scholarly journals An Enhanced Sampling Approach to the Induced Fit Docking Problem in Protein-Ligand Binding: the case of mono-ADP-ribosylation hydrolases inhibitors

2021 ◽  
Author(s):  
Qianqian Zhao ◽  
Riccardo Capelli ◽  
Paolo Carloni ◽  
Bernhard Luescher ◽  
Jinyu Li ◽  
...  
Keyword(s):  
Ligand Binding ◽  
Absolute Error ◽  
Enhanced Sampling ◽  
Free Energies ◽  
Induced Fit ◽  
Adp Ribosylation ◽  
Free Energy Landscapes ◽  
First Time ◽  
Enzyme Complexes ◽  
Binding Free Energies

A variety of enhanced sampling methods can predict free energy landscapes associated with protein/ligand binding events, characterizing in a precise way the intermolecular interactions involved. Unfortunately, these approaches are challenged by not uncommon induced fit mecchanisms. Here, we present a variant of the recently reported volume-based metadynamics (MetaD) method which describes ligand binding even when it affects protein structure. The validity of the approach is established by applying it to a substrate/enzyme complexes of pharmacological relevance: this is the mono-ADP-ribose (ADPr) in complex with mono-ADP-ribosylation hydrolases (MacroD1 and MacroD2), where induced-fit phenomena are known to be operative. The calculated binding free energies are consistent with experiments, with an absolute error less than 0.5 kcal/mol. Our simulations reveal that in all circumstances the active loops, delimiting the boundaries of the binding site, rearrange from an open to a closed conformation upon ligand binding. The calculations further provide, for the first time, the molecular basis of the experimentally observed affinity changes in ADPr binding on passing from MacroD1 to MacroD2 and all its mutants. Our study paves the way to investigate in a completely general manner ligand binding to proteins and receptors.

scholarly journals Hit-to-lead and lead optimization binding free energy calculations for G protein-coupled receptors

2020 ◽  
Vol 10 (6) ◽  
pp. 20190128 ◽  
Author(s):  
Shunzhou Wan ◽  
Andrew Potterton ◽  
Fouad S. Husseini ◽  
David W. Wright ◽  
Alexander Heifetz ◽  
...  
Keyword(s):  
G Protein ◽  
Binding Free Energy ◽  
Absolute Error ◽  
Free Energy Calculations ◽  
Lead Optimization ◽  
Receptor Protein ◽  
Enhanced Sampling ◽  
Free Energies ◽  
G Protein Coupled ◽  
Binding Free Energies

We apply the hit-to-lead ESMACS (enhanced sampling of molecular dynamics with approximation of continuum solvent) and lead-optimization TIES (thermodynamic integration with enhanced sampling) methods to compute the binding free energies of a series of ligands at the A 1 and A 2A adenosine receptors, members of a subclass of the GPCR (G protein-coupled receptor) superfamily. Our predicted binding free energies, calculated using ESMACS, show a good correlation with previously reported experimental values of the ligands studied. Relative binding free energies, calculated using TIES, accurately predict experimentally determined values within a mean absolute error of approximately 1 kcal mol −1 . Our methodology may be applied widely within the GPCR superfamily and to other small molecule–receptor protein systems.

scholarly journals The role of water in host-guest interaction

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Valerio Rizzi ◽  
Luigi Bonati ◽  
Narjes Ansari ◽  
Michele Parrinello
Keyword(s):  
Ligand Binding ◽  
Degrees Of Freedom ◽  
Enhanced Sampling ◽  
Free Energies ◽  
Collective Variables ◽  
Complex Behaviour ◽  
Field Quality ◽  
Non Local ◽  
Binding Free Energies

AbstractOne of the main applications of atomistic computer simulations is the calculation of ligand binding free energies. The accuracy of these calculations depends on the force field quality and on the thoroughness of configuration sampling. Sampling is an obstacle in simulations due to the frequent appearance of kinetic bottlenecks in the free energy landscape. Very often this difficulty is circumvented by enhanced sampling techniques. Typically, these techniques depend on the introduction of appropriate collective variables that are meant to capture the system’s degrees of freedom. In ligand binding, water has long been known to play a key role, but its complex behaviour has proven difficult to fully capture. In this paper we combine machine learning with physical intuition to build a non-local and highly efficient water-describing collective variable. We use it to study a set of host-guest systems from the SAMPL5 challenge. We obtain highly accurate binding free energies and good agreement with experiments. The role of water during the binding process is then analysed in some detail.

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...

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...

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>

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