Molecular simulation of polymer assisted protein refolding

2005 ◽  
Vol 123 (13) ◽  
pp. 134903 ◽  
Author(s):  
Diannan Lu ◽  
Zheng Liu
Keyword(s):  
Molecular Simulation ◽  
Protein Refolding

Molecular simulation of surfactant-assisted protein refolding

2005 ◽  
Vol 122 (13) ◽  
pp. 134902 ◽  
Author(s):  
Diannan Lu ◽  
Zheng Liu ◽  
Zhixia Liu ◽  
Minlian Zhang ◽  
Pingkai Ouyang
Keyword(s):  
Molecular Simulation ◽  
Protein Refolding ◽  
Surfactant Assisted

Low temperature protein refolding suggested by molecular simulation

2019 ◽  
Vol 151 (18) ◽  
pp. 185101 ◽  
Author(s):  
Daniel J. Kozuch ◽  
Frank H. Stillinger ◽  
Pablo G. Debenedetti
Keyword(s):  
Low Temperature ◽  
Molecular Simulation ◽  
Protein Refolding

Adsorption Isotherm Predictions for Multiple Molecules in MOFs Using the Same Deep Learning Model

2019 ◽  
Author(s):  
Ryther Anderson ◽  
Achay Biong ◽  
Diego Gómez-Gualdrón
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Molecular Simulation ◽  
Large Scale ◽  
Learning Model ◽  
Operating Conditions ◽  
Small Subset ◽  
Screening Methods ◽  
Large Set ◽  
Metal Organic

<div>Tailoring the structure and chemistry of metal-organic frameworks (MOFs) enables the manipulation of their adsorption properties to suit specific energy and environmental applications. As there are millions of possible MOFs (with tens of thousands already synthesized), molecular simulation, such as grand canonical Monte Carlo (GCMC), has frequently been used to rapidly evaluate the adsorption performance of a large set of MOFs. This allows subsequent experiments to focus only on a small subset of the most promising MOFs. In many instances, however, even molecular simulation becomes prohibitively time consuming, underscoring the need for alternative screening methods, such as machine learning, to precede molecular simulation efforts. In this study, as a proof of concept, we trained a neural network as the first example of a machine learning model capable of predicting full adsorption isotherms of different molecules not included in the training of the model. To achieve this, we trained our neural network only on alchemical species, represented only by their geometry and force field parameters, and used this neural network to predict the loadings of real adsorbates. We focused on predicting room temperature adsorption of small (one- and two-atom) molecules relevant to chemical separations. Namely, argon, krypton, xenon, methane, ethane, and nitrogen. However, we also observed surprisingly promising predictions for more complex molecules, whose properties are outside the range spanned by the alchemical adsorbates. Prediction accuracies suitable for large-scale screening were achieved using simple MOF (e.g. geometric properties and chemical moieties), and adsorbate (e.g. forcefield parameters and geometry) descriptors. Our results illustrate a new philosophy of training that opens the path towards development of machine learning models that can predict the adsorption loading of any new adsorbate at any new operating conditions in any new MOF.</div>

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