Application of the PM6 method to modeling proteins

Click Chemistry Derived Hexa-ferrocenylated 1,3,5-Triphenylbenzene for the Detection of Divalent Transition Metal Cations

The 1,3-dipolar cycloaddition reaction (click chemistry approach) was employed to create a hexa-ferrocenylated 1,3,5-triphenylbenzene derivative. Leveraging the presence of metal-chelating sites associated with 1,2,3-triazole moieties and 1,4-dinitrogen systems (ethylenediamine-like), as well as tridentate chelating sites (1,4,7-trinitrogen, diethylene triamine-like) systems, the application of this molecule as a chemosensor for divalent transition metal cations was investigated. The interactions were probed voltammetrically and spectrofluorimetrically against seven selected cations: iron(II) (Fe2+), cobalt(II) (Co2+), nickel(II) (Ni2+), copper(II) (Cu2+), zinc(II) (Zn2+), cadmium(II) (Cd2+), and manganese(II) (Mn2+). Electrochemical assays revealed good detection properties, with very low limits of detection (LOD), for Co2+, Cu2+, and Cd2+ in aqueous solution (0.03-0.09 μM). Emission spectroscopy experiments demonstrated that the title compound exhibited versatile detection properties in solution, specifically turn-off fluorescence behavior upon the addition of each tested transition metal cation. The systems were characterized by satisfactory Stern-Volmer constant values (105-106 M-1) and low LOD, especially for Zn2+ and Co2+ (at the nanomolar concentration level).

Influence of Solvent Polarity on the Conformer Ratio of Bicalutamide in Saturated Solutions: Insights from NOESY NMR Analysis and Quantum-Chemical Calculations

The study presents a thorough and detailed analysis of bicalutamide's structural and conformational properties. Quantum chemical calculations were employed to explore the conformational properties of the molecule, identifying significant energy differences between conformers. Analysis revealed that hydrogen bonds stabilise the conformers, with notable variations in torsion angles. Conformers were classified into 'closed' and 'open' types based on the relative orientation of the cyclic fragments. NOE spectroscopy in different solvents (CDCl3 and DMSO-d6) was used to study the conformational preferences of the molecule. NOESY experiments provided the predominance of 'closed' conformers in non-polar solvents and a significant presence of 'open' conformers in polar solvents. The proportions of open conformers were 22.7 ± 3.7% in CDCl3 and 59.8 ± 6.2% in DMSO-d6, while closed conformers accounted for 77.3 ± 3.7% and 40.2 ± 6.2%, respectively. This comprehensive study underscores the solvent environment's impact on its structural behaviour. The findings significantly contribute to a deeper understanding of conformational dynamics, stimulating further exploration in drug development.

In Silico Comparative Analysis of Ivermectin and Nirmatrelvir Inhibitors Interacting with the SARS-CoV-2 Main Protease

Exploring therapeutic options is crucial in the ongoing COVID-19 pandemic caused by SARS-CoV-2. Nirmatrelvir, which is a potent inhibitor that targets the SARS-CoV-2 Mpro, shows promise as an antiviral treatment. Additionally, Ivermectin, which is a broad-spectrum antiparasitic drug, has demonstrated effectiveness against the virus in laboratory settings. However, its clinical implications are still debated. Using computational methods, such as molecular docking and 100 ns molecular dynamics simulations, we investigated how Nirmatrelvir and Ivermectin interacted with SARS-CoV-2 Mpro(A). Calculations using density functional theory were instrumental in elucidating the behavior of isolated molecules, primarily by analyzing the frontier molecular orbitals. Our analysis revealed distinct binding patterns: Nirmatrelvir formed strong interactions with amino acids, like MET49, MET165, HIS41, HIS163, HIS164, PHE140, CYS145, GLU166, and ASN142, showing stable binding, with a root-mean-square deviation (RMSD) of around 2.0 Å. On the other hand, Ivermectin interacted with THR237, THR239, LEU271, LEU272, and LEU287, displaying an RMSD of 1.87 Å, indicating enduring interactions. Both ligands stabilized Mpro(A), with Ivermectin showing stability and persistent interactions despite forming fewer hydrogen bonds. These findings offer detailed insights into how Nirmatrelvir and Ivermectin bind to the SARS-CoV-2 main protease, providing valuable information for potential therapeutic strategies against COVID-19.

Structural Evaluation of a Nitroreductase Engineered for Improved Activation of the 5-Nitroimidazole PET Probe SN33623

Bacterial nitroreductase enzymes capable of activating imaging probes and prodrugs are valuable tools for gene-directed enzyme prodrug therapies and targeted cell ablation models. We recently engineered a nitroreductase (E. coli NfsB F70A/F108Y) for the substantially enhanced reduction of the 5-nitroimidazole PET-capable probe, SN33623, which permits the theranostic imaging of vectors labeled with oxygen-insensitive bacterial nitroreductases. This mutant enzyme also shows improved activation of the DNA-alkylation prodrugs CB1954 and metronidazole. To elucidate the mechanism behind these enhancements, we resolved the crystal structure of the mutant enzyme to 1.98 Å and compared it to the wild-type enzyme. Structural analysis revealed an expanded substrate access channel and new hydrogen bonding interactions. Additionally, computational modeling of SN33623, CB1954, and metronidazole binding in the active sites of both the mutant and wild-type enzymes revealed key differences in substrate orientations and interactions, with improvements in activity being mirrored by reduced distances between the N5-H of isoalloxazine and the substrate nitro group oxygen in the mutant models. These findings deepen our understanding of nitroreductase substrate specificity and catalytic mechanisms and have potential implications for developing more effective theranostic imaging strategies in cancer treatment.

Generating High-Precision Force Fields for Molecular Dynamics Simulations to Study Chemical Reaction Mechanisms Using Molecular Configuration Transformer

Theoretical studies on chemical reaction mechanisms have been crucial in organic chemistry. Traditionally, calculating the manually constructed molecular conformations of transition states for chemical reactions using quantum chemical calculations is the most commonly used method. However, this way is heavily dependent on individual experience and chemical intuition. In our previous study, we proposed a research paradigm that used enhanced sampling in molecular dynamics simulations to study chemical reactions. This approach can directly simulate the entire process of a chemical reaction. However, the computational speed limited the use of high-precision potential energy functions for simulations. To address this issue, we presented a scheme for training high-precision force fields for molecular modeling using a previously developed graph-neural-network-based molecular model, molecular configuration transformer. This potential energy function allowed for highly accurate simulations at a low computational cost, leading to more precise calculations of the mechanism of chemical reactions. We applied this approach to study a Claisen rearrangement reaction and a carbonyl insertion reaction catalyzed by manganese.

MISATO: machine learning dataset of protein-ligand complexes for structure-based drug discovery

Large language models have greatly enhanced our ability to understand biology and chemistry, yet robust methods for structure-based drug discovery, quantum chemistry and structural biology are still sparse. Precise biomolecule-ligand interaction datasets are urgently needed for large language models. To address this, we present MISATO, a dataset that combines quantum mechanical properties of small molecules and associated molecular dynamics simulations of ~20,000 experimental protein-ligand complexes with extensive validation of experimental data. Starting from the existing experimental structures, semi-empirical quantum mechanics was used to systematically refine these structures. A large collection of molecular dynamics traces of protein-ligand complexes in explicit water is included, accumulating over 170 μs. We give examples of machine learning (ML) baseline models proving an improvement of accuracy by employing our data. An easy entry point for ML experts is provided to enable the next generation of drug discovery artificial intelligence models.

In Silico Prediction and Molecular Docking of SNPs in NRP1 Gene Associated with SARS-COV-2

Neuropilin-1 (NRP1) which is a main transmembrane cell surface receptor acts as a host cell mediator resulting in increasing the SARS-Cov-2 infectivity and also plays a role in neuronal development, angiogenesis and axonal outgrowth. The goal of this study is to estimate the impact of single nucleotide polymorphisms (SNPs) in the NRP1 gene on the function, structure and stabilization of protein as well as on the miRNA-mRNA binding regions using bioinformatical tools. It is also aimed to investigate the changes caused by SNPs in NRP1 on interactions with drug molecule and spike protein. The missense type of SNPs was analyzed using SIFT, PolyPhen-2, SNAP2, PROVEAN, Mutation Assessor, SNPs&GO, PhD-SNP, I-Mutant 3.0, MUpro, STRING, Project HOPE, ConSurf, and PolymiRTS. Docking analyses were conducted by AutoDock Vina program. As a result, a total of 733 missense SNPs were determined within the NRP1 gene and nine SNPs were specified as damaging to the protein. The modelling results showed that wild and mutant type amino acids had some different properties such as size, charge, and hydrophobicity. Additionally, their three-dimensional structures of protein were utilized for confirmation of these differences. After evaluating the results, nine polymorphisms rs141633354, rs142121081, rs145954532, rs200028992, rs200660300, rs369312020, rs370117610, rs370551432, rs370641686 were determined to be damaging on the structure and function of NRP1 protein and located in conserved regions. The results of molecular docking showed that the binding affinity values are nearly the same for wild-type and mutant structures support that the mutations carried out are not in the focus of the binding site, therefore the ligand does not affect the binding energy. It is expected that the results will be useful for future studies.

Theoretical Insights into the Generation Mechanism of the Tyr122 Radical Catalyzed by Intermediate X in Class Ia Ribonucleotide Reductase

Ribonucleotide reductase (RNR) catalyzes the reduction of ribonucleotides to deoxyribonucleotides in all organisms. There is an ∼35 Å long-range electron-hole transfer pathway during the catalytic process of class Ia RNR, which can be described as Tyr122β ↔ [Trp48β]? ↔ Tyr356β ↔ Tyr731α ↔ Tyr730α ↔ Cys439α. The formation of the Y122• radical initiates this long-range radical transfer process. However, the generation mechanism of Y122• is not yet clear due to confusion over the intermediate X structures. Based on the two reported X structures, we examined the possible mechanisms of Y122• generation by density functional theory (DFT) calculations. Our examinations revealed that the generation of the Y122• radical from the two different core structures of X was via a similar two-step reaction, with the first step of proton transfer for the formation of the proton receptor of Y122 and the second step of a proton-coupled long-range electron transfer reaction with the proton transfer from the Y122 hydroxyl group to the terminal hydroxide ligand of Fe1III and simultaneously electron transfer from the side chain of Y122 to Fe2IV. These findings provide an insight into the formation mechanism of Y122• catalyzed by the double-iron center of the β subunit of class Ia RNR.

The redox-sensing mechanism of Agrobacterium tumefaciens NieR as a thiol-based oxidation sensor for hypochlorite stress

NieR is a TetR family transcriptional repressor previously shown to regulate the NaOCl-inducible efflux pump NieAB in Agrobacterium tumefaciens. NieR is an ortholog of Escherichia coli NemR that specifically senses hypochlorite through the redox switch of a reversible sulfenamide bond between C106 and K175. The amino acid sequence of NieR contains only one cysteine. NieR has C104 and R166, which correspond to C106 and K175 of NemR, respectively. The aim of this study was to investigate the redox-sensing mechanism of NieR under NaOCl stress. C104 and R166 were subjected to mutagenesis to determine their roles. Although the substitution of R166 by alanine slightly reduced its DNA-binding activity, NieR retained its repressor function. By contrast, the DNA-binding and repression activities of NieR were completely lost when C104 was replaced by alanine. C104 substitution with serine only partially impaired the repressor function. Mass spectrometry analysis revealed an intermolecular disulfide bond between the C104 residues of NieR monomers. This study demonstrates the engagement of C104 in the mechanism of NaOCl sensing. C104 oxidation induced the formation of a disulfide-linked dimer that was likely to alter conformation, thus abolishing the DNA-binding ability of NieR and derepressing the target genes.

Regulation of Protein-Induced Apoptosis and Autophagy in Human Hepatocytes Treated with Metformin and Paclitaxel In Silico and In Vitro

Metformin and paclitaxel therapy offer promising outcomes in the treatment of liver cancer. Combining paclitaxel with metformin enhances treatment effectiveness and mitigates the adverse effects associated with paclitaxel alone. This study explored the anticancer properties of metformin and paclitaxel in HepG2 liver cancer cells, MCF-7 breast cancer cells, and HCT116 colon cancer cells. The results demonstrated that the combination of these agents exhibited a lower IC50 in the tested cell lines compared to paclitaxel monotherapy. Notably, treating the HepG2 cell line with this combination led to a reduction in the G0/G1 phase and an increase in the S and G2/M phases, ultimately triggering early apoptosis. To further investigate the interaction between the cellular proteins with paclitaxel and metformin, an in silico study was conducted using proteins chosen from a protein data bank (PDB). Among the proteins studied, AMPK-α, EGFRK, and FKBP12-mTOR exhibited the highest binding free energy, with values of -11.01, -10.59, and -15.63 kcal/mol, respectively, indicating strong inhibitory or enhancing effects on these proteins. When HepG2 cells were exposed to both paclitaxel and metformin, there was an upregulation in the gene expression of AMPK-α, a key regulator of the energy balance in cancer growth, as well as apoptotic markers such as p53 and caspase-3, along with autophagic markers including beclin1 and ATG4A. This combination therapy of metformin and paclitaxel exhibited significant potential as a treatment option for HepG2 liver cancer. In summary, the combination of metformin and paclitaxel not only enhances treatment efficacy but also reduces side effects. It induces cell cycle alterations and apoptosis and modulates key cellular proteins involved in cancer growth, making it a promising therapy for HepG2 liver cancer.

Combining fragmentation method and high-performance computing: Geometry optimization and vibrational spectra of proteins

Exploring the structures and spectral features of proteins with advanced quantum chemical methods is an uphill task. In this work, a fragment-based molecular tailoring approach (MTA) is appraised for the CAM-B3LYP/aug-cc-pVDZ-level geometry optimization and vibrational infrared (IR) spectra calculation of ten real proteins containing up to 407 atoms and 6617 basis functions. The use of MTA and the inherently parallel nature of the fragment calculations enables a rapid and accurate calculation of the IR spectrum. The applicability of MTA to optimize the protein geometry and evaluate its IR spectrum employing a polarizable continuum model with water as a solvent is also showcased. The typical errors in the total energy and IR frequencies computed by MTA vis-à-vis their full calculation (FC) counterparts for the studied protein are 5-10 millihartrees and 5 cm-1, respectively. Moreover, due to the independent execution of the fragments, large-scale parallelization can also be achieved. With increasing size and level of theory, MTA shows an appreciable advantage in computer time as well as memory and disk space requirement over the corresponding FCs. The present study suggests that the geometry optimization and IR computations on the biomolecules containing ∼1000 atoms and/or ∼15 000 basis functions using MTA and HPC facility can be clearly envisioned in the near future.

Ultra-fast semi-empirical quantum chemistry for high-throughput computational campaigns with Sparrow

Semi-empirical quantum chemical approaches are known to compromise accuracy for the feasibility of calculations on huge molecules. However, the need for ultrafast calculations in interactive quantum mechanical studies, high-throughput virtual screening, and data-driven machine learning has shifted the emphasis toward calculation runtimes recently. This comes with new constraints for the software implementation as many fast calculations would suffer from a large overhead of the manual setup and other procedures that are comparatively fast when studying a single molecular structure, but which become prohibitively slow for high-throughput demands. In this work, we discuss the effect of various well-established semi-empirical approximations on calculation speed and relate this to data transfer rates from the raw-data source computer to the results of the visualization front end. For the former, we consider desktop computers, local high performance computing, and remote cloud services in order to elucidate the effect on interactive calculations, for web and cloud interfaces in local applications, and in world-wide interactive virtual sessions. The models discussed in this work have been implemented into our open-source software SCINE Sparrow.

Mechanistic Investigation of Lysine-Targeted Covalent Inhibition of PI3Kδ via ONIOM QM:QM Computations

Phosphoinositide 3-kinase (PI3K) enzymes are important drug targets, especially in oncology, and several inhibitors are currently under investigation in clinical trials for the treatment of lymphocytic leukemia, follicular lymphoma, breast, thyroid, colorectal, and lung cancer. Targeted covalent inhibitors hold significant promise for drug discovery research especially for kinases. Targeting the lysine residues attracts attention as a new strategy in designing targeted covalent inhibitors, since the lysine residue provides several advantages over the traditional cysteine residue. Recently, new highly selective covalent inhibitors of PI3Kδ with activated ester warheads, targeting the conserved Lys779 residue, were reported. Based on the observed kinetics, a covalent inhibition mechanism was proposed, but the atomistic details of the reaction are still not understood. Therefore, in the present work, we have conducted quantum chemical ONIOM M06-2X/6-31+G(d,p):PM6 calculations on the active site cluster structure of PI3Kδ to elucidate the microscopic details of the mechanism of the aminolysis reaction between Lys779 and the ester inhibitors. Our calculations clearly discriminate the noncovalent methyl ester inhibitor and the covalent inhibitors with activated phenolic esters. For the representative p-NO₂, p-F, p-H, and p-OCH₃ phenolic esters, the Gibbs free energy profiles of alternative mechanistic paths through either Asp782 or Asp911 demonstrate the modulatory role of active site aspartate residues. The most plausible path alters depending on the electron-withdrawing/donating nature of the p-substituted phenolate leaving group. Inhibitors with sufficiently strong electron-withdrawing group prefer direct dissociation of the leaving group from the tetrahedral zwitterion intermediate, while the ones with electron-donating group favor the formation of a neutral tetrahedral intermediate prior to the dissociation. The relative Gibbs free energy barriers of p-NO₂ < p-F < p-H < p-OCH₃ substituted phenyl esters display the same qualitative trend as the experimentally measured k_inact/K_I values. Our results provide in depth insight into the mechanism, which can pave the way for optimizing the inhibitor efficiency.

Sustainable Production of Biosurfactant from Agro-Industrial Oil Wastes by Bacillus subtilis and Its Potential Application as Antioxidant and ACE Inhibitor

The microbial conversion of agro-industrial oil wastes into biosurfactants shows promise as a biomass refinery approach. In this study, Bacillus subtilis #309 was applied to produce surfactin using rapeseed and sunflower cakes, the most common oil processing side products in Europe. Studies of the chemical composition of the substrates were performed, to determine the feasibility of oil cakes for surfactin production. Initially, screening of proteolytic and lipolytic activity was performed to establish the capability of B. subtilis #309 for substrate utilization and hence effective surfactin production. B. subtilis #309 showed both proteolytic and lipolytic activity. The process of surfactin production was carefully analyzed by measurement of the surfactin concentration, pH, surface tension (ST) and emulsification index (E₂₄). The maximal surfactin concentration in the sunflower and rapeseed cake medium reached 1.19 ± 0.03 and 1.45 ± 0.09 g/L, respectively. At the same time, a progressive decrease in the surface tension and increase in emulsification activity were observed. The results confirmed the occurrence of various surfactin homologues, while the surfactin C₁₅ was the dominant one. Finally, the analysis of surfactin biological function exhibited antioxidant activity and significant angiotensin-converting enzyme (ACE)-inhibitory activity. The half-maximal inhibitory concentration (IC₅₀) value for ACE inhibition was found to be 0.62 mg/mL for surfactin. Molecular docking of the surfactin molecule to the ACE domains confirmed its inhibitory activity against ACE. Several interactions, such as hydrophobic terms, hydrogen bonds and van der Waals interactions, were involved in the complex stabilization. To the best of our knowledge, this is the first report describing the effect of a lipopeptide biosurfactant, surfactin, produced by B. subtilis for multifunctional properties in vitro, namely the ACE-inhibitory activity and the antioxidant properties, using different assays, such as 2,2-azinobis (3-ethyl-benzothiazoline-6-sulfonic acid (ABTS), 2,2-diphenyl-1-picrylhydrazyl (DPPH) and ferric reducing antioxidant power (FRAP). Thus, the ACE-inhibitory lipopeptide biosurfactant shows promise to be used as a natural antihypertensive agent.

QM/MM Well-Tempered Metadynamics Study of the Mechanism of XBP1 mRNA Cleavage by Inositol Requiring Enzyme 1α RNase

A range of in silico methodologies were herein employed to study the unconventional XBP1 mRNA cleavage mechanism performed by the unfolded protein response (UPR) mediator Inositol Requiring Enzyme 1α (IRE1). Using Protein-RNA molecular docking along with a series of extensive restrained/unrestrained atomistic molecular dynamics (MD) simulations, the dynamical behavior of the system was evaluated and a reliable model of the IRE1/XBP1 mRNA complex was constructed. From a series of well-converged quantum mechanics molecular mechanics well-tempered metadynamics (QM/MM WT-MetaD) simulations using the Grimme dispersion interaction corrected semiempirical parametrization method 6 level of theory (PM6-D3) and the AMBER14SB-OL3 force field, the free energy profile of the cleavage mechanism was determined, along with intermediates and transition state structures. The results show two distinct reaction paths based on general acid-general base type mechanisms, with different activation energies that perfectly match observations from experimental mutagenesis data. The study brings unique atomistic insights into the cleavage mechanism of XBP1 mRNA by IRE1 and clarifies the roles of the catalytic residues H910 and Y892. Increased understanding of the details in UPR signaling can assist in the development of new therapeutic agents for its modulation.

Decolorization mechanisms of reactive yellow 145 and ponceau S in microbial fuel cells during simultaneous electricity production

Environmental pollution is increasing in parallel with the increase in the world population. Azo dyes are one of the most important causes of environmental pollution. Microbial electrochemical cells are biotechnological systems that generate energy from renewable sources such as electricity. This study investigated simultaneous electricity generation with the decolorization of two different azo dyes in microbial fuel cells. And also, changes in pH values, chemical oxygen demand analysis, hourly color removal rate, dye spectral scanning were investigated. Reactive Yellow 145 dye with a concentration of 10 mg/L, 20 mg/L, and 40 mg/L, and Ponceau S dye with 20 mg/L and 40 mg/L concentration were tested in microbial fuel cells, respectively. Results indicate that the maximum voltage obtained was 0.11 V at the same time as the 100% decolorization rate in Reactive Yellow 145 and was achieved at a concentration of 10 mg/L also, the maximum voltage obtained was 0.24 V at the same time as the 100% decolorization rate in Ponceau S. It was achieved at a concentration of 20 mg/L. In conclusion, microbial fuel cells appear to be promising tools in treating textile azo dye wastewaters, and computational methods can be applied to estimate the degradation mechanisms of complex organic molecules found in wastewaters.

Development and Evaluation of Geometry Optimization Algorithms in Conjunction with ANI Potentials

An efficient yet accurate method for producing a large amount of energy data for molecular mechanical force field (MMFF) parameterization is on demand, especially for torsional angle parameters which are typically derived to reproduce ab initio rotational profiles or torsional potential energy surfaces (PESs). Recently, an active learning potential (ANI-1x) for organic molecules which can produce smooth and physically meaningful PESs has been developed. The high efficiency and accuracy make ANI-1x especially attractive for geometry optimization at low cost. To apply the ANI-1x potential in MMFF parameterization, one needs to perform constrained geometry optimization. In this work, we first developed a computational protocol to constrain rotatable torsional angles and other geometric parameters for a molecule whose geometry is described by Cartesian coordinates. The constraint is successfully achieved by force projection for the two conjugated gradient (CG) algorithms. We then conducted large-scale assessments on ANI-1x along with four different optimization algorithms in reproducing DFT energies and geometries for two CG algorithms, CG backtracking line search (CG-BS) and CG Wolfe line search (CG-WS), and two quasi-Newton algorithms, Broyden-Fletcher-Goldfarb-Shanno (BFGS) and low-memory BFGS (L-BFGS). Note that CG-BS is a new algorithm we developed in this work. All four algorithms take the ANI energies and forces to optimize a molecule geometry. Last, we conducted a large-scale assessment of applying ANI-1x in MMFF development in three aspects. First, we performed full optimizations for 100 drug molecules, each consisting of five distinct conformations. The average root-mean-square error (RMSE) between ANI-1x and DFT is about 1.3 kcal/mol, and the root-mean-square displacement (RMSD) of heavy atoms is about 0.35 Å. Second, we generated torsional PESs for 160 organic molecules, and constrained optimizations were performed for up to 18 conformations for each PES. We found that the RMSE of all the conformers is 1.23 kcal/mol. Last, we carried out constrained optimizations for alanine dipeptide with both ϕ and φ angles being frozen. The Ramachandran plots indicate that the two CG algorithms in conjunction with the ANI-1x potential could well reproduce the DFT-optimized geometries and torsional PESs. We concluded that CG-BS and CG-WS are good choices for generating PESs, while CG-WS or BFGS is ideal for performing full geometry optimization. With the continuously increased quality of ANI, it is expected that the computational algorithms and protocols presented in this work will have great applications in improving the quality of an existing small-molecule MMFF.

Triethylsulfonium-Based Ionic Liquids Enforce Lithium Salts Electrolytes

The demand for energy cheap production and efficient storage is huge nowadays. Sulfonium-based ionic liquids were shown to exhibit a useful set of physical-chemical and electrochemical properties which make them...

The Potential Antimicrobial Action of Human Mucin 7 15-Mer Peptide and Its Metal Complexes

Mucin 7 (encoded byMUC7) is a human salivary protein that has a role in the natural immune system. Fragments of mucin 7 exhibit antimicrobial activity against bacteria and yeast. Although the antimicrobial properties of peptides have been known and studied for decades, the exact mechanism of action of antimicrobial peptides (AMPs) is still unclear. It is known that some AMPs require divalent metal ions to activate their activity. Herein, we investigated three 15-mer MUC7 peptides, one of which (mother peptide, sequence, L3) is a synthetic analog of a fragment naturally excised from MUC7 (with His3, His8, and His 14) and its two structural analogs, containing only two histidine residues, His3, His13 and His8, His13 (L2 and L1, respectively). Since there is a correlation between lipophilicity, the presence of metal ions (such as Cu(II) and Zn(II)) and antimicrobial activity of AMP, antimicrobial properties of the studied peptides, as well as their complexes with Cu(II) and Zn(II) ions, were tested for activity against Gram-positive (Enterococcus faecalis, Staphylococcus epidermidis) and Gram-negative (Escherichia coli, Pseudomonas aeruginosa) bacteria and fungi (Candida albicans). The results were correlated with their lipophilicity. Coordination and thermodynamic studies (potentiometry, UV-Vis, CD) revealed the formation of mainly mononuclear complexes in solution for all studied systems with different stability in the physiological pH range.

From molecular dynamics to quantum mechanics of misfolded proteins and amyloid-like macroaggregates applied to neurodegenerative diseases

A misfolded protein compared with its native state lacks its biological function resulting in cell dysregulations and often death. Outdated hypotheses on protein folding must be revised: More realistic molecular models, focusing not only on classical molecular dynamics (MD) but also on ab initio quantum mechanics (QM) at the molecular orbitals (MOs) scale, which is not experimentally achievable, are presented to improve our understanding of the thermodynamics of the protein-protein interactions leading to misfolding and neurodegenerative diseases for future drug design. Protein misfolding is characterized by the formation of highly reactive beta-sheets oligomers leading to fibrillar macroscopic aggregates, which are studied with the models given herein that can be useful for the development of new immunotherapies against the Alzheimer's disease and prion, e.g. The example of the prion - an intrinsically disordered protein - is studied, but the models can be generalized to other misfolding diseases. The binding free energy and interactions in a complex of a misfolded prion with a native prion are first analyzed by MD and compared to a complex of two native conformers. A conversion of residues to toxic beta-sheets is observed in the optimized misfolded complex. Then, QM is used to compute, with a much better accuracy than that of MD, the binding free energy of the hydrophobic binding site, responsible of the aggregation, between the bound misfolded and native conformers in the misfolded complex. The latter quantity is significantly negative, so that aggregation is strong and fast. The frontier MOs from QM are used for docking to determine how the first repetitive beta-sheets building blocks of the nanofibrils can be assembled from initial cleaved complexes of the native and misfolded proteins. Successive aggregation of multiple monomers leads to an amyloid-like nanofibril that grows along a principal elongation direction, as also observed experimentally.

Vanillin enones as selective inhibitors of the cancer associated carbonic anhydrase isoforms IX and XII. The out of the active site pocket for the design of selective inhibitors?

New C-glycosides and α,β-unsaturated ketones incorporating the 4-hydroxy-3-methoxyphenyl (vanillin) moiety as inhibitors of carbonic anhydrase (CA, EC 4.2.1.1) isoforms have been investigated. The inhibition profile of these compounds is presented against four human CA (hCA) isozymes, comprising hCAs I and II (cytosolic, ubiquitous enzymes) and hCAs IX and XII (tumour associated isozymes). Docking analysis of the inhibitors within the active sites of these enzymes has been performed and is discussed, showing that the observed selectivity could be explained in terms of an alternative pocket out of the CA active site where some of these compounds may bind. Several derivatives were identified as selective inhibitors of the tumour-associated hCA IX and XII. Their discovery might be a step in the strategy for finding an effective non-sulfonamide CA inhibitor useful in therapy/diagnosis of hypoxic tumours or other pathologies in which CA isoforms are involved.

Ab initio multi-level layered elongation method and its application to local interaction analysis between DNA bulge and ligand molecules

A multi-level layered elongation method was developed for efficiently analyzing the electronic states of local structures in large bio/nano-systems at the full ab initio level of theory. The original elongation method developed during the last three decades in our group has focused on the system in one direction from one terminal to the other terminal to sequentially construct the electronic states of a polymer, called a theoretical synthesis of polymers. In this study, an important region termed the central (C) part is targeted in a large polymer and the remainder are terminal (T) parts. The electronic structures along with polymer elongation are calculated repeatedly from both end T parts to the C central part at the same time. The important C part is treated with large basis sets (high level) and the other regions are treated with small basis sets (low level) in the ab initio theoretical framework. The electronic structures besides the C part can be reused for other systems with different structures at the C part, which renders the method computationally efficient. This multi-level layered elongation method was applied to the investigation on DNA single bulge recognition of small molecules (ligands). The reliability and validity of our approach were examined in comparison with the results obtained by direct calculations using a conventional quantum chemical method for the entire system. Furthermore, stabilization energies by the formation of the complex of bulge DNA and a ligand were estimated with basis set superposition error corrections incorporated into the elongation method.

Robust and Efficient Implicit Solvation Model for Fast Semiempirical Methods

We present a robust and efficient method to implicitly account for solvation effects in modern semiempirical quantum mechanics and force fields. A computationally efficient yet accurate solvation model based on the analytical linearized Poisson-Boltzmann (ALPB) model is parameterized for the extended tight binding (xTB) and density functional tight binding (DFTB) methods as well as for the recently proposed GFN-FF general force field. The proposed methods perform well over a broad range of systems and applications, from conformational energies over transition-metal complexes to large supramolecular association reactions of charged species. For hydration free energies of small molecules, GFN1-xTB(ALPB) is reaching the accuracy of sophisticated explicitly solvated approaches, with a mean absolute deviation of only 1.4 kcal/mol compared to the experiment. Logarithmic octanol-water partition coefficients (log K_ow) are computed with a mean absolute deviation of about 0.65 using GFN2-xTB(ALPB) compared to experimental values indicating a consistent description of differential solvent effects. Overall, more than twenty solvents for each of the six semiempirical methods are parameterized and tested. They are readily available in the xtb and dftb+ programs for diverse computational applications.

Biological activity and molecular docking studies of some new quinolines as potent anticancer agents

The objective of this study is to investigate the antiproliferative and cytotoxic properties and the action mechanism of substituted quinoline and tetrahydroquinolines 3, 4, 5, 7, and 8 against rat glioblastoma (C6), human cervical cancer (HeLa), human adenocarcinoma (HT29) cancer cell lines by BrdU Cell Proliferation ELISA, Lactate Dehydrogenase, DNA laddering and Topoisomerase I assays. The results of the study showed that 6,8-dibromotetrahydroquinoline 3 possess in vitro antiproliferative activity against C6, HeLa, and HT29 cell lines while morpholine/piperazine substituted quinoline 7 and 8 showed selective antiproliferative activity on C6 cell line with IC₅₀ values 47.5 and 46.3 µg/mL, respectively. Moreover, 6,8-dibromoTHQ 3 caused DNA fragmentation while it did not inhibit the Topoisomerase I (Topo I) enzyme. On the other hand, compound 8 did not cause DNA laddering while 8 inhibited the Topo I enzyme. According to these results, 6,8-dibromoTHQ 3 stimulates apoptosis on the C6 cell line while 6,8-dibromo-3-morhonilylquinoline (8) inhibits the Topo I enzyme to cause antiproliferative activity.

Comparative QM/MM Molecular Dynamics and Umbrella Sampling Simulations: Interaction of the Zinc-Bound Intermediate Gem-Diolate Trapoxin A Inhibitor and Acetyl-l-lysine Substrate with Histone Deacetylase 8

Targeting the genetic material without destruction is a priority to develop safe anticancer drugs. Histone deacetylase 8 (HDAC8), which is proved to be involved in carcinogenesis, is an enzyme associated with the chromatin for post-translational deacetylation of acetylated lysine. In this study, HDAC8 co-crystallized with the intermediate state tetrapeptide Trapoxin A (TA) inhibitor and the holoenzyme are utilized to find their conformational ensembles. Furthermore, the co-crystallized intermediate gem-diolate TA was used to find optimum interactions with the active site residues by conventional molecular dynamics (MD) simulation and QM/MM umbrella sampling. Finally, the intermediate state of the acetyl-l-lysine substrate was explored by QM/MM steered MD and compared to the binding of the intermediate state of the inhibitor. This research showed that HDAC8 is flexible and exists in conformational ensembles in its holoenzyme state. Binding of the intermediate state TA stabilizes its conformation. The optimum binding to the active site of HDAC8 for structures of gem-diolate TA (intermediate state) and acetyl-l-lysine (intermediate state) was determined according to the corresponding energy profiles. The use of these models will aid in the design of potentially reversible, potent, and selective inhibitors of HDAC8 for cancer treatment.

Conformational Shifts of Stacked Heteroaromatics: Vacuum vs. Water Studied by Machine Learning

Stacking interactions play a crucial role in drug design, as we can find aromatic cores or scaffolds in almost any available small molecule drug. To predict optimal binding geometries and enhance stacking interactions, usually high-level quantum mechanical calculations are performed. These calculations have two major drawbacks: they are very time consuming, and solvation can only be considered using implicit solvation. Therefore, most calculations are performed in vacuum. However, recent studies have revealed a direct correlation between the desolvation penalty, vacuum stacking interactions and binding affinity, making predictions even more difficult. To overcome the drawbacks of quantum mechanical calculations, in this study we use neural networks to perform fast geometry optimizations and molecular dynamics simulations of heteroaromatics stacked with toluene in vacuum and in explicit solvation. We show that the resulting energies in vacuum are in good agreement with high-level quantum mechanical calculations. Furthermore, we show that using explicit solvation substantially influences the favored orientations of heteroaromatic rings thereby emphasizing the necessity to include solvation properties starting from the earliest phases of drug design.

The critical role of QM/MM X-ray refinement and accurate tautomer/protomer determination in structure-based drug design

Conventional protein:ligand crystallographic refinement uses stereochemistry restraints coupled with a rudimentary energy functional to ensure the correct geometry of the model of the macromolecule-along with any bound ligand(s)-within the context of the experimental, X-ray density. These methods generally lack explicit terms for electrostatics, polarization, dispersion, hydrogen bonds, and other key interactions, and instead they use pre-determined parameters (e.g. bond lengths, angles, and torsions) to drive structural refinement. In order to address this deficiency and obtain a more complete and ultimately more accurate structure, we have developed an automated approach for macromolecular refinement based on a two layer, QM/MM (ONIOM) scheme as implemented within our DivCon Discovery Suite and "plugged in" to two mainstream crystallographic packages: PHENIX and BUSTER. This implementation is able to use one or more region layer(s), which is(are) characterized using linear-scaling, semi-empirical quantum mechanics, followed by a system layer which includes the balance of the model and which is described using a molecular mechanics functional. In this work, we applied our Phenix/DivCon refinement method-coupled with our XModeScore method for experimental tautomer/protomer state determination-to the characterization of structure sets relevant to structure-based drug design (SBDD). We then use these newly refined structures to show the impact of QM/MM X-ray refined structure on our understanding of function by exploring the influence of these improved structures on protein:ligand binding affinity prediction (and we likewise show how we use post-refinement scoring outliers to inform subsequent X-ray crystallographic efforts). Through this endeavor, we demonstrate a computational chemistry ↔ structural biology (X-ray crystallography) "feedback loop" which has utility in industrial and academic pharmaceutical research as well as other allied fields.

Non-Covalent Interactions Atlas Benchmark Data Sets 3: Repulsive Contacts

The new R739×5 data set from the Non-Covalent Interactions Atlas series (www.nciatlas.org) focuses on repulsive contacts in molecular complexes, covering organic molecules, sulfur, phosphorus, halogens, and noble gases. Information on the repulsive parts of the potential energy surface is crucial for the development of robust empirically parametrized computational methods. We use the new data set of highly accurate CCSD(T)/CBS interaction energies to test selected density functional theory (DFT) and semiempirical quantum-mechanical methods. The double-hybrid functionals were the best performing, with the revDSD-PBEP86-D3 being the most accurate DFT method, followed by the range-separated ωB97X functionals. Out of semiempirical methods, GFN2-xTB yielded the best results. On the example of the PM6 method, we analyze the source of error and its relation to the difficulties in the description of conformational energies, and we also devise an immediately applicable correction that fixes the most serious uncorrected issues previously encountered in practical calculations.

Remote Water-Mediated Proton Transfer Triggers Inter-Cu Electron Transfer: Nitrite Reduction Activation in Copper-Containing Nitrite Reductase

The copper-containing nitrite reductase (CuNiR) catalyzes the biological conversion of nitrite to nitric oxide; key long-range electron/proton transfers are involved in the catalysis. However, the details of the electron-/proton-transfer mechanism are still unknown. In particular, the driving force of the electron transfer from the type-1 copper (T1Cu) site to the type-2 copper (T2Cu) site is ambiguous. Here, we explored the two possible proton-transfer channels, the high-pH proton channel and the primary proton channel, by using two-layered ONIOM calculations. Our calculation results reveal that the driving force for electron transfer from T1Cu to T2Cu comes from a remote water-mediated triple-proton-coupled electron-transfer mechanism. In the high-pH proton channel, the water-mediated triple-proton transfer occurs from Glu113 to an intermediate water molecule, whereas in the primary channel, the transfer is from Lys128 to His260. Subsequently, the two channels employ another two or three distinct proton-transfer steps to deliver the proton to the nitrite substrate at the T2Cu site. These findings explain the detailed proton-/electron-transfer mechanisms of copper-containing nitrite reductase and could extend our understanding of the diverse proton-coupled electron-transfer mechanisms in complicated proteins.

819 molecular knot: a theoretical analysis of the electronic structure using an ONIOM approach

The present work analyzes the electronic and molecular properties of the 8₁₉ ([Fe(II)₄]Cℓ) and metal-free knot ligand complexes obtained from X-ray crystal structure of molecular 8₁₉ knot complex [Fe(II)₄(PF₆)₇]Cℓ. The studies were theoretically investigated by means of DFT, TD-DFT, and ONIOM approaches. Basis sets functions from all-electron calculations for bromine, iodine, and iron atoms were adapted to be used along with relativistic effective core potential, while H, C, N, O, and Cℓ atoms were described by Pople basis sets. The diffusion effect of halogen into the 8₁₉ cavity, UV-Vis, and Electronic Circular Dichroism spectra were also analyzed. All calculations were performed using solvent effect through the SCRF/SMD model and dispersion effects by Grimme methodology. The value of mean separation distance between Cℓ and iron atom (7.218 Å) is in good agreement with X-ray experimental result (7.258 Å). Circular dichroism spectrum of metal-free 8₁₉ knot ligand was calculated and the maximum absorption in 262 nm, Δ𝜖 obtained was 67 L mol^- 1 cm^- 1. These results are qualitatively similar to those obtained experimentally, 295 nm and 80 L mol^- 1 cm^- 1, respectively. In this study, we report the electronic and molecular properties of the 8₁₉ ([Fe(II)₄]Cl and metal-free knot ligand complexes and compare with the results obtained from X-ray crystallographic data of 8₁₉ knot complex [Fe(II)₄(PF₆)₇]Cl. The 8₁₉ knot were investigated by means of DFT, TD-DFT, and ONIOM approaches. Basis sets functions from all-electron for Br, I, and Fe atoms were adapted to be used along with relativistic effective core potential, while H, C, N, O, and Cl atoms were described by Pople basis sets. The objective was to understand the stability of the 8₁₉ knot as a function of the substitution of the central halogen atom (Cl), and the signal in the circular dichroism spectra. From the equilibrium geometries, we have obtained good results for values of the bond distance, bond angle, and dihedral angle along the molecular structure when these variables are compared with the results obtained from X-ray data. The diffusion effect of halogen into the 8₁₉ cavity, UV-Vis, and Electronic Circular Dichroism spectra was also analyzed. Circular dichroism spectrum of metal-free 8₁₉ knot ligand was calculated, and the maximum absorption is in good agreement with the experimental value. The ONIOM methodology combined with the relativistic effective core potential and the atomic basis sets provide good results for systems with a complex topology, such as knots.

Effective Fragment Potentials for Flexible Molecules: Transferability of Parameters and Amino Acid Database

An accurate but efficient description of noncovalent interactions is a key to predictive modeling of biological and materials systems. The effective fragment potential (EFP) is an ab initio-based force field that provides a physically meaningful decomposition of noncovalent interactions of a molecular system into Coulomb, polarization, dispersion, and exchange-repulsion components. An EFP simulation protocol consists of two steps, preparing parameters for molecular fragments by a series of ab initio calculations on each individual fragment, and calculation of interaction energy and properties of a total molecular system based on the prepared parameters. As the fragment parameters (distributed multipoles, polarizabilities, localized wave function, etc.) depend on a fragment geometry, straightforward application of the EFP method requires recomputing parameters of each fragment if its geometry changes, for example, during thermal fluctuations of a molecular system. Thus, recomputing fragment parameters can easily become both computational and human bottlenecks and lead to a loss of efficiency of a simulation protocol. An alternative approach, in which fragment parameters are adjusted to different fragment geometries, referred to as "flexible EFP", is explored here. The parameter adjustment is based on translations and rotations of local coordinate frames associated with fragment atoms. The protocol is validated on extensive benchmark of amino acid dimers extracted from molecular dynamics snapshots of a cryptochrome protein. A parameter database for standard amino acids is developed to automate flexible EFP simulations in proteins. To demonstrate applicability of flexible EFP in large-scale protein simulations, binding energies and vertical electron ionization and electron attachment energies of a lumiflavin chromophore of the cryptochrome protein are computed. The results obtained with flexible EFP are in a close agreement with the standard EFP procedure but provide a significant reduction in computational cost.

PubChemQC PM6: Data Sets of 221 Million Molecules with Optimized Molecular Geometries and Electronic Properties

We report on optimized molecular geometries and electronic properties calculated by the PM6 method for 94.0% of the 91.6 million molecules cataloged in PubChem Compounds retrieved on August 29, 2016. In addition to neutral states, we also calculated those for cationic, anionic, and spin flipped electronic states of 56.2%, 49.7%, and 41.3% of the molecules, respectively. Thus, the grand total of the PM6 calculations amounted to 221 million. We compared the resulting molecular geometries with B3LYP/6-31G* optimized geometries for 2.6 million molecules. The root-mean-square deviations in bond length and bond angle were approximately 0.016 Å and 1.7°, respectively. Then, using linear regression to examine the HOMO energy levels E(HOMO) in the B3LYP and PM6 calculations, we found that E_B3LYP(HOMO) = 0.876E_PM6(HOMO) + 1.975 (eV) and calculated the coefficient of determination to be 0.803. Likewise, we examined the LUMO energy levels and found E_B3LYP(LUMO) = 1.069E_PM6(LUMO) - 0.420 (eV); the coefficient of determination was 0.842. We also generated four subdata sets, each of which was composed of molecules with molecular weights less than 500. Subdata set i contained C, H, O and N, ii contained C, H, N, O, P, and S, iii contained C, H, N, O, P, S, F, and Cl, and iv contained C, H, N, O, P, S, F, Cl, Na, K, Mg, and Ca. The data sets are available at http://pubchemqc.riken.jp/pm6_datasets.html under a Creative Commons Attribution 4.0 International license.

GPU algorithms for density matrix methods on MOPAC: linear scaling electronic structure calculations for large molecular systems

Purification of the density matrix methods should be employed when dealing with complex chemical systems containing many atoms. The running times for these methods scale linearly with the number of atoms if we consider the sparsity from the density matrix. Since the efficiency expected from those methods is closely tied to the underlying parallel implementations of the linear algebra operations (e.g., P² = P × P), we proposed a central processing unit (CPU) and graphics processing unit (GPU) parallel matrix-matrix multiplication in SVBR (symmetrical variable block row) format for energy calculations through the SP2 algorithm. This algorithm was inserted in MOPAC's MOZYME method, using the original LMO Fock matrix assembly, and the atomic integral calculation implemented on it. Correctness and performance tests show that the implemented SP2 is accurate and fast, as the GPU is able to achieve speedups up to 40 times for a water cluster system with 42,312 orbitals running in one NVIDIA K40 GPU card compared to the single-threaded version. The GPU-accelerated SP2 algorithm using the MOZYME LMO framework enables the calculations of semiempirical wavefunction with stricter SCF criteria for localized charged molecular systems, as well as the single-point energies of molecules with more than 100.000 LMO orbitals in less than 1 h. Graphical abstract Parallel CPU and GPU purification algorithms for electronic structure calculations were implemented in MOPAC's MOZYME method. Some matrices in these calculations, e.g., electron density P, are compressed, and the developed linear algebra operations deal with non-zero entries only. We employed the NVIDIA/CUDA platform to develop GPU algorithms, and accelerations up to 40 times for larger systems were achieved.

Sensitive detection of iron (II) sulfate with a novel reagent using spectrophotometry

In this study, a novel reagent was developed for sensitive detection of iron (II) sulfate, spectrophotometrically. A novel thio-anthraquinone derivative, 1-(Dodecylthio)anthracene-9,10-dione (3), was synthesized from the chemical reaction of 1-Chloroanthraquinone (1) and 1-Dodecanethiol (2) by an original reaction method and was used in the preparation of the novel reagent called Catal's reagent. A synthesized thio-anthraquinone analogue (3) was purified by column chromatography, and its chemical structure was characterized by spectroscopic methods such as Fourier-transform infrared spectroscopy (FT-IR), nuclear magnetic resonance (NMR) spectroscopy, mass spectrometry (MS), and ultraviolet (UV)-visible spectrophotometry. The chemical and molecular structure of the developed thio-antraquinone derivative (3) was illuminated using computational methods with the GaussView5 and Gaussian09 programs. Various solvents including ethanol, methanol, and acetonitrile were examined in the preparation of the reagent. A concentration range from 0.2 mg mL^-1 up to 10 mg mL^-1 of iron (II) sulfate heptahydrate solution in distilled water was prepared. The absorption spectra of Catal's reagent (0.816 mM) showed three peaks between 185 nm-700 nm of wavelength. However, after the reaction with H₂O₂ and the 30 mM trisodium citrate dihydrate mixture in the presence of an iron sulfate (II) solution, a single peak was observed, producing a stable and reddish/brownish homogenous solution (λ max = 304 nm). The following concentrations of iron (II) sulfate heptahydrate was examined using developed protocol and the reagent, and the concentrations were measured spectrophotometrically at 304 nm, 0.2-1 mg mL^-1. Absorbances of reaction mixtures of iron (II) sulfate remained stable up to 48 h. The results indicated that the novel Catal's reagent can be used for sensitive spectrophotometric detection of iron (II) sulfate in aqueous solutions.

Theoretical characterization of the shikimate 5-dehydrogenase reaction from Mycobacterium tuberculosis by hybrid QC/MM simulations and quantum chemical descriptors

In this study, we have investigated the enzyme shikimate 5-dehydrogenase from the causative agent of tuberculosis, Mycobacterium tuberculosis. We have employed a mixture of computational techniques, including molecular dynamics, hybrid quantum chemical/molecular mechanical potentials, relaxed surface scans, quantum chemical descriptors and free-energy simulations, to elucidate the enzyme's reaction pathway. Overall, we find a two-step mechanism, with a single transition state, that proceeds by an energetically uphill hydride transfer, followed by an energetically downhill proton transfer. Our mechanism and calculated free energy barrier for the reaction, 64.9 kJ mol^- 1, are in good agreement with those predicted from experiment. An analysis of quantum chemical descriptors along the reaction pathway indicated a possibly important, yet currently unreported, role of the active site threonine residue, Thr65.

Superiority of cilostazol among antiplatelet FDA-approved drugs against COVID 19 Mpro and spike protein: Drug repurposing approach

Coronavirus disease 2019 (COVID 19) was first identified in Wuhan, China near the end of 2019. To date, COVID‐19 had spread to almost 235 countries and territories due to its highly infectious nature. Moreover, there is no vaccine or Food and Drug Administration (FDA)‐approved drug. More time is needed to establish one of them. Consequently, the drug repurposing approach seems to be the most attractive and quick solution to accommodate this crisis. In this regard, we performed molecular docking‐based virtual screening of antiplatelet FDA‐approved drugs on the key two viral target proteins: main protease (M^pro) and spike glycoprotein (S) as potential inhibitor candidates for COVID‐19. In the present study, 15 antiplatelet FDA‐approved drugs were investigated against the concerned targets using the Molecular Docking Server. Our study revealed that only cilostazol has the most favorable binding interaction on M^pro (PDB ID: 6LU7) and cilostazol, iloprost, epoprostenol, prasugrel, and icosapent ethyl have a higher binding affinity on spike glycoprotein (S) (PDB ID: 6VYB) compared with recent anti‐CoVID‐19. Therefore, cilostazol is a promising FDA drug against COVID‐19 by inhibiting both M^pro and S protein. The insights gained in this study may be useful for quick approach against COVID‐19 in the future.

Investigating the biomolecular interactions between model proteins and glycine betaine surfactant with reference to the stabilization of emulsions and antimicrobial properties

Binding effect and interaction of 2-pentadecanoyloxymethyl)trimethylammonium bromide (DMGM-14) with bovine serum albumin (BSA) and hen egg white lysozyme (HEWL) were systematically investigated by the fluorescence spectroscopy, circular dichroism (CD) spectroscopy, isothermal titration calorimetry (ITC), surface tension analysis, and molecular docking studies. The emulsion properties and particle size distribution of surfactant/protein complexes containing sunflower oil were studied using static light scattering and confocal laser scanning microscopy (CLSM). The fluorescence spectroscopy and ITC analysis confirmed the complexes formation of DMGM-14 with BSA and HEWL which was also verified by surface tension measurements. CD results explained the conformational changes in BSA and HEWL upon DMGM-14 complexation. Molecular docking study provides insight into the binding of DMGM-14 into the specific sites of BSA and HEWL. Besides, the studies drew a detailed picture on the emulsification properties of DMGM-14 with BSA and HEWL. In addition, the in vitro experiment revealed a broad antibacterial spectrum of DMGM-14 and DMGM-14/HEWL complex including activity against Gram-positive and Gram-negative bacteria. In conclusion, the present study revealed that the interaction between DMGM-14 with BSA and HEWL is important for the pharmaceutical, biological, and food products.

Quantum Chemical Calculation of Molecular and Periodic Peptide and Protein Structures

Special-purpose classical force fields (FFs) provide good accuracy at very low computational cost, but their application is limited to systems for which potential energy functions are available. This excludes most metal-containing proteins or those containing cofactors. In contrast, the GFN2-xTB semiempirical quantum chemical method is parametrized for almost the entire periodic table. The accuracy of GFN2-xTB is assessed for protein structures with respect to experimental X-ray data. Furthermore, the results are compared with those of two special-purpose FFs, HF-3c, PM6-D3H4X, and PM7. The test sets include proteins without any prosthetic groups as well as metalloproteins. Crystal packing effects are examined for a set of smaller proteins to validate the molecular approach. For the proteins without prosthetic groups, the special purpose FF OPLS-2005 yields the smallest overall RMSD to the X-ray data but GFN2-xTB provides similarly good structures with even better bond-length distributions. For the metalloproteins with up to 5000 atoms, a good overall structural agreement is obtained with GFN2-xTB. The full geometry optimizations of protein structures with on average 1000 atoms in wall-times below 1 day establishes the GFN2-xTB method as a versatile tool for the computational treatment of various biomolecules with a good accuracy/computational cost ratio.

Study of interaction energies between residues of the active site of Hsp90 and geldanamycin analogues using quantum mechanics/molecular mechanics methods

Background: Heat shock protein (Hsp90KDa) is a molecular chaperone involved in the process of cellular oncogenesis, hence its importance as a therapeutic target. Geldanamycin is an inhibitor of Hsp90 chaperone activity, which binds to the ATP binding site in the N-terminal domain of Hsp90. However, geldanamycin has shown hepatotoxic damage in clinical trials; for this reason, its use is not recommended. Taking advantage that geldanamycin binds successfully to Hsp90, many efforts have focused on the search for similar analogues, which have the same or better biological response and reduce the side effects of its predecessor; 17-AAG and 17-DMAG are examples of these analogues. Methods: In order to know the chemical factors influencing the growth or decay of the biological activity of geldanamycin analogues, different computational techniques such as docking, 3DQSAR and quantum similarity were used.  Moreover, the study quantified the interaction energy between amino acids residues of active side and geldanamycin analogues, through hybrid methodology (Autodock-PM6) and DFT indexes. Results: The evaluation of interaction energies showed that the interaction with Lys58 residue is essential for the union of the analogues to the active site of Hsp90, and improves its biological activity. This union is formed through a substituent on C-11 of the geldanamycin macrocycle. A small and attractor group was found as the main steric and electrostatic characteristic that substituents on C11 need in order to interact with Lys 58; behavior was observed with hydroxy and methoxy series of geldanamycin analogues, under study. Conclusion: This study contributes with new hybrid methodology (Autodock-PM6) for the generation of 3DQSAR models, which to consider the interactions between compounds and amino acids residues of Hsp90´s active site in the alignment generation. Additionally, quantum similarity and reactivity indices calculations using DFT were performed to know the non-covalent stabilization in the active site of these compounds.

Steric and Electrostatic Control of the pH-Regulated Interconversion of Au16(SR)12 and Au18(SR)14 (SR: Deprotonated Captopril)

An understanding of the response of nanomaterials to specific environmental parameters is an essential prerequisite for their practical use, especially in living systems. Herein, we disclose the preparation of a water-soluble nanocluster Au₁₆(SR)₁₂ (SR denotes deprotonated captopril) and its characterization by a combination of theoretical (e.g., density functional theory calculations) and experimental (UV-vis, electrospray ionization mass spectrometry, etc.) methods. Interestingly, Au₁₆(SR)₁₂ was found to convert to Au₁₈(SR)₁₄ under acidic conditions, while the reverse conversion from Au₁₈(SR)₁₄ to Au₁₆(SR)₁₂ occurred upon the addition of base. A mechanistic investigation determined this pH regulation to originate from the distinct steric and electrostatic properties of these two clusters. This study is the first to report the susceptibility of Au₁₈(SR)₁₄ and Au₁₆(SR)₁₂ to pH, and the distinct pH stability unambiguously reveals the importance of size-tracking of nanomaterials in living systems for future clinical applications.

Modeling of spin–spin distance distributions for nitroxide labeled biomacromolecules

Combining CREST and MD simulations based on GFN-FF for the automated computation of distance distributions for nitroxide labeled (metallo-) proteins.

Binding Free Energy Calculation Using Quantum Mechanics Aimed for Drug Lead Optimization

The routine use of in silico tools is already established in drug lead design. Besides the use of molecular docking methods to screen large chemical libraries and thus prioritize compounds for purchase or synthesis, more accurate calculations of protein-ligand binding free energy has shown the potential to guide lead optimization, thus saving time and resources. Theoretical developments and advances in computing power have allowed quantum mechanical-based methods applied to calculations on biomacromolecules to be increasingly explored and used, with the purpose of providing a more accurate description of protein-ligand interactions and an enhanced level of accuracy in the calculation of binding affinities. It should be noted that the quantum mechanical formulation includes, in principle, all contributions to the energy, considering terms usually neglected in molecular mechanics force fields, such as electronic polarization, metal coordination, and covalent binding; moreover, quantum mechanical approaches are systematically improvable. By treating all elements and interactions on equal footing, and avoiding the need of system-dependent parameterizations, they provide a greater degree of transferability. In this review, we illustrate the increasing relevance of quantum mechanical methods for binding free energy calculation in the context of structure-based drug lead optimization, showing representative applications of the different approaches available.