- and sequence-specific manner and future directions to improve the encodable scaffold. Additional supporting data can be found after Chapter 3 in the Appendix.

Purchased from AmBeed: ;

Vallabh Suresh ; Kaleb Byers ; Ummadisetti Chinna Rajesh ; Francesco Caiazza ; Gina Zhu ; Charles S. Craik , et al.

Abstract: The classification of pancreatic cyst fluids can provide a basis for the early detection of pancreatic cancer while eliminating unnecessary procedures. A candidate biomarker, gastricsin (pepsin C), was found to be present in potentially malignant mucinous pancreatic cyst fluids. A gastricsin activity assay using a magnetic bead-based platform has been developed using immobilized peptide substrates selective for gastricsin bearing a dimeric rhodamine dye. The unique dye structure allows quantitation of enzyme-cleaved product by both fluorescence and surface enhanced Raman spectroscopy (SERS). The performance of this assay was compared with ELISA assays of pepsinogen C and the standard of care, carcinoembryonic antigen (CEA), in the same clinical sample cohort. A retrospective cohort of mucinous (n = 40) and non-mucinous (n = 29) classes of pancreatic cyst fluid samples were analyzed using the new protease activity assay. For both assay detection modes, successful differentiation of mucinous and non-mucinous cyst fluid was achieved using 1 µL clinical samples. The activity-based assays in combination with CEA exhibit optimal sensitivity and specificity of 87% and 93%, respectively. The use of this gastricsin activity assay requires a minimal volume of clinical specimen, offers a rapid assay time, and shows improvements in the differentiation of mucinous and non-mucinous cysts using an accurate standardized readout of product formation, all without interfering with the clinical standard of care.

Keywords: pancreatic cancer ; early detection ; minimal volume ; mucinous ; non-mucinous ; matrix effects ; surface-enhanced Raman spectroscopy (SERS)

Purchased from AmBeed:

Alternative Products

Product Details of [ 6485-79-6 ]

CAS No. :6485-79-6
Formula : C9H22Si
M.W : 158.36
SMILES Code : CC([SiH](C(C)C)C(C)C)C
MDL No. :MFCD00009657
InChI Key :ZGYICYBLPGRURT-UHFFFAOYSA-N
Pubchem ID :6327611

Safety of [ 6485-79-6 ]

GHS Pictogram:
Signal Word:Danger
Hazard Statements:H225-H315-H319
Precautionary Statements:P501-P240-P210-P233-P243-P241-P242-P264-P280-P370+P378-P337+P313-P305+P351+P338-P362+P364-P303+P361+P353-P332+P313-P403+P235
Class:3
UN#:1993
Packing Group:

Computational Chemistry of [ 6485-79-6 ] Show Less

Physicochemical Properties

Num. heavy atoms 10
Num. arom. heavy atoms 0
Fraction Csp3 1.0
Num. rotatable bonds 3
Num. H-bond acceptors 0.0
Num. H-bond donors 0.0
Molar Refractivity 53.58
TPSA ?

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

0.0 Ų

Lipophilicity

Log Po/w (iLOGP)?

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

3.05
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

4.33
Log Po/w (WLOGP)?

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

3.44
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.

3.61
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

1.3
Consensus Log Po/w?

Consensus Log Po/w: Average of all five predictions

3.14

Water Solubility

Log S (ESOL):?

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

-3.35
Solubility 0.0705 mg/ml ; 0.000445 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.

-4.04
Solubility 0.0143 mg/ml ; 0.0000903 mol/l
Class?

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

Moderately 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

-2.44
Solubility 0.575 mg/ml ; 0.00363 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

Low
BBB permeant?

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

Yes
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.

-4.19 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

2.0
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

1.0 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)

2.95

Application In Synthesis of [ 6485-79-6 ]

* 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 [ 6485-79-6 ]

[ 6485-79-6 ] Synthesis Path-Downstream   1~1

  • 1
  • [ 6485-79-6 ]
  • [ 1798-85-2 ]
  • (3-(3-bromophenyl)propyl)triisopropylsilane [ No CAS ]
 

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