A New Computational Method for Protein–Ligand Binding Thermodynamics

2019 ◽  
Vol 40 (2) ◽  
pp. 180-185 ◽  
Author(s):  
Song‐Ho Chong ◽  
Sihyun Ham
Keyword(s):  
Ligand Binding ◽  
Computational Method ◽  
Binding Thermodynamics

scholarly journals An Efficient Computational Method for Calculating Ligand Binding Affinities

PLoS ONE ◽  
2012 ◽  
Vol 7 (8) ◽  
pp. e42846 ◽  
Author(s):  
Atsushi Suenaga ◽  
Noriaki Okimoto ◽  
Yoshinori Hirano ◽  
Kazuhiko Fukui
Keyword(s):  
Ligand Binding ◽  
Computational Method ◽  
Binding Affinities

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 Tomographic docking suggests the mechanism of auxin receptor TIR1 selectivity

Open Biology ◽  
2016 ◽  
Vol 6 (10) ◽  
pp. 160139 ◽  
Author(s):  
Veselina V. Uzunova ◽  
Mussa Quareshy ◽  
Charo I. del Genio ◽  
Richard M. Napier
Keyword(s):  
Ligand Binding ◽  
Plant Hormone ◽  
Binding Pocket ◽  
Computational Method ◽  
Final Position ◽  
Auxin Receptor ◽  
Back Wall ◽  
Different Depths ◽  
Critical Residues ◽  
Step Mechanism

We study the binding of plant hormone IAA on its receptor TIR1, introducing a novel computational method that we call tomographic docking and that accounts for interactions occurring along the depth of the binding pocket. Our results suggest that selectivity is related to constraints that potential ligands encounter on their way from the surface of the protein to their final position at the pocket bottom. Tomographic docking helps develop specific hypotheses about ligand binding, distinguishing binders from non-binders, and suggests that binding is a three-step mechanism, consisting of engagement with a niche in the back wall of the pocket, interaction with a molecular filter which allows or precludes further descent of ligands, and binding on the pocket base. Only molecules that are able to descend the pocket and bind at its base allow the co-receptor IAA7 to bind on the complex, thus behaving as active auxins. Analysing the interactions at different depths, our new method helps in identifying critical residues that constitute preferred future study targets and in the quest for safe and effective herbicides. Also, it has the potential to extend the utility of docking from ligand searches to the study of processes contributing to selectivity.

scholarly journals Author Correction: A computational method for detection of ligand-binding proteins from dose range thermal proteome profiles

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Nils Kurzawa ◽  
Isabelle Becher ◽  
Sindhuja Sridharan ◽  
Holger Franken ◽  
André Mateus ◽  
...  
Keyword(s):  
Ligand Binding ◽  
Binding Proteins ◽  
Dose Range ◽  
Computational Method

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.

Elucidating the Multiple Roles of Hydration in Protein-Ligand Binding via Layerwise Relevance Propagation and Big Data Analytics

2019 ◽  
Author(s):  
Markus Lill ◽  
Ying Yang ◽  
Amr Mahmoud ◽  
Matthew Masters
Keyword(s):  
Big Data ◽  
Ligand Binding ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Computational Method ◽  
Correct Prediction ◽  
Pose Prediction ◽  
Learning Approaches ◽  
Protein Targets ◽  
Trained Neural Network

Hydration is a key player in protein-ligand association. No computational method for modeling hydration has so far consistently improved the scoring performance of docking approaches. Using molecular dynamics on thousands of proteins in conjunction with modern deep learning approaches allowed the successful modeling of hydration during scoring of protein-ligand binding poses. This on-the-fly inclusion of hydration information resulted in unprecedented accuracy in binding pose prediction.<br>Big-data analytics based on relevance deduced from the trained neural network<br>revealed that the correct prediction of binding poses depends on three essential pillars of hydration, i.e. water-mediated interactions, desolvation, and enthalpically stable water layers around the bound ligand. The latter form of hydration may open new avenues for optimizing ligands for diverse protein targets.

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