scholarly journals Global and local metric geometry of ligand binding thermodynamics.

1991 ◽  
Vol 88 (8) ◽  
pp. 3494-3497 ◽  
Author(s):  
E. Di Cera ◽  
J. Wyman
Keyword(s):  
Ligand Binding ◽  
Metric Geometry ◽  
Binding Thermodynamics ◽  
Global And Local

scholarly journals Binding thermodynamics of host-guest systems with SMIRNOFF99Frosst 1.0.5 from the Open Force Field Initiative

2019 ◽  
Author(s):  
David Slochower ◽  
Niel Henriksen ◽  
Lee-Ping Wang ◽  
John Chodera ◽  
David Mobley ◽  
...  
Keyword(s):  
Ligand Binding ◽  
Force Field ◽  
Small Molecule ◽  
Force Fields ◽  
Free Energy Calculations ◽  
Model Systems ◽  
Conformational Space ◽  
Free Energies ◽  
Binding Thermodynamics ◽  
Binding Free Energies

<div><div><div><p>Designing ligands that bind their target biomolecules with high affinity and specificity is a key step in small- molecule drug discovery, but accurately predicting protein-ligand binding free energies remains challenging. Key sources of errors in the calculations include inadequate sampling of conformational space, ambiguous protonation states, and errors in force fields. Noncovalent complexes between a host molecule with a binding cavity and a drug-like guest molecules have emerged as powerful model systems. As model systems, host-guest complexes reduce many of the errors in more complex protein-ligand binding systems, as their small size greatly facilitates conformational sampling, and one can choose systems that avoid ambiguities in protonation states. These features, combined with their ease of experimental characterization, make host-guest systems ideal model systems to test and ultimately optimize force fields in the context of binding thermodynamics calculations.</p><p><br></p><p>The Open Force Field Initiative aims to create a modern, open software infrastructure for automatically generating and assessing force fields using data sets. The first force field to arise out of this effort, named SMIRNOFF99Frosst, has approximately one tenth the number of parameters, in version 1.0.5, compared to typical general small molecule force fields, such as GAFF. Here, we evaluate the accuracy of this initial force field, using free energy calculations of 43 α and β-cyclodextrin host-guest pairs for which experimental thermodynamic data are available, and compare with matched calculations using two versions of GAFF. For all three force fields, we used TIP3P water and AM1-BCC charges. The calculations are performed using the attach-pull-release (APR) method as implemented in the open source package, pAPRika. For binding free energies, the root mean square error of the SMIRNOFF99Frosst calculations relative to experiment is 0.9 [0.7, 1.1] kcal/mol, while the corresponding results for GAFF 1.7 and GAFF 2.1 are 0.9 [0.7, 1.1] kcal/mol and 1.7 [1.5, 1.9] kcal/mol, respectively, with 95% confidence ranges in brackets. These results suggest that SMIRNOFF99Frosst performs competitively with existing small molecule force fields and is a parsimonious starting point for optimization.</p></div></div></div>

scholarly journals Molecular simulations reveal the impact of RAMP1 on ligand binding and dynamics of CGRPR heterodimer

2021 ◽  
Author(s):  
Busecan Aksoydan ◽  
Serdar Durdagi
Keyword(s):  
Ligand Binding ◽  
Pain Perception ◽  
Md Simulations ◽  
Binding Pocket ◽  
Binding Modes ◽  
Cgrp Receptor ◽  
Tm Domain ◽  
The Stability ◽  
Global And Local ◽  
The Impact

The release of the neuropeptide of calcitonin gene-related peptide (CGRP) plays a key role in the mechanisms of migraine pathology and pain perception as it causes vasodilatation, neurogenic inflammation, mast cell degranulation, sensory signal activation and peripheral sensitivity. Although the findings on the effectiveness of CGRP-targeted therapies in migraine provide new information about the pathophysiology of migraine, questions remain on how the CGRP mechanisms fit into the overall migraine theory. The cryo-EM structure of Gs-protein complexed human CGRP receptor (CGRPR) with bound endogenous CGRP neuropeptide paved the way of understanding the insights into the CGRP receptor function. With several molecular modeling approaches, molecular dynamics (MD) simulations and post-MD analyzes, we aimed to investigate the importance of RAMP1 in the stability of calcitonin receptor-like receptor (CLR). Moreover, we compared the binding modes of the CGRP neuropeptide and CGRPR antagonists (i.e., telcagepant and rimegepant) within the presence or absence of RAMP1. We also investigated the global and local effects of bound molecules on CGRPR as well as their effects on the CLR-RAMP1 interaction interfaces. Results showed that although these molecules stay stable at the ectodomain binding site, they can also bind to the orthosteric ligand binding pocket and form the crucial interactions occurred in the CGRP agonism, which may be interpreted as non-specificity of the ligands, however, most of these interactions at orthosteric site are not sustainable or weak. Particularly, RAMP1 may also be important for the stability of TM domain of CLR hereby stabilizing the orthosteric binding pocket.

scholarly journals Ligand Gaussian accelerated molecular dynamics (LiGaMD): Characterization of ligand binding thermodynamics and kinetics

2020 ◽  
Author(s):  
Yinglong Miao ◽  
Apurba Bhattarai ◽  
Jinan Wang
Keyword(s):  
Experimental Data ◽  
Molecular Dynamics ◽  
Ligand Binding ◽  
Free Energies ◽  
Kinetic Rate ◽  
Binding Thermodynamics ◽  
Accelerated Molecular Dynamics ◽  
Thermodynamics And Kinetics ◽  
Kinetic Rate Constants ◽  
Binding Free Energies

AbstractCalculations of ligand binding free energies and kinetic rates are important for drug design. However, such tasks have proven challenging in computational chemistry and biophysics. To address this challenge, we have developed a new computational method “LiGaMD”, which selectively boosts the ligand non-bonded interaction potential energy based on the Gaussian accelerated molecular dynamics (GaMD) enhanced sampling technique. Another boost potential could be applied to the remaining potential energy of the entire system in a dual-boost algorithm (LiGaMD_Dual) to facilitate ligand binding. LiGaMD has been demonstrated on host-guest and protein-ligand binding model systems. Repetitive guest binding and unbinding in the β-cyclodextrin host were observed in hundreds-of-nanosecond LiGaMD simulations. The calculated binding free energies of guest molecules with sufficient sampling agreed excellently with experimental data (< 1.0 kcal/mol error). In comparison with previous microsecond-timescale conventional molecular dynamics simulations, accelerations of ligand kinetic rate constants in LiGaMD simulations were properly estimated using Kramers’ rate theory. Furthermore, LiGaMD allowed us to capture repetitive dissociation and binding of the benzamidine inhibitor in trypsin within 1 μs simulations. The calculated ligand binding free energy and kinetic rate constants compared well with the experimental data. In summary, LiGaMD provides a promising approach for characterizing ligand binding thermodynamics and kinetics simultaneously, which is expected to facilitate computer-aided drug design.

scholarly journals A look at ligand binding thermodynamics in drug discovery

2017 ◽  
Vol 12 (4) ◽  
pp. 363-377 ◽  
Author(s):  
Rafael Claveria-Gimeno ◽  
Sonia Vega ◽  
Olga Abian ◽  
Adrian Velazquez-Campoy
Keyword(s):  
Drug Discovery ◽  
Ligand Binding ◽  
Binding Thermodynamics

scholarly journals Binding thermodynamics of host-guest systems with SMIRNOFF99Frosst 1.0.5 from the Open Force Field Initiative

2019 ◽  
Author(s):  
David Slochower ◽  
Niel Henriksen ◽  
Lee-Ping Wang ◽  
John Chodera ◽  
David Mobley ◽  
...  
Keyword(s):  
Ligand Binding ◽  
Force Field ◽  
Small Molecule ◽  
Force Fields ◽  
Free Energy Calculations ◽  
Model Systems ◽  
Conformational Space ◽  
Free Energies ◽  
Binding Thermodynamics ◽  
Binding Free Energies

<div><div><div><p>Designing ligands that bind their target biomolecules with high affinity and specificity is a key step in small- molecule drug discovery, but accurately predicting protein-ligand binding free energies remains challenging. Key sources of errors in the calculations include inadequate sampling of conformational space, ambiguous protonation states, and errors in force fields. Noncovalent complexes between a host molecule with a binding cavity and a drug-like guest molecules have emerged as powerful model systems. As model systems, host-guest complexes reduce many of the errors in more complex protein-ligand binding systems, as their small size greatly facilitates conformational sampling, and one can choose systems that avoid ambiguities in protonation states. These features, combined with their ease of experimental characterization, make host-guest systems ideal model systems to test and ultimately optimize force fields in the context of binding thermodynamics calculations.</p><p><br></p><p>The Open Force Field Initiative aims to create a modern, open software infrastructure for automatically generating and assessing force fields using data sets. The first force field to arise out of this effort, named SMIRNOFF99Frosst, has approximately one tenth the number of parameters, in version 1.0.5, compared to typical general small molecule force fields, such as GAFF. Here, we evaluate the accuracy of this initial force field, using free energy calculations of 43 α and β-cyclodextrin host-guest pairs for which experimental thermodynamic data are available, and compare with matched calculations using two versions of GAFF. For all three force fields, we used TIP3P water and AM1-BCC charges. The calculations are performed using the attach-pull-release (APR) method as implemented in the open source package, pAPRika. For binding free energies, the root mean square error of the SMIRNOFF99Frosst calculations relative to experiment is 0.9 [0.7, 1.1] kcal/mol, while the corresponding results for GAFF 1.7 and GAFF 2.1 are 0.9 [0.7, 1.1] kcal/mol and 1.7 [1.5, 1.9] kcal/mol, respectively, with 95% confidence ranges in brackets. These results suggest that SMIRNOFF99Frosst performs competitively with existing small molecule force fields and is a parsimonious starting point for optimization.</p></div></div></div>

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