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Articoli di riviste sul tema "Chemical affinity"

Autore: Grafiati

Pubblicato: 4 giugno 2021

Ultima modifica: 29 luglio 2024

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1

Košíková, Božena, Elena Sláviková, and Juraj Lábaj. "Affinity of lignin preparations towards genotoxic compounds." BioResources 4, no. 1 (November 17, 2008): 72–79. http://dx.doi.org/10.15376/biores.4.1.72-79.

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The carcinogenicity and mutagenicity of chemicals may be modulated by other chemicals, including those prepared by organic synthesis. Considering the several drawbacks of synthetic compounds vis-a-vis the human organism, the lignin biomass component was examined for this purpose. The binding affinity of lignin samples prepared by chemical and biological modification of lignin products derived from chemical wood treatment towards for N-nitrosodiethylamine (NDA) was examined. The protective role of the lignin samples against carcinogenesis was tested on a well-known model carcinogen, N-methyl-N´

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2

Kauvar, Lawrence M., Hugo O. Villar, J. Richard Sportsman, Deborah L. Higgins, and Donald E. Schmidt. "Protein affinity map of chemical space." Journal of Chromatography B: Biomedical Sciences and Applications 715, no. 1 (September 1998): 93–102. http://dx.doi.org/10.1016/s0378-4347(98)00045-0.

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3

PARIKH, INDU, and PEDRO CUATRECASAS. "AFFINITY CHROMATOGRAPHY." Chemical & Engineering News 63, no. 34 (August 26, 1985): 17–32. http://dx.doi.org/10.1021/cen-v063n034.p017.

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4

Ohno, K., Y. Inoue, and M. Sakurai. "Quantum Chemical Study of Antibody Affinity Maturation." Seibutsu Butsuri 43, supplement (2003): S50. http://dx.doi.org/10.2142/biophys.43.s50_4.

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5

Alcantara, R. E., C. Xu, T. G. Spiro, and V. Guallar. "A quantum-chemical picture of hemoglobin affinity." Proceedings of the National Academy of Sciences 104, no. 47 (November 14, 2007): 18451–55. http://dx.doi.org/10.1073/pnas.0706026104.

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6

Hornung, M. W., M. A. Tapper, J. S. Denny, R. C. Kolanczyk, B. R. Sheedy, P. C. Hartig, H. Aladjov, T. R. Henry, and P. K. Schmieder. "Effects-based chemical category approach for prioritization of low affinity estrogenic chemicals." SAR and QSAR in Environmental Research 25, no. 4 (April 3, 2014): 289–323. http://dx.doi.org/10.1080/1062936x.2014.898692.

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7

Schmieder, P., Ovanes Mekenyan, Steven Bradbury, and G. Veith. "QSAR prioritization of chemical inventories for endocrine disruptor testing." Pure and Applied Chemistry 75, no. 11-12 (January 1, 2003): 2389–96. http://dx.doi.org/10.1351/pac200375112389.

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Binding affinity between chemicals and the estrogen receptor (ER) serves as an indicator of the potential to cause endocrine disruption through this receptor-mediated endocrine pathway. Estimating ER-binding affinity is, therefore, one strategic approach to reducing the costs of screening chemicals for potential risks of endocrine disruption. While measuring ER binding with in vitro assays may be the first choice in prioritizing chemicals for additional in vitro or in vivo estrogenicity testing, the time and costs associated with screening thousands of chemicals is prohibitive. Recent advances

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8

Freidin, A. B. "On the chemical affinity tensor for chemical reactions in deformable materials." Mechanics of Solids 50, no. 3 (May 2015): 260–85. http://dx.doi.org/10.3103/s0025654415030048.

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9

Nayarisseri, Anuraj. "Experimental and Computational Approaches to Improve Binding Affinity in Chemical Biology and Drug Discovery." Current Topics in Medicinal Chemistry 20, no. 19 (September 14, 2020): 1651–60. http://dx.doi.org/10.2174/156802662019200701164759.

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Drug discovery is one of the most complicated processes and establishment of a single drug may require multidisciplinary attempts to design efficient and commercially viable drugs. The main purpose of drug design is to identify a chemical compound or inhibitor that can bind to an active site of a specific cavity on a target protein. The traditional drug design methods involved various experimental based approaches including random screening of chemicals found in nature or can be synthesized directly in chemical laboratories. Except for the long cycle design and time, high cost is also the majo

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10

Nowak, Damian, Rafał Adam Bachorz, and Marcin Hoffmann. "Neural Networks in the Design of Molecules with Affinity to Selected Protein Domains." International Journal of Molecular Sciences 24, no. 2 (January 16, 2023): 1762. http://dx.doi.org/10.3390/ijms24021762.

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Drug design with machine learning support can speed up new drug discoveries. While current databases of known compounds are smaller in magnitude (approximately 108), the number of small drug-like molecules is estimated to be between 1023 and 1060. The use of molecular docking algorithms can help in new drug development by sieving out the worst drug-receptor complexes. New chemical spaces can be efficiently searched with the application of artificial intelligence. From that, new structures can be proposed. The research proposed aims to create new chemical structures supported by a deep neural n

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11

Koike, A. "Comparison of methods for chemical-compound affinity prediction." SAR and QSAR in Environmental Research 17, no. 5 (October 2006): 497–514. http://dx.doi.org/10.1080/10629360600934168.

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12

Torres Castillo, C. S., C. Bruel, and J. R. Tavares. "Chemical affinity and dispersibility of boron nitride nanotubes." Nanoscale Advances 2, no. 6 (2020): 2497–506. http://dx.doi.org/10.1039/d0na00136h.

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The chemical affinity of purified boron nitride nanotubes (BNNTs) is measured in terms of Hansen solubility parameters, taking into account relative sedimentation time in various solvents. This improves predictions for the dispersion of BNNTs.

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13

Albrow, Victoria E., Carla Fernandes, David M. Beal, Matthew D. Selby, Mireia Fernandez-Ocaña, Klaus C. Rumpel, and Lyn H. Jones. "Quantitative affinity-based chemical proteomics of TrkA inhibitors." Med. Chem. Commun. 3, no. 3 (2012): 322–25. http://dx.doi.org/10.1039/c2md00271j.

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14

Saikhanov, M. B. "Chemical Affinity and the Density of Energy Levels." Journal of Modern Physics 06, no. 11 (2015): 1452–55. http://dx.doi.org/10.4236/jmp.2015.611149.

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15

Freidin, Alexander B., Igor K. Korolev, Sergey P. Aleshchenko, and Elena N. Vilchevskaya. "Chemical affinity tensor and chemical reaction front propagation: theory and FE-simulations." International Journal of Fracture 202, no. 2 (October 26, 2016): 245–59. http://dx.doi.org/10.1007/s10704-016-0155-1.

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16

Míka, V., V. Kubáň, B. Klejdus, V. Odstrčilová, and P. Nerušil. "Phenolic compounds as chemical markers of low taxonomic levels in the family Poaceae." Plant, Soil and Environment 51, No. 11 (November 20, 2011): 506–12. http://dx.doi.org/10.17221/3624-pse.

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The spectra of non-structural phenolics in herbage were used to study genetic affinity in polyploid complex of Dactylis L., next in complex of octoploid brome species, and in red fescue cultivar collection. The diploid (2n = 14) subspecies of Dactylis revealed distinct differences from the tetraploid (2n = 28) ones as a clearly specialized group. In the genus Bromus the affinity bounds of the species inside the section (e.g. Ceratochloa) were not very tight in comparison to those between the species from various sections. Also remarkable differences in affinity bonds among red fescue cultivars

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17

Salas-Banuet, Guillermo, José Ramírez-Vieyra, Oscar Jaime Restrepo Baena, María Noguez-Amaya, and Bryan Cockrell. "The importance of being chemical affinity. Part VI: The harvest." DYNA 82, no. 190 (May 11, 2015): 13–22. http://dx.doi.org/10.15446/dyna.v82n190.48730.

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The quantitative scale of electronegativity, obtained by Linus Pauling, as a result of qualitative electron affinity background, generated multiple different and interesting proposals until today, which have proved to be the effort of ingenuity to get a universal concept of affinity, which has resulted incomplete. Thermodynamics, specifically its thermochemical branch, has offered an explanation, which has been accepted by The International Union of Pure and Applied Chemistry (IUPAC), as incomplete as the former. In both cases, it is thought that the error is in regard to affinity as a propert

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18

Bzowej, Natalie H., Hyman B. Niznik, and Philip Seeman. "Dopamine D1 receptors with enhanced agonist affinity and reduced antagonist affinity revealed by chemical modification." Biochemical and Biophysical Research Communications 152, no. 2 (April 1988): 933–39. http://dx.doi.org/10.1016/s0006-291x(88)80130-x.

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19

Sharma, Chiranjeev, Dickson Donu, Alyson M. Curry, Elizabeth Barton, and Yana Cen. "Multifunctional activity-based chemical probes for sirtuins." RSC Advances 13, no. 17 (2023): 11771–81. http://dx.doi.org/10.1039/d3ra02133e.

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20

Zou, Xiangman, Zhi Liu, Liya Liu, Wei Shi, Wanzhen Li, Zifen Guo, Feng Tang, and Wei Huang. "Enhanced transglycosylation activity of an Endo-F3 mutant by ligand-directed localization." Organic & Biomolecular Chemistry 20, no. 15 (2022): 3086–95. http://dx.doi.org/10.1039/d2ob00030j.

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Proximity-directed Endo-F3 D165A was developed by chemically arming with an Fc affinity tag, which exhibited excellent transglycosylation activities towards IgG, indicating a promising strategy for enzyme activity enhancement by chemical tools.

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21

Pachl, Fiona, Patrik Plattner, Benjamin Ruprecht, Guillaume Médard, Norbert Sewald, and Bernhard Kuster. "Characterization of a Chemical Affinity Probe Targeting Akt Kinases." Journal of Proteome Research 12, no. 8 (July 3, 2013): 3792–800. http://dx.doi.org/10.1021/pr400455j.

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22

Matsuno, Koichiro, and Stanley N. Salthe. "Chemical Affinity as Material Agency for Naturalizing Contextual Meaning." Information 3, no. 1 (January 16, 2012): 21–35. http://dx.doi.org/10.3390/info3010021.

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23

Iliuk, Anton, Xiaofeng Wu, Li Li, Jie Sun, Marco Hadisurya, Ronald S. Boris, and W. Andy Tao. "Plasma-Derived Extracellular Vesicle Phosphoproteomics through Chemical Affinity Purification." Journal of Proteome Research 19, no. 7 (May 12, 2020): 2563–74. http://dx.doi.org/10.1021/acs.jproteome.0c00151.

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24

Kuznik, G., B. Hörsch, G. Kretzschmar, and C. Unverzagt. "Chemical and enzymatic synthesis of high-affinity selectin ligands." Bioorganic & Medicinal Chemistry Letters 7, no. 5 (March 1997): 577–80. http://dx.doi.org/10.1016/s0960-894x(97)00051-6.

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25

Tomioka, Kanji, Hideki Fukuda, and Hiroshi Taniguchi. "Insulin delivery system based on an affinity chemical valve." Journal of Fermentation and Bioengineering 77, no. 4 (January 1994): 442–44. http://dx.doi.org/10.1016/0922-338x(94)90022-1.

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26

Mulhollan, G. A., and J. C. Bierman. "Enhanced chemical immunity for negative electron affinity GaAs photoemitters." Journal of Vacuum Science & Technology A: Vacuum, Surfaces, and Films 26, no. 5 (September 2008): 1195–97. http://dx.doi.org/10.1116/1.2965816.

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27

Soraya, hiva, Ruohollah Seddigh, Fatemeh Hadi, and Mohammad Faramarzi. "Chemical cannabis; The New Trend of addiction in Iran." Iranian Journal of Psychiatry and Clinical Psychology 28, no. 1 (April 20, 2022): 10. http://dx.doi.org/10.32598/ijpcp.28.1.4010.1.

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Synthetic cannabinoids (SC) are a heterogeneous group of substances with a high affinity for cannabinoid receptors. Unlike Δ9-tetrahydrocannabinol (THC), synthetic cannabinoids are incredibly potent, highly productive, have more affinity for the Cannabinoid receptor type 1 (CB1), and Cannabinoid receptor type 2 (CB2), and are designed to accelerate the effects of tetrahydrocannabinol. Also, there is experimental evidence that SCs acts on non-cannabinoid receptors, such as the 5-HT2B receptor or dopaminergic receptors. (1, 2).

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28

Gabler, Anna Maria, Annalena Ludwig, Florian Biener, Magdalena Waldner, Corinna Dawid, and Oliver Frank. "Chemical Characterization of Red Wine Polymers and Their Interaction Affinity with Odorants." Foods 13, no. 4 (February 8, 2024): 526. http://dx.doi.org/10.3390/foods13040526.

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In order to characterize red wine polymers with regard to their binding properties to aroma compounds (odorants), a qualitative and quantitative analysis of chemical degradation products after different chemical treatments (thiolytic, acidic, and alkaline depolymerization) of high -molecular-weight (HMW) fractions of red wine was performed. Using 1H NMR, LC-ToF-MS, LCMS/MS, and HPIC revealed key structural features such as carbohydrates, organic acids, phenolic compounds, anthocyanins, anthocyanidins, amino acids, and flavan-3-ols responsible for odorant-polymer interactions. Further, NMR-base

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29

Pratama, Mohammad Rizki Fadhil, та Sutomo S. "CHEMICAL STRUCTURE OPTIMIZATION OF LUPEOL AS ER-Α AND HER2 INHIBITOR". Asian Journal of Pharmaceutical and Clinical Research 11, № 6 (7 червня 2018): 298. http://dx.doi.org/10.22159/ajpcr.2018.v11i6.24226.

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Objectives: Lupeol, a triterpenoid isolated from Kasturi (Mangifera casturi) fruit has been known for having several pharmacological activities, including anticancer properties. Lupeol showed antiproliferative activity toward many cancer cells line including breast cancer. Lupeol showed promising potency as both ER-α and HER2 inhibitors, although still lower than known ER-α and HER2 Inhibitors. Chemical structure optimization of lupeol was predicted could increase the affinity of lupeol derivatives against ER-α and HER2. This study aims to determine lupeol derivative with the highest affinity

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30

Vodyanitskii, Yuri, and Dmitry Vlasov. "Integrated Assessment of Affinity to Chemical Fractions and Environmental Pollution with Heavy Metals: A New Approach Based on Sequential Extraction Results." International Journal of Environmental Research and Public Health 18, no. 16 (August 10, 2021): 8458. http://dx.doi.org/10.3390/ijerph18168458.

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To assess the affinity degree of heavy metals (HMs) to geochemical phases, many indices with several limitations are used. Thus, this study aims to develop a new complex index for assessing contamination level and affinity to chemical fractions in various solid environmental media. For this, a new integrated approach using the chemical affinity index (CAF) is proposed. Comparison of CAF with %F on the literature examples on fractionation of HMs from soils, bottom sediments, atmospheric PM10, and various particle size fractions of road dust proved a less significant role of the residual HMs fra

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31

Kim (book author), Mi Gyung, and Noel G. Coley (review author). "Affinity, That Elusive Dream: A Genealogy of the Chemical Revolution." Aestimatio: Critical Reviews in the History of Science 2 (December 21, 2015): 88–93. http://dx.doi.org/10.33137/aestimatio.v2i0.25747.

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32

Van Eygen, Gilles, Bart Van der Bruggen, Anita Buekenhoudt, and Patricia Luis Alconero. "Efficient membrane-based affinity separations for chemical applications: A review." Chemical Engineering and Processing - Process Intensification 169 (December 2021): 108613. http://dx.doi.org/10.1016/j.cep.2021.108613.

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33

Salas-Banuet, Guillermo, José Ramírez-Vieyra, Oscar Jaime Restrepo Baena, María Noguez-Amaya, and Bryan Cockrell. "The importance of being chemical affinity. Part V: The fruits." DYNA 81, no. 187 (October 24, 2014): 267–75. http://dx.doi.org/10.15446/dyna.v81n187.46092.

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34

Sakata, K., S. Nakajima, Y. Inoue, and M. Sakurai. "1D0900 Quantum chemical study on the anion affinity of Halorhodopsin." Seibutsu Butsuri 42, supplement2 (2002): S22. http://dx.doi.org/10.2142/biophys.42.s22_2.

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35

Zhang, Chunfang, Dale Fredericks, Eva M. Campi, Pas Florio, Christina Jespersgaard, Christine Bruun Schiødt, and Milton T. W. Hearn. "Purification of monoclonal antibodies by chemical affinity mixed mode chromatography." Separation and Purification Technology 142 (March 2015): 332–39. http://dx.doi.org/10.1016/j.seppur.2015.01.006.

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36

Urdal, D. L., S. M. Call, J. L. Jackson, and S. K. Dower. "Affinity purification and chemical analysis of the interleukin-1 receptor." Journal of Biological Chemistry 263, no. 6 (February 1988): 2870–77. http://dx.doi.org/10.1016/s0021-9258(18)69149-5.

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37

Ohno, Kazuki, Mitsutoshi Wada, Seiji Saito, Yoshio Inoue, and Minoru Sakurai. "Quantum chemical study on the affinity maturation of 48G7 antibody." Journal of Molecular Structure: THEOCHEM 722, no. 1-3 (May 2005): 203–11. http://dx.doi.org/10.1016/j.theochem.2004.11.061.

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38

Gutsev, G. L., and A. I. Boldyrev. "Problem of second and higher electron affinity of chemical compounds." Bulletin of the Academy of Sciences of the USSR Division of Chemical Science 38, no. 7 (July 1989): 1541–43. http://dx.doi.org/10.1007/bf00978456.

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39

Barshop, William D., Hee Jong Kim, Xiaorui Fan, Jihui Sha, Shima Rayatpisheh, and James A. Wohlschlegel. "Chemical Derivatization of Affinity Matrices Provides Protection from Tryptic Proteolysis." Journal of Proteome Research 18, no. 10 (September 9, 2019): 3586–96. http://dx.doi.org/10.1021/acs.jproteome.9b00254.

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40

Wang, Hangxiang, Yoichiro Koshi, Daishiro Minato, Hiroshi Nonaka, Shigeki Kiyonaka, Yasuo Mori, Shinya Tsukiji, and Itaru Hamachi. "Chemical Cell-Surface Receptor Engineering Using Affinity-Guided, Multivalent Organocatalysts." Journal of the American Chemical Society 133, no. 31 (August 10, 2011): 12220–28. http://dx.doi.org/10.1021/ja204422r.

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41

Bartmess, John E. "Gas-phase equilibrium affinity scales and chemical ionization mass spectrometry." Mass Spectrometry Reviews 8, no. 5 (September 1989): 297–343. http://dx.doi.org/10.1002/mas.1280080502.

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42

Lien Thuong, Nguyen Thi. "SCREENING OF PEPTIDE RECEPTOR SEQUENCES FOR AMITROL DETECTION USING CHROMATOGRAPHIC BIOPANNING." Vietnam Journal of Science and Technology 54, no. 2A (March 19, 2018): 6. http://dx.doi.org/10.15625/2525-2518/54/2a/11903.

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Endocrine disrupting chemicals (EDCs) have been known as health threatening compounds and much attention has been paid in a research related with detection and/or removal from food sources and living environment. Amitrol, classified as one of EDCs, is hard to be detected and removed due to its chemical characteristics such as small molecular weight, low reactivity and high solubility. Some of organic compounds were investigated to find the corresponding binding peptides to those by biopanning protocol. However, peptides with affinity to amitrol remained to be searched for because amitrol is ha

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43

Yang, Bo-Lun, and Shigeo Goto. "Affinity Purification by Tapered Bed." JOURNAL OF CHEMICAL ENGINEERING OF JAPAN 26, no. 6 (1993): 752–54. http://dx.doi.org/10.1252/jcej.26.752.

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44

Lee, Alpha A., Michael P. Brenner, and Lucy J. Colwell. "Predicting protein–ligand affinity with a random matrix framework." Proceedings of the National Academy of Sciences 113, no. 48 (November 16, 2016): 13564–69. http://dx.doi.org/10.1073/pnas.1611138113.

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Rapid determination of whether a candidate compound will bind to a particular target receptor remains a stumbling block in drug discovery. We use an approach inspired by random matrix theory to decompose the known ligand set of a target in terms of orthogonal “signals” of salient chemical features, and distinguish these from the much larger set of ligand chemical features that are not relevant for binding to that particular target receptor. After removing the noise caused by finite sampling, we show that the similarity of an unknown ligand to the remaining, cleaned chemical features is a robus

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45

Bansal, Rachit, Zehra Elgundi, Andrew Care, Sophia C. Goodchild, Megan S. Lord, Alison Rodger, and Anwar Sunna. "Elucidating the Binding Mechanism of a Novel Silica-Binding Peptide." Biomolecules 10, no. 1 (December 18, 2019): 4. http://dx.doi.org/10.3390/biom10010004.

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Linker-protein G (LPG) is a bifunctional fusion protein composed of a solid-binding peptide (SBP, referred as the “linker”) with high affinity to silica-based compounds and a Streptococcus protein G (PG), which binds antibodies. The binding mechanisms of LPG to silica-based materials was studied using different biophysical techniques and compared to that of PG without the linker. LPG displayed high binding affinity to a silica surface (KD = 34.77 ± 11.8 nM), with a vertical orientation, in comparison to parent PG, which exhibited no measurable binding affinity. Incorporation of the linker in t

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46

Glad, Cristina, Karin Sjödin, and Bo Mattiasson. "Streaming potential—a general affinity sensor." Biosensors 2, no. 2 (January 1986): 89–100. http://dx.doi.org/10.1016/0265-928x(86)80012-8.

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47

Liapis, Athanasios I. "Modelling Affinity Chromatography." Separation and Purification Methods 19, no. 2 (January 1990): 133–210. http://dx.doi.org/10.1080/03602549008050935.

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48

S, Anaghashree, and Chaitra H. "Review of Impact of Dooshi Visha on Female Infertility." International Research Journal of Ayurveda & Yoga 05, no. 10 (2022): 110–17. http://dx.doi.org/10.47223/irjay.2022.51018.

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Abstract (sommario):

Dooshi visha is a unique concept mentioned in Ayurveda, which has the potential to accumulate in various bodily tissues depending on their affinity and harm the body, by causing chronic illness. Present day exposure to various harmful chemicals can be considered as dooshi visha as they get accumulated in bodily tissues leading to cumulative toxicity causing chronic illnesses, instead of causing death immediately. Chemicals acting over the female reproductive system at different doses of exposure, and concept of dooshi visha was studied thoroughly. Chemicals having their affinity towards female

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49

Litovchick, Alexander, Xia Tian, Michael I. Monteiro, Kaitlyn M. Kennedy, Marie-Aude Guié, Paolo Centrella, Ying Zhang, Matthew A. Clark, and Anthony D. Keefe. "Novel Nucleic Acid Binding Small Molecules Discovered Using DNA-Encoded Chemistry." Molecules 24, no. 10 (May 27, 2019): 2026. http://dx.doi.org/10.3390/molecules24102026.

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Inspired by the many reported successful applications of DNA-encoded chemical libraries in drug discovery projects with protein targets, we decided to apply this platform to nucleic acid targets. We used a 120-billion-compound set of 33 distinct DNA-encoded chemical libraries and affinity-mediated selection to discover binders to a panel of DNA targets. Here, we report the successful discovery of small molecules that specifically interacted with DNA G-quartets, which are stable structural motifs found in G-rich regions of genomic DNA, including in the promoter regions of oncogenes. For this st

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50

Chi, Shu-Chun, Hsing-Cheng Hsi, and Chia Ming Chang. "Quantum Chemical GA-MLR, Cluster Model, and Conceptual DFT Descriptors Studies on the Binding Interaction of Estrogen Receptor Alpha with Endocrine Disrupting Chemicals." Crystals 13, no. 2 (January 27, 2023): 228. http://dx.doi.org/10.3390/cryst13020228.

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In the present study, the predication of the binding affinity (log RBA) of estrogen receptor alpha with three categories of environmental endocrine disrupting chemicals (EDCs), namely, PCB, phenol, and DDT, is performed by the quantum chemical genetic algorithm multiple linear regression (GA-MLR) method. The result of the optimal model indicates that log RBA increases with increasing the electrophilicity and hydrophobicity of EDCs. However, by using the quantum chemical cluster model approach, the modeling results reveal that electrostatic interaction and hydrogen bonding play a significant ro

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