scholarly journals A new one-site coarse-grained model for water: Bottom-up many-body projected water (BUMPer). I. General theory and model

2021 ◽  
Vol 154 (4) ◽  
pp. 044104
Author(s):  
Jaehyeok Jin ◽  
Yining Han ◽  
Alexander J. Pak ◽  
Gregory A. Voth
Keyword(s):  
General Theory ◽  
Coarse Grained ◽  
Many Body ◽  
Bottom Up ◽  
Coarse Grained Model ◽  
Water Bottom ◽  
Theory And Model

Towards a coarse-grained model of the peptoid backbone: the case of N,N-dimethylacetamide

2018 ◽  
Vol 20 (36) ◽  
pp. 23386-23396 ◽  
Author(s):  
Pu Du ◽  
Steven W. Rick ◽  
Revati Kumar
Keyword(s):  
Coarse Grained ◽  
Many Body ◽  
Coarse Grained Model

Coarse-grained model of DMA, containing the basic motif of the peptoid backbone, based on short ranged many-body ranged interactions.

scholarly journals Quantitative Prediction of pH-Controlled Ionization of Oligopeptides

2020 ◽  
Author(s):  
Raju Lunkad ◽  
Anastasiia Murmiliuk ◽  
Pascal Hebbeker ◽  
Milan Boublík ◽  
Zdeněk Tošner ◽  
...  
Keyword(s):  
Electrostatic Interactions ◽  
Weak Acid ◽  
Coarse Grained ◽  
Quantitative Prediction ◽  
Bottom Up ◽  
Coarse Grained Model ◽  
Flexible Polymer ◽  
Acid And Base ◽  
Base Side ◽  
Capillary Zone

Weak ampholytes are ubiquitous in nature and commonly found in artificial pH-responsive systems. However, our limited understanding of their ionisation response and the lack of predictive capabilities hinder the bottom-up design of such systems. Here, we used a coarse-grained model of a flexible polymer with weakly ionisable monomer units to quantitatively analyse the ionisation behaviour of two oligopeptides. Differences in ionisation response between oligopeptides and monomeric amino acids showed that electrostatic interactions between weak acid and base side chains play a key role in oligopeptide ionisation, as predicted by our model. Moreover, by comparing our simulations with experimental results from potentiometric titration, capillary zone electrophoresis and NMR, we demonstrated that our model reliably predicts the ionisation response and electrophoretic mobilities of various peptide sequences. Ultimately, our model is the first step towards using predictive bottom-up design of responsive ampholytes to tailor their properties as a function of charge and pH.<br>

scholarly journals Assessment of an anisotropic coarse-grained model for cis-1,4-polybutadiene: a bottom-up approach

Soft Matter ◽  
2021 ◽  
Author(s):  
Ioannis Tanis ◽  
Bernard Rousseau ◽  
Laurent Soulard ◽  
Claire A. Lemarchand
Keyword(s):  
Polymer Melts ◽  
Coarse Grained ◽  
Bottom Up ◽  
Coarse Grained Model

This work presents a generic anisotropic bottom-up coarse-grained approach for polymer melts and it is tested thoroughly and successfully.

scholarly journals Quantitative Prediction of pH-Controlled Ionization of Oligoampholytes

2020 ◽  
Author(s):  
Raju Lunkad ◽  
Anastasiia Murmiliuk ◽  
Pascal Hebbeker ◽  
Milan Boublík ◽  
Zdeněk Tošner ◽  
...  
Keyword(s):  
Electrostatic Interactions ◽  
Weak Acid ◽  
Coarse Grained ◽  
Quantitative Prediction ◽  
Bottom Up ◽  
Coarse Grained Model ◽  
Flexible Polymer ◽  
Acid And Base ◽  
Base Side ◽  
Capillary Zone

Weak ampholytes are ubiquitous in nature and commonly found in artificial pH-responsive systems. However, our limited understanding of their ionisation response and the lack of predictive capabilities hinder the bottom-up design of such systems. Here, we used a coarse-grained model of a flexible polymer with weakly ionisable monomer units to quantitatively analyse the ionisation behaviour of two oligopeptides. Differences in ionisation response between oligopeptides and monomeric amino acids showed that electrostatic interactions between weak acid and base side chains play a key role in oligopeptide ionisation, as predicted by our model. Moreover, by comparing our simulations with experimental results from potentiometric titration, capillary zone electrophoresis and NMR, we demonstrated that our model reliably predicts the ionisation response and electrophoretic mobilities of various peptide sequences. Ultimately, our model is the first step towards using predictive bottom-up design of responsive ampholytes to tailor their properties as a function of charge and pH.<br>

scholarly journals Learning neural network potentials from experimental data via Differentiable Trajectory Reweighting

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Stephan Thaler ◽  
Julija Zavadlav
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Coarse Graining ◽  
Md Simulations ◽  
Coarse Grained ◽  
Thermodynamic Perturbation Theory ◽  
Top Down ◽  
Bottom Up ◽  
Coarse Grained Model ◽  
Gradient Computation

AbstractIn molecular dynamics (MD), neural network (NN) potentials trained bottom-up on quantum mechanical data have seen tremendous success recently. Top-down approaches that learn NN potentials directly from experimental data have received less attention, typically facing numerical and computational challenges when backpropagating through MD simulations. We present the Differentiable Trajectory Reweighting (DiffTRe) method, which bypasses differentiation through the MD simulation for time-independent observables. Leveraging thermodynamic perturbation theory, we avoid exploding gradients and achieve around 2 orders of magnitude speed-up in gradient computation for top-down learning. We show effectiveness of DiffTRe in learning NN potentials for an atomistic model of diamond and a coarse-grained model of water based on diverse experimental observables including thermodynamic, structural and mechanical properties. Importantly, DiffTRe also generalizes bottom-up structural coarse-graining methods such as iterative Boltzmann inversion to arbitrary potentials. The presented method constitutes an important milestone towards enriching NN potentials with experimental data, particularly when accurate bottom-up data is unavailable.

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