scholarly journals Comparison of Implicit and Explicit Solvent Models for the Calculation of Solvation Free Energy in Organic Solvents

2017 ◽  
Vol 13 (3) ◽  
pp. 1034-1043 ◽  
Author(s):  
Jin Zhang ◽  
Haiyang Zhang ◽  
Tao Wu ◽  
Qi Wang ◽  
David van der Spoel
Keyword(s):  
Free Energy ◽  
Organic Solvents ◽  
Solvation Free Energy ◽  
Solvent Models ◽  
Explicit Solvent ◽  
Implicit And Explicit

Thermodynamics of Helix formation in small peptides of varying lengthin vacuo, implicit solvent and explicit solvent: Comparison between AMBER force fields

2019 ◽  
Vol 18 (03) ◽  
pp. 1950015
Author(s):  
Zhaoxi Sun ◽  
Xiaohui Wang
Keyword(s):  
Amino Acids ◽  
Free Energy ◽  
Hydrogen Bonds ◽  
Force Fields ◽  
Implicit Solvent ◽  
Conformational Ensemble ◽  
Helix Formation ◽  
Solvent Models ◽  
Explicit Solvent ◽  
Amber Force Fields

Helix formation is of great significance in protein folding. The helix-forming tendencies of amino acids are accumulated along the sequence to determine the helix-forming tendency of peptides. Computer simulation can be used to model this process in atomic details and give structural insights. In the current work, we employ equilibrate-state free energy simulation to systematically study the folding/unfolding thermodynamics of a series of mutated peptides. Two AMBER force fields including AMBER99SB and AMBER14SB are compared. The new 14SB force field uses refitted torsion parameters compared with 99SB and they share the same atomic charge scheme. We find that in vacuo the helix formation is mutation dependent, which reflects the different helix propensities of different amino acids. In general, there are helix formers, helix indifferent groups and helix breakers. The helical structure becomes more favored when the length of the sequence becomes longer, which arises from the formation of additional backbone hydrogen bonds in the lengthened sequence. Therefore, the helix indifferent groups and helix breakers will become helix formers in long sequences. Also, protonation-dependent helix formation is observed for ionizable groups. In 14SB, the helical structures are more stable than in 99SB and differences can be observed in their grouping schemes, especially in the helix indifferent group. In solvents, all mutations are helix indifferent due to protein–solvent interactions. The decrease in the number of backbone hydrogen bonds is the same with the increase in the number of protein–water hydrogen bonds. The 14SB in explicit solvent is able to capture the free energy minima in the helical state while 14SB in implicit solvent, 99SB in explicit solvent and 99SB in implicit solvent cannot. The helix propensities calculated under 14SB agree with the corresponding experimental values, while the 99SB results obviously deviate from the references. Hence, implicit solvent models are unable to correctly describe the thermodynamics even for the simple helix formation in isolated peptides. Well-developed force fields and explicit solvents are needed to correctly describe the protein dynamics. Aside from the free energy, differences in conformational ensemble under different force fields in different solvent models are observed. The numbers of hydrogen bonds formed under different force fields agree and they are mostly determined by the solvent model.

scholarly journals Comparison of free-energy profiles of small peptides between the generalized Born and the explicit solvent models

2003 ◽  
Vol 43 (supplement) ◽  
pp. S35
Author(s):  
T. Ishizuka ◽  
T. Terada ◽  
S. Nakamura ◽  
K. Shimizu
Keyword(s):  
Free Energy ◽  
Small Peptides ◽  
Generalized Born ◽  
Energy Profiles ◽  
Solvent Models ◽  
Explicit Solvent ◽  
Free Energy Profiles

scholarly journals Assessment of the SM12, SM8, and SMD Solvation Models for Predicting Limiting Activity Coefficients at 298.15 K

2020 ◽  
Author(s):  
Sydnee N. Roese ◽  
Justin D. Heintz ◽  
Cole B. Uzat ◽  
Alexa J. Schmidt ◽  
Griffin Margulis ◽  
...  
Keyword(s):  
Free Energy ◽  
Phase Equilibrium ◽  
Activity Coefficients ◽  
Solvation Free Energy ◽  
Excess Gibbs Free Energy ◽  
Implicit Solvent ◽  
Solvent Models ◽  
Comparable Performance ◽  
Solvation Models ◽  
Solvation Free Energies

The SM<i>x</i> (<i>x</i>= 12, 8, or D) universal solvent models are implicit solvent models which using electronic structure calculations can compute solvation free energies at 298.15 K. While solvation free energy is an important thermophysical property, within the thermodynamic modeling of phase equilibrium, limiting (or infinite dilution) activity coefficients are preferred since they may be used to parameterize excess Gibbs free energy models to model phase equilibrium. Conveniently, the two quantities are related. Therefore the present study was performed to assess the ability to use the SM<i>x</i> universal solvent models to predict limiting activity coefficients. Two methods of calculating the limiting activity coefficient where compared: 1) The solvation free energy and self-solvation free energy were both predicted and 2) the self-solvation free energy was computed using readily available vapor pressure data. Overall the first method is preferred as it results in a cancellation of errors, specifically for the case in which water is a solute. The SM12 model was compared to both UNIFAC and MOSCED. MOSCED was the highest performer, yet had the smallest available compound inventory. UNIFAC and SM12 exhibited comparable performance. Therefore further exploration and research should be conducted into the viability of using the SM<i>x</i> models for phase equilibrium calculations.

scholarly journals Bridging the experiment-calculation divide: machine learning corrections to redox potential calculations in implicit and explicit solvent models

2021 ◽  
Author(s):  
Eugen Hruska ◽  
Ariel Gale ◽  
Fang Liu
Keyword(s):  
Machine Learning ◽  
Redox Potential ◽  
Density Functional ◽  
Implicit Solvent ◽  
Systematic Bias ◽  
Redox Potentials ◽  
Solvent Models ◽  
Explicit Solvent ◽  
Solvent Model ◽  
Implicit And Explicit

Prediction of redox potentials is essential for catalysis and energy storage. Although density functional theory (DFT) calculations have enabled rapid redox potential predictions for numerous compounds, prominent errors persist compared to experimental measurements. In this work, we develop machine learning (ML) models to reduce the errors of redox potential calculations in both implicit and explicit solvent models. Training and testing of the ML correction models are based on the diverse ROP313 dataset with experimental redox potentials measured for organic and organometallic compounds in a variety of solvents. For the implicit solvent approach, our ML models can reduce both the systematic bias and the number of outliers. ML corrected redox potentials also demonstrate less sensitivity to DFT functional choice. For the explicit solvent approach, we significantly reduce the computational costs by embedding the microsolvated cluster in implicit bulk solvent, obtaining converged redox potential results with a smaller solvation shell. This combined implicit-explicit solvent model, together with GPU-accelerated quantum chemistry methods, enabled rapid generation of a large dataset of explicit-solvent-calculated redox potentials for 165 organic compounds, allowing detailed investigation of the error sources in explicit solvent redox potential calculations.

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