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Journal articles on the topic 'Protein-ligand complex'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 April 2022

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1

Roche, Olivier, Ryuichi Kiyama, and Charles L. Brooks. "Ligand−Protein DataBase: Linking Protein−Ligand Complex Structures to Binding Data." Journal of Medicinal Chemistry 44, no. 22 (October 2001): 3592–98. http://dx.doi.org/10.1021/jm000467k.

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2

Debreczeni, Judit É., and Paul Emsley. "Ligand complex structures in protein crystallography." Acta Crystallographica Section D Structural Biology 73, no. 2 (February 1, 2017): 77–78. http://dx.doi.org/10.1107/s2059798317001644.

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3

Karthikeyan, Muthukumarasamy, Deepak Pandit, and Renu Vyas. "Protein Ligand Complex Guided Approach for Virtual Screening." Combinatorial Chemistry & High Throughput Screening 18, no. 6 (September 2, 2015): 577–90. http://dx.doi.org/10.2174/1386207318666150703112620.

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4

Vonrhein, C., O. S. Smart, A. Sharff, C. Flensburg, P. Keller, W. Paciorek, T. O. Womack, and G. Bricogne. "Improving the quality of protein–ligand complex structures." Acta Crystallographica Section A Foundations of Crystallography 68, a1 (August 7, 2012): s87. http://dx.doi.org/10.1107/s0108767312098303.

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5

Kolář, Michal, Jindřich Fanfrlík, and Pavel Hobza. "Ligand Conformational and Solvation/Desolvation Free Energy in Protein−Ligand Complex Formation." Journal of Physical Chemistry B 115, no. 16 (April 28, 2011): 4718–24. http://dx.doi.org/10.1021/jp2010265.

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6

Emsley, Paul. "Protein-Ligand Analysis and Validation." Acta Crystallographica Section A Foundations and Advances 70, a1 (August 5, 2014): C1480. http://dx.doi.org/10.1107/s2053273314085192.

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A number of tools related to handling of ligands have been added to Coot in recent years - these include 2D depictions, ligand binding pocket layout and a ligand scoring system. Coot also incorporates a number interface to other tools (CCP4's Refmac, Molprobity's probe and reduce and the CCDC's Mogul) to generate score for protein ligand complexes. This scoring system has been applied to models (with data) from the PDB. The details of the ligand scoring, and its application to one's own complex structure will be discussed.

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7

Moriarty, Nigel W., and Paul D. Adams. "High-throughput protein–ligand complex structure solution with Phenix." Acta Crystallographica Section A Foundations and Advances 74, a1 (July 20, 2018): a445. http://dx.doi.org/10.1107/s0108767318095557.

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8

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 (November 27, 2012): 284–90. http://dx.doi.org/10.1111/cbdd.12078.

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9

Masetti, Matteo, Andrea Cavalli, Maurizio Recanatini, and Francesco Luigi Gervasio. "Exploring Complex Protein−Ligand Recognition Mechanisms with Coarse Metadynamics." Journal of Physical Chemistry B 113, no. 14 (April 9, 2009): 4807–16. http://dx.doi.org/10.1021/jp803936q.

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10

Müller, Ilka. "Guidelines for the successful generation of protein–ligand complex crystals." Acta Crystallographica Section D Structural Biology 73, no. 2 (February 1, 2017): 79–92. http://dx.doi.org/10.1107/s2059798316020271.

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With continuous technical improvements at synchrotron facilities, data-collection rates have increased dramatically. This makes it possible to collect diffraction data for hundreds of protein–ligand complexes within a day, provided that a suitable crystal system is at hand. However, developing a suitable crystal system can prove challenging, exceeding the timescale of data collection by several orders of magnitude. Firstly, a useful crystallization construct of the protein of interest needs to be chosen and its expression and purification optimized, before screening for suitable crystallization and soaking conditions can start. This article reviews recent publications analysing large data sets of crystallization trials, with the aim of identifying factors that do or do not make agoodcrystallization construct, and gives guidance in the design of an expression construct. It provides an overview of common protein-expression systems, addresses how ligand binding can be both help and hindrance for protein purification, and describes ligand co-crystallization and soaking, with an emphasis on troubleshooting.

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11

Shao, Qiang, and Weiliang Zhu. "Exploring the Ligand Binding/Unbinding Pathway by Selectively Enhanced Sampling of Ligand in a Protein–Ligand Complex." Journal of Physical Chemistry B 123, no. 38 (September 3, 2019): 7974–83. http://dx.doi.org/10.1021/acs.jpcb.9b05226.

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12

Malham, Richard, Sarah Johnstone, Richard J. Bingham, Elizabeth Barratt, Simon E. V. Phillips, Charles A. Laughton, and Steve W. Homans. "Strong Solute−Solute Dispersive Interactions in a Protein−Ligand Complex." Journal of the American Chemical Society 127, no. 48 (December 2005): 17061–67. http://dx.doi.org/10.1021/ja055454g.

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13

Shin, Woong-Hee, Jae-Kwan Kim, Deok-Soo Kim, and Chaok Seok. "GalaxyDock2: Protein-ligand docking using beta-complex and global optimization." Journal of Computational Chemistry 34, no. 30 (September 24, 2013): 2647–56. http://dx.doi.org/10.1002/jcc.23438.

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14

RAMESH, Vasudevan, and Tom BROWN. "1H-NMR characterization of l-tryptophan binding to TRAP, the trp RNA-binding attenuation protein of Bacillus subtilis." Biochemical Journal 315, no. 3 (May 1, 1996): 895–900. http://dx.doi.org/10.1042/bj3150895.

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A 1H-NMR study of the binding of L-tryptophan to the trp RNA-binding attenuation protein of Bacillus subtilis (TRAP), an ondecamer (91.6 kDa), has been implemented. The assignment of the aromatic indole ring proton resonances of the bound tryptophan ligand has been successfully carried out by two-dimensional chemical exchange experiments. The observation of only a single set of chemical shifts of the bound ligand demonstrates that the tryptophan binding site is identical in all the 11 subunits of the protein. Further, the large change in ligand chemical shifts suggests that the conformation of tryptophan ligand undergoes a significant rearrangement after complex formation with TRAP. This is further substantiated by the extensive ligand-induced chemical shift changes observed to the protein resonances and identification of several strong ligand–protein intermolecular nuclear Overhauser effects. A correlation of these preliminary NMR data with the X-ray crystal structure of the TRAP–tryptophan complex also suggests, tentatively, that the observed changes to the NMR spectra of the protein might correspond to changes associated with residues surrounding the tryptophan binding pocket owing to complex formation.

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15

Wiggers, Felix, Samuel Wohl, Artem Dubovetskyi, Gabriel Rosenblum, Wenwei Zheng, and Hagen Hofmann. "Diffusion of a disordered protein on its folded ligand." Proceedings of the National Academy of Sciences 118, no. 37 (September 9, 2021): e2106690118. http://dx.doi.org/10.1073/pnas.2106690118.

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Intrinsically disordered proteins often form dynamic complexes with their ligands. Yet, the speed and amplitude of these motions are hidden in classical binding kinetics. Here, we directly measure the dynamics in an exceptionally mobile, high-affinity complex. We show that the disordered tail of the cell adhesion protein E-cadherin dynamically samples a large surface area of the protooncogene β-catenin. Single-molecule experiments and molecular simulations resolve these motions with high resolution in space and time. Contacts break and form within hundreds of microseconds without a dissociation of the complex. The energy landscape of this complex is rugged with many small barriers (3 to 4 kBT) and reconciles specificity, high affinity, and extreme disorder. A few persistent contacts provide specificity, whereas unspecific interactions boost affinity.

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16

Fukunishi, Yoshifumi, and Haruki Nakamura. "Prediction of protein–ligand complex structure by docking software guided by other complex structures." Journal of Molecular Graphics and Modelling 26, no. 6 (February 2008): 1030–33. http://dx.doi.org/10.1016/j.jmgm.2007.07.001.

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17

Pozharski, Edwin. "Evidence vs Expectations: How to validate your ligand in a protein structure." Acta Crystallographica Section A Foundations and Advances 70, a1 (August 5, 2014): C1479. http://dx.doi.org/10.1107/s2053273314085209.

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Determination of a protein-ligand complex structure is essential in many areas of structural biology. Details of the interactions between protein and a small molecule ligand often represent major findings from a crystal structure. Thorough validation of interpretation of such structural data is particularly important given high expectation of confirming prior experimental findings regarding targeted protein-ligand interaction. Modern methods of ligand validation are discussed and illustrated.

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18

Anand, Praveen, Deepesh Nagarajan, Sumanta Mukherjee, and Nagasuma Chandra. "ABS–Scan: In silico alanine scanning mutagenesis for binding site residues in protein–ligand complex." F1000Research 3 (September 9, 2014): 214. http://dx.doi.org/10.12688/f1000research.5165.1.

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Most physiological processes in living systems are fundamentally regulated by protein–ligand interactions. Understanding the process of ligand recognition by proteins is a vital activity in molecular biology and biochemistry. It is well known that the residues present at the binding site of the protein form pockets that provide a conducive environment for recognition of specific ligands. In many cases, the boundaries of these sites are not well defined. Here, we provide a web-server to systematically evaluate important residues in the binding site of the protein that contribute towards the ligand recognition through in silico alanine-scanning mutagenesis experiments. Each of the residues present at the binding site is computationally mutated to alanine. The ligand interaction energy is computed for each mutant and the corresponding ΔΔG values are computed by comparing it to the wild type protein, thus evaluating individual residue contributions towards ligand interaction. The server will thus provide clues to researchers about residues to obtain loss-of-function mutations and to understand drug resistant mutations. This web-tool can be freely accessed through the following address: http://proline.biochem.iisc.ernet.in/abscan/.

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19

Anand, Praveen, Deepesh Nagarajan, Sumanta Mukherjee, and Nagasuma Chandra. "ABS–Scan: In silico alanine scanning mutagenesis for binding site residues in protein–ligand complex." F1000Research 3 (December 1, 2014): 214. http://dx.doi.org/10.12688/f1000research.5165.2.

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Most physiological processes in living systems are fundamentally regulated by protein–ligand interactions. Understanding the process of ligand recognition by proteins is a vital activity in molecular biology and biochemistry. It is well known that the residues present at the binding site of the protein form pockets that provide a conducive environment for recognition of specific ligands. In many cases, the boundaries of these sites are not well defined. Here, we provide a web-server to systematically evaluate important residues in the binding site of the protein that contribute towards the ligand recognition through in silico alanine-scanning mutagenesis experiments. Each of the residues present at the binding site is computationally mutated to alanine. The ligand interaction energy is computed for each mutant and the corresponding ΔΔG values are calculated by comparing it to the wild type protein, thus evaluating individual residue contributions towards ligand interaction. The server will thus provide a ranked list of residues to the user in order to obtain loss-of-function mutations. This web-tool can be freely accessed through the following address: http://proline.biochem.iisc.ernet.in/abscan/.

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20

Ahmed, Asad, Bhavika Mam, and Ramanathan Sowdhamini. "DEELIG: A Deep Learning Approach to Predict Protein-Ligand Binding Affinity." Bioinformatics and Biology Insights 15 (January 2021): 117793222110303. http://dx.doi.org/10.1177/11779322211030364.

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Protein-ligand binding prediction has extensive biological significance. Binding affinity helps in understanding the degree of protein-ligand interactions and is a useful measure in drug design. Protein-ligand docking using virtual screening and molecular dynamic simulations are required to predict the binding affinity of a ligand to its cognate receptor. Performing such analyses to cover the entire chemical space of small molecules requires intense computational power. Recent developments using deep learning have enabled us to make sense of massive amounts of complex data sets where the ability of the model to “learn” intrinsic patterns in a complex plane of data is the strength of the approach. Here, we have incorporated convolutional neural networks to find spatial relationships among data to help us predict affinity of binding of proteins in whole superfamilies toward a diverse set of ligands without the need of a docked pose or complex as user input. The models were trained and validated using a stringent methodology for feature extraction. Our model performs better in comparison to some existing methods used widely and is suitable for predictions on high-resolution protein crystal (⩽2.5 Å) and nonpeptide ligand as individual inputs. Our approach to network construction and training on protein-ligand data set prepared in-house has yielded significant insights. We have also tested DEELIG on few COVID-19 main protease-inhibitor complexes relevant to the current public health scenario. DEELIG-based predictions can be incorporated in existing databases including RSCB PDB, PDBMoad, and PDBbind in filling missing binding affinity data for protein-ligand complexes.

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21

Noresson, A. L., O. Aurelius, C. T. Öberg, O. Engström, A. P. Sundin, M. Håkansson, O. Stenström, et al. "Designing interactions by control of protein–ligand complex conformation: tuning arginine–arene interaction geometry for enhanced electrostatic protein–ligand interactions." Chemical Science 9, no. 4 (2018): 1014–21. http://dx.doi.org/10.1039/c7sc04749e.

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22

Nobile, S., and J. Deshusses. "Detection of ligand-protein binding by direct electrophoresis of the complex." Journal of Chromatography A 449 (January 1988): 331–36. http://dx.doi.org/10.1016/s0021-9673(00)94394-2.

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23

Kitova, Elena N., Weijie Wang, David R. Bundle, and John S. Klassen. "Retention of Bioactive Ligand Conformation in a Gaseous Protein−Trisaccharide Complex." Journal of the American Chemical Society 124, no. 47 (November 2002): 13980–81. http://dx.doi.org/10.1021/ja0281380.

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24

Sakata, M., T. Kajikawa, E. Nishibori, H. Ago, K. Hamada, J. Ishijima, and M. Miyano. "Structure refinements of protein-ligand complex by the maximum entropy method." Acta Crystallographica Section A Foundations of Crystallography 61, a1 (August 23, 2005): c422. http://dx.doi.org/10.1107/s010876730508222x.

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25

Bradley, A. R., I. D. Wall, F. von Delft, D. V. S. Green, C. M. Deane, and B. D. Marsden. "WONKA: objective novel complex analysis for ensembles of protein–ligand structures." Journal of Computer-Aided Molecular Design 29, no. 10 (September 19, 2015): 963–73. http://dx.doi.org/10.1007/s10822-015-9866-z.

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26

Ługowska, Magdalena, and Marcin Pacholczyk. "PDBrt: A free database of complexes with measured drug-target residence time." F1000Research 10 (December 3, 2021): 1236. http://dx.doi.org/10.12688/f1000research.73420.1.

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Background: Difficulties in translating the in vitro potency determined by cellular assays into in vivo efficacy in living organisms complicates the design and development of drugs. However, the residence time of a drug in its molecular target is becoming a key parameter in the design and optimization of new drugs, as recent studies show that residence time can reliably predict drug efficacy in vivo. Experimental approaches to binding kinetics and target ligand complex solutions are currently available, but known bioinformatics databases do not usually report information about the ligand residence time in its molecular target. Methods: To extend existing databases we developed the Protein Data Bank (PDB) residence time database (PDBrt) which reports drug residence time. The database is implemented as an open access web-based tool. The front end uses Bootstrap with Hypertext Markup Language (HTML), jQuery for the interface and 3Dmol.js to visualize the complexes. The server-side code uses Python web application framework, Django Rest Framework and backend database PostgreSQL. Results: The PDBrt database is a free, non-commercial repository for 3D protein-ligand complex data, including the measured ligand residence time inside the binding pocket of the specific biological macromolecules as deposited in The Protein Data Bank. The PDBrt database contains information about both the protein and the ligand separately, as well as the protein-ligand complex, binding kinetics, and time of the ligand residence inside the protein binding site. Availability: https://pdbrt.polsl.pl

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27

Clarkson, J., and I. D. Campbell. "Studies of protein–ligand interactions by NMR." Biochemical Society Transactions 31, no. 5 (October 1, 2003): 1006–9. http://dx.doi.org/10.1042/bst0311006.

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Solution-state NMR has become an accepted method for studying the structure of small proteins in solution. This has resulted in over 3000 NMR-based co-ordinate sets being deposited in the Protein Databank. It is becoming increasingly apparent, however, that NMR is also a very powerful tool for accessing interactions between macromolecules and various ligands. These interactions can be assessed at a wide variety of levels, e.g. qualitative screening of libraries of pharmaceuticals and ‘chemical shift mapping’. Dissociation constants can sometimes be obtained in such cases. Another example would be the complete three-dimensional structure determination of a protein–ligand complex. Here we briefly describe a few of the principles involved and illustrate the method with recent examples.

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28

Almahmoud, Suliman, and Haizhen A. Zhong. "Molecular Modeling Studies on the Binding Mode of the PD-1/PD-L1 Complex Inhibitors." International Journal of Molecular Sciences 20, no. 18 (September 19, 2019): 4654. http://dx.doi.org/10.3390/ijms20184654.

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The programmed cell death protein 1 (PD-1)/programmed cell death ligand 1 (PD-L1) is an immune checkpoint (ICP) overexpressed in various types of tumors; thus, it has been considered as an important target for cancer therapy. To determine important residues for ligand binding, we applied molecular docking studies to PD-1/PD-L1 complex inhibitors against the PD-L1 protein. Our data revealed that the residues Tyr56, Asp122, and Lys124 play critical roles in ligand binding to the PD-L1 protein and they could be used to design ligands that are active against the PD-1/PD-L1 complex. The formation of H-bonds with Arg125 of the PD-L1 protein may enhance the potency of the PD-1/PD-L1 binding.

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29

Morris, Connor J., and Dennis Della Corte. "Using molecular docking and molecular dynamics to investigate protein-ligand interactions." Modern Physics Letters B 35, no. 08 (February 18, 2021): 2130002. http://dx.doi.org/10.1142/s0217984921300027.

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Molecular docking and molecular dynamics (MD) are powerful tools used to investigate protein-ligand interactions. Molecular docking programs predict the binding pose and affinity of a protein-ligand complex, while MD can be used to incorporate flexibility into docking calculations and gain further information on the kinetics and stability of the protein-ligand bond. This review covers state-of-the-art methods of using molecular docking and MD to explore protein-ligand interactions, with emphasis on application to drug discovery. We also call for further research on combining common molecular docking and MD methods.

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30

Bolon, P. J., H. M. Al-Hashimi, and J. H. Prestegard. "Residual dipolar coupling derived orientational constraints on ligand geometry in a 53 kDa protein-ligand complex." Journal of Molecular Biology 293, no. 1 (October 1999): 107–15. http://dx.doi.org/10.1006/jmbi.1999.3133.

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31

Shimizu, Hiroki, Art Donohue-Rolfe, and Steve W. Homans. "Derivation of the Bound-State Conformation of a Ligand in a Weakly Aligned Ligand−Protein Complex." Journal of the American Chemical Society 121, no. 24 (June 1999): 5815–16. http://dx.doi.org/10.1021/ja990586t.

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32

Maeki, Masatoshi, Sho Ito, Reo Takeda, Go Ueno, Akihiko Ishida, Hirofumi Tani, Masaki Yamamoto, and Manabu Tokeshi. "Room-temperature crystallography using a microfluidic protein crystal array device and its application to protein–ligand complex structure analysis." Chemical Science 11, no. 34 (2020): 9072–87. http://dx.doi.org/10.1039/d0sc02117b.

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33

Kazlauskas, Arunas, Sara Sundström, Lorenz Poellinger, and Ingemar Pongratz. "The hsp90 Chaperone Complex Regulates Intracellular Localization of the Dioxin Receptor." Molecular and Cellular Biology 21, no. 7 (April 1, 2001): 2594–607. http://dx.doi.org/10.1128/mcb.21.7.2594-2607.2001.

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ABSTRACT The molecular chaperone complex hsp90-p23 interacts with the dioxin receptor, a ligand-dependent basic helix-loop-helix (bHLH)/Per-Arnt-Sim domain transcription factor. Whereas biochemical and genetic evidence indicates that hsp90 is important for maintenance of a high-affinity ligand binding conformation of the dioxin receptor, the role of hsp90-associated proteins in regulation of the dioxin receptor function remains unclear. Here we demonstrate that the integrity of the hsp90 complex characterized by the presence of the hsp90-associated cochaperone p23 and additional cochaperone proteins is important for regulation of the intracellular localization of the dioxin receptor by two mechanisms. First, in the absence of ligand, the dioxin receptor-hsp90 complex was associated with the immunophilin-like protein XAP2 to mediate cytoplasmic retention of the dioxin receptor. Second, upon exposure to ligand, the p23-associated hsp90 complex mediated interaction of the dioxin receptor with the nuclear import receptor protein pendulin and subsequent nuclear translocation of the receptor. Interestingly, these two modes of regulation target two distinct functional domains of the dioxin receptor. Whereas the nuclear localization signal-containing and hsp90-interacting bHLH domain of the receptor regulates ligand-dependent nuclear import, the interaction of the p23-hsp90-XAP2 complex with the ligand binding domain of the dioxin receptor was essential to mediate cytoplasmic retention of the ligand-free receptor form. In conclusion, these data suggest a novel role of the hsp90 molecular chaperone complex in regulation of the intracellular localization of the dioxin receptor.

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34

Burkitt, W. I., P. J. Derrick, D. Lafitte, and I. Bronstein. "Protein–ligand and protein–protein interactions studied by electrospray ionization and mass spectrometry." Biochemical Society Transactions 31, no. 5 (October 1, 2003): 985–89. http://dx.doi.org/10.1042/bst0310985.

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Electrospray ionization has made possible the transference of non-covalently bound complexes from solution phase to high vacuum. In the process, a complex acquires a net charge and becomes amenable to measurement by MS. FTICR (Fourier-transform ion cyclotron resonance) MS allows these ions to be measured with sufficiently high resolution for the isotopomers of complexes of small proteins to be resolved from each other (true for complexes up to about 100 kDa for the most powerful FTICR instruments), which is of crucial significance in the interpretation of spectra. Results are presented for members of the S100 family of proteins, demonstrating how non-covalently bound complexes can be distinguished unambiguously from covalently bound species. Consideration relevant both to determination of binding constants in solution from the gas-phase results and to the elucidation of protein folding and unfolding in solution are discussed. The caveats inherent to the basic approach of using electrospray and MS to characterize protein complexes are weighed and evaluated.

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35

Stepanenko, Olga V., Olesya V. Stepanenko, Alexander V. Fonin, Vladislav V. Verkhusha, Irina M. Kuznetsova, and Konstantin K. Turoverov. "Protein-Ligand Interactions of the D-Galactose/D-Glucose-Binding Protein as a Potential Sensing Probe of Glucose Biosensors." Spectroscopy: An International Journal 27 (2012): 373–79. http://dx.doi.org/10.1155/2012/169579.

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In this paper we have studied peculiarities of protein-ligand interaction under different conditions. We have shown that guanidine hydrochloride (GdnHCI) unfolding-refolding of GGBP in the presence of glucose (Glc) is reversible, but the equilibrium curves of complex refolding-unfolding have been attained only after 10-day incubation of GGBP/Glc in the presence of GdnHCl. This effect has not been revealed at heat-induced GGBP/Glc denaturation. Slow equilibration between the native protein in GGBP/Glc complex and the unfolded state of protein in the GdnHCl presence is connected with increased viscosity of solution at moderate and high GdnHCl concentrations which interferes with diffusion of glucose molecules. Thus, the limiting step of the unfolding-refolding process of the complex GGBP/Glc is the disruption/tuning of the configuration fit between the protein in the native state and the ligand.

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36

Medda, Pankaj, and Wilhelm Hasselbach. "Formation and Decay of the Vanadate Complex of the Sarcoplasmic Reticulum Calcium Transport Protein." Zeitschrift für Naturforschung C 40, no. 11-12 (October 1, 1985): 876–79. http://dx.doi.org/10.1515/znc-1985-11-1221.

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Abstract The calcium free sarcoplasmic reticulum calcium transport ATPase incorporates in the presence of magnesium ions approx. 8 nmol monovanadate per mg protein, indicating the formation of a complex containing one vanadate residue per enzyme molecule. On ligand-removal or dilution, the saturated enzyme complex displays biphasic decay kinetics, while the unsaturated complex slowly dissociates monophasically. - Ligand competition by raising the concentrations of unlabeled vanadate results in a progressive decrease of the dissociation rate of the unsaturated enzyme. The complicated dissociation kinetics indicate a sequential mode of interaction between two ligand binding sites. The one to one stoichiometry of the complex suggests that the two sites are located at adjacent ATPase molecules. - It appears unlikely that the decay of the enzyme, vanadate complex is retarded by the formation of a stable quaternary complex between the enzyme, magnesium, mono-and polyvanadate.

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37

Hossain, Belal M., Douglas A. Simmons, and Lars Konermann. "Do electrospray mass spectra reflect the ligand binding state of proteins in solution?" Canadian Journal of Chemistry 83, no. 11 (November 1, 2005): 1953–60. http://dx.doi.org/10.1139/v05-194.

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Electrospray ionization (ESI) mass spectrometry (MS) has become a popular tool for monitoring ligandprotein and proteinprotein interactions. Due to the "gentle" nature of the ionization process, it is often possible to transfer weakly bound complexes into the gas phase, thus making them amenable to MS detection. One problem with this technique is the potential occurrence of fragmentation events during ESI. Also, some analytes tend to cluster together during ionization, thus forming nonspecific gas-phase assemblies that do not represent solution-phase complexes. In this work, we implemented a hydrogendeuterium exchange (HDX) approach that can reveal whether or not the free and (or) bound constituents of a complex observed in ESI-MS reflect the binding situation in solution. Proteins are subjected to ESI immediately following an isotopic labeling pulse; only ligand-free and ligand-bound protein ions that were formed directly from the corresponding solution-phase species showed different HDX levels. Using myoglobin as a model system, it is demonstrated that this approach can readily distinguish scenarios where the hemeprotein interactions were disrupted in solution from those where dissociation of the complex occurred in the gas phase. Experiments on cytochrome c strongly suggest that dimeric protein ions observed in ESI-MS reflect aggregates that were formed in solution.Key words: electrospray mass spectrometry, ligandprotein interaction, noncovalent complex, hydrogendeuterium exchange, protein folding.

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38

Ghosh, Swagatha, Chi-Li Yu, Daniel J. Ferraro, Sai Sudha, Samir Kumar Pal, Wayne F. Schaefer, David T. Gibson, and S. Ramaswamy. "Blue protein with red fluorescence." Proceedings of the National Academy of Sciences 113, no. 41 (September 29, 2016): 11513–18. http://dx.doi.org/10.1073/pnas.1525622113.

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The walleye (Sander vitreus) is a golden yellow fish that inhabits the Northern American lakes. The recent sightings of the blue walleye and the correlation of its sighting to possible increased UV radiation have been proposed earlier. The underlying molecular basis of its adaptation to increased UV radiation is the presence of a protein (Sandercyanin)–ligand complex in the mucus of walleyes. Degradation of heme by UV radiation results in the formation of Biliverdin IXα (BLA), the chromophore bound to Sandercyanin. We show that Sandercyanin is a monomeric protein that forms stable homotetramers on addition of BLA to the protein. A structure of the Sandercyanin–BLA complex, purified from the fish mucus, reveals a glycosylated protein with a lipocalin fold. This protein–ligand complex absorbs light in the UV region (λmax of 375 nm) and upon excitation at this wavelength emits in the red region (λmax of 675 nm). Unlike all other known biliverdin-bound fluorescent proteins, the chromophore is noncovalently bound to the protein. We provide here a molecular rationale for the observed spectral properties of Sandercyanin.

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39

Cheskis, B., and L. P. Freedman. "Ligand modulates the conversion of DNA-bound vitamin D3 receptor (VDR) homodimers into VDR-retinoid X receptor heterodimers." Molecular and Cellular Biology 14, no. 5 (May 1994): 3329–38. http://dx.doi.org/10.1128/mcb.14.5.3329.

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Protein dimerization facilitates cooperative, high-affinity interactions with DNA. Nuclear hormone receptors, for example, bind either as homodimers or as heterodimers with retinoid X receptors (RXR) to half-site repeats that are stabilized by protein-protein interactions mediated by residues within both the DNA- and ligand-binding domains. In vivo, ligand binding among the subfamily of steroid receptors unmasks the nuclear localization and DNA-binding domains from a complex with auxiliary factors such as the heat shock proteins. However, the role of ligand is less clear among nuclear receptors, since they are constitutively localized to the nucleus and are presumably associated with DNA in the absence of ligand. In this study, we have begun to explore the role of the ligand in vitamin D3 receptor (VDR) function by examining its effect on receptor homodimer and heterodimer formation. Our results demonstrate that VDR is a monomer in solution; VDR binding to a specific DNA element leads to the formation of a homodimeric complex through a monomeric intermediate. We find that 1,25-dihydroxyvitamin D3, the ligand for VDR, decreases the amount of the DNA-bound VDR homodimer complex. It does so by significantly decreasing the rate of conversion of DNA-bound monomer to homodimer and at the same time enhancing the dissociation of the dimeric complex. This effectively stabilizes the bound monomeric species, which in turn serves to favor the formation of a VDR-RXR heterodimer. The ligand for RXR, 9-cis retinoic acid, has the opposite effect of destabilizing the heterodimeric-DNA complex. These results may explain how a nuclear receptor can bind DNA constitutively but still act to regulate transcription in a fully hormone-dependent manner.

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40

Cheskis, B., and L. P. Freedman. "Ligand modulates the conversion of DNA-bound vitamin D3 receptor (VDR) homodimers into VDR-retinoid X receptor heterodimers." Molecular and Cellular Biology 14, no. 5 (May 1994): 3329–38. http://dx.doi.org/10.1128/mcb.14.5.3329-3338.1994.

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Protein dimerization facilitates cooperative, high-affinity interactions with DNA. Nuclear hormone receptors, for example, bind either as homodimers or as heterodimers with retinoid X receptors (RXR) to half-site repeats that are stabilized by protein-protein interactions mediated by residues within both the DNA- and ligand-binding domains. In vivo, ligand binding among the subfamily of steroid receptors unmasks the nuclear localization and DNA-binding domains from a complex with auxiliary factors such as the heat shock proteins. However, the role of ligand is less clear among nuclear receptors, since they are constitutively localized to the nucleus and are presumably associated with DNA in the absence of ligand. In this study, we have begun to explore the role of the ligand in vitamin D3 receptor (VDR) function by examining its effect on receptor homodimer and heterodimer formation. Our results demonstrate that VDR is a monomer in solution; VDR binding to a specific DNA element leads to the formation of a homodimeric complex through a monomeric intermediate. We find that 1,25-dihydroxyvitamin D3, the ligand for VDR, decreases the amount of the DNA-bound VDR homodimer complex. It does so by significantly decreasing the rate of conversion of DNA-bound monomer to homodimer and at the same time enhancing the dissociation of the dimeric complex. This effectively stabilizes the bound monomeric species, which in turn serves to favor the formation of a VDR-RXR heterodimer. The ligand for RXR, 9-cis retinoic acid, has the opposite effect of destabilizing the heterodimeric-DNA complex. These results may explain how a nuclear receptor can bind DNA constitutively but still act to regulate transcription in a fully hormone-dependent manner.

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41

Mazal, Hisham, Haim Aviram, Inbal Riven, and Gilad Haran. "Effect of ligand binding on a protein with a complex folding landscape." Physical Chemistry Chemical Physics 20, no. 5 (2018): 3054–62. http://dx.doi.org/10.1039/c7cp03327c.

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42

Onufriev, Alexey V., and Emil Alexov. "Protonation and pK changes in protein–ligand binding." Quarterly Reviews of Biophysics 46, no. 2 (May 2013): 181–209. http://dx.doi.org/10.1017/s0033583513000024.

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AbstractFormation of protein–ligand complexes causes various changes in both the receptor and the ligand. This review focuses on changes in pK and protonation states of ionizable groups that accompany protein–ligand binding. Physical origins of these effects are outlined, followed by a brief overview of the computational methods to predict them and the associated corrections to receptor–ligand binding affinities. Statistical prevalence, magnitude and spatial distribution of the pK and protonation state changes in protein–ligand binding are discussed in detail, based on both experimental and theoretical studies. While there is no doubt that these changes occur, they do not occur all the time; the estimated prevalence varies, both between individual complexes and by method. The changes occur not only in the immediate vicinity of the interface but also sometimes far away. When receptor–ligand binding is associated with protonation state change at particular pH, the binding becomes pH dependent: we review the interplay between sub-cellular characteristic pH and optimum pH of receptor–ligand binding. It is pointed out that there is a tendency for protonation state changes upon binding to be minimal at physiologically relevant pH for each complex (no net proton uptake/release), suggesting that native receptor–ligand interactions have evolved to reduce the energy cost associated with ionization changes. As a result, previously reported statistical prevalence of these changes – typically computed at the same pH for all complexes – may be higher than what may be expected at optimum pH specific to each complex. We also discuss whether proper account of protonation state changes appears to improve practical docking and scoring outcomes relevant to structure-based drug design. An overview of some of the existing challenges in the field is provided in conclusion.

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Pandya, Pujan N., Archana U. Mankad, Rakesh M. Rawal, and Kumar S. Prasanth. "Screening of diverse phytochemicals with Aurora Kinase C protein: An In Silico approach." Journal of Drug Delivery and Therapeutics 9, no. 1-s (February 15, 2019): 67–74. http://dx.doi.org/10.22270/jddt.v9i1-s.2249.

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Aurora Kinase C, a vital serine-threonine protein Kinase, is an important member of the Aurora Kinase protein family which plays an important role in mitosis is a part of Chromosomal Passenger Complex (CPC). Aurora Kinase C over expression is found to be linked with several cancer cell lines which demonstrate its oncogenic involvement and activity. Aurora C over expression in certain cancer types makes it an important target to be considered for cancer therapeutics. The present research work focuses on the Aurora Kinase C as an important target for computational studies. The protein model of Aurora Kinase C, as a proten target on docking with 1500 natural compounds (phytochemicals) reveals the binding of the natural ligand 3-beta,23,28-trihydroxy-12-oleanene 23-caffeate belonging to the terpenoid class with highest docking score. This best bound ligand with the protein Aurora Kinase C was chosen for further understanding their protein-ligand interactions at the the molecular level using the molecular dynamic simulation approach. Stability of the protein-ligand complex and its conformation helps in disclosing the potentiality of the best bound ligand to be further chosen as an important small molecule inhibitor that would help playing a lead role in further drug discovery process Keywords: Aurora Kinase C, Cancer, Phytochemicals, Docking, Molecular Dynamics

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44

Shinada, Nicolas K., Peter Schmidtke, and Alexandre G. de Brevern. "Accurate Representation of Protein-Ligand Structural Diversity in the Protein Data Bank (PDB)." International Journal of Molecular Sciences 21, no. 6 (March 24, 2020): 2243. http://dx.doi.org/10.3390/ijms21062243.

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The number of available protein structures in the Protein Data Bank (PDB) has considerably increased in recent years. Thanks to the growth of structures and complexes, numerous large-scale studies have been done in various research areas, e.g., protein–protein, protein–DNA, or in drug discovery. While protein redundancy was only simply managed using simple protein sequence identity threshold, the similarity of protein-ligand complexes should also be considered from a structural perspective. Hence, the protein-ligand duplicates in the PDB are widely known, but were never quantitatively assessed, as they are quite complex to analyze and compare. Here, we present a specific clustering of protein-ligand structures to avoid bias found in different studies. The methodology is based on binding site superposition, and a combination of weighted Root Mean Square Deviation (RMSD) assessment and hierarchical clustering. Repeated structures of proteins of interest are highlighted and only representative conformations were conserved for a non-biased view of protein distribution. Three types of cases are described based on the number of distinct conformations identified for each complex. Defining these categories decreases by 3.84-fold the number of complexes, and offers more refined results compared to a protein sequence-based method. Widely distinct conformations were analyzed using normalized B-factors. Furthermore, a non-redundant dataset was generated for future molecular interactions analysis or virtual screening studies.

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Jaremko, ukasz, M. Jaremko, K. Giller, S. Becker, and M. Zweckstetter. "Structure of the Mitochondrial Translocator Protein in Complex with a Diagnostic Ligand." Science 343, no. 6177 (March 20, 2014): 1363–66. http://dx.doi.org/10.1126/science.1248725.

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46

Kwon, Yongbeom, Woong-Hee Shin, Junsu Ko, and Juyong Lee. "AK-Score: Accurate Protein-Ligand Binding Affinity Prediction Using an Ensemble of 3D-Convolutional Neural Networks." International Journal of Molecular Sciences 21, no. 22 (November 10, 2020): 8424. http://dx.doi.org/10.3390/ijms21228424.

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Accurate prediction of the binding affinity of a protein-ligand complex is essential for efficient and successful rational drug design. Therefore, many binding affinity prediction methods have been developed. In recent years, since deep learning technology has become powerful, it is also implemented to predict affinity. In this work, a new neural network model that predicts the binding affinity of a protein-ligand complex structure is developed. Our model predicts the binding affinity of a complex using the ensemble of multiple independently trained networks that consist of multiple channels of 3-D convolutional neural network layers. Our model was trained using the 3772 protein-ligand complexes from the refined set of the PDBbind-2016 database and tested using the core set of 285 complexes. The benchmark results show that the Pearson correlation coefficient between the predicted binding affinities by our model and the experimental data is 0.827, which is higher than the state-of-the-art binding affinity prediction scoring functions. Additionally, our method ranks the relative binding affinities of possible multiple binders of a protein quite accurately, comparable to the other scoring functions. Last, we measured which structural information is critical for predicting binding affinity and found that the complementarity between the protein and ligand is most important.

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47

Holm, Jan, and Steen Ingemann Hansen. "Effect of Hydrogen Ion Concentration and Buffer Composition on Ligand Binding Characteristics and Polymerization of Cow's Milk Folate Binding Protein." Bioscience Reports 21, no. 6 (December 1, 2001): 745–53. http://dx.doi.org/10.1023/a:1015528606487.

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The ligand binding and aggregation behavior of cow's milk folate binding protein depends on hydrogen ion concentration and buffer composition. At pH 5.0, the protein polymerizes in Tris-HCl subsequent to ligand binding. No polymerization occurs in acetate, and binding is markedly weaker in acetate or citrate buffers as compared to Tris-HCl. Polymerization of ligand-bound protein was far more pronounced at pH 7.4 as compared to pH 5.0 regardless of buffer composition. Binding affinity increased with decreasing concentration of protein both at pH 7.4 and 5.0. At pH 5.0 this effect seemed to level off at a protein concentration of 10−6 M which is 100–1000 fold higher than at pH 7.4. The data can be interpreted in terms of complex models for ligand binding systems polymerizing both in the absence or presence of ligand (pH 7.4) as well as only subsequent to ligand binding (pH 5.0).

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Hodošček, Milan, and Nadia Elghobashi-Meinhardt. "Simulations of NPC1(NTD):NPC2 Protein Complex Reveal Cholesterol Transfer Pathways." International Journal of Molecular Sciences 19, no. 9 (September 4, 2018): 2623. http://dx.doi.org/10.3390/ijms19092623.

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The Niemann Pick type C (NPC) proteins, NPC1 and NPC2, are involved in the lysosomal storage disease, NPC disease. The formation of a NPC1–NPC2 protein–protein complex is believed to be necessary for the transfer of cholesterol and lipids out of the late endosomal (LE)/lysosomal (Lys) compartments. Mutations in either NPC1 or NPC2 can lead to an accumulation of cholesterol and lipids in the LE/Lys, the primary phenotype of the NPC disease. We investigated the NPC1(NTD)–NPC2 protein–protein complex computationally using two putative binding interfaces. A combination of molecular modeling and molecular dynamics simulations reveals atomic details that are responsible for interface stability. Cholesterol binding energies associated with each of the binding pockets for the two models are calculated. Analyses of the cholesterol binding in the two models support bidirectional ligand transfer when a particular interface is established. Based on the results, we propose that, depending on the location of the cholesterol ligand, a dynamical interface between the NPC2 and NPC1(NTD) proteins exists. Structural features of a particular interface can lower the energy barrier and stabilize the passage of the cholesterol substrate from NPC2 to NPC1(NTD).

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Nicholls, Robert A. "Ligand fitting withCCP4." Acta Crystallographica Section D Structural Biology 73, no. 2 (February 1, 2017): 158–70. http://dx.doi.org/10.1107/s2059798316020143.

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Crystal structures of protein–ligand complexes are often used to infer biology and inform structure-based drug discovery. Hence, it is important to build accurate, reliable models of ligands that give confidence in the interpretation of the respective protein–ligand complex. This paper discusses key stages in the ligand-fitting process, including ligand binding-site identification, ligand description and conformer generation, ligand fitting, refinement and subsequent validation. TheCCP4 suite contains a number of software tools that facilitate this task:AceDRGfor the creation of ligand descriptions and conformers,LidiaandJLigandfor two-dimensional and three-dimensional ligand editing and visual analysis,Cootfor density interpretation, ligand fitting, analysis and validation, andREFMAC5 for macromolecular refinement. In addition to recent advancements in automatic carbohydrate building inCoot(LO/Carb) and ligand-validation tools (FLEV), the release of theCCP4i2 GUI provides an integrated solution that streamlines the ligand-fitting workflow, seamlessly passing results from one program to the next. The ligand-fitting process is illustrated using instructive practical examples, including problematic cases such as post-translational modifications, highlighting the need for careful analysis and rigorous validation.

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Bai, Bing, Rongfeng Zou, H. C. Stephen Chan, Hongchun Li, and Shuguang Yuan. "MolADI: A Web Server for Automatic Analysis of Protein–Small Molecule Dynamic Interactions." Molecules 26, no. 15 (July 30, 2021): 4625. http://dx.doi.org/10.3390/molecules26154625.

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Protein–ligand interaction analysis is important for drug discovery and rational protein design. The existing online tools adopt only a single conformation of the complex structure for calculating and displaying the interactions, whereas both protein residues and ligand molecules are flexible to some extent. The interactions evolved with time in the trajectories are of greater interest. MolADI is a user-friendly online tool which analyzes the protein–ligand interactions in detail for either a single structure or a trajectory. Interactions can be viewed easily with both 2D graphs and 3D representations. MolADI is available as a web application.

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