A Cu metal-organic framework (MOF), [Cu (PMTD) (H O) ]⋅20 H O, 1, (where PMTD is 1,4-phenylenebis(5-methyl-4H-1,2,4-triazole-3,4-diyl)bis(5-carboxylato-3,1-phenylene)bis(hydroperoxymethanide)), with a rare chiral O -type cage, and dual functionalities of open metal sites and Lewis basic sites, based on a designed U-shaped ligand, was synthesized by hydrothermal methods. It exhibits high CO , C , and C hydrocarbon storage capacity under atmospheric pressure, as well as high H (1.96 wt.%) adsorption capacity at 77 K. Methane purification capacity was tested and verified step by step. Isosteric heats (Q ) studies reveal that CH has the weakest van der Waals host-guest interactions among the seven gases at 298 K. Ideal adsorbed solution theory (IAST) calculation reveals that compound 1 is more selective toward CO , C H , and C H over CH in further calculating its separation capacity, as exemplified for CO /CH (50:50, 5:95), C H /CH (50:50, 5:95), or C H /CH (50:50, 5:95) binary gas mixtures. Breakthrough experiments show that 1 has a significantly higher adsorption capacity for CO , C H , and C H than CH . The selective adsorption properties of 1 make it a promising candidate for methane purification.

Molecular dynamics (MD) simulations are a widely used technique in modeling complex nanoscale interactions of atoms and molecules. These simulations can provide detailed insight into how molecules behave under certain environmental conditions. This work explores a machine learning (ML) solution to predicting long-term properties of SARS-CoV-2 spike glycoproteins (S-protein) through the analysis of its nanosecond backbone RMSD (root-mean-square deviation) MD simulation data at varying temperatures. The simulation data were denoised with fast Fourier transforms. The performance of the models was measured by evaluating their mean squared error (MSE) accuracy scores in recurrent forecasts for long-term predictions. The models evaluated include k-nearest neighbors (kNN) regression models, as well as GRU (gated recurrent unit) neural networks and LSTM (long short-term memory) autoencoder models. Results demonstrated that the kNN model achieved the greatest accuracy in forecasts with MSE scores over around 0.01 nm less than those of the GRU model and the LSTM autoencoder. Furthermore, it demonstrated that the kNN model accuracy increases with data size but can still forecast relatively well when trained on small amounts of data, having achieved MSE scores of around 0.02 nm when trained on 10,000 ns of simulation data. This study provides valuable information on the feasibility of accelerating the MD simulation process through training and predicting supervised ML models, which is particularly applicable in time-sensitive studies. Graphic abstract SARS-CoV-2 spike glycoprotein molecular dynamics simulation. Extraction and denoising of backbone RMSD data. Evaluation of k-nearest neighbors regression, GRU neural network, and LSTM autoencoder models in recurrent forecasting for long-term property predictions.

The spike glycoprotein (S protein) of the SARS-CoV-2 that has be studied extensively in vitro is modeled by all-atom molecular dynamics for its conformational states at six pH values ranging from 2 to 11.5. The MD simulations up to 3.7 T demonstrate interesting discoveries while confirming known facts. (1). At pH2, the protein’s time averaged RMSD is 62.5% higher than that of pH7, as the control group, and the receptor binding domain (RBD) deviates from that of pH7 by 200%. (2). For pH4 through pH10.5, the S protein remains relatively stable evident by the invariance of the side chain H bond counts and RMSD from pH7, suggesting high tolerance of the S protein to a wide range of pH values other than the extreme acidic and basic conditions. (3). For pH2 to pH4, the structure of the S protein alters significantly, suggesting the existence of a critical pH value at which the S protein responds to acid sharply. (4). In the residue-based relative entropy analysis, we identify several RBM and RBD residue clusters with maximum deviations that cause the overall protein structure changes.

We calculate the thermal and conformational states of the spike glycoprotein (S-protein) of SARS-CoV-2 at seven temperatures ranging from 3°C to 95°C by all-atom molecular dynamics (MD) µs-scale simulations with the objectives to understand the structural variations on the temperatures and to determine the potential phase transition while trying to correlate such findings of the S-protein with the observed properties of the SARS-CoV2. Our simulations revealed the following thermal properties of the S-protein: 1) It is structurally stable at 3°C, agreeing with observations that the virus stays active for more than two weeks in the cold supply chain; 2) Its structure varies more significantly at temperature values of 60°C–80°C; 3) The sharpest structural variations occur near 60°C, signaling a plausible critical temperature nearby; 4) The maximum deviation of the receptor-binding domain at 37°C, corroborating the anecdotal observations that the virus is most infective at 37°C; 5) The in silico data agree with reported experiments of the SARS-CoV-2 survival times from weeks to seconds by our clustering approach analysis. Our MD simulations at µs scales demonstrated the S-protein’s thermodynamics of the critical states at around 60°C, and the stable and denatured states for temperatures below and above this value, respectively.