scholarly journals Combining Machine Learning and Enhanced Sampling Techniques for Efficient and Accurate Calculation of Absolute Binding Free Energies

2020 ◽  
Vol 16 (7) ◽  
pp. 4641-4654 ◽  
Author(s):  
Rhys Evans ◽  
Ladislav Hovan ◽  
Gareth A. Tribello ◽  
Benjamin P. Cossins ◽  
Carolina Estarellas ◽  
...  
Keyword(s):  
Machine Learning ◽  
Sampling Techniques ◽  
Enhanced Sampling ◽  
Free Energies ◽  
Accurate Calculation ◽  
Binding Free Energies

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.

Accurate Calculation of Relative Binding Free Energies between Ligands with Different Net Charges

2018 ◽  
Vol 14 (12) ◽  
pp. 6346-6358 ◽  
Author(s):  
Wei Chen ◽  
Yuqing Deng ◽  
Ellery Russell ◽  
Yujie Wu ◽  
Robert Abel ◽  
...  
Keyword(s):  
Free Energies ◽  
Accurate Calculation ◽  
Relative Binding ◽  
Net Charges ◽  
Binding Free Energies

scholarly journals Sensitivity in binding free energies due to protein reorganization

2016 ◽  
Author(s):  
Nathan M. Lim ◽  
Lingle Wang ◽  
Robert Abel ◽  
David L. Mobley
Keyword(s):  
Ligand Binding ◽  
Conformational Changes ◽  
Binding Free Energy ◽  
Binding Pocket ◽  
Free Energy Calculations ◽  
Computer Hardware ◽  
Sampling Techniques ◽  
Free Energies ◽  
Sampling Algorithms ◽  
Binding Free Energies

AbstractTremendous recent improvements in computer hardware, coupled with advances in sampling techniques and force fields, are now allowing protein-ligand binding free energy calculations to be routinely used to aid pharmaceutical drug discovery projects. However, despite these recent innovations, there are still needs for further improvement in sampling algorithms to more adequately sample protein motion relevant to protein-ligand binding. Here, we report our work identifying and studying such clear and remaining needs in the apolar cavity of T4 Lysozyme L99A. In this study, we model recent experimental results that show the progressive opening of the binding pocket in response to a series of homologous ligands.1 Even while using enhanced sampling techniques, we demonstrate that the predicted relative binding free energies (RBFE) are sensitive to the initial protein conformational state. Particularly, we highlight the importance of sufficient sampling of protein conformational changes and demonstrate how inclusion of three key protein residues in the ‘hot’ region of the FEP/REST simulation improves the sampling and resolves this sensitivity.

scholarly journals Peptide Gaussian accelerated molecular dynamics (Pep-GaMD): Enhanced sampling and free energy and kinetics calculations of peptide binding

2020 ◽  
Author(s):  
Jinan Wang ◽  
Yinglong Miao
Keyword(s):  
Molecular Dynamics ◽  
Potential Energy ◽  
Protein Interactions ◽  
Peptide Binding ◽  
Computational Method ◽  
Enhanced Sampling ◽  
Free Energies ◽  
Accelerated Molecular Dynamics ◽  
High Flexibility ◽  
Binding Free Energies

AbstractPeptides mediate up to 40% of known protein-protein interactions in higher eukaryotes and play an important role in cellular signaling. However, it is challenging to simulate both binding and unbinding of peptides and calculate peptide binding free energies through conventional molecular dynamics, due to long biological timescales and extremely high flexibility of the peptides. Based on the Gaussian accelerated molecular dynamics (GaMD) enhanced sampling technique, we have developed a new computational method “Pep-GaMD”, which selectively boosts essential potential energy of the peptide in order to effectively model its high flexibility. In addition, another boost potential is applied to the remaining potential energy of the entire system in a dual-boost algorithm. Pep-GaMD has been demonstrated on binding of three model peptides to the SH3 domains. Independent 1 μs dual-boost Pep-GaMD simulations have captured repetitive peptide dissociation and binding events, which enable us to calculate peptide binding thermodynamics and kinetics. The calculated binding free energies and kinetic rate constants agreed very well with available experimental data. Furthermore, the all-atom Pep-GaMD simulations have provided important insights into the mechanism of peptide binding to proteins that involves long-range electrostatic interactions and mainly conformational selection. In summary, Pep-GaMD provides a highly efficient, easy-to-use approach for unconstrained enhanced sampling and calculations of peptide binding free energies and kinetics.Significance StatementWe have developed a new computational method “Pep-GaMD” for enhanced sampling of peptide-protein interactions based on the Gaussian accelerated molecular dynamics (GaMD) technique. Pep-GaMD works by selectively boosting the essential potential energy of the peptide to effectively model its high flexibility. In addition, another boost potential can be applied to the remaining potential energy of the entire system in a dual-boost algorithm. Pep-GaMD has been demonstrated on binding of three model peptides to the SH3 domains. Dual-boost Pep-GaMD has captured repetitive peptide dissociation and binding events within significantly shorter simulation time (microsecond) than conventional molecular dynamics. Compared with previous enhanced sampling methods, Pep-GaMD is easier to use and more efficient for unconstrained enhanced sampling of peptide binding and unbinding, which provides a novel physics-based approach to calculating peptide binding free energies and kinetics.

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 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 A Machine Learning Based Intramolecular Potential for a Flexible Organic Molecule

2019 ◽  
Author(s):  
Daniel Cole ◽  
Letif Mones ◽  
Gábor Csányi
Keyword(s):  
Machine Learning ◽  
Ligand Binding ◽  
Gaussian Approximation ◽  
P38 Map Kinase ◽  
Simulation Software ◽  
Free Energies ◽  
Analytical Potential ◽  
Leukotriene A4 Hydrolase ◽  
Leukotriene A4 ◽  
Binding Free Energies

<div><div><div><p>One limitation of the accuracy of computational predictions of protein-ligand binding free energies is the fixed functional form of the intramolecular component of the molecular mechanics force fields. Here, we employ the kernel regression machine learning technique to construct an analytical potential, using the Gaussian Approximation Potential software and framework, that reproduces the quantum mechanical potential energy surface of a small, flexible, drug-like molecule, 3-(benzyloxy)pyridin-2-amine. Challenges linked to the high dimensionality of the configurational space of the molecule are overcome by developing an iterative training protocol and employing a representation that separates short and long range interactions. The analytical model is connected to the MCPRO simulation software, which allows us to perform Monte Carlo simulations of the small molecule bound to two proteins, p38 MAP kinase and leukotriene A4 hydrolase, as well as in water. We demonstrate corrections to absolute protein-ligand binding free energies obtained with our machine learning based intramolecular model of up to 2 kcal/mol.</p></div></div></div>

scholarly journals A Machine Learning Based Intramolecular Potential for a Flexible Organic Molecule

2019 ◽  
Author(s):  
Daniel Cole ◽  
Letif Mones ◽  
Gábor Csányi
Keyword(s):  
Machine Learning ◽  
Ligand Binding ◽  
Gaussian Approximation ◽  
P38 Map Kinase ◽  
Simulation Software ◽  
Free Energies ◽  
Analytical Potential ◽  
Leukotriene A4 Hydrolase ◽  
Leukotriene A4 ◽  
Binding Free Energies

<div><div><div><p>One limitation of the accuracy of computational predictions of protein–ligand binding free energies is the fixed functional form of the intramolecular component of the molecular mechanics force fields. Here, we employ the kernel regression machine learning technique to construct an analytical potential, using the Gaussian Approximation Potential software and framework, that reproduces the quantum mechanical potential energy surface of a small, flexible, drug-like molecule, 3-(benzyloxy)pyridin-2-amine. Challenges linked to the high dimensionality of the configurational space of the molecule are overcome by developing an iterative training protocol and employing a representation that separates short and long range interactions. The analytical model is connected to the MCPRO simulation software, which allows us to perform Monte Carlo simulations of the small molecule bound to two proteins, p38 MAP kinase and leukotriene A4 hydrolase, as well as in water. We demonstrate that the accuracy of our machine learning based intramolecular model is retained in the condensed phase, and that corrections to absolute protein–ligand binding free energies of up to 2 kcal/mol are obtained.</p></div></div></div>

Validation of AMBER/GAFF for Relative Free Energy Calculations

2019 ◽  
Author(s):  
Lin Song ◽  
Tai-Sung Lee ◽  
Chun Zhu ◽  
Darrin M. York ◽  
Kenneth M. Merz Jr.
Keyword(s):  
Free Energy ◽  
Force Field ◽  
Free Energy Calculations ◽  
Enhanced Sampling ◽  
Free Energies ◽  
Data Set ◽  
Relative Free Energy ◽  
Relative Binding ◽  
Sampling Approach ◽  
Binding Free Energies

We computed relative binding free energies using GPU accelerated Thermodynamic Integration (GPU-TI) on a dataset originally assembled by Schrödinger, Inc.. Using their GPU enabled free energy code (FEP+) and the OPLS2.1 force field combined with REST2 enhanced sampling approach, these authors obtained an overall MUE of 0.9 kcal/mol and an overall RMSD of 1.14 kcal/mol.<b> </b>In our study using GPU-TI of AMBER with the AMBER14SB/GAFF1.8 force field but without enhanced sampling, we obtained an overall MUE of 1.17 kcal/mol and an overall RMSD of 1.50 kcal/mol for the 330 mutations contained in this data set.

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