Targeting Selectivity: Improving Golgi \u03b1-Mannosidase II (GMII) Inhibitors Through In Silico Studies

Results of ab initio molecular dynamics (AIMD) simulations (density functional theory+PBE96) of the dynamics of waters in the hydration shells surrounding the Zn(2+) ion (T approximately 300 K, rho approximately 1 gm/cm(3)) are compared to simulations using a combined quantum and classical molecular dynamics [AIMD/molecular mechanical (MM)] approach. Both classes of simulations were performed with 64 solvating water molecules ( approximately 15 ps) and used the same methods in the electronic structure calculation (plane-wave basis set, time steps, effective mass, etc.). In the AIMD/MM calculation, only six waters of hydration were included in the quantum mechanical (QM) region. The remaining 58 waters were treated with a published flexible water-water interaction potential. No reparametrization of the water-water potential was attempted. Additional AIMD/MM simulations were performed with 256 water molecules. The hydration structures predicted from the AIMD and AIMD/MM simulations are found to agree in detail with each other and with the structural results from x-ray data despite the very limited QM region in the AIMD/MM simulation. To further evaluate the agreement of these parameter-free simulations, predicted extended x-ray absorption fine structure (EXAFS) spectra were compared directly to the recently obtained EXAFS data and they agree in remarkable detail with the experimental observations. The first hydration shell contains six water molecules in a highly symmetric octahedral structure is (maximally located at 2.13-2.15 A versus 2.072 A EXAFS experiment). The widths of the peak of the simulated EXAFS spectra agree well with the data (8.4 A(2) versus 8.9 A(2) in experiment). Analysis of the H-bond structure of the hydration region shows that the second hydration shell is trigonally bound to the first shell water with a high degree of agreement between the AIMD and AIMD/MM calculations. Beyond the second shell, the bonding pattern returns to the tetrahedral structure of bulk water. The AIMD/MM results emphasize the importance of a quantum description of the first hydration shell to correctly describe the hydration region. In these calculations the full d(10) electronic structure of the valence shell of the Zn(2+) ion is retained. The simulations show substantial and complex charge relocation on both the Zn(2+) ion and the first hydration shell. The dipole moment of the waters in the first hydration shell is 3.4 D (3.3 D AIMD/MM) versus 2.73 D bulk. Little polarization is found for the waters in the second hydration shell (2.8 D). No exchanges were seen between the first and the second hydrations shells; however, many water transfers between the second hydration shell and the bulk were observed. For 64 waters, the AIMD and AIMD/MM simulations give nearly identical results for exchange dynamics. However, in the larger particle simulations (256 waters) there is a significant reduction in the second shell to bulk exchanges.

Of the two approaches of density-functional theory molecular dynamics, quantum molecular dynamics is limited at high temperature by computational cost whereas orbital-free molecular dynamics, based on an approximation of the kinetic electronic free energy, can be implemented in this domain. In the case of deuterium, it is shown how orbital-free molecular dynamics can be regarded as the limit of quantum molecular dynamics at high temperature for the calculation of the equation of state. To this end, accurate quantum molecular dynamics calculations are performed up to 20 eV at mass densities as low as 0.5g/cm^{3} and up to 10 eV at mass densities as low as 0.2g/cm^{3}. As a result, the limitation in temperature so far attributed to quantum molecular dynamics is overcome and an approach combining quantum and orbital-free molecular dynamics is used to construct an equation of state of deuterium. The thermodynamic domain addressed is that of the fluid phase above 1 eV and 0.2g/cm^{3}. Both pressure and internal energy are calculated as functions of temperature and mass density, and various exchange-correlation contributions are compared. The generalized gradient approximation of the exchange-correlation functional, corrected to approximately include the influence of temperature, is retained and the results obtained are compared to other approaches and to experimental shock data; in parts of the thermodynamic domain addressed, these results significantly differ from those obtained in other first-principles investigations which themselves disagree. The equations of state of hydrogen and tritium above 1 eV and above, respectively, 0.1g/cm^{3} and 0.3g/cm^{3}, can be simply obtained by mass density scaling from the results found for deuterium. This ab initio approach allows one to consistently cover a very large domain of temperature on the domain of mass density outlined above.

Sampling of potential energy surfaces (PES) lies at the core of soft matter self-assembly and material property characterization using molecular simulations. Yet, PES roughness and poor sampling efficiency must be overcome with advanced molecular dynamics (MD) techniques. We introduce the PES concept and detail the current PES-sampling progress using multiscale simulations, including methods in quantum mechanics, all-atomistic MD, and coarse-grained MD. Advanced techniques such as data-driven, machine-learned, and statistical-modeling methods are also introduced. We then focus on several enhanced sampling methods to accurately characterize the PES for probing the material properties of supramolecular soft matter. A variety of simulation software is also showcased to guide hands-on implementation. Finally, we summarize some recent characterization of soft material phenomena using MD methods with enhanced PES sampling. This chapter overviews methods for PES sampling and aims to stimulate broader accessibility of enhanced PES sampling of soft matter self-assembly and characterization.

Designing small molecule inhibitors that are highly selective for Golgi alpha mannosidase II (GMII) over lysosomal alpha mannosidase (LM) remains crucial for the development of novel anticancer drugs targeting the N-glycosylation pathway. Studies have previously identified an unconserved ‘anchor site’ in GMII that represents an attractive target for achieving selectivity. In this study we conduct molecular dynamics simulations and free energy calculations of GMII and LM with their natural oligosaccharide substrates to investigate the potential of the anchor site. Our findings reveal that the corresponding N-acetylglucosamine residue remains tightly bound to the anchor site in GMII, helping stabilize overall substrate binding to GMII, while the lack of a similar conserved site in LM allows the substrate to fluctuate more freely. Our simulations also suggest that besides stabilizing the catalytic site mannose for cleavage, the anchor site residue may play a role in facilitating the release of the holding site mannose and its transition to the catalytic site in GMII. Subsequently, free energy calculations reveal that the anchor site N-acetylglucosamine contributes 4.107 kcal/mol to the free energy of binding in GMII, with a significantly smaller contribution of 1.035 kcal/mol in LM. This difference of 3.072 kcal/mol in the free energy of binding represents potential gains in selectivity that could be achieved by targeting the anchor site in GMII. Taken together, these findings provide further evidence on the potential of targeting the anchor site in the design of highly selective GMII inhibitors.

Quantum mechanics (QM) revolutionizes drug discovery by providing precise molecular insights unattainable with classical methods. This review explores QM’s role in computational drug design, detailing key methods like density functional theory (DFT), Hartree–Fock (HF), quantum mechanics/molecular mechanics (QM/MM), and fragment molecular orbital (FMO). These methods model electronic structures, binding affinities, and reaction mechanisms, enhancing structure-based and fragment-based drug design. This article highlights the applicability of QM to various drug classes, including small-molecule kinase inhibitors, metalloenzyme inhibitors, covalent inhibitors, and fragment-based leads. Quantum computing’s potential to accelerate quantum mechanical (QM) calculations is discussed alongside novel applications in biological drugs (e.g., gene therapies, monoclonal antibodies, biosimilars), protein–receptor dynamics, and new therapeutic indications. A molecular dynamics (MD) simulation exercise is included to teach QM/MM applications. Future projections for 2030–2035 emphasize QM’s transformative impact on personalized medicine and undruggable targets. The qualifications and tools required for researchers, including advanced degrees, programming skills, and software such as Gaussian and Qiskit, are outlined, along with sources for training and resources. Specific publications on quantum mechanics (QM) in drug discovery relevant to QM and molecular dynamics (MD) studies are incorporated. Challenges, such as computational cost and expertise requirements, are addressed, offering a roadmap for educators and researchers to leverage quantum mechanics (QM) and molecular dynamics (MD) in drug discovery.

During this coronavirus pandemic, when a lot of people are already severely afflicted with SARS-CoV-19, the dispersion of black fungus is making it worse, especially in the Indian subcontinent. Considering this situation, the idea for an in silico study to identify the potential inhibitor against black fungal infection is envisioned and computational analysis has been conducted with isatin derivatives that exhibit considerable antifungal activity. Through this in silico study, several pharmacokinetics properties like absorption, distribution, metabolism, excretion, and toxicity (ADMET) are estimated for various derivatives. Lipinski rules have been used to observe the drug likeliness property, and to study the electronic properties of the molecules, quantum mechanism was analyzed using the density functional theory (DFT). After applying molecular docking of the isatin derivatives with sterol 14-alpha demethylase enzyme of black fungus, a far higher docking affinity score has been observed for the isatin sulfonamide-34 (derivative 1) than the standard fluconazole. Lastly, molecular dynamic (MD) simulation has been performed for 100ns to examine the stability of the proposed drug complex by estimating Root Mean Square Deviation (RMSD), Radius of gyration (Rg), Solvent accessible surface area (SASA), Root Mean Square Fluctuation (RMSF), as well as hydrogen bond. Listed ligands have precisely satisfied every pharmacokinetics requirement for a qualified drug candidate and they are non-toxic, non-carcinogenic, and have high stability. This natural molecule known as isatin derivative 1 has shown the potential of being a drug for fungal treatment. However, the impact of the chemicals on living cells requires more investigation and research.

Ab initio quantum mechanical (QM) and molecular dynamics (MD) methods have been used to study the catalytic mechanism of aspartylglucosaminidase (AGA)-catalyzed hydrolysis of an amide bond. QM active-site models were used to calculate the reaction sequences of the acylation and deacylation parts of the catalytic reaction at the MP2/6-31G*//HF/6-31G* level. MD simulations, in which the structures of the active sites were constrained to the geometries obtained from the QM model calculations, were carried out for all the species of the reaction sequence. Structural information from the MD simulations was used in the subsequent QM calculations in which the charge distribution of the protein environment and solvent was included as a set of atomic point charges. These QM calculations provided an estimate of the effects of the protein environment on the energetics of the catalytic reaction. The reaction mechanism was also simulated using a continuum model for the aqueous solvent, in order to differentiate genera...

The SARS-CoV-2 pandemic at the end of 2019 had major worldwide health and economic consequences. Until effective vaccination approaches were created, the healthcare sectors endured a shortage of operative treatments that might prevent the infection's spread. As a result, academia and the pharmaceutical industry prioritized the development of SARS-CoV2 antiviral medication. Pyranopyrazoles have been shown to play a prominent function in pharmaceutical chemistry and drug sighting because of their significant bioactive properties. We provide herein a novel sequence of pyranopyrazoles and their annulated systems whose antiviral efficacy and cytotoxicity were explored versus human coronavirus 229E (HCoV-229E) Vero-E6 cell lines as a model for the Coronaviridae family. Fifteen synthetic congeners pointed out miscellaneous antiviral efficacies against HCoV-229E with variable inhibition degrees. Compound 18 showed a high selectivity index (SI = 12.6) that established spectacular inhibitory capacity against human coronavirus 229E. Compounds 6, 7, and 14 exposed moderate efficacies. Compounds 6, 7, 14, and 18 exhibited substantial antiviral action through the replication phase with reduction percentages extending from 53.6%, 60.7%, and 55% to 82.2%, correspondingly. Likewise, when assessed to the positive control tipranavir (88.6%), the inhibitory efficiency of compounds 6, 7, 14, and 18 versus the SARS-CoV2 Mpro provided high percentages of 80.4%, 73.1%, 81.4% and up to 84.5%, respectively. In silico studies were performed to investigate further the biological activity and the target compounds' physical and chemical features, including molecular dynamic (MD) simulations, protein-ligand docking, ADME studies, and density functional theory (DFT) calculations. These inquiries demonstrated that this series of metabolically stable pyranopyrazoles and their annulated systems are effective human coronavirus inhibitors that inhibit the viral Mpro protein and may have emerged as a novel COVID-19 curative option.

Editorial: A Tribute to Michele Parrinello: From Physics via Chemistry to Biology

Infrared spectroscopy is a powerful technique to unravel the structure and dynamics of molecular systems of ever increasing complexity. For isolated molecules in the gas phase theoretical approaches that directly rely on solving the Schrödinger equation, either approximately or quasi-exactly, are well established. A distinctly different approach to compute infrared spectra can be based on advanced molecular dynamics, itself being based on classical Newtonian dynamics, in conjunction with concurrent first principles electronic structure calculations. At variance with traditional methods, which are formulated in terms of the Schrödinger representation of quantum mechanics, the molecular dynamics approach stems from Heisenberg's representation and thus relies on computing thermal expectation values of time-correlation functions. Crucial in addition to generating the spectra themselves is their decomposition in terms of modes, which can be assigned to correlated atomic motion. This ab initio molecular dynamics route to compute infrared spectra, and its recent extension to quasiclassical techniques relying on approximate path integral dynamics, is covered in the review part of this Perspective. The usefulness of this unconventional approach, which can be generalized beyond infrared spectroscopy, is demonstrated in detail by applying the full machinery in computing and assigning the infrared spectra of protonated methane and its isotopologues. This particular molecule is often considered to be the most prominent member of the class of floppy or fluxional molecules. CH5(+) has been a longstanding challenge for theoretical infrared spectroscopy because it undergoes intricate large-amplitude motion, which is also reviewed. Molecular dynamics based infrared spectroscopy is general and can be applied to diverse systems such as molecular complexes in the gas phase, chromophores in biomolecular environments, and solute-solvent systems in the liquid phase.

MolTwister – a molecular systems construction, manipulation and statistical mechanical calculation tool

Quantum and classical molecular dynamics simulations of shocked polyurea and polyurethane

We present a comparative study of classical and ab-initio molecular dynamics (MD) simulations of methane in the liquid state. The atom wise radial distribution function (RDF) of liquid methane for both classical and ab initio simulations is calculated. It is observed that the peaks of RDF are lowered and broadened when quantum effects are considered. Also, the peaks are shifted towards the slightly lower values of intermolecular distance r. The diffusion coefficient from the slope of Mean Square Displacement (MSD) and the partial density of states has also been calculated for Quantum MD. The bond angles of the final configuration obtained after running the simulations show more fluctuations in classical MD as compared to quantum MD simulations.

It has been just over twenty years since the publication of the seminal paper on molecular dynamics with ab initio methods by Car and Parrinello [1], and the contribution of density functional theory (DFT) and the related techniques to physics, chemistry, materials science, earth science and biochemistry has been huge. Nevertheless, significant improvements are still being made to the performance of these standard techniques; recent work suggests that speed improvements of one or even two orders of magnitude are possible [2]. One of the areas where major progress has long been expected is in O(N), or linear scaling, DFT, in which the computer effort is proportional to the number of atoms. Linear scaling DFT methods have been in development for over ten years [3] but we are now in an exciting period where more and more research groups are working on these methods. Naturally there is a strong and continuing effort to improve the efficiency of the methods and to make them more robust. But there is also a growing ambition to apply them to challenging real-life problems.

We show how orbital-free molecular dynamics can be regarded as the limit of quantum molecular dynamics at high temperature for the calculation of the equation of state of carbon. As a result, we construct an approach that allows to reproduce the quantum molecular dynamics equation of state of carbon at high temperature by combining quantum and orbital-free molecular dynamics. We apply this approach to a wide range of density and temperature conditions of the liquid or plasma state: $30\ensuremath{\ge}\ensuremath{\rho}\ensuremath{\ge}0.8\phantom{\rule{0.16em}{0ex}}\mathrm{g}/{\mathrm{cm}}^{3}, T\ensuremath{\ge}1$ eV. Then we test the sensitivity of the equation of state obtained to the choice of the exchange-correlation functional. We retain the generalized gradient approximation of this functional, corrected to approximately include the influence of temperature, and compare the results obtained to published first-principles calculations and to experimental shock data.