On the calculation of free energies over Hamiltonian and order parameters via perturbation and thermodynamic integration

2021 ◽  
Vol 155 (11) ◽  
pp. 114112
Author(s):  
Fernando A. Escobedo
Keyword(s):  
Thermodynamic Integration ◽  
Order Parameters ◽  
Free Energies

Understanding defects in DSA: calculation of free energies of block copolymer DSA systems via thermodynamic integration of a mesoscale block-copolymer model

2014 ◽  
Author(s):  
Andrew J. Peters ◽  
Richard A. Lawson ◽  
Benjamin D. Nation ◽  
Peter J. Ludovice ◽  
Clifford L. Henderson
Keyword(s):  
Block Copolymer ◽  
Thermodynamic Integration ◽  
Free Energies

Statistical Mechanics: Entropy, Order Parameters, and Complexity

2021 ◽  
Author(s):  
James P. Sethna
Keyword(s):  
Statistical Mechanics ◽  
Topological Defects ◽  
Linear Response Theory ◽  
Continuous Phase ◽  
Quantum Systems ◽  
Order Parameters ◽  
Free Energies ◽  
Onset Of Chaos ◽  
The Social ◽  
Essential Insight

This text distills the core ideas of statistical mechanics to make room for new advances important to information theory, complexity, active matter, and dynamical systems. Chapters address random walks, equilibrium systems, entropy, free energies, quantum systems, calculation and computation, order parameters and topological defects, correlations and linear response theory, and abrupt and continuous phase transitions. Exercises explore the enormous range of phenomena where statistical mechanics provides essential insight — from card shuffling to how cells avoid errors when copying DNA, from the arrow of time to animal flocking behavior, from the onset of chaos to fingerprints. The text is aimed at graduates, undergraduates, and researchers in mathematics, computer science, engineering, biology, and the social sciences as well as to physicists, chemists, and astrophysicists. As such, it focuses on those issues common to all of these fields, background in quantum mechanics, thermodynamics, and advanced physics should not be needed, although scientific sophistication and interest will be important.

Use of thermodynamic integration to calculate the hydration free energies of n-alkanes

2002 ◽  
Vol 116 (6) ◽  
pp. 2361-2369 ◽  
Author(s):  
J. T. Wescott ◽  
L. R. Fisher ◽  
S. Hanna
Keyword(s):  
Thermodynamic Integration ◽  
Free Energies

scholarly journals Alchemical Free Energy Estimators and Molecular Dynamics Engines: Accuracy, Precision and Reproducibility

2021 ◽  
Author(s):  
Alexander Wade ◽  
Agastya Bhati ◽  
Shunzhou Wan ◽  
Peter Coveney
Keyword(s):  
Molecular Dynamics ◽  
Free Energy ◽  
Binding Free Energy ◽  
Thermodynamic Integration ◽  
Free Energy Perturbation ◽  
Free Energies ◽  
Ensemble Averaging ◽  
Relative Binding ◽  
Energy Perturbation ◽  
Binding Free Energies

The binding free energy between a ligand and its target protein is an essential quantity to know at all stages of the drug discovery pipeline. Assessing this value computationally can offer insight into where efforts should be focused in the pursuit of effective therapeutics to treat myriad diseases. In this work we examine the computation of alchemical relative binding free energies with an eye to assessing reproducibility across popular molecular dynamics packages and free energy estimators. The focus of this work is on 54 ligand transformations from a diverse set of protein targets: MCL1, PTP1B, TYK2, CDK2 and thrombin. These targets are studied with three popular molecular dynamics packages: OpenMM, NAMD2 and NAMD3. Trajectories collected with these packages are used to compare relative binding free energies calculated with thermodynamic integration and free energy perturbation methods. The resulting binding free energies show good agreement between molecular dynamics packages with an average mean unsigned error between packages of 0.5 $kcal/mol$ The correlation between packages is very good with the lowest Spearman's, Pearson's and Kendall's tau correlation coefficient between two packages being 0.91, 0.89 and 0.74 respectively. Agreement between thermodynamic integration and free energy perturbation is shown to be very good when using ensemble averaging.

Prediction of Solvation Free Energies with Thermodynamic Integration Using the General Amber Force Field

2014 ◽  
Vol 10 (8) ◽  
pp. 3570-3577 ◽  
Author(s):  
Silvia A. Martins ◽  
Sergio F. Sousa ◽  
Maria João Ramos ◽  
Pedro A. Fernandes
Keyword(s):  
Force Field ◽  
Thermodynamic Integration ◽  
Free Energies ◽  
Amber Force Field ◽  
Solvation Free Energies

scholarly journals Comparison of enveloping distribution sampling and thermodynamic integration to calculate binding free energies of phenylethanolamine N-methyltransferase inhibitors

2011 ◽  
Vol 135 (2) ◽  
pp. 024105 ◽  
Author(s):  
Sereina Riniker ◽  
Clara D. Christ ◽  
Niels Hansen ◽  
Alan E. Mark ◽  
Pramod C. Nair ◽  
...  
Keyword(s):  
Thermodynamic Integration ◽  
Free Energies ◽  
Binding Free Energies

scholarly journals Symmetry-adapted order parameters and free energies for solids undergoing order-disorder phase transitions

2017 ◽  
Vol 96 (13) ◽  
Author(s):  
Anirudh Raju Natarajan ◽  
John C. Thomas ◽  
Brian Puchala ◽  
Anton Van der Ven
Keyword(s):  
Phase Transitions ◽  
Order Parameters ◽  
Free Energies

scholarly journals Thermodynamic Integration in 3n Dimensions without Biases or Alchemy for Protein Interactions

2017 ◽  
Author(s):  
Liao Y Chen
Keyword(s):  
Free Energy ◽  
Protein Interactions ◽  
Protein Complexes ◽  
Thermodynamic Integration ◽  
Interaction Parameters ◽  
Free Energies ◽  
Biologically Relevant ◽  
Error Amplification ◽  
Relative Errors ◽  
Colicin E9

ABSTRACTThermodynamic integration (TI), a powerful formalism for computing the Gibbs free energy, has been implemented for many biophysical processes characterized by one-dimensional order parameters with alchemical schemes that require delicate human efforts to choose/design biasing potentials for sampling the desired biophysical events and to remove their artifactitious consequences afterwards. Theoretically, an alchemical scheme is exact but practically, it causes error amplification. Small relative errors in the interaction parameters can be amplified many times in their propagation into the computed free energy [due to subtraction of similar numbers such as (105 ± 5) − (100 ± 5) = 5 ± 7], which would render the results significantly less accurate than the input interaction parameters. In this paper, we present an unsophisticated implementation of TI in 3n dimensions (3nD) (n=1,2,3…) without alchemy or biasing potentials. In TI3nD, the errors in the interaction parameters will not be amplified and human efforts are not required to design biasing potentials that generate unphysical consequences. Using TI3nD, we computed the standard free energies of three protein complexes: trometamol in Salmonella effector SpvD (n=1), biotin in avidin (n=2), and Colicin E9 endonuclease with cognate immunity protein Im9 (n=3) and the hydration energies of ten biologically relevant compounds (n=1 for water, acetamide, urea, glycerol, trometamol, ammonium and n=2 for erythritol, 1,3-propanediol, xylitol, biotin). The computed results all agree with available experimental data. Each of the 13 computations is accomplishable within two (for a hydration problem) to ten (for the protein-recognition problem) days on an inexpensive workstation (two Xeon E5-2665 2.4GHz CPUs and one nVidia P5000 GPU).

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