Water in Nanopores and Biological Channels: A Molecular Simulation Perspective

Biological channels facilitate the exchange of small molecules across membranes, but surprisingly there is a lack of general tools for the identification and quantification of transport (i.e., translocation and binding). Analyzing the ion current fluctuation of a typical channel with its constriction region in the middle does not allow a direct conclusion on successful transport. For this, we created an additional barrier acting as a molecular counter at the exit of the channel. To identify permeation, we mainly read the molecule residence time in the channel lumen as the indicator whether the molecule reached the exit of the channel. As an example, here we use the well-studied porin, OmpF, an outer membrane channel from E. coli. Inspection of the channel structure suggests that aspartic acid at position 181 is located below the constriction region (CR) and we subsequently mutated this residue to cysteine, where else cysteine free and functionalized it by covalent binding with 2-sulfonatoethyl methanethiosulfonate (MTSES) or the larger glutathione (GLT) blockers. Using the dwell time as the signal for transport, we found that both mono-arginine and tri-arginine permeation process is prolonged by 20% and 50% respectively through OmpFE181CMTSES, while the larger sized blocker modification OmpFE181CGLT drastically decreased the permeation of mono-arginine by 9-fold and even blocked the pathway of the tri-arginine. In case of the hepta-arginine as substrate, both chemical modifications led to an identical ‘blocked’ pattern observed by the dwell time of ion current fluctuation of the OmpFwt. As an instance for antibiotic permeation, we analyzed norfloxacin, a fluoroquinolone antimicrobial agent. The modulation of the interaction dwell time suggests possible successful permeation of norfloxacin across OmpFwt. This approach may discriminate blockages from translocation events for a wide range of substrates. A potential application could be screening for scaffolds to improve the permeability of antibiotics.

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

10.26434/chemrxiv.9894224.v1 ◽

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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