scholarly journals Improved Sampling in Ab Initio Free Energy Calculations of Biomolecules at Solid–Liquid Interfaces: Tight-Binding Assessment of Charged Amino Acids on TiO2 Anatase (101)

Computation ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 12 ◽  
Author(s):  
Lorenzo Agosta ◽  
Erik G. Brandt ◽  
Alexander Lyubartsev
Keyword(s):  
Amino Acids ◽  
Free Energy ◽  
Ab Initio ◽  
Tight Binding ◽  
Free Energy Calculations ◽  
Free Energies ◽  
Ab Initio Simulations ◽  
Adsorption Free Energy ◽  
Charged Amino Acids ◽  
Solid Liquid

Atomistic simulations can complement the scarce experimental data on free energies of molecules at bio-inorganic interfaces. In molecular simulations, adsorption free energy landscapes are efficiently explored with advanced sampling methods, but classical dynamics is unable to capture charge transfer and polarization at the solid–liquid interface. Ab initio simulations do not suffer from this flaw, but only at the expense of an overwhelming computational cost. Here, we introduce a protocol for adsorption free energy calculations that improves sampling on the timescales relevant to ab initio simulations. As a case study, we calculate adsorption free energies of the charged amino acids Lysine and Aspartate on the fully hydrated anatase (101) TiO2 surface using tight-binding forces. We find that the first-principle description of the system significantly contributes to the adsorption free energies, which is overlooked by calculations with previous methods.

scholarly journals Improved Sampling in Ab Initio-Based Free Energy Calculations of Amino Acids at Solid-Liquid Interfaces: A Tight-Binding Assessment on TiO2 Anatase (101)

2019 ◽  
Author(s):  
Lorenzo Agosta ◽  
Erik G. Brandt ◽  
Alexander Lyubartsev
Keyword(s):  
Amino Acids ◽  
Free Energy ◽  
Ab Initio ◽  
Tight Binding ◽  
Free Energy Calculations ◽  
Surface Binding ◽  
Free Energy Of Adsorption ◽  
Tio2 Anatase ◽  
Advanced Sampling ◽  
Solid Liquid

Atomistic simulations are powerful for probing molecules at bioinorganic interfaces and excellent complements to scarcely available experimental techniques. The free energy controls the adsorption behavior of molecules on nanosurfaces, and is therefore a quantity of particular importance. Advanced sampling techniques can efficiently explore the adsorption free energy landscape, but molecular simulations with classical (Newtownian) dynamics fail to capture charge transfer and polarization at the solid-liquid interface. First principle simulations do not suffer from this limitation but come with a heavy computational load. Here, we introduce an efficient protocol to explore the free energy of adsorption in the ab initio framework. This approach accurately models the complex phenomena at bio-inorganic surfaces on the nanoscale and properly samples the relevant thermodynamic properties. We present a case study of adsorption of the Lysine and Aspartate amino acids on the anatase (101) TiO<sub>2</sub> surface with the tight binding method. The high values of the calculated adsorption free energies highlight the importance of a proper description of the electronic state for surface binding processes.

scholarly journals Improved Sampling in Ab Initio-Based Free Energy Calculations of Amino Acids at Solid-Liquid Interfaces: A Tight-Binding Assessment on TiO2 Anatase (101)

2019 ◽  
Author(s):  
Lorenzo Agosta ◽  
Erik G. Brandt ◽  
Alexander Lyubartsev
Keyword(s):  
Amino Acids ◽  
Free Energy ◽  
Ab Initio ◽  
Tight Binding ◽  
Free Energy Calculations ◽  
Surface Binding ◽  
Free Energy Of Adsorption ◽  
Tio2 Anatase ◽  
Advanced Sampling ◽  
Solid Liquid

Atomistic simulations are powerful for probing molecules at bioinorganic interfaces and excellent complements to scarcely available experimental techniques. The free energy controls the adsorption behavior of molecules on nanosurfaces, and is therefore a quantity of particular importance. Advanced sampling techniques can efficiently explore the adsorption free energy landscape, but molecular simulations with classical (Newtownian) dynamics fail to capture charge transfer and polarization at the solid-liquid interface. First principle simulations do not suffer from this limitation but come with a heavy computational load. Here, we introduce an efficient protocol to explore the free energy of adsorption in the ab initio framework. This approach accurately models the complex phenomena at bio-inorganic surfaces on the nanoscale and properly samples the relevant thermodynamic properties. We present a case study of adsorption of the Lysine and Aspartate amino acids on the anatase (101) TiO<sub>2</sub> surface with the tight binding method. The high values of the calculated adsorption free energies highlight the importance of a proper description of the electronic state for surface binding processes.

scholarly journals Improved Sampling in Ab Initio-Based Free Energy Calculations of Amino Acids at Solid-Liquid Interfaces: A Tight-Binding Assessment on TiO2 Anatase (101)

2019 ◽  
Author(s):  
Lorenzo Agosta ◽  
Erik G. Brandt ◽  
Alexander Lyubartsev
Keyword(s):  
Amino Acids ◽  
Free Energy ◽  
Ab Initio ◽  
Tight Binding ◽  
Free Energy Calculations ◽  
Surface Binding ◽  
Free Energy Of Adsorption ◽  
Tio2 Anatase ◽  
Advanced Sampling ◽  
Solid Liquid

Atomistic simulations are powerful for probing molecules at bioinorganic interfaces and excellent complements to scarcely available experimental techniques. The free energy controls the adsorption behavior of molecules on nanosurfaces, and is therefore a quantity of particular importance. Advanced sampling techniques can efficiently explore the adsorption free energy landscape, but molecular simulations with classical (Newtownian) dynamics fail to capture charge transfer and polarization at the solid-liquid interface. First principle simulations do not suffer from this limitation but come with a heavy computational load. Here, we introduce an efficient protocol to explore the free energy of adsorption in the ab initio framework. This approach accurately models the complex phenomena at bio-inorganic surfaces on the nanoscale and properly samples the relevant thermodynamic properties. We present a case study of adsorption of the Lysine and Aspartate amino acids on the anatase (101) TiO<sub>2</sub> surface with the tight binding method. The high values of the calculated adsorption free energies highlight the importance of a proper description of the electronic state for surface binding processes.

Ab Initio Free Energy Calculations at Multiple Electronic Structure Levels Made Affordable: An Effective Combination of Perturbation Theory and Machine Learning

2020 ◽  
Author(s):  
Tomas Bucko ◽  
Monika Gešvandtnerová ◽  
Dario Rocca
Keyword(s):  
Machine Learning ◽  
Free Energy ◽  
Perturbation Theory ◽  
Ab Initio ◽  
Single Point ◽  
Free Energy Calculations ◽  
Thermodynamic Quantities ◽  
Free Energies ◽  
Energy Calculations ◽  
Ab Initio Theory

<div>While free energies are fundamental thermodynamic quantities to characterize chemical reactions, their calculation based on ab initio theory is usually limited by the high computational cost. This is particularly true if multiple levels of theory have to be tested to establish their relative accuracy, if highly expensive quantum mechanical approximations are of interest, and also if several different temperatures have to be considered. We present an ab initio approach that effectively couples perturbation theory and machine learning to make ab initio free energy calculations more affordable. Starting from results based on a certain production ab initio theory, perturbation theory is applied to obtain free energies. The large number of single point calculations required by a brute force application of this approach are here significantly decreased by applying machine learning techniques. Importantly, the </div><div>training of the machine learning model requires only a small amount of data and does not need to be </div><div>performed again when the temperature is decreased.</div><div>The accuracy and efficiency of this method is demonstrated by computing the free energy of activation of the </div><div>proton exchange reaction in the zeolite chabazite. Starting from an ab initio calculation based on a semilocal</div><div>approximation of density functional theory, free energies based on significantly </div><div>more expensive non-local van der Waals and hybrid functionals are obtained with only a few tens</div><div>of additional single point calculations. In this way this work paves the route to</div><div>quick free energy calculations using different levels of theory or approximations that would be</div><div>too computationally expensive to be directly employed in molecular dynamics or Monte Carlo simulations.</div>

scholarly journals Ab initio calculations of free energy of activation at multiple electronic structure levels made affordable: An effective combination of perturbation theory and machine learning

2020 ◽  
Author(s):  
Tomas Bucko ◽  
Monika Gešvandtnerová ◽  
Dario Rocca
Keyword(s):  
Machine Learning ◽  
Free Energy ◽  
Perturbation Theory ◽  
Ab Initio ◽  
Single Point ◽  
Free Energy Calculations ◽  
Free Energies ◽  
Energy Calculations ◽  
Free Energy Of Activation ◽  
Ab Initio Theory

<div>While free energies are fundamental thermodynamic quantities to characterize chemical reactions, their calculation based on ab initio theory is usually limited by the high computational cost. This is particularly true if multiple levels of theory have to be tested to establish their relative accuracy, if highly expensive quantum mechanical approximations are of interest, and also if several different temperatures have to be considered. We present an ab initio approach that effectively couples perturbation theory and machine learning to make ab initio free energy calculations more affordable. Starting from results based on a certain production ab initio theory, perturbation theory is applied to obtain free energies. The large number of single point calculations required by a brute force application of this approach are here significantly decreased by applying machine learning techniques. Importantly, the training of the machine learning model requires only a small amount of data and does not need to be performed again when the temperature is decreased. The accuracy and efficiency of this method is demonstrated by computing the free energy of activation of the proton exchange reaction in the zeolite chabazite. Starting from an ab initio calculation based on a semilocal approximation of density functional theory, free energies based on significantly more expensive non-local van der Waals and hybrid functionals are obtained with only a few tens of additional single point calculations. In this way this work paves the route to quick free energy calculations using different levels of theory or approximations that would be too computationally expensive to be directly employed in molecular dynamics or Monte Carlo simulations.</div>

scholarly journals Ab initio calculations of free energy of activation at multiple electronic structure levels made affordable: An effective combination of perturbation theory and machine learning

2020 ◽  
Author(s):  
Tomas Bucko ◽  
Monika Gešvandtnerová ◽  
Dario Rocca
Keyword(s):  
Machine Learning ◽  
Free Energy ◽  
Perturbation Theory ◽  
Ab Initio ◽  
Single Point ◽  
Free Energy Calculations ◽  
Free Energies ◽  
Energy Calculations ◽  
Free Energy Of Activation ◽  
Ab Initio Theory

<div>While free energies are fundamental thermodynamic quantities to characterize chemical reactions, their calculation based on ab initio theory is usually limited by the high computational cost. This is particularly true if multiple levels of theory have to be tested to establish their relative accuracy, if highly expensive quantum mechanical approximations are of interest, and also if several different temperatures have to be considered. We present an ab initio approach that effectively couples perturbation theory and machine learning to make ab initio free energy calculations more affordable. Starting from results based on a certain production ab initio theory, perturbation theory is applied to obtain free energies. The large number of single point calculations required by a brute force application of this approach are here significantly decreased by applying machine learning techniques. Importantly, the training of the machine learning model requires only a small amount of data and does not need to be performed again when the temperature is decreased. The accuracy and efficiency of this method is demonstrated by computing the free energy of activation of the proton exchange reaction in the zeolite chabazite. Starting from an ab initio calculation based on a semilocal approximation of density functional theory, free energies based on significantly more expensive non-local van der Waals and hybrid functionals are obtained with only a few tens of additional single point calculations. In this way this work paves the route to quick free energy calculations using different levels of theory or approximations that would be too computationally expensive to be directly employed in molecular dynamics or Monte Carlo simulations.</div>

SAMPL6 Octanol-Water Partition Coefficients from Alchemical Free Energy Calculations with MBIS Atomic Charges

2019 ◽  
Author(s):  
Maximiliano Riquelme ◽  
Esteban Vöhringer-Martinez
Keyword(s):  
Free Energy ◽  
Electron Density ◽  
Free Energy Calculations ◽  
Basis Set ◽  
Energetic Cost ◽  
Atomic Charges ◽  
Free Energies ◽  
Energy Calculations ◽  
Alchemical Free Energy ◽  
Alchemical Free Energy Calculations

In molecular modeling the description of the interactions between molecules forms the basis for a correct prediction of macroscopic observables. Here, we derive atomic charges from the implicitly polarized electron density of eleven molecules in the SAMPL6 challenge using the Hirshfeld-I and Minimal Basis Set Iterative Stockholder(MBIS) partitioning method. These atomic charges combined with other parameters in the GAFF force field and different water/octanol models were then used in alchemical free energy calculations to obtain hydration and solvation free energies, which after correction for the polarization cost, result in the blind prediction of the partition coefficient. From the tested partitioning methods and water models the S-MBIS atomic charges with the TIP3P water model presented the smallest deviation from the experiment. Conformational dependence of the free energies and the energetic cost associated with the polarization of the electron density are discussed.

scholarly journals Automation of absolute protein-ligand binding free energy calculations for docking refinement and compound evaluation

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Germano Heinzelmann ◽  
Michael K. Gilson
Keyword(s):  
Free Energy ◽  
Early Stage ◽  
Binding Free Energy ◽  
Low Cost ◽  
Free Energy Calculations ◽  
Free Energies ◽  
Energy Calculations ◽  
Drug Candidates ◽  
Binding Free Energy Calculations ◽  
Binding Free Energies

AbstractAbsolute binding free energy calculations with explicit solvent molecular simulations can provide estimates of protein-ligand affinities, and thus reduce the time and costs needed to find new drug candidates. However, these calculations can be complex to implement and perform. Here, we introduce the software BAT.py, a Python tool that invokes the AMBER simulation package to automate the calculation of binding free energies for a protein with a series of ligands. The software supports the attach-pull-release (APR) and double decoupling (DD) binding free energy methods, as well as the simultaneous decoupling-recoupling (SDR) method, a variant of double decoupling that avoids numerical artifacts associated with charged ligands. We report encouraging initial test applications of this software both to re-rank docked poses and to estimate overall binding free energies. We also show that it is practical to carry out these calculations cheaply by using graphical processing units in common machines that can be built for this purpose. The combination of automation and low cost positions this procedure to be applied in a relatively high-throughput mode and thus stands to enable new applications in early-stage drug discovery.

scholarly journals A Hybrid Alchemical Free Energy and Machine Learning Methodology for the Calculation of Absolute Hydration Free Energies of Small Molecules

2020 ◽  
Author(s):  
Jenke Scheen ◽  
Wilson Wu ◽  
Antonia S. J. S. Mey ◽  
Paolo Tosco ◽  
Mark Mackey ◽  
...  
Keyword(s):  
Machine Learning ◽  
Free Energy ◽  
Free Energy Calculations ◽  
Learning Approaches ◽  
Free Energies ◽  
Training Set ◽  
Set Size ◽  
Correction Terms ◽  
Alchemical Free Energy ◽  
Alchemical Free Energy Calculations

A methodology that combines alchemical free energy calculations (FEP) with machine learning (ML) has been developed to compute accurate absolute hydration free energies. The hybrid FEP/ML methodology was trained on a subset of the FreeSolv database, and retrospectively shown to outperform most submissions from the SAMPL4 competition. Compared to pure machine-learning approaches, FEP/ML yields more precise estimates of free energies of hydration, and requires a fraction of the training set size to outperform standalone FEP calculations. The ML-derived correction terms are further shown to be transferable to a range of related FEP simulation protocols. The approach may be used to inexpensively improve the accuracy of FEP calculations, and to flag molecules which will benefit the most from bespoke forcefield parameterisation efforts.

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