scholarly journals Practical protocols for calculations of protein-ligand binding free energies in daily drug-design projects using AMBER GPU-TI with ff14SB/GAFF

2020 ◽  
Author(s):  
Xibing He ◽  
Junmei Wang
Keyword(s):  
Drug Design ◽  
Ligand Binding ◽  
Free Energies ◽  
Design Projects ◽  
Binding Free Energies

scholarly journals Alchemical Absolute Protein-Ligand Binding Free Energies for Drug Design

2021 ◽  
Author(s):  
Yuriy Khalak ◽  
Gary Tresdern ◽  
Matteo Aldeghi ◽  
Hannah Magdalena Baumann ◽  
David L. Mobley ◽  
...  
Keyword(s):  
Free Energy ◽  
Drug Discovery ◽  
Drug Design ◽  
Ligand Binding ◽  
Binding Free Energy ◽  
Free Energy Calculations ◽  
Free Energies ◽  
Energy Calculations ◽  
Binding Free Energy Calculations ◽  
Binding Free Energies

The recent advances in relative protein-ligand binding free energy calculations have shown the value of alchemical methods in drug discovery. Accurately assessing absolute binding free energies, although highly desired, remains...

scholarly journals Alchemical Absolute Protein-Ligand Binding Free Energies for Drug Design

2021 ◽  
Author(s):  
Yuriy Khalak ◽  
Gary Tresadern ◽  
Matteo Aldeghi ◽  
Hannah Magdalena Baumann ◽  
David L. Mobley ◽  
...  
Keyword(s):  
Drug Design ◽  
Ligand Binding ◽  
Free Energies ◽  
Binding Free Energies

scholarly journals Protein-ligand free energies of binding from full-protein DFT calculations: convergence and choice of exchange-correlation functional

2021 ◽  
Author(s):  
Lennart Gundelach ◽  
Christofer S Tautermann ◽  
Thomas Fox ◽  
Chris-Kriton Skylaris
Keyword(s):  
Drug Discovery ◽  
Ligand Binding ◽  
Dft Calculations ◽  
Quantum Mechanical ◽  
Free Energies ◽  
Ligand Interaction ◽  
Exchange Correlation ◽  
Ligand Free ◽  
Protein Ligand Interaction ◽  
Binding Free Energies

The accurate prediction of protein-ligand binding free energies with tractable computational methods has the potential to revolutionize drug discovery. Modeling the protein-ligand interaction at a quantum mechanical level, instead of...

Receptor–Ligand Binding Free Energies from a Consecutive Histograms Monte Carlo Sampling Method

2020 ◽  
Vol 16 (10) ◽  
pp. 6645-6655
Author(s):  
Hao Liu ◽  
Jianpeng Deng ◽  
Zhou Luo ◽  
Yawei Lin ◽  
Kenneth M. Merz ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Ligand Binding ◽  
Sampling Method ◽  
Monte Carlo Sampling ◽  
Free Energies ◽  
Receptor Ligand ◽  
Binding Free Energies

A water-swap reaction coordinate for the calculation of absolute protein–ligand binding free energies

2011 ◽  
Vol 134 (5) ◽  
pp. 054114 ◽  
Author(s):  
Christopher J. Woods ◽  
Maturos Malaisree ◽  
Supot Hannongbua ◽  
Adrian J. Mulholland
Keyword(s):  
Ligand Binding ◽  
Reaction Coordinate ◽  
Free Energies ◽  
Binding Free Energies

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 Structural Perspective on Revealing and Altering Molecular Functions of Genetic Variants Linked with Diseases

2019 ◽  
Vol 20 (3) ◽  
pp. 548 ◽  
Author(s):  
Yunhui Peng ◽  
Emil Alexov ◽  
Sankar Basu
Keyword(s):  
Drug Design ◽  
Structural Information ◽  
Radical Change ◽  
Biological Macromolecules ◽  
Free Energies ◽  
Biophysical Properties ◽  
Structure Based Drug Design ◽  
Promising Alternative ◽  
Intrinsically Disordered ◽  
Binding Free Energies

Structural information of biological macromolecules is crucial and necessary to deliver predictions about the effects of mutations—whether polymorphic or deleterious (i.e., disease causing), wherein, thermodynamic parameters, namely, folding and binding free energies potentially serve as effective biomarkers. It may be emphasized that the effect of a mutation depends on various factors, including the type of protein (globular, membrane or intrinsically disordered protein) and the structural context in which it occurs. Such information may positively aid drug-design. Furthermore, due to the intrinsic plasticity of proteins, even mutations involving radical change of the structural and physico–chemical properties of the amino acids (native vs. mutant) can still have minimal effects on protein thermodynamics. However, if a mutation causes significant perturbation by either folding or binding free energies, it is quite likely to be deleterious. Mitigating such effects is a promising alternative to the traditional approaches of designing inhibitors. This can be done by structure-based in silico screening of small molecules for which binding to the dysfunctional protein restores its wild type thermodynamics. In this review we emphasize the effects of mutations on two important biophysical properties, stability and binding affinity, and how structures can be used for structure-based drug design to mitigate the effects of disease-causing variants on the above biophysical properties.

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