scholarly journals Implicit Solvents for the Polarizable Atomic Multipole AMOEBA Force Field

2020 ◽  
Author(s):  
Rae Corrigan ◽  
Guowei Qi ◽  
Andrew Thiel ◽  
Jack Lynn ◽  
Brandon Walker ◽  
...  
Keyword(s):  
Force Field ◽  
Protein Design ◽  
Numerical Solutions ◽  
Target Function ◽  
Model Parameters ◽  
Implicit Solvent ◽  
Electrostatic Model ◽  
Implicit Solvents ◽  
Poisson Boltzmann Equation ◽  
Amoeba Force Field

Computational protein design, ab initio protein/RNA folding, and protein-ligand screening can be too computationally demanding for explicit treatment of solvent. For these applications, implicit solvent offers a compelling alternative, which we describe here for the polarizable atomic multipole AMOEBA force field based on three treatments of continuum electrostatics: numerical solutions to the Poisson-Boltzmann equation (PBE), the domain-decomposition Conductor-like Screening Model (ddCOSMO) approximation to the PBE, and the analytic generalized Kirkwood (GK) approximation. The continuum electrostatic models are combined with a nonpolar estimator based on novel cavitation and dispersion terms. Electrostatic model parameters are numerically optimized using a least squares style target function based on a library of 103 small molecule solvation free energy differences. Mean signed errors for the APBS, ddCOSMO, and GK models are 0.05, 0.00, and 0.00 kcal/mol, respectively, while the mean unsigned errors are 0.70, 0.63, and 0.51 kcal/mol, respectively. Validation of the electrostatic response of the resulting implicit solvents, which are available in the Tinker (or Tinker-HP), OpenMM, and Force Field X software packages, is based on comparisons to explicit solvent simulations for a series of proteins and nucleic acids. Overall, the emergence of performative implicit solvent models for polarizable force fields will open the door to their use for folding and design applications.<br>

scholarly journals Implicit Solvents for the Polarizable Atomic Multipole AMOEBA Force Field

2020 ◽  
Author(s):  
Rae Corrigan ◽  
Guowei Qi ◽  
Andrew Thiel ◽  
Jack Lynn ◽  
Brandon Walker ◽  
...  
Keyword(s):  
Force Field ◽  
Protein Design ◽  
Numerical Solutions ◽  
Target Function ◽  
Model Parameters ◽  
Implicit Solvent ◽  
Electrostatic Model ◽  
Implicit Solvents ◽  
Poisson Boltzmann Equation ◽  
Amoeba Force Field

Computational protein design, ab initio protein/RNA folding, and protein-ligand screening can be too computationally demanding for explicit treatment of solvent. For these applications, implicit solvent offers a compelling alternative, which we describe here for the polarizable atomic multipole AMOEBA force field based on three treatments of continuum electrostatics: numerical solutions to the Poisson-Boltzmann equation (PBE), the domain-decomposition Conductor-like Screening Model (ddCOSMO) approximation to the PBE, and the analytic generalized Kirkwood (GK) approximation. The continuum electrostatic models are combined with a nonpolar estimator based on novel cavitation and dispersion terms. Electrostatic model parameters are numerically optimized using a least squares style target function based on a library of 103 small molecule solvation free energy differences. Mean signed errors for the APBS, ddCOSMO, and GK models are 0.05, 0.00, and 0.00 kcal/mol, respectively, while the mean unsigned errors are 0.70, 0.63, and 0.51 kcal/mol, respectively. Validation of the electrostatic response of the resulting implicit solvents, which are available in the Tinker (or Tinker-HP), OpenMM, and Force Field X software packages, is based on comparisons to explicit solvent simulations for a series of proteins and nucleic acids. Overall, the emergence of performative implicit solvent models for polarizable force fields will open the door to their use for folding and design applications.<br>

scholarly journals Evaluation of Docking Target Functions by the Comprehensive Investigation of Protein-Ligand Energy Minima

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Igor V. Oferkin ◽  
Ekaterina V. Katkova ◽  
Alexey V. Sulimov ◽  
Danil C. Kutov ◽  
Sergey I. Sobolev ◽  
...  
Keyword(s):  
Ligand Binding ◽  
Force Field ◽  
Quantum Chemical ◽  
Target Function ◽  
Binding Energies ◽  
Low Energy ◽  
Implicit Solvent ◽  
Energy Calculation ◽  
Solvent Models ◽  
Docking Program

The adequate choice of the docking target function impacts the accuracy of the ligand positioning as well as the accuracy of the protein-ligand binding energy calculation. To evaluate a docking target function we compared positions of its minima with the experimentally known pose of the ligand in the protein active site. We evaluated five docking target functions based on either the MMFF94 force field or the PM7 quantum-chemical method with or without implicit solvent models: PCM, COSMO, and SGB. Each function was tested on the same set of 16 protein-ligand complexes. For exhaustive low-energy minima search the novel MPI parallelized docking program FLM and large supercomputer resources were used. Protein-ligand binding energies calculated using low-energy minima were compared with experimental values. It was demonstrated that the docking target function on the base of the MMFF94 force field in vacuo can be used for discovery of native or near native ligand positions by finding the low-energy local minima spectrum of the target function. The importance of solute-solvent interaction for the correct ligand positioning is demonstrated. It is shown that docking accuracy can be improved by replacement of the MMFF94 force field by the new semiempirical quantum-chemical PM7 method.

scholarly journals An optimized charge penetration model for use with the AMOEBA force field

2017 ◽  
Vol 19 (1) ◽  
pp. 276-291 ◽  
Author(s):  
Joshua A. Rackers ◽  
Qiantao Wang ◽  
Chengwen Liu ◽  
Jean-Philip Piquemal ◽  
Pengyu Ren ◽  
...  
Keyword(s):  
Nucleic Acid ◽  
Ligand Binding ◽  
Force Field ◽  
Short Range ◽  
Electrostatic Model ◽  
Base Stacking ◽  
Acid Base ◽  
Nucleic Acid Base ◽  
Range Error ◽  
Amoeba Force Field

Inclusion of charge penetration corrects the short-range error in the electrostatic model of the AMOEBA force field, and improves the accuracy of interactions ranging from nucleic acid base stacking to protein–ligand binding.

Implicit Solvents for the Polarizable Atomic Multipole AMOEBA Force Field

2021 ◽  
Vol 17 (4) ◽  
pp. 2323-2341
Author(s):  
Rae A. Corrigan ◽  
Guowei Qi ◽  
Andrew C. Thiel ◽  
Jack R. Lynn ◽  
Brandon D. Walker ◽  
...  
Keyword(s):  
Force Field ◽  
Implicit Solvents ◽  
Amoeba Force Field

scholarly journals Supercomputer investigation of the protein-ligand system low-energy minima

2015 ◽  
Vol 61 (6) ◽  
pp. 712-716 ◽  
Author(s):  
I.V. Oferkin ◽  
A.V. Sulimov ◽  
E.V. Katkova ◽  
D.K. Kutov ◽  
F.V. Grigoriev ◽  
...  
Keyword(s):  
Binding Energy ◽  
Ligand Binding ◽  
Force Field ◽  
Search Algorithm ◽  
Target Function ◽  
Binding Energies ◽  
Low Energy ◽  
Implicit Solvent ◽  
Solvent Model ◽  
Monte Carlo Search

The accuracy ofthe protein-ligand binding energy calculations andligand positioning isstrongly influenced by the choice of the docking target function. This work demonstrates the evaluation of the five different target functions used in docking: functions based on MMFF94 force field and functions based on PM7 quantum-chemical method accounting orwithout accounting the implicit solvent model (PCM, COSMO or SGB). For these purposes the ligand positions corresponding to the minima of the target function and the experimentally known ligand positions in the protein active site (crystal ligand positions) were compared. Each function was examined on the same test-set of 16 protein-ligand complexes. The new parallelized docking program FLM based on Monte Carlo search algorithm was developed to perform the comprehensive low-energy minima search and to calculate the protein-ligand binding energy. This study demonstrates that the docking target function based on the MMFF94 force field can be used to detect the crystal or near crystal positions of the ligand by the finding the low-energy local minima spectrum of the target function. The importance of solvent accounting in the docking process for the accurate ligand positioning is also shown. The accuracy of the ligand positioning as well as the correlation between the calculated and experimentally determined protein-ligand binding energies are improved when the MMFF94 force field is substituted by the new PM7 method with implicit solvent accounting.

Unsupervised labelling of remote sensing images based on force field clustering

2021 ◽  
pp. 1-14
Author(s):  
Zhenggang Wang ◽  
Jin Jin
Keyword(s):  
Remote Sensing ◽  
Force Field ◽  
Image Data ◽  
Model Parameters ◽  
Remote Sensing Images ◽  
Initial Cluster ◽  
Data Points ◽  
Global Optimal ◽  
Density Force

Remote sensing image segmentation provides technical support for decision making in many areas of environmental resource management. But, the quality of the remote sensing images obtained from different channels can vary considerably, and manually labeling a mass amount of image data is too expensive and Inefficiently. In this paper, we propose a point density force field clustering (PDFC) process. According to the spectral information from different ground objects, remote sensing superpixel points are divided into core and edge data points. The differences in the densities of core data points are used to form the local peak. The center of the initial cluster can be determined by the weighted density and position of the local peak. An iterative nebular clustering process is used to obtain the result, and a proposed new objective function is used to optimize the model parameters automatically to obtain the global optimal clustering solution. The proposed algorithm can cluster the area of different ground objects in remote sensing images automatically, and these categories are then labeled by humans simply.

Implications of Using Dual Number Derivatives With a Numerical Solution

2013 ◽  
Author(s):  
Suryanarayana R. Pakalapati ◽  
Hayri Sezer ◽  
Ismail B. Celik
Keyword(s):  
Automatic Differentiation ◽  
Numerical Solutions ◽  
Computer Code ◽  
Model Parameters ◽  
Model Parameter ◽  
Systematic Assessment ◽  
Dual Number ◽  
Advection Diffusion Equation ◽  
Model Equations ◽  
Derivatives Of

Dual number arithmetic is a well-known strategy for automatic differentiation of computer codes which gives exact derivatives, to the machine accuracy, of the computed quantities with respect to any of the involved variables. A common application of this concept in Computational Fluid Dynamics, or numerical modeling in general, is to assess the sensitivity of mathematical models to the model parameters. However, dual number arithmetic, in theory, finds the derivatives of the actual mathematical expressions evaluated by the computer code. Thus the sensitivity to a model parameter found by dual number automatic differentiation is essentially that of the combination of the actual mathematical equations, the numerical scheme and the grid used to solve the equations not just that of the model equations alone as implied by some studies. This aspect of the sensitivity analysis of numerical simulations using dual number auto derivation is explored in the current study. A simple one-dimensional advection diffusion equation is discretized using different schemes of finite volume method and the resulting systems of equations are solved numerically. Derivatives of the numerical solutions with respect to parameters are evaluated automatically using dual number automatic differentiation. In addition the derivatives are also estimated using finite differencing for comparison. The analytical solution was also found for the original PDE and derivatives of this solution are also computed analytically. It is shown that a mathematical model could potentially show different sensitivity to a model parameter depending on the numerical method employed to solve the equations and the grid resolution used. This distinction is important since such inter-dependence needs to be carefully addressed to avoid confusion when reporting the sensitivity of predictions to a model parameter using a computer code. A systematic assessment of numerical uncertainty in the sensitivities computed using automatic differentiation is presented.

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