Variations in Proteins Dielectric Constants

Using a new semi-empirical method for calculating molecular polarizabilities and the Clausius−Mossotti relation, we calculated the static dielectric constants of dry proteins for all structures in the protein data bank (PDB). The mean dielectric constant of more than 150,000 proteins is (Formula presented.) with a standard deviation of 0.04, which agrees well with previous measurement for dry proteins. The small standard deviation results from the strong correlation between the molecular polarizability and the volume of the proteins. We note that non-amino acid cofactors such as Chlorophyll may alter the dielectric environment significantly. Furthermore, our model shows anisotropies of the dielectric constant within the same molecule according to the constituents amino acids and cofactors. Finally, by changing the amino acid protonation states, we show that a change of pH does not have a significant effect on the dielectric constants of proteins

Predicting the Oxidation States of Mn ions in the Oxygen Evolving Complex of Photosystem II Using Supervised and Unsupervised Machine Learning

Serial Femtosecond Crystallography at the X-ray Free Electron Laser (XFEL) sources enabled the imaging of the catalytic intermediates of the oxygen evolution reaction (OEC) of Photosystem II. However, due to the incoherent transition of the S-states, the resolved structures are a convolution from different catalytic states. Here, we train Decision Tree Classifier and K-mean clustering models on Mn compounds obtained from the Cambridge Crystallographic Database to predict the S-state of the X-ray, XFEL, and CryoEm structures by predicting the Mn's oxidation states in the OEC. The model agrees mostly with the XFEL structures in the dark S1 state. However, significant discrepancies are observed for the excited XFEL states (S2, S3, and S0) and the dark states of the X-ray and CryoEm structures. Furthermore, there is a mismatch between the predicted S-states within the two monomers of the same dimer, mainly in the excited states. The model suggests that improving the resolution is crucial to precisely resolve the geometry of the illuminated S-states to overcome the noncoherent S-state transition. In addition, significant radiation damage is observed in X-ray and CryoEM structures, particularly at the dangler Mn center (Mn4). Our model represents a valuable tool for investigating the electronic structure of the catalytic metal cluster of PSII to understand the water splitting mechanism

The new SARS-CoV-2 strain shows a stronger binding affinity to ACE2due to N501Y mutant

SARS-CoV-2 is a global challenge due to its ability to spread much faster than the SARS-CoV, which was attributed to the mutations in the receptor binding domain (RBD). These mutations enhanced the electrostatic interactions. Recently, a new strain is reported in the UK that includes a mutation (N501Y) in the RBD, that is possibly increasing the infection rate. Here, using Molecular Dynamics simulations (MD) and Monte Carlo (MC) sampling, we show that the N501 mutation enhanced the electrostatic interactions due to the formation of a strong hydrogen bond between SARS-CoV-2-T500 and ACE2-D355 near the mutation site. In addition, we observed that the electrostatic interactions between the SARS-CoV-2 and ACE2 in the wild type and the mutant are dominated by salt-bridges formed between SARS-CoV-2-K417 and ACE2-D30, SARS-CoV-2-K458, ACE2-E23, and SARS-CoV-2-R403 and ACE2-E37. These interactions contributed more than 40% of the total binding energies

CMInject:Python framework for the numerical simulation of nanoparticle injection pipelines

CMInject simulates nanoparticle injection experiments of particles with diameters in the micrometer to nanometer-regime, e.g., for single-particle-imaging experiments. Particle-particle interactions and particle-induced changes in the surrounding fields are disregarded, due to low nanoparticle concentration in these experiments. CMInject's focus lies on the correct modeling of different forces on such particles, such as fluid-dynamics or light-induced interactions, to allow for simulations that further the scientific development of nanoparticle injection pipelines. To provide a usable basis for this framework and allow for a variety of experiments to be simulated, we implemented first specific force models: fluid drag forces, Brownian motion, and photophoretic forces. For verification, we benchmarked a drag-force-based simulation against a nanoparticle focusing experiment. We envision its use and further development by experimentalists, theorists, and software developers. Program summary: Program Title: CMInject CPC Library link to program files: https://doi.org/10.17632/rbpgn4fk3z.1 Developer's repository link: https://github.com/cfel-cmi/cminject Code Ocean capsule: https://codeocean.com/capsule/5146104 Licensing provisions: GPLv3 Programming language: Python 3 Supplementary material: Code to reproduce and analyze simulation results, example input and output data, video files of trajectory movies Nature of problem: Well-defined, reproducible, and interchangeable simulation setups of experimental injection pipelines for biological and artificial nanoparticles, in particular such pipelines that aim to advance the field of single-particle imaging. Solution method: The definition and implementation of an extensible Python 3 framework to model and execute such simulation setups based on object-oriented software design, making use of parallelization facilities and modern numerical integration routines. Additional comments including restrictions and unusual features: Supplementary executable scripts for quantitative and visual analyses of result data are also part of the framework

The New SARS-CoV-2 Strain Shows a Stronger Binding Affinity to ACE2 Due to N501Y Mutation

SARS-CoV-2 is a global challenge due to its ability to spread much faster than SARS-CoV, which was attributed to the mutations in the receptor binding domain (RBD). These mutations enhanced the electrostatic interactions. Recently, a new strain was reported in the UK that includes a mutation (N501Y) in the RBD, that possibly increases the infection rate. Using Molecular Dynamics simulations (MD) and Monte Carlo (MC) sampling, we showed that the N501 mutation enhances the electrostatic interactions due to the formation of a strong hydrogen bond between SARS-CoV-2-T500 and ACE2-D355 near the mutation site. In addition, we observed that the electrostatic interactions between the SARS-CoV-2 and ACE2 in the wild type and the mutant are dominated by salt-bridges formed between SARS-CoV-2-K417 and ACE2-D30, SARS-CoV-2-K458, ACE2-E23, and SARS-CoV-2-R403 and ACE2-E37. These interactions contributed more than 40 % of the total binding energies