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Journal articles on the topic 'Study of protein-ligand interactions'

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Published: 4 June 2021

Last updated: 8 February 2022

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1

Chappuis, Quentin, Jonas Milani, Basile Vuichoud, et al. "Hyperpolarized Water to Study Protein–Ligand Interactions." Journal of Physical Chemistry Letters 6, no. 9 (2015): 1674–78. http://dx.doi.org/10.1021/acs.jpclett.5b00403.

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2

Banner, D. "Using thrombin to study protein–ligand interactions." Acta Crystallographica Section A Foundations of Crystallography 60, a1 (2004): s27. http://dx.doi.org/10.1107/s0108767304099477.

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3

Biswas, Priyanka. "Modern Biophysical Approaches to Study Protein–Ligand Interactions." Biophysical Reviews and Letters 13, no. 04 (2018): 133–55. http://dx.doi.org/10.1142/s1793048018300013.

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Protein–ligand interactions act as a pivot to the understanding of most of the biological interactions. The study of interactions between proteins and cellular molecules has led to the establishment and identification of various important pathways that control biological systems. Investigators working in different fields of biological sciences have an intrinsic interest in this field and complement their findings by the application of different biophysical approaches and tools to quantify protein–ligand interactions that include protein–small molecules, protein–DNA, protein–RNA, protein–protei

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4

Singh, Omkar, Kunal Sawariya, and Polamarasetty Aparoy. "Graphlet signature-based scoring method to estimate protein–ligand binding affinity." Royal Society Open Science 1, no. 4 (2014): 140306. http://dx.doi.org/10.1098/rsos.140306.

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Over the years, various computational methodologies have been developed to understand and quantify receptor–ligand interactions. Protein–ligand interactions can also be explained in the form of a network and its properties. The ligand binding at the protein-active site is stabilized by formation of new interactions like hydrogen bond, hydrophobic and ionic. These non-covalent interactions when considered as links cause non-isomorphic sub-graphs in the residue interaction network. This study aims to investigate the relationship between these induced sub-graphs and ligand activity. Graphlet sign

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5

Kumar, Prashant, and Paulina Maria Dominiak. "Combining Molecular Dynamic Information and an Aspherical-Atom Data Bank in the Evaluation of the Electrostatic Interaction Energy in Multimeric Protein-Ligand Complex: A Case Study for HIV-1 Protease." Molecules 26, no. 13 (2021): 3872. http://dx.doi.org/10.3390/molecules26133872.

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Computational analysis of protein–ligand interactions is of crucial importance for drug discovery. Assessment of ligand binding energy allows us to have a glimpse of the potential of a small organic molecule to be a ligand to the binding site of a protein target. Available scoring functions, such as in docking programs, all rely on equations that sum each type of protein–ligand interactions in order to predict the binding affinity. Most of the scoring functions consider electrostatic interactions involving the protein and the ligand. Electrostatic interactions constitute one of the most import

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6

Fu, Yi, Ji Zhao, and Zhiguo Chen. "Insights into the Molecular Mechanisms of Protein-Ligand Interactions by Molecular Docking and Molecular Dynamics Simulation: A Case of Oligopeptide Binding Protein." Computational and Mathematical Methods in Medicine 2018 (December 4, 2018): 1–12. http://dx.doi.org/10.1155/2018/3502514.

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Protein-ligand interactions are a necessary prerequisite for signal transduction, immunoreaction, and gene regulation. Protein-ligand interaction studies are important for understanding the mechanisms of biological regulation, and they provide a theoretical basis for the design and discovery of new drug targets. In this study, we analyzed the molecular interactions of protein-ligand which was docked by AutoDock 4.2 software. In AutoDock 4.2 software, we used a new search algorithm, hybrid algorithm of random drift particle swarm optimization and local search (LRDPSO), and the classical Lamarck

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7

Apaydin, M. S., C. E. Guestrin, C. Varma, D. L. Brutlag, and J. C. Latombe. "Stochastic roadmap simulation for the study of ligand-protein interactions." Bioinformatics 18, Suppl 2 (2002): S18—S26. http://dx.doi.org/10.1093/bioinformatics/18.suppl_2.s18.

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8

Wati, Widia, Gunawan Pamudji Widodo, and Rina Herowati. "Prediction of Pharmacokinetics Parameter and Molecular Docking Study of Antidiabetic Compounds from Syzygium polyanthum and Syzygium cumini." Jurnal Kimia Sains dan Aplikasi 23, no. 6 (2020): 189–95. http://dx.doi.org/10.14710/jksa.23.6.189-195.

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Syzygium polyanthum leaf extract and Syzygium cumini herbs extract have been reported to have antidiabetic activity. This study aimed to predict the molecular target of chemical constituents of S. polyanthum and S. cumini as well as study their interactions with various macromolecular targets of an antidiabetic agent. Molecular docking of all ligands was studied using the Autodock Vina program in PyRx, and the results are presented as binding affinity values (kcal/mol) of ligand against the protein. PyMOL is used to visualize the 3D molecular of docked conformation and ligand-protein interacti

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9

Barone, G., F. Catanzano, P. Del Vecchio, C. Giancola, and G. Graziano. "Differential scanning calorimetry as a tool to study protein-ligand interactions." Pure and Applied Chemistry 67, no. 11 (1995): 1867–72. http://dx.doi.org/10.1351/pac199567111867.

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10

Stoneman, Michael R., Naomi Raicu, Gabriel Biener, and Valerică Raicu. "Fluorescence-based Methods for the Study of Protein-Protein Interactions Modulated by Ligand Binding." Current Pharmaceutical Design 26, no. 44 (2020): 5668–83. http://dx.doi.org/10.2174/1381612826666201116120934.

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Background: The growing evidence that G protein-coupled receptors (GPCRs) not only form oligomers but that the oligomers also may modulate the receptor function provides a promising avenue in the area of drug design. Highly selective drugs targeting distinct oligomeric sub-states offer the potential to increase efficacy while reducing side effects. In this regard, determining the various oligomeric configurations and geometric sub-states of a membrane receptor is of utmost importance. Methods: In this report, we have reviewed two techniques that have proven to be valuable in monitoring the qua

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11

Rausell, Antonio, David Juan, Florencio Pazos, and Alfonso Valencia. "Protein interactions and ligand binding: From protein subfamilies to functional specificity." Proceedings of the National Academy of Sciences 107, no. 5 (2010): 1995–2000. http://dx.doi.org/10.1073/pnas.0908044107.

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The divergence accumulated during the evolution of protein families translates into their internal organization as subfamilies, and it is directly reflected in the characteristic patterns of differentially conserved residues. These specifically conserved positions in protein subfamilies are known as “specificity determining positions” (SDPs). Previous studies have limited their analysis to the study of the relationship between these positions and ligand-binding specificity, demonstrating significant yet limited predictive capacity. We have systematically extended this observation to include th

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12

Hossen, J., and T. K. Pal. "DFT Study of Efinaconazole, an Antifungal Drug and Its Molecular Docking against a Holoenzyme (pdb id: 3IDB)." Journal of Scientific Research 13, no. 2 (2021): 679–94. http://dx.doi.org/10.3329/jsr.v13i2.51731.

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Efinaconazole (ECZ) is an antifungal drug. Various non-covalent interactions between ECZ and a holoenzyme (protein id: 3idb) has been investigated through computational study. The structure of ECZ was optimized using density functional theory (DFT) applying B3LYP/6-311G+(d,p) method. HOMO, LUMO, chemical hardness and softness, several thermochemical parameters, electrostatic potential surface, vibrational spectrum, total energy, and maximum internal force and maximum internal displacement with respect to optimization step number have been determined. The optimized ECZ ligand was subjected to m

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13

Li, Cheuk-Wing, Guodong Yu, Jingyun Jiang, et al. "A microfluidic linear node array for the study of protein–ligand interactions." Lab Chip 14, no. 20 (2014): 3993–99. http://dx.doi.org/10.1039/c4lc00779d.

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14

Vandevenne, M., G. Gaspard, N. Yilmaz, et al. "Rapid and easy development of versatile tools to study protein/ligand interactions." Protein Engineering Design and Selection 21, no. 7 (2008): 443–51. http://dx.doi.org/10.1093/protein/gzn021.

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15

Stanczak, A., T. Magdziarz, A. Raczynska, and A. Góra. "Hot spot identification by ligand-protein surface interactions mapping – in silico study." New Biotechnology 44 (October 2018): S93. http://dx.doi.org/10.1016/j.nbt.2018.05.953.

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16

Breuker, K. "The study of protein–ligand interactions by mass spectrometry—a personal view." International Journal of Mass Spectrometry 239, no. 1 (2004): 33–41. http://dx.doi.org/10.1016/j.ijms.2004.09.004.

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17

Poongodi, T., and TH Nazeema. "Network Pharmacology study on the mechanism of MKA Polyherbal Formulation in combating Respiratory Diseases." Journal of Phytopharmacology 9, no. 6 (2020): 385–91. http://dx.doi.org/10.31254/phyto.2020.9601.

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The Multi-targeted action of Polyherbal formulation is responsible for enhanced therapeutic efficacy in combating various diseases. But, understanding the mode of action of herbal medicine remains a challenge because of its complex metabolomics. Network pharmacology-based approach enables to explore the mechanism of action of polyherbal formulation in biological system. In present investigation, we have explored the molecular mechanism of action of the Polyherbal formulation MKA comprising of three botanicals Mimusops elengi L., Kedrostis foetidissima (Jacq.) Cogn. and Artemisia vulgaris L. in

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18

Karasev, Dmitry, Boris Sobolev, Alexey Lagunin, Dmitry Filimonov, and Vladimir Poroikov. "Prediction of Protein–ligand Interaction Based on Sequence Similarity and Ligand Structural Features." International Journal of Molecular Sciences 21, no. 21 (2020): 8152. http://dx.doi.org/10.3390/ijms21218152.

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Computationally predicting the interaction of proteins and ligands presents three main directions: the search of new target proteins for ligands, the search of new ligands for targets, and predicting the interaction of new proteins and new ligands. We proposed an approach providing the fuzzy classification of protein sequences based on the ligand structural features to analyze the latter most complicated case. We tested our approach on five protein groups, which represented promised targets for drug-like ligands and differed in functional peculiarities. The training sets were built with the or

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19

Lemmens, Irma, Sam Lievens, and Jan Tavernier. "MAPPIT: a versatile tool to study cytokine receptor signalling." Biochemical Society Transactions 36, no. 6 (2008): 1448–51. http://dx.doi.org/10.1042/bst0361448.

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MAPPIT (mammalian protein–protein interaction trap) is a cytokine receptor-based two-hybrid method that operates in intact mammalian cells. A bait is fused C-terminally to a STAT (signal transducer and activator of transcription) recruitment-deficient receptor, whereas the prey is linked to functional STAT-binding sites. When bait and prey interact a ligand-dependent complementation of the STAT recruitment deficiency occurs, leading to activation of a STAT-responsive reporter. MAPPIT is very well suited to study protein interactions involving activated cytokine receptors as the technique allow

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20

Hussain, Rohanah, Edoardo Longo, and Giuliano Siligardi. "UV-Denaturation Assay to Assess Protein Photostability and Ligand-Binding Interactions Using the High Photon Flux of Diamond B23 Beamline for SRCD." Molecules 23, no. 8 (2018): 1906. http://dx.doi.org/10.3390/molecules23081906.

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Light irradiation with high photon flux in the vacuum and far-UV region is known to denature the conformation of biopolymers. Measures are in place at Diamond Light Source B23 beamline for Synchrotron Radiation Circular Dichroism (SRCD) to control and make this effect negligible. However, UV denaturation of proteins can also be exploited as a novel method for assessing biopolymer photostability as well as ligand-binding interactions. Usually, host–ligand binding interactions can be assessed monitoring CD changes of the host biopolymer upon ligand addition. The novel method of identifying ligan

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21

Lu, Qiangna, Lian-Wen Qi, and Jinfeng Liu. "Improving protein–ligand binding prediction by considering the bridging water molecules in Autodock." Journal of Theoretical and Computational Chemistry 18, no. 05 (2019): 1950027. http://dx.doi.org/10.1142/s0219633619500275.

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Water plays a significant role in determining the protein–ligand binding modes, especially when water molecules are involved in mediating protein–ligand interactions, and these important water molecules are receiving more and more attention in recent years. Considering the effects of water molecules has gradually become a routine process for accurate description of the protein–ligand interactions. As a free docking program, Autodock has been most widely used in predicting the protein–ligand binding modes. However, whether the inclusion of water molecules in Autodock would improve its docking p

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22

Shah, Vraj R., Jaydip D. Bhaliya, and Gautam M. Patel. "In silico approach: docking study of oxindole derivatives against the main protease of COVID-19 and its comparison with existing therapeutic agents." Journal of Basic and Clinical Physiology and Pharmacology 32, no. 3 (2021): 197–214. http://dx.doi.org/10.1515/jbcpp-2020-0262.

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Abstract Objectives Presently, the pandemic of COVID-19 has worsened the situation worldwide and received global attention. The United States of America have the highest numbers of a patient infected by this disease followed by Brazil, Russia, India and many other countries. Moreover, lots of research is going on to find out effective vaccines or medicine, but still, no potent vaccine or drug is discovered to cure COVID-19. As a consequence, many types of research have designated that computer-based studies, such as protein–ligand interactions, structural dynamics, and chembio modeling are the

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23

Kim, Keehun, Shayla Paulekas, Fredrik Sadler та ін. "β2-adrenoceptor ligand efficacy is tuned by a two-stage interaction with the Gαs C terminus". Proceedings of the National Academy of Sciences 118, № 11 (2021): e2017201118. http://dx.doi.org/10.1073/pnas.2017201118.

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Classical pharmacological models have incorporated an “intrinsic efficacy” parameter to capture system-independent effects of G protein–coupled receptor (GPCR) ligands. However, the nonlinear serial amplification of downstream signaling limits quantitation of ligand intrinsic efficacy. A recent biophysical study has characterized a ligand “molecular efficacy” that quantifies the influence of ligand-dependent receptor conformation on G protein activation. Nonetheless, the structural translation of ligand molecular efficacy into G protein activation remains unclear and forms the focus of this st

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24

Jayaraman, Narayanaswamy. "Multivalent ligand presentation as a central concept to study intricate carbohydrate–protein interactions." Chemical Society Reviews 38, no. 12 (2009): 3463. http://dx.doi.org/10.1039/b815961k.

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25

Sahai, Michelle A., and Philip C. Biggin. "Quantifying Water-Mediated Protein–Ligand Interactions in a Glutamate Receptor: A DFT Study." Journal of Physical Chemistry B 115, no. 21 (2011): 7085–96. http://dx.doi.org/10.1021/jp200776t.

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26

Souiri, Mina, Laurence Mora-Ponsonnet, Karine Glinel, Ali Othmane, Thierry Jouenne, and Anthony C. Duncan. "Surface assembly on biofunctional magnetic nanobeads for the study of protein–ligand interactions." Colloids and Surfaces B: Biointerfaces 68, no. 2 (2009): 125–29. http://dx.doi.org/10.1016/j.colsurfb.2008.07.006.

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27

Zheng, Fang, and Chang-Guo Zhan. "Computational Modeling of Solvent Effects on Protein-Ligand Interactions Using Fully Polarizable Continuum Model and Rational Drug Design." Communications in Computational Physics 13, no. 1 (2013): 31–60. http://dx.doi.org/10.4208/cicp.130911.121011s.

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AbstractThis is a brief review of the computational modeling of protein-ligand interactions using a recently developed fully polarizable continuum model (FPCM) and rational drug design. Computational modeling has become a powerful tool in understanding detailed protein-ligand interactions at molecular level and in rational drug design. To study the binding of a protein with multiple molecular species of a ligand, one must accurately determine both the relative free energies of all of the molecular species in solution and the corresponding microscopic binding free energies for all of the molecu

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28

Kanai, Chisato, Enzo Kawasaki, Ryuta Murakami, Yusuke Morita, and Atsushi Yoshimori. "Computational Prediction of Compound–Protein Interactions for Orphan Targets Using CGBVS." Molecules 26, no. 17 (2021): 5131. http://dx.doi.org/10.3390/molecules26175131.

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A variety of Artificial Intelligence (AI)-based (Machine Learning) techniques have been developed with regard to in silico prediction of Compound–Protein interactions (CPI)—one of which is a technique we refer to as chemical genomics-based virtual screening (CGBVS). Prediction calculations done via pairwise kernel-based support vector machine (SVM) is the main feature of CGBVS which gives high prediction accuracy, with simple implementation and easy handling. We studied whether the CGBVS technique can identify ligands for targets without ligand information (orphan targets) using data from G pr

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29

Sousa, Paulo Robson M., Nelson Alberto N. de Alencar, Anderson H. Lima, Jerônimo Lameira, and Cláudio Nahum Alves. "Protein-Ligand Interaction Study ofCpOGA in Complex with GlcNAcstatin." Chemical Biology & Drug Design 81, no. 2 (2012): 284–90. http://dx.doi.org/10.1111/cbdd.12078.

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30

Merugu, Ramchander, Uttam Kumar Neerudu, Karunakar Dasa, and Kalpana V. Singh. "Molecular docking studies of deacetylbisacodyl with intestinal sucrase-maltase enzyme." International Journal of Advances in Scientific Research 2, no. 12 (2017): 191. http://dx.doi.org/10.7439/ijasr.v2i12.3821.

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Molecular docking of sucrase-isomaltase with ligand deacetylbisacodyl when subjected to docking analysis using docking server, predicted in-silico result with a free energy of -3.36 Kcal/mol which was agreed well with physiological range for protein-ligand interaction, making bisacodyl probable potent anti-isomaltase molecule. According to docking server Inhibition constant is 5.98Mm. which predicts that the ligand is going to inhibits enzyme and result in a clinically relevant drug interaction with a substrate for the enzyme. Hydrogen bond with bond length 3.45is formed between Pro 64 (A) of

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31

Thapa, Bishnu, Daniel Beckett, Jon Erickson, and Krishnan Raghavachari. "Theoretical Study of Protein–Ligand Interactions Using the Molecules-in-Molecules Fragmentation-Based Method." Journal of Chemical Theory and Computation 14, no. 10 (2018): 5143–55. http://dx.doi.org/10.1021/acs.jctc.8b00531.

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32

Helms, V., and R. C. Wade. "Thermodynamics of water mediating protein-ligand interactions in cytochrome P450cam: a molecular dynamics study." Biophysical Journal 69, no. 3 (1995): 810–24. http://dx.doi.org/10.1016/s0006-3495(95)79955-6.

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33

WINGARD, LEMUEL B., and KRISHNA NARASIMHAN. "Application of Fourier Transform Infrared (FTIR) Spectroscopy to the Study of Protein-Ligand Interactions." Annals of the New York Academy of Sciences 542, no. 1 Enzyme Engine (1988): 480–84. http://dx.doi.org/10.1111/j.1749-6632.1988.tb25875.x.

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34

Li, Qingxin, and CongBao Kang. "A Practical Perspective on the Roles of Solution NMR Spectroscopy in Drug Discovery." Molecules 25, no. 13 (2020): 2974. http://dx.doi.org/10.3390/molecules25132974.

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Solution nuclear magnetic resonance (NMR) spectroscopy is a powerful tool to study structures and dynamics of biomolecules under physiological conditions. As there are numerous NMR-derived methods applicable to probe protein–ligand interactions, NMR has been widely utilized in drug discovery, especially in such steps as hit identification and lead optimization. NMR is frequently used to locate ligand-binding sites on a target protein and to determine ligand binding modes. NMR spectroscopy is also a unique tool in fragment-based drug design (FBDD), as it is able to investigate target-ligand int

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35

Gorska, Katarzyna, Julien Beyrath, Sylvie Fournel, Gilles Guichard, and Nicolas Winssinger. "Ligand dimerization programmed by hybridization to study multimeric ligand–receptor interactions." Chemical Communications 46, no. 41 (2010): 7742. http://dx.doi.org/10.1039/c0cc02852e.

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36

Uhrín, Duan, A. V. Krishna Prasad, Jean-Robert Brisson, and David R. Bundle. "Carbohydrate-antibody interactions by NMR for a 13C-labelled disaccharide ligand." Canadian Journal of Chemistry 80, no. 8 (2002): 904–7. http://dx.doi.org/10.1139/v02-063.

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Incorporation of a 13C label into a carbohydrate ligand, methyl 3-O-(3,6-dideoxy-α-D-xylohexopyranosyl)-2-O-methyl-α-D-mannopyranoside permitted by NMR spectroscopy the study of its binding to the Fab from a monoclonal antibody, Se 155-4. The signals of the free and bound form were observed in the 13C spectrum of the carbohydrate-protein complex. The dissociation rate constants were consequently determined by full lineshape analysis of the 13C spectrum. Comparison with simplified analyses relying only on the linewidth of the 1H and 13C signals of the free ligand were made and the justification

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37

Guariento, Mara, Michael Assfalg, Serena Zanzoni, Dimitrios Fessas, Renato Longhi, and Henriette Molinari. "Chicken ileal bile-acid-binding protein: a promising target of investigation to understand binding co-operativity across the protein family." Biochemical Journal 425, no. 2 (2009): 413–24. http://dx.doi.org/10.1042/bj20091209.

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Protein–bile acid interactions are crucial microscopic events at the basis of both physiological and pathological biochemical pathways. BABPs (bile-acid-binding proteins) are intracellular transporters able to bind ligands with different stoichiometry, selectivity and co-operativity. The molecular determinants and energetics of interaction are the observables that connect the microscopic to the macroscopic frameworks. The present paper addresses the study and proposes a mechanism for the multi-site interaction of bile acids with chicken I-BABP (ileal BABP) with the aim of elucidating the deter

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Angira, Deekshi, Nalini Natarajan, Samir R. Dedania, Darshan H. Patel, and Vijay Thiruvenkatam. "Characterization of P. aeruginosa Glucose 6- Phosphate Isomerase: A Functional Insight via In-Vitro Activity Study." Current Topics in Medicinal Chemistry 20, no. 29 (2020): 2651–61. http://dx.doi.org/10.2174/1568026620666200820153751.

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Background: Glucose-6-phosphate isomerase (G6PI) catalyses the second step in glycolysis in the reversible interconversion of an aldohexose glucose 6-phosphate, a six membered ring moiety to a ketohexose, fructose 6-phosphate five membered ring moiety. This enzyme is of utmost importance due to its multifunctional role like neuroleukin, autocrine motility factor, etc. in various species. G6PI from Pseudomonas aeruginosa is less explored for its moonlighting properties. These properties can be predicted by studying the active site conservation of residues and their interaction with the specific

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Vagrys, Darius, James Davidson, Ijen Chen, Roderick E. Hubbard, and Ben Davis. "Exploring IDP–Ligand Interactions: Tau K18 as a Test Case." International Journal of Molecular Sciences 21, no. 15 (2020): 5257. http://dx.doi.org/10.3390/ijms21155257.

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Over the past decade intrinsically disordered proteins (IDPs) have emerged as a biologically important class of proteins, many of which are of therapeutic relevance. Here, we investigated the interactions between a model IDP system, tau K18, and nine literature compounds that have been reported as having an effect on tau in order to identify a robust IDP–ligand system for the optimization of a range of biophysical methods. We used NMR, surface plasmon resonance (SPR) and microscale thermophoresis (MST) methods to investigate the binding of these compounds to tau K18; only one showed unambiguou

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CHANG, DARBY TIEN-HAO, JUNG-HSIN LIN, CHIH-HUNG HSIEH, and YEN-JENG OYANG. "ON THE DESIGN OF OPTIMIZATION ALGORITHMS FOR PREDICTION OF MOLECULAR INTERACTIONS." International Journal on Artificial Intelligence Tools 19, no. 03 (2010): 267–80. http://dx.doi.org/10.1142/s0218213010000182.

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This article presents a comprehensive study on the main characteristics of a novel optimization algorithm specifically designed for simulation of protein-ligand interactions. Though design of optimization algorithms has been a research issue extensively studied by computer scientists for decades, the emerging applications in bioinformatics such as simulation of protein-ligand interactions and protein folding introduce additional challenges due to (1) the high dimensionality nature of the problem and (2) the highly rugged landscape of the energy function. As a result, optimization algorithms th

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Lecas, Lucile, Jérôme Randon, Alain Berthod, Vincent Dugas та Claire Demesmay. "Monolith weak affinity chromatography for μg-protein-ligand interaction study". Journal of Pharmaceutical and Biomedical Analysis 166 (березень 2019): 164–73. http://dx.doi.org/10.1016/j.jpba.2019.01.012.

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42

AG, Patil, Ojha MD, Bhandari PA, and Kulkarni S. "Protein folding dynamics study for protein-protein interactions." International Journal of Chemical Research 1, no. 2 (2009): 18–23. http://dx.doi.org/10.9735/0975-3699.1.2.18-23.

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43

Pandey, Vishnudatt, Gargi Tiwari, and Rajendra Prasad Ojha. "A Comparative Study of Binding of Different Drugs on gp120: Insight from Molecular Dynamics Simulation Study." Oriental Journal of Chemistry 34, no. 6 (2018): 2954–62. http://dx.doi.org/10.13005/ojc/340635.

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HIV-I cellular infection triggered by CD4 receptor protein and viral envelop glycoprotein gp120 binding event. CD4:gp120 surface is directed by the contact points of a hydrophobic gp120 cavity capped by Phe43CD4 and ionic bonds residues Arg59CD4 and Asp368gp120. The binding sites originated by gp120 and CD4 interaction leads to the entry of HIV-I into the host membrane, where, gp120 and a CD4 binding site becomes the main mark for plenty of drug uncovering program. Here, we took the crystal structure of small-molecule of gp120 in a complex that concurrently pursues both of the hotspots of gp12

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Li, Jing, Kyungho Kim, Si-Yeon Jeong та ін. "Platelet Protein Disulfide Isomerase Promotes Glycoprotein Ibα–Mediated Platelet-Neutrophil Interactions Under Thromboinflammatory Conditions". Circulation 139, № 10 (2019): 1300–1319. http://dx.doi.org/10.1161/circulationaha.118.036323.

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Background: Platelet-neutrophil interactions contribute to vascular occlusion and tissue damage in thromboinflammatory disease. Platelet glycoprotein Ibα (GPIbα), a key receptor for the cell-cell interaction, is believed to be constitutively active for ligand binding. Here, we established the role of platelet-derived protein disulfide isomerase (PDI) in reducing the allosteric disulfide bonds in GPIbα and enhancing the ligand-binding activity under thromboinflammatory conditions. Methods: Bioinformatic analysis identified 2 potential allosteric disulfide bonds in GPIbα. Agglutination assays, f

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Polakovičová, M., та R. Čižmáriková. "Molecular Docking Study on the Binding Mode of Cardioselective Phenoxyaminopropanol Blocker into β-adrenergic Receptor Subtypes". Acta Facultatis Pharmaceuticae Universitatis Comenianae 59, № 2 (2012): 44–53. http://dx.doi.org/10.2478/v10219-012-0024-6.

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AbstractStructural understanding of subtype specific ligand-binding pocket variations and interactions of ligand with receptor may facilitate design of novel selective drugs. To gain insights into the subtype selectivity of β-blockers we performed flexible molecular docking study to analyze the interaction mode of cardioselective phenoxyaminopropanol blocker into the β1 and β2-adrenergic receptor. The binding site analysis reveals a strong identity between important amino acid residues and interactions with ligand in orthosteric catecholamine- binding pocket. The differences in the binding mod

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Hung, Tzu-Chieh, Tung-Ti Chang, Ming-Jen Fan, Cheng-Chun Lee, and Calvin Yu-Chian Chen. "In SilicoInsight into Potent of Anthocyanin Regulation of FKBP52 to Prevent Alzheimer’s Disease." Evidence-Based Complementary and Alternative Medicine 2014 (2014): 1–20. http://dx.doi.org/10.1155/2014/450592.

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Alzheimer’s disease (AD) is caused by the hyperphosphorylation of Tau protein aggregation. FKBP52 (FK506 binding protein 52) has been found to inhibit Tau protein aggregation. This study found six different kinds of anthocyanins that have high binding potential. After analyzing the docking positions, hydrophobic interactions, and hydrogen bond interactions, several amino acids were identified that play important roles in protein and ligand interaction. The proteins’ variation is described using eigenvectors and the distance between the amino acids during a molecular dynamics simulation (MD). T

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Kitova, Elena N., Mikyung Seo, Pierre-Nicholas Roy, and John S. Klassen. "Elucidating the Intermolecular Interactions within a Desolvated Protein−Ligand Complex. An Experimental and Computational Study." Journal of the American Chemical Society 130, no. 4 (2008): 1214–26. http://dx.doi.org/10.1021/ja075333b.

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Verma, Niraj, Xingming Qu, Francesco Trozzi, et al. "SSnet: A Deep Learning Approach for Protein-Ligand Interaction Prediction." International Journal of Molecular Sciences 22, no. 3 (2021): 1392. http://dx.doi.org/10.3390/ijms22031392.

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Computational prediction of Protein-Ligand Interaction (PLI) is an important step in the modern drug discovery pipeline as it mitigates the cost, time, and resources required to screen novel therapeutics. Deep Neural Networks (DNN) have recently shown excellent performance in PLI prediction. However, the performance is highly dependent on protein and ligand features utilized for the DNN model. Moreover, in current models, the deciphering of how protein features determine the underlying principles that govern PLI is not trivial. In this work, we developed a DNN framework named SSnet that utiliz

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Benyamini, Hadar, and Assaf Friedler. "Using peptides to study protein–protein interactions." Future Medicinal Chemistry 2, no. 6 (2010): 989–1003. http://dx.doi.org/10.4155/fmc.10.196.

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Beeckmans, Sonia. "Chromatographic Methods to Study Protein–Protein Interactions." Methods 19, no. 2 (1999): 278–305. http://dx.doi.org/10.1006/meth.1999.0857.

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