Computational Insights into Protein Aging: Spontaneous Deamidation of Glutamine.

Spontaneous deamidation of amino acids is a physiologically important process, particularly for protein aging and diseases. Despite its widespread occurrence, the mechanism of glutamine deamidation particularly within proteins remains poorly understood. We have used a multiscale computational approach to investigate glutamine deamidation in the tripeptide Glycine-Glutamine-Glycine (Gly-Gln-Gly) and γS-Crystallin protein. Specifically, both the 5- and 6-membered water-assisted deamidation pathways in the tripeptide have been elucidated and compared. Both are found to occur in three stages: iminol formation, cyclization, and deamination. The rate-limiting step in each mechanism is nucleophilic attack of the backbone iminol nitrogen, formed in the first stage, at the glutamine's side-chain carbonyl carbon. For the 6- and 5-membered mechanisms, this occurs with a free energy cost of 136.4 and 179.5 kJ mol-1, respectively. Thus, overall, in the Gly-Gln-Gly tripeptide, the 6-membered pathway is preferred. Furthermore, the free energies for forming cyclic intermediates and products at selected Gln residues (based on experimentally reported % deamidation) in γS-Crystallin have been obtained. It is found that the 5-membered product complex is exergonic at -25.3 kJ mol-1, while the 6-membered product complex is calculated to be endergonic at 90.7 kJ mol-1. Thus, the deamidation pathway in folded and constrained proteins may not exclusively follow the 6-membered route. Molecular dynamics (MD) simulations of γS-Crystallin indicate that deamidation is more likely to occur when two or more water molecules are in the proximity of the glutamine residue. Consequently, significant conformational changes are found to accompany Gln120 deamidation in γS-Crystallin. This in turn can influence water availability at the other Gln residues considered and hence potentially their deamidation. Collectively, these results provide comprehensive insights into spontaneous water-assisted deamidation of glutamine residues in peptides and into the role and impact of Gln deamidation in proteins.

Uncovering Structure-Activity Relationships of Phenethylamines: Paving the Way for Innovative Mental Health Treatments.

The rapidly evolving psychedelic industry has garnered considerable attention due to 3,4-methylenedioxymethamphetamine-assisted psychotherapy's ground-breaking success in treating moderate-to-severe Post-traumatic Stress Disorder in two Phase 3 clinical trials. This has opened Pandora's box for the development of innovative therapeutic modalities. Of particular interest are the phenethylamines and their ability to inhibit monoamine transporters. In this study, we employed the quantitative structure-activity relationship methodology to develop three vigorous models for the reuptake of serotonin, dopamine, and norepinephrine through monoamine transporters. These models were thoroughly validated using various criteria, including fitting (R2DAT = 0.869, R2SERT = 0.828, and R2NET = 0.887), internal (Q2looDAT = 0.795, Q2looSERT = 0.784, and Q2looNET = 0.820), and external (RMSEextDAT = 0.373, R2extDAT = 0.831, RMSEextSERT = 0.200, R2extSERT = 0.955, RMSEextNET = 0.318, and R2extNET = 0.711) criteria.

Applications and Potential of In Silico Approaches for Psychedelic Chemistry.

Molecular-level investigations of the Central Nervous System have been revolutionized by the development of computational methods, computing power, and capacity advances. These techniques have enabled researchers to analyze large amounts of data from various sources, including genomics, in vivo, and in vitro drug tests. In this review, we explore how computational methods and informatics have contributed to our understanding of mental health disorders and the development of novel drugs for neurological diseases, with a special focus on the emerging field of psychedelics. In addition, the use of state-of-the-art computational methods to predict the potential of drug compounds and bioinformatic tools to integrate disparate data sources to create predictive models is also discussed. Furthermore, the challenges associated with these methods, such as the need for large datasets and the diversity of in vitro data, are explored. Overall, this review highlights the immense potential of computational methods and informatics in Central Nervous System research and underscores the need for continued development and refinement of these techniques and more inclusion of Quantitative Structure-Activity Relationships (QSARs).

Computational Insights into the Formation and Structure of S-N Containing Cyclic Peptides.

Cyclic peptides are known to have biologically important roles and may also be applicable to the pharmaceutical and other industries. Furthermore, thiols and amines, which are found throughout biological systems, can react to form S-N bonds and to date, ∼100 biomolecules containing such a bond have been identified. However, while there are in principle numerous S-N containing peptide-derived rings possible, only a few are presently known to occur in biochemical systems. Density functional theory-based calculations have been used to consider the formation and structure of S-N containing cyclic peptides from systematic series of linear peptides in which a cysteinyl has first been oxidized to a sulfenic or sulfonic acid. In addition, the possible effect of the cysteine's vicinal residue on the free energy of formation has also been considered. In general, when the cysteine is first oxidized to a sulfenic acid, only the formation of smaller S-N containing rings is calculated to be exergonic in aqueous solution. In contrast, when the cysteine is first oxidized to a sulfonic acid, the formation of all rings considered (with one exception) is calculated to be endergonic in aqueous solution. The nature of vicinal residue can influence ring formation through stabilizing or destabilizing intramolecular interactions.

The Hydrolysis Rate of Paraoxonase-1 Q and R Isoenzymes: An In Silico Study Based on In Vitro Data.

Human serum paraoxonase-1 (PON1) is an important hydrolase-type enzyme found in numerous tissues. Notably, it can exist in two isozyme-forms, Q and R, that exhibit different activities. This study presents an in silico (QSAR, Docking, MD and QM/MM) study of a set of compounds on the activity towards the PON1 isoenzymes (QPON1 and RPON1). Different rates of reaction for the Q and R isoenzymes were analyzed by modelling the effect of Q192R mutation on active sites. It was concluded that the Q192R mutation is not even close to the active site, while it is still changing the geometry of it. Using the combined genetic algorithm with multiple linear regression (GA-MLR) technique, several QSAR models were developed and relative activity rates of the isozymes of PON1 explained. From these, two QSAR models were selected, one each for the QPON1 and RPON1. Best selected models are four-variable MLR models for both Q and R isozymes with squared correlation coefficient R2 values of 0.87 and 0.83, respectively. In addition, the applicability domain of the models was analyzed based on the Williams plot. The results were discussed in the light of the main factors that influence the hydrolysis activity of the PON1 isozymes.

QSPR modeling of optical rotation of amino acids using specific quantum chemical descriptors.

Many chemical phenomena occur in solution. Different solvents can change the optical activity of chiral molecules. The optical rotation angles of solutes of 75 amino acids in dimethylformamide, water and methanol were analyzed using the quantitative structure-activity relationships approach. For an accurate description of chirality, we used specific quantum chemical descriptors, which reflect the properties of a chiral center, and continuous symmetry measures. The set of specific quantum chemical descriptors for atoms located near the chiral center, such as Mulliken charges, the sum of Mulliken charges on an atom (with the hydrogen charges summed up with the adjacent non-hydrogen atoms), and nuclear magnetic resoncance tensors was applied. To represent solvent effects, we used mixture-like structural simplex descriptors and quantum chemical descriptors obtained for structures optimized for specified solvent using PBE1PBE/6-31G** level of theory with the polarizable continuum model. Multiple linear regression, M5P, and locally weighted learning techniques were used to achieve accurate predictions. The specific quantum chemical descriptors proposed here demonstrated high specificity in the majority of the developed models and established direct quantitative structure-property relationships.

A DFT-based QSAR study on inhibition of human dihydrofolate reductase.

Diaminopyrimidine derivatives are frequently used as inhibitors of human dihydrofolate reductase, for example in treatment of patients whose immune system are affected by human immunodeficiency virus. Forty-seven dicyclic and tricyclic potential inhibitors of human dihydrofolate reductase were analyzed using the quantitative structure-activity analysis supported by DFT-based and DRAGON-based descriptors. The developed model yielded an RMSE deviation of 1.1 a correlation coefficient of 0.81. The prediction set was characterized by R2=0.60 and RMSE=3.59. Factors responsible for inhibition process were identified and discussed. The resulting model was validated via cross validation and Y-scrambling procedure. From the best model, we found several mass-related descriptors and Sanderson electronegativity-related descriptors that have the best correlations with the investigated inhibitory concentration. These descriptors reflect results from QSAR studies based on characteristics of human dihydrofolate reductase inhibitors.

Reduction of aflatoxin B1 to aflatoxicol: a comprehensive DFT study provides clues to its toxicity.

BACKGROUND: Aflatoxicol (AFL) is one of most the important metabolites of aflatoxin B1 (AFB1). AFL can be formed through enzymatic or synthetic reduction of AFB1. Various experimental and theoretical studies have been focused on the AFB1 due to its high toxicity and carcinogenicity. RESULTS: The selective reduction of AFB1 carbonyls, molecular structure of AFL and its effect on toxicity has been studied here by the density functional theory (DFT) method. Although the toxicity of AFL is 18 times lower than that of AFB1, it has been concluded that both molecular structures have similar potency to form an exo-epoxide (AFEP) analogue which can bind to DNA. CONCLUSION: Calculations revealed that only one of the three possible tautomers of AFL is stable, both in the gas phase and water. The electronic properties of aflatoxicol are calculated as similar to aflatoxin B1 and this may be an explanation of similar carcinogenicity and toxicity of these compounds, which has been proved by experimental results.

Modeling the intermolecular interactions: molecular structure of N-3-hydroxyphenyl-4-methoxybenzamide.

The title compound, N-3-hydroxyphenyl-4-methoxybenzamide (3) was prepared by the acylation reaction of 3-aminophenol (1) and 4-metoxybenzoylchloride (2) in THF and characterized by ¹H NMR, ¹³C NMR and elemental analysis. Molecular structure of the crystal was determined by single crystal X-ray diffraction and DFT calculations. 3 crystallizes in monoclinic P21/c space group. The influence of intermolecular interactions (dimerization and crystal packing) on molecular geometry has been evaluated by calculations performed for three different models; monomer (3), dimer (4) and dimer with added unit cell contacts (5). Molecular structure of 3, 4 and 5 was optimized by applying B3LYP method with 6-31G+(d,p) basis set in gas phase and compared with X-ray crystallographic data including bond lengths, bond angles and selected dihedral angles. It has been concluded that although the crystal packing and dimerization have a minor effect on bond lengths and angles, however, these interactions are important for the dihedral angles and the rotational conformation of aromatic rings.

Detection of tautomer proportions of dimedone in solution: a new approach based on theoretical and FT-IR viewpoint.

Molecular structures of stable tautomers of dimedone [5,5-dimethyl-cyclohexane-1,3-dione and 3-hydroxy-5,5-dimethylcyclohex-2-enone] were optimized and vibrational frequencies were calculated in five different organic solvents (dimethylsulfoxide, methanol, acetonitrile, dichloromethane and chloroform). Geometry optimizations and harmonic vibrational frequency calculations were performed at DFT 6-31+G(d,p), DFT 6-311++G(2d,2p), MP2 6-311++G (2d,2p) and MP2 aug-cc-pVDZ levels for both stable forms of dimedone. Experimental FT-IR spectra of dimedone have also been recorded in the same solvents. A new approach was developed in order to determine tautomers' ratio using both experimental and theoretical data in Lambert-Beer equation. Obtained results were compared with experimental results published in literature. It has been concluded that while DFT 6-31+G(d,p) method provides accurate enol ratio in DMSO, MeOH, and DCM, in order to obtain accurate results for the other solvents the MP2 aug-cc-pVDZ level calculations should be used for CH3CN and CHCl3 solutions.

Anomeric and rotameric preferences of glucopyranose in vacuo, water and organic solvents.

Glucopyranose is the most stable form of glucose in solution. Identification of molecular structure of glucopyranose is very important because of its biological and synthetic significance; it is not an easy task because of the large number of possible configurations. Relative energies of exocyclic hydroxymethyl rotamers and α-ß anomers of D-glucopyranose have been determined at the reference MP2/6-31G(d,p) level geometry by ab initio calculations at the infinite basis set limit of MP2 approach and with inclusion of CCSD(T) correction term evaluated with the aug-cc-pVDZ basis set in vacuum, water, dimethylsulfoxide, tetrahydrofurane and ethanol. The infinite basis set limit of MP2 level was determined by two point extrapolation using aug-cc-pVTZ and aug-cc-pVQZ basis sets. Solvent effects, relative energies and binding energies have been considered applying explicit calculations and implicit solvent models.