scholarly journals Characterization of protein–ligand interactions by SABRE

2021 ◽  
Author(s):  
Ratnamala Mandal ◽  
Pierce Pham ◽  
Christian Hilty
Keyword(s):  
Ligand Binding ◽  
Polarization Transfer ◽  
Binding Interactions ◽  
Protein Ligand Interactions ◽  
Ligand Interactions

Protein–ligand binding interactions are characterized by the para-H2 based hyperpolarization technique SABRE and flow-NMR. Binding to the protein is identified by R2 change of a ligand first interacting with the Ir polarization transfer catalyst.

Characterization of the r-state insulin hexamer and its derivatives. The hexamer is stabilized by heterotropic ligand binding interactions

Biochemistry ◽  
1991 ◽  
Vol 30 (27) ◽  
pp. 6636-6645 ◽  
Author(s):  
Mark L. Brader ◽  
Niels C. Kaarsholm ◽  
Robert W. K. Lee ◽  
Michael F. Dunn
Keyword(s):  
Ligand Binding ◽  
Binding Interactions

Improving protein–ligand binding prediction by considering the bridging water molecules in Autodock

2019 ◽  
Vol 18 (05) ◽  
pp. 1950027 ◽  
Author(s):  
Qiangna Lu ◽  
Lian-Wen Qi ◽  
Jinfeng Liu
Keyword(s):  
Ligand Binding ◽  
Considerable Improvement ◽  
Ligand Docking ◽  
Water Molecules ◽  
Binding Modes ◽  
Binding Prediction ◽  
Docking Simulations ◽  
Protein Ligand Interactions ◽  
Ligand Interactions ◽  
Docking Program

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 performance has not been systematically investigated. Here, we incorporate important bridging water molecules into Autodock program, and systematically investigate the effectiveness of these water molecules in protein–ligand docking. This approach was evaluated using 18 structurally diverse protein–ligand complexes, in which several water molecules bridge the protein–ligand interactions. Different treatment of water molecules were tested by using the fixed and rotatable water molecules, and a considerable improvement in successful docking simulations was found when including these water molecules. This study illustrates the necessity of inclusion of water molecules in Autodock docking, and emphasizes the importance of a proper treatment of water molecules in protein–ligand binding predictions.

scholarly journals Estimation of Binding Rates and Affinities from Multiensemble Markov Models and Ligand Decoupling

2021 ◽  
Author(s):  
Yunhui Ge ◽  
Vincent Voelz
Keyword(s):  
Ligand Binding ◽  
Markov Models ◽  
Time Correlation ◽  
Test System ◽  
Binding Affinities ◽  
Limited Data ◽  
Markov State Model ◽  
Protein Ligand Interactions ◽  
Ligand Interactions ◽  
Kinetics Of

Accurate and efficient simulation of the thermodynamics and kinetics of protein-ligand interactions is crucial for computational drug discovery. Multiensemble Markov Model (MEMM) estimators can provide estimates of both binding rates and affinities from collections of short trajectories, but have not been systematically explored for situations when a ligand is decoupled through scaling of non-bonded interactions. In this work, we compare the performance of two MEMM approaches for estimating ligand binding affinities and rates: (1) the transition-based reweighting analysis method (TRAM) and (2) a Maximum Caliber (MaxCal) based method. As a test system, we construct a small host-guest system where the ligand is a single uncharged Lennard-Jones (LJ) particle, and the receptor is an 11-particle icosahedral pocket made from the same atom type. To realistically mimic a protein-ligand binding system, the LJ ε parameter was tuned, and the system placed in a periodic box with 860 TIP3P water molecules. A benchmark was performed using over 80 μs of unbiased simulation, and an 18-state Markov state model used to estimate reference binding affinities and rates. We then tested the performance of TRAM and MaxCal when challenged with limited data. Both TRAM and MaxCal approaches perform better than conventional MSMs, with TRAM showing better convergence and accuracy. We find that subsampling of trajectories to remove time correlation improves the accuracy of both TRAM and MaxCal, and that in most cases only a single biased ensemble to enhance sampled transitions is required to make accurate estimates.

scholarly journals BINANA 2.0: Characterizing Protein/Ligand Interactions in Python and JavaScript

2021 ◽  
Author(s):  
Jade Young ◽  
Neerja Garikipati ◽  
Jacob D. Durrant
Keyword(s):  
Chemical Biology ◽  
Web Browser ◽  
Receptor Ligand ◽  
Binding Interactions ◽  
Protein Ligand Interactions ◽  
Ligand Interactions ◽  
The Web ◽  
Browser Application ◽  
Proof Of Principle

AbstractBINding ANAlyzer (BINANA) is an algorithm for identifying and characterizing protein/ligand interactions and other factors that contribute to binding. We recently updated BINANA to make the algorithm more accessible to a broader audience. We have also ported the Python3 codebase to JavaScript, thus enabling BINANA analysis in the web browser. As proof of principle, we created a web-browser application so students and chemical-biology researchers can quickly visualize receptor/ligand complexes and their unique binding interactions.

scholarly journals Characterization of protein-ligand binding interactions of enoyl-ACP reductase (FabI) by native MS reveals allosteric effects of coenzymes and the inhibitor triclosan

2019 ◽  
Author(s):  
P. Matthew Joyner ◽  
Denise P. Tran ◽  
Muhammad A. Zenaidee ◽  
Joseph A. Loo
Keyword(s):  
Fatty Acid Synthase ◽  
Antibacterial Drug ◽  
Allosteric Effect ◽  
The Past ◽  
Protein Ligand Interactions ◽  
Native Ms ◽  
Ligand Interactions ◽  
Enoyl Acp Reductase ◽  
The Subject

AbstractThe enzyme enoyl-ACP reductase (also called FabI in bacteria) is an essential member of the fatty acid synthase II pathway in plants and bacteria. This enzyme is the target of the antibacterial drug triclosan and has been the subject of extensive studies for the past 20 years. Despite the large number of reports describing the biochemistry of this enzyme, there have been no studies that provided direct observation of the protein and its various ligands. Here we describe the use of native MS to characterize the protein-ligand interactions of FabI with its coenzymes NAD+ and NADH and with the inhibitor triclosan. Measurements of the gas-phase affinities of the enzyme for these ligands yielded values that are in close agreement with solution-phase affinity measurements. Additionally, FabI is a homotetramer and we were able to measure the affinity of each subunit for each coenzyme, which revealed that both coenzymes exhibit a positive homotropic allosteric effect. An allosteric effect was also observed in association with the inhibitor triclosan. These observations provide new insights into this well-studied enzyme and suggest that there may still be gaps in the existing mechanistic models that explain FabI inhibition.

NMR investigation of protein–ligand interactions for G-protein coupled receptors

2019 ◽  
Vol 11 (14) ◽  
pp. 1811-1825 ◽  
Author(s):  
Claire Raingeval ◽  
Isabelle Krimm
Keyword(s):  
Conformational Changes ◽  
Functional Characterization ◽  
Allosteric Modulation ◽  
G Protein Coupled Receptors ◽  
Nmr Studies ◽  
Protein Ligand Interactions ◽  
Ligand Interactions ◽  
Therapeutic Compounds ◽  
G Protein Coupled

In this review, we report NMR studies of ligand–GPCR interactions, including both ligand-observed and protein-observed NMR experiments. Published studies exemplify how NMR can be used as a powerful tool to design novel GPCR ligands and investigate the ligand-induced conformational changes of GPCRs. The strength of NMR also lies in its capability to explore the diverse signaling pathways and probe the allosteric modulation of these highly dynamic receptors. By offering unique opportunities for the identification, structural and functional characterization of GPCR ligands, NMR will likely play a major role for the generation of novel molecules both as new tools for the understanding of the GPCR function and as therapeutic compounds for a large diversity of pathologies.

scholarly journals Site-Resolved and Quantitative Characterization of Very Weak Protein–Ligand Interactions

2019 ◽  
Vol 14 (7) ◽  
pp. 1398-1402 ◽  
Author(s):  
Brian Fuglestad ◽  
Nicole E. Kerstetter ◽  
A. Joshua Wand
Keyword(s):  
Quantitative Characterization ◽  
Protein Ligand Interactions ◽  
Ligand Interactions
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