Abstract

Accuracy on Minnesota Solvation Database (MNSOL) There are classical and quantum-mechanical simulation studies that use the MNSOL as the reference database. We choose solvation model based on density(SMD) for comparison with our CIGIN Model. We also compared CIGIN Model with semi-empirical methods, pure COSMO, and COSMO-RS.

Abstract

Energetically unfavorable Watson–Crick (WC)-like tautomeric forms of nucleobases are known to introduce spontaneous mutations, and contribute to replication, transcription, and translation errors. Recent NMR relaxation dispersion techniques were able to show that wobble (w) G•U mispair exists in equilibrium with the short-lived, low-population WC-like enolic tautomers. Presently, we have investigated the wG•U → WC-like enolic reaction pathway using various theoretical methods: quantum mechanics (QM), molecular dynamics (MD), and combined quantum mechanics/molecular mechanics (QM/MM). The previous studies on QM gas phase calculations were inconsistent with experimental data. We have also explored the environmental effects on the reaction energies by adding explicit water. While the QM-profile clearly becomes endoergic in the presence of water, the QM/MM-profile remains consistently endoergic in the presence and absence of water. Hence, by including microsolvation and QM/MM calculations, the experimental data can be explained. For the G•Uenol → Genol•U pathway, the latter appears to be energetically more favorable throughout all computational models. This study can be considered as a benchmark of various computational models of wG•U to WC-like tautomerization pathways with and without the environmental effects, and may contribute on further studies of other mispairs as well.

Abstract

The coronavirus disease 2019 (COVID-19), caused by the virus SARS-CoV-2, is an acute respiratory disease that has been classified as a pandemic by the World Health Organization (WHO). The sudden spike in the number of infections and high mortality rates have put immense pressure on the public healthcare systems. Hence, it is crucial to identify the key factors for mortality prediction to optimize patient treatment strategy. Different routine blood test results are widely available compared to other forms of data like X-rays, CT-scans, and ultrasounds for mortality prediction. This study proposes machine learning (ML) methods based on blood tests data to predict COVID-19 mortality risk. A powerful combination of five features: neutrophils, lymphocytes, lactate dehydrogenase (LDH), high-sensitivity C-reactive protein (hs-CRP), and age helps to predict mortality with 96% accuracy. Various ML models (neural networks, logistic regression, XGBoost, random forests, SVM, and decision trees) have been trained and performance compared to determine the model that achieves consistently high accuracy across the days that span the disease. The best performing method using XGBoost feature importance and neural network classification, predicts with an accuracy of 90% as early as 16 days before the outcome. Robust testing with three cases based on days to outcome confirms the strong predictive performance and practicality of the proposed model. A detailed analysis and identification of trends was performed using these key biomarkers to provide useful insights for intuitive application. This study provide solutions that would help accelerate the decision-making process in healthcare systems for focused medical treatments in an accurate, early, and reliable manner

Abstract

Aqueous urea stabilizes the unfolded states of protein due to their ability to solvate both hydrophilic and hydrophobic residues favorably. The nature of interactions that stabilize different types of amino acid side chains in their solvent exposed state is still not understood. To gain insights into the molecular level details of urea interactions with proteins in their unfolded states, we have performed atomistic molecular dynamics simulations and free energy calculations using the thermodynamic integration method on model systems representing side chains of all amino acids in different solvent environments (water and varying concentrations of aqueous urea). A systematic analysis of structural, energetic and dynamic parameters has been done to understand the detailed atomistic mechanism. The main aim of the current study is to unravel the nature of urea-amino acid interactions by emphasizing on the chemical nature …

Abstract

The transport of water through aquaporins is a dynamic process that involves rapid movement of a chain of water molecules through the pore of the aquaporin. Structures of aquaporins solved using X-ray crystallography have provided some insights into how water is transported through these channels, and how certain structural features of the pore might help exclude other solutes from passing through the pore. However, such techniques provide only a static picture, and a dynamic picture of the transport and selectivity mechanism at work in aquaporins is possible with molecular dynamics (MD) simulations. In MD simulations, the forces between the different atoms in a system are computed, and the atoms are then allowed to move under the influence of these forces. This allows the sampling of different conformations of the molecule being studied, including conformations that are crucial in driving biological phenomena like water transport. Simulation studies have provided insights into a number of aspects of aquaporins, including the role of the asparagine-proline-alanine (NPA) motif and the aromatic/arginine (ar/R) constriction, water transport mechanism, mechanisms defining the selectivity of the channel, interaction with lipids, response to external electric field, and binding of putative drug molecules. This chapter provides a brief review of the current status of computational modeling of aquaporins using MD simulations. Initially, a brief account of force fields and MD simulations is presented followed by an account of how MD simulations have contributed to further our understanding of different aspects of aquaporins.

Abstract

The optical activity of a metal nanocluster (NC) is induced either by an asymmetric arrangement of constituents or by a dissymmetric field of a chiral ligand layer. Herein, we unveil the origin of chirality in Ag29 NCs, which is attributed to the intrinsically chiral atomic arrangement. The X-ray crystal structure of a Ag29(BDT)12(TPP)4 NC (BDT: 1,3-benzenedithiol; TPP: triphenylphosphine) manifested the presence of intrinsic chirality in the outer shell capping the icosahedral achiral Ag13 core. The enantiomers of the Ag29(BDT)12(TPP)4 NC are separated by high-performance liquid chromatography (HPLC) using a chiral column for the first time, showing mirror-image circular dichroism (CD) spectra. The CD spectra are reproduced by time-dependent density functional theory (TDDFT) calculations based on enantiomeric Ag29 models with achiral 1,3-propanedithiolate ligands. The mechanism of chiral induction in the synthesis of Ag29(DHLA)12 (DHLA: a-dihydrolipoic acid) NCs with a chiral ligand system is further discussed with the aid of DFT calculations. The use of the enantiomeric DHLA ligand preferentially leads to a one-handed atomic arrangement which is more stable than the opposite one, inducing the enantiomeric excess in the population of intrinsically chiral Ag29 NCs with CD activity

Abstract

The fifty–year proposal of nondissociative racemization reaction of a tetracoordinated tetrahedral center from one enantiomer to another via a planar transition state by Hoffmann and coworkers has been explored by many research groups during the past five decades. A number of stable molecules with planar tetracoordinate and higher-coordinate centers have been designed and experimentally realized; however, there has not been a single example of molecular system that can possibly undergo such racemization. Here we show examples of molecular species that undergo inversion of stereochemistry around tetrahedral centers (Si, Al− and P+) either via a planar transition state or an intermediate state using quantum mechanical, ab initio quasi-classical dynamics calculations, and Born-Oppenheimer molecular dynamics (BOMD) simulations. This work is expected to provide potential leads for future studies on this fundamental phenomenon in chemistry.

Abstract

Noncovalent interactions are key determinants in both chemical and biological processes. Among such processes, the hydrophobic interactions play an eminent role in folding of proteins, nucleic acids, formation of membranes, protein-ligand recognition, etc.. Though this interaction is mediated through the aqueous solvent, the stability of the above biomolecules can be highly sensitive to any small external perturbations, such as temperature, pressure, pH, or even cosolvent additives, like, urea-a highly soluble small organic molecule utilized by various living organisms to regulate osmotic pressure. A plethora of detailed studies exist covering both experimental and theoretical regimes, to understand how urea modulates the stability of biological macromolecules. While experimentalists have been primarily focusing on the thermodynamic and kinetic aspects, theoretical modeling predominantly involves mechanistic information at the molecular level, calculating atomistic details applying the force field approach to the high level electronic details using the quantum mechanical methods. The review focuses mainly on examples with biological relevance, such as (1) urea-assisted protein unfolding, (2) urea-assisted RNA unfolding, (3) urea lesion interaction within damaged DNA, (4) urea conduction through membrane proteins, and (5) protein-ligand interactions those explicitly address the vitality of hydrophobic interactions involving exclusively the urea-aromatic moiety.

Abstract

Recent advances in artificial intelligence along with the development of large data sets of energies calculated using quantum mechanical (QM)/density functional theory (DFT) methods have enabled prediction of accurate molecular energies at reasonably low computational cost. However, machine learning models that have been reported so far require the atomic positions obtained from geometry optimizations using high‐level QM/DFT methods as input in order to predict the energies and do not allow for geometry optimization. In this study, a transferable and molecule size‐independent machine learning model bonds (B), angles (A), nonbonded (N) interactions, and dihedrals (D) neural network (BAND NN) based on a chemically intuitive representation inspired by molecular mechanics force fields is presented. The model predicts the atomization energies of equilibrium and nonequilibrium structures as sum of energy contributions from bonds (B), angles (A), nonbonds (N), and dihedrals (D) at remarkable accuracy. The robustness of the proposed model is further validated by calculations that span over the conformational, configurational, and reaction space. The transferability of this model on systems larger than the ones in the data set is demonstrated by performing calculations on selected large molecules. Importantly, employing the BAND NN model, it is possible to perform geometry optimizations starting from nonequilibrium structures along with predicting their energies. © 2019 Wiley Periodicals, Inc.

Abstract

Solubility of drug molecules is related to pharmacokinetic properties such as absorption and distribution, which affects the amount of drug that is available in the body for its action. Computational or experimental evaluation of solvation free energies of drug-like molecules/solute that quantify solubilities is an arduous task and hence development of reliable computationally tractable models is sought after in drug discovery tasks in pharmaceutical industry. Here, we report a novel method based on graph neural network to predict solvation free energies. Previous studies considered only the solute for solvation free energy prediction and ignored the nature of the solvent, limiting their practical applicability. The proposed model is an end-to-end framework comprising three phases namely, message passing, interaction and prediction phases. In the first phase, message passing neural network was used to compute inter-atomic interaction within both solute and solvent molecules represented as molecular graphs. In the interaction phase, features from the preceding step is used to calculate a solute-solvent interaction map, since the solvation free energy depends on how (un)favorable the solute and solvent molecules interact with each other. The calculated interaction map that captures the solute-solvent interactions along with the features from the message passing phase is used to predict the solvation free energies in the final phase. The model predicts solvation free energies involving a large number of solvents with high accuracy. We also show that the interaction map captures the electronic and steric factors that govern the solubility of drug-like molecules and hence is chemically interpretable.