The shielding effect of metal complexes on the binding affinities of ligands to metalloproteins

2019 ◽  
Vol 21 (1) ◽  
pp. 205-216 ◽  
Author(s):  
Deliang Chen ◽  
Yibao Li ◽  
Wei Guo ◽  
Yongdong Li ◽  
Tor Savidge ◽  
...  
Keyword(s):  
Ligand Binding ◽  
Metal Complexes ◽  
Shielding Effect ◽  
Binding Affinities ◽  
Ligand Interactions ◽  
Metal Ligand ◽  
Shielding Effects

The contributions of metal–ligand interactions to the ligand binding affinities are largely reduced by the shielding effects of metal complexes.

Syntheses of Chelating Tetrazole-Containing Ligands and Studies of Their Palladium(II) and Ruthenium(II) Complexes

1995 ◽  
Vol 48 (9) ◽  
pp. 1625 ◽  
Author(s):  
AJ Downard ◽  
PJ Steel ◽  
J Steenwijk
Keyword(s):  
Cyclic Voltammetry ◽  
Transition Metal ◽  
Metal Complexes ◽  
Transition Metal Complexes ◽  
Absorption Spectroscopy ◽  
Electronic Absorption ◽  
Ph Control ◽  
Strong Electron ◽  
Ligand Interactions ◽  
Metal Ligand

Eleven chelating tetrazole -containing ligands have been synthesized, and their complexes with palladium(II) and ruthenium(II) prepared. Proton n.m.r. spectroscopy, electronic absorption spectroscopy and cyclic voltammetry have been used to study the nature of the metal-ligand interactions in these complexes. The negatively charged tetrazolate group is shown to be a strong electron donor with very different properties to those of the protonated or alkylated tetrazole group. This leads to pH control of the properties of transition metal complexes containing such ligands.

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 Theoretical Understanding of the Thermodynamics and Interactions in Transcriptional Regulator TtgR-Ligand Binding

2019 ◽  
Author(s):  
Zhaoxi Sun ◽  
Xiaohui Wang ◽  
John Z. H. Zhang
Keyword(s):  
Ligand Binding ◽  
Transcriptional Regulator ◽  
Current Work ◽  
Experimental Result ◽  
Bacterial Cells ◽  
Binding Affinities ◽  
Transcriptional Repressors ◽  
Theoretical Understanding ◽  
Ligand Interactions ◽  
Noxious Chemicals

<p> The transcriptional regulator TtgR belongs to the TetR family of transcriptional repressors. It depresses the transcription of the TtgABC operon and itself and thus regulates the extrusion of noxious chemicals with efflux pumps in bacterial cells. As the ligand binding domain of TtgR is rather flexible, it can bind with a number of structurally diverse ligands, such as antibiotics, flavonoids and aromatic solvents. In the current work, we perform equilibrium and nonequilibrium alchemical free energy simulation to predict the binding affinities of a series of ligands targeting the TtgR protein and the agreement between the theoretical prediction and the experimental result is observed. End-point methods of MM/PBSA and MM/GBSA are also employed for comparison. We further study the interaction maps and identify important interactions in the protein-ligand binding cases. The current work sheds light on atomic and thermodynamic understanding on the TtgR-ligand interactions.</p>

scholarly journals Theoretical Understanding of the Thermodynamics and Interactions in Transcriptional Regulator TtgR-Ligand Binding

2019 ◽  
Author(s):  
Zhaoxi Sun ◽  
Xiaohui Wang ◽  
John Z. H. Zhang
Keyword(s):  
Ligand Binding ◽  
Transcriptional Regulator ◽  
Current Work ◽  
Experimental Result ◽  
Bacterial Cells ◽  
Binding Affinities ◽  
Transcriptional Repressors ◽  
Theoretical Understanding ◽  
Ligand Interactions ◽  
Noxious Chemicals

<p> The transcriptional regulator TtgR belongs to the TetR family of transcriptional repressors. It depresses the transcription of the TtgABC operon and itself and thus regulates the extrusion of noxious chemicals with efflux pumps in bacterial cells. As the ligand binding domain of TtgR is rather flexible, it can bind with a number of structurally diverse ligands, such as antibiotics, flavonoids and aromatic solvents. In the current work, we perform equilibrium and nonequilibrium alchemical free energy simulation to predict the binding affinities of a series of ligands targeting the TtgR protein and the agreement between the theoretical prediction and the experimental result is observed. End-point methods of MM/PBSA and MM/GBSA are also employed for comparison. We further study the interaction maps and identify important interactions in the protein-ligand binding cases. The current work sheds light on atomic and thermodynamic understanding on the TtgR-ligand interactions.</p>

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 Rapid decomposition and visualisation of protein–ligand binding free energies by residue and by water

2014 ◽  
Vol 169 ◽  
pp. 477-499 ◽  
Author(s):  
Christopher J. Woods ◽  
Maturos Malaisree ◽  
Julien Michel ◽  
Ben Long ◽  
Simon McIntosh-Smith ◽  
...  
Keyword(s):  
Free Energy ◽  
Ligand Binding ◽  
Binding Free Energy ◽  
Chem Phys ◽  
Binding Affinities ◽  
Rapid Decomposition ◽  
Protein Ligand Interactions ◽  
Ligand Interactions ◽  
Site Water ◽  
Dynamics Simulations

Recent advances in computational hardware, software and algorithms enable simulations of protein–ligand complexes to achieve timescales during which complete ligand binding and unbinding pathways can be observed. While observation of such events can promote understanding of binding and unbinding pathways, it does not alone provide information about the molecular drivers for protein–ligand association, nor guidance on how a ligand could be optimised to better bind to the protein. We have developed the waterswap (C. J. Woods et al., J. Chem. Phys., 2011, 134, 054114) absolute binding free energy method that calculates binding affinities by exchanging the ligand with an equivalent volume of water. A significant advantage of this method is that the binding free energy is calculated using a single reaction coordinate from a single simulation. This has enabled the development of new visualisations of binding affinities based on free energy decompositions to per-residue and per-water molecule components. These provide a clear picture of which protein–ligand interactions are strong, and which active site water molecules are stabilised or destabilised upon binding. Optimisation of the algorithms underlying the decomposition enables near-real-time visualisation, allowing these calculations to be used either to provide interactive feedback to a ligand designer, or to provide run-time analysis of protein–ligand molecular dynamics simulations.

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