Quantum Mechanical Calculations Research Articles

Machine learning-based screening and molecular simulations for discovering novel PARP-1 inhibitors targeting DNA repair mechanisms for breast cancer therapy.

Cancer remains one of the leading causes of death worldwide, with the rising incidence of breast cancer being a significant public health concern. Poly (ADP-ribose) polymerase-1 (PARP-1) has emerged as a promising therapeutic target for breast cancer treatment due to its crucial role in DNA repair. This study aimed to discover novel, targeted, and non-toxic PARP-1 inhibitors using an integrated approach that combines machine learning-based screening, molecular docking simulations, and quantum mechanical calculations. We trained a widely used machine learning models, Random Forest, using bioactivity data from known PARP-1 inhibitors. After evaluating the performance, it was used to screen an FDA-approved drug library, successfully identifying Atazanavir, Brexpiprazole, Raltegravir, and Nisoldipine as potential PARP-1 inhibitors. These compounds were further validated through molecular docking and all-atom molecular dynamics simulations, highlighting their potential for breast cancer therapy. The binding free energies indicated that Atazanavir at -41.86kJ/mol and Brexpiprazole at -45.44kJ/mol exhibited superior binding affinity compared to the control drug at -30.42kJ/mol, highlighting their promise as candidates for breast cancer therapy. Subsequent optimized geometries and electron density mappings of the two molecular structures revealed a Gibbs free energy of -2334.610 Ha for the first molecule and -1682.278316 Ha for the second, confirming enhanced stability compared to the standard drug. This study not only highlights the efficacy of machine learning in drug discovery but also underscores the importance of quantum mechanics in validating molecular stability, setting a robust foundation for future pharmacological explorations. Additionally, this approach could revolutionize the drug repurposing process by significantly reducing the time and cost associated with traditional drug development methods. Our results establish a promising basis for subsequent research aimed at optimizing these PARP-1 inhibitors for clinical use, potentially offering more effective treatment options for breast cancer patients.

Photoelasticity of crystals with the scheelite structure: quantum mechanical calculations.

We report a complete set of elastic, piezooptic and photoelastic tensor constants of scheelite crystals CaMoO4, BaMoO4, BaWO4 and PbWO4 determined by density functional theory (DFT) calculations using the quantum chemical software package CRYSTAL17. The modulation parameter, i.e. the change in the crystal optical path normalized by thickness and mechanical stress, was calculated based on piezooptic and elastic compliance tensor constants. For the geometries of the most effective piezo-optic interactions, this parameter reaches rather large values (16-17) × 10-12 m2 N-1. Anisotropy of the photoelastic and acoustooptic effects is explored by means of indicative surfaces, considering the directions of light propagation and polarization, the direction of uniaxial compression or lattice distortion caused by the propagation of the acoustic wave. DFT calculations indicate BaWO4 and PbWO4 crystals as the most effective acousto-optic materials, predicting the figure of merit constant M2 ∼ 20 × 10-15 s3 kg-1. The methodology proposed combines the DFT calculations and photoelasticity caused by uniaxial compression of the crystal lattice, with particular emphasis on its anisotropy. It can be considered as part of optical engineering aimed at preliminary assessment of the photoelastic properties of crystal materials, thus assisting in their selection for synthesis and relevant applications.

Insights into the separation, enantioseparation and recognition mechanisms of methamphetamine isotopologues on achiral and polysaccharide-based chiral columns in high-performance liquid chromatography.

Isotopologues resulting from the labelling of molecules with deuterium have attracted interest due to the isotope effect observed in chemistry and biosciences. Isotope effect may also play out in noncovalent interactions and mechanisms leading to intermolecular recognition. In chromatography, differences in retention time between isotopologues, as well as between isotopomers have been observed resulting in two different elution sequences (isotope effects): the normal isotope effect when heavier isotopologues retain longer than lighter analogues, and the inverse isotope effect featuring the opposite elution order. Although several cases of deuterium isotope effects have been reported so far, the molecular bases of these phenomena remain unclear. We report the separation of isotopologues of methamphetamine (N-methyl-1-phenylpropan-2-amine) (MET), featuring different number and location of deuterium substituents, on an achiral column by high-performance liquid chromatography (HPLC), as well as the simultaneous separation of isotopologues and their enantiomers on some polysaccharide-based chiral columns. The effects of the number and location of deuterium substituents introduced in MET's structure, of the surface chemistry of the adsorbent, and the mobile phase composition and pH on the retention and separation of isotopologues and their enantiomers were examined. In several cases, the effect of temperature was also evaluated, and the thermodynamic quantities associated with isotopologue adsorption, separation, and enantioseparation were also calculated. To elucidate the molecular bases of the experimental observations, quantum mechanics calculations were performed focusing on the vibrational degree of freedom calculated for models of isotopologue-selector complexes. On this basis, zero-point vibrational energies were computed as useful descriptors to differentiate computationally between deuterated isotopologues. Using HPLC as experimental technique with the following dual function: 1) separation of MET isotopologues and their enantiomers; 2) exploring noncovalent interactions underlying their separation. The degree of deuteration, location of deuterium substituents, and mobile phase pH play key roles in isotopologue separations. By integrating experimental and computational analyses, noncovalent interactions underlying normal and inverse isotope effects were deconvoluted, highlighting the contribution of hydrogen bond, hydrophobic and dispersive forces in isotopologue and enantiomer separation.

New Approach as Inhibitor Against Head-Neck Cancer by In silico, DFT, FMOs, Docking, Molecular Dynamic, and ADMET of Euphorbia tirucalli (Pencil Cactus)

Background: Head and neck cancer (HNC) is on the rise worldwide, endangering lives and straining healthcare systems in both developing and developed nations. Despite the availability of a number of therapy options, the success rate for treating and controlling head and neck cancer remains dismal. To combat the aggressiveness and drug resistance of Epstein-Barr virus (EBV)-positive Head-Neck cancer cells, this study looks into the potential of Euphorbia tirucalli (pencil cactus) leaf extract. Objectives: The goal of this study is to identify prospective therapeutic candidates from the extract of Euphorbia tirucalli (pencil cactus) leaves, which have the ability to inhibit Epstein-Barr virus (EBV)-positive Head- Neck cancer cells. Materials and Methods: The thirteen most important chemical components found in Euphorbia tirucalli (pencil cactus) leaves were analyzed by means of molecular modeling techniques such as Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET), Quantum Mechanics (QM) calculation, molecular docking, and molecular dynamics (MD) simulations. Using the Prediction of Activity Spectra for Substances (PASS) model, we assess the potency of these compounds. Important molecular properties such as chemical potential, electronegativity, hardness, and softness can be determined with the use of quantum chemical calculations employing HOMO-LUMO analysis. These drugs' safety and toxicological characteristics are better understood to assessments of their pharmacokinetics and ADMET. Finally, molecular dynamics simulations are employed to verify binding interactions and assess the stability of docked complexes. Results: The molecular docking analysis identifies ligands (01), (02), and (10) as strong competitors, with strong binding affinity for the Epstein-Barr virus (EBV)-positive Head-Neck cancer cell line. Not only do the ligands (01), (02), and (10) match the criteria for a potential new inhibitor of head-neck cancer, but they also outperform the present FDA-approved treatment. Conclusion: Taraxerol, euphol, and ephorginol, three phytochemicals isolated from the leaves of the Euphorbia tirucalli (pencil cactus), have been identified as effective anti-cancer agents with the potential to serve as a foundation for novel head-neck cancer therapies, particularly those targeting the Epstein-Barr virus (EBV)-overexpressing subtype of this disease. An effective, individualized treatment plan for head-neck cancer is a long way off, but this study is a major step forward that could change the lives of patients and reduce the global burden of this disease.

Machine learning photodynamics decode multiple singlet fission channels in pentacene crystal

Crystalline pentacene is a model solid-state light-harvesting material because its quantum efficiencies exceed 100% via ultrafast singlet fission. The singlet fission mechanism in pentacene crystals is disputed due to insufficient electronic information in time-resolved experiments and intractable quantum mechanical calculations for simulating realistic crystal dynamics. Here we combine a multiscale multiconfigurational approach and machine learning photodynamics to understand competing singlet fission mechanisms in crystalline pentacene. Our simulations reveal coexisting charge-transfer-mediated and coherent mechanisms via the competing channels in the herringbone and parallel dimers. The predicted singlet fission time constants (61 and 33 fs) are in excellent agreement with experiments (78 and 35 fs). The trajectories highlight the essential role of intermolecular stretching between monomers in generating the multi-exciton state and explain the anisotropic phenomenon. The machine-learning-photodynamics resolved the elusive interplay between electronic structure and vibrational relations, enabling fully atomistic excited-state dynamics with multiconfigurational quantum mechanical quality for crystalline pentacene.

Electrocatalytic Pathways and Efficiency of Cuprous Oxide (Cu2O) Surfaces in CO2 Electrochemical Reduction (CO2ER) to Methanol: A Computational Approach

Carbon dioxide (CO2) can be electrochemically, thermally, and photochemically reduced into valuable products such as carbon monoxide (CO), formic acid (HCOOH), methane (CH4), and methanol (CH3OH), contributing to carbon footprint mitigation. Extensive research has focused on catalysts, combining experimental approaches with computational quantum mechanics to elucidate reaction mechanisms. Although computational studies face challenges due to a lack of accurate approximations, they offer valuable insights and assist in selecting suitable catalysts for specific applications. This study investigates the electrocatalytic pathways of CO2 reduction on cuprous oxide (Cu2O) catalysts, utilizing the computational hydrogen electrode (CHE) model based on density functional theory (DFT). The electrocatalytic performance of flat Cu2O (100) and hexagonal Cu2O (111) surfaces was systematically analysed, using the standard hydrogen electrode (SHE) as a reference. Key parameters, including free energy changes (ΔG), adsorption energies (Eads), reaction mechanisms, and pathways for various intermediates were estimated. The results showed that CO2 was reduced to CO(g) on both Cu2O surfaces at low energies. However, methanol (CH3OH) production was observed preferentially on Cu2O (111) at ΔG = −1.61 eV, whereas formic acid (HCOOH) and formaldehyde (HCOH) formation were thermodynamically unfavourable at interfacial sites. The CO2-to-methanol conversion on Cu2O (100) exhibited a total ΔG of −3.38 eV, indicating lower feasibility compared to Cu2O (111) with ΔG = −5.51 eV. These findings, which are entirely based on a computational approach, highlight the superior catalytic efficiency of Cu2O (111) for methanol synthesis. This approach also holds the potential for assessing the catalytic performance of other transition metal oxides (e.g., nickel oxide, cobalt oxide, zinc oxide, and molybdenum oxide) and their modified forms through doping or alloying with various elements.

Improving Bond Dissociations of Reactive Machine Learning Potentials through Physics-Constrained Data Augmentation.

In the field of computational chemistry, predicting bond dissociation energies (BDEs) presents well-known challenges, particularly due to the multireference character of reactive systems. Many chemical reactions involve configurations where single-reference methods fall short, as the electronic structure can significantly change during bond breaking. As generating training data for partially broken bonds is a challenging task, even state-of-the-art reactive machine learning interatomic potentials (MLIPs) often fail to predict reliable BDEs and smooth dissociation curves. By contrast, simple and inexpensive physics-based models, such as the well-established Morse potential, do not suffer from any such limitations. This work leverages the Morse potential to improve reactive MLIPs by augmenting the training data set with inexpensive Morse data along the dissociation pathways. This physics-constrained data augmentation (PCDA) approach results in MLIPs with smooth bond dissociation curves as well as near coupled-cluster level BDEs, all without requiring any expensive multireference quantum mechanical calculations. A case study for methane combustion demonstrates how the PCDA approach can improve an existing reactive MLIP, namely, ANI-1xnr. Not only are the BDEs and bond dissociation curves for all radicals and molecules significantly improved compared to ANI-1xnr but the PCDA-trained MLIP retains the reliability of ANI-1xnr when performing reactive molecular dynamics simulations.

Enhancing Activation Energy Predictions under Data Constraints Using Graph Neural Networks.

Accurately predicting activation energies is crucial for understanding chemical reactions and modeling complex reaction systems. However, the high computational cost of quantum chemistry methods often limits the feasibility of large-scale studies, leading to a scarcity of high-quality activation energy data. In this work, we explore and compare three innovative approaches (transfer learning, delta learning, and feature engineering) to enhance the accuracy of activation energy predictions using graph neural networks, specifically focusing on methods that incorporate low-cost, low-level computational data. Using the Chemprop model, we systematically evaluated how these methods leverage data from semiempirical quantum mechanics (SQM) calculations to improve predictions. Delta learning, which adjusts low-level SQM activation energies to align with high-level CCSD(T)-F12a targets, emerged as the most effective method, achieving high accuracy with substantially reduced data requirements. Notably, delta learning trained with just 20-30% of high-level data matched or exceeded the performance of other methods trained with full data sets, making it advantageous in data-scarce scenarios. However, its reliance on transition state searches imposes significant computational demands during model application. Transfer learning, which pretrains models on large data sets of low-level data, provided mixed results, particularly when there was a mismatch in the reaction distributions between the training and target data sets. Feature engineering, which involves adding computed molecular properties as input features, showed modest gains, particularly in thermodynamic properties. Our study highlights the trade-offs between accuracy and computational demand in selecting the best approach for enhancing activation energy predictions. These insights provide valuable guidelines for researchers aiming to apply machine learning in chemical reaction engineering, helping to balance accuracy with resource constraints.

Reaction of NHOs with Bisphosphanes - Designing Diradicaloids, Zwitterions and Radicals.

The linkage of an imidazole-based N-heterocyclic olefin (NHO), containing a terminal CH2 donor group, with a phosphorus-centered diradical molecular fragment leads to an open-shell singlet diphospha-indenylide system, a new class of P-heterocycles, which can be interpreted both as a phosphorus-centered diradicaloid and as a zwitterion with a permanent, overall charge separation between the N- and P-heterocyclic ring systems. The rotation of the imidazole ring, which is thermally possible due to a central C-C bond with a weakened π-component, changes both the charge separation and diradical character depending on the dihedral angle, as quantum mechanical calculations indicate. By varying the bulkiness of substituents at the imidazole-based NHO, it was possible to obtain different diphospha-indenylide species with different rotation angles in the solid state and hence varying diradical character. Imidazolium-diphospha-indenyli-des represent a new class of NHO-based zwitterions with diradical character. Their synthesis, structure, and activation chemistry are described, as well as the quantum mechanical description of the electronic structure in these unusual heterocycles. In addition, along the synthesis route to diphospha-indenylide, we also succeeded in isolating a highly reactive monoradical anion, which was also fully characterized.

Reaction of NHOs with Bisphosphanes – Designing Diradicaloids, Zwitterions and Radicals

The linkage of an imidazole‐based N‐heterocyclic olefin (NHO), containing a terminal CH2 donor group, with a phosphorus‐centered diradical molecular fragment leads to an open‐shell singlet diphospha‐indenylide system, a new class of P‐heterocycles, which can be interpreted both as a phosphorus‐centered diradicaloid and as a zwitterion with a permanent, overall charge separation between the N‐ and P‐heterocyclic ring systems. The rotation of the imidazole ring, which is thermally possible due to a central C‐C bond with a weakened π‐component, changes both the charge separation and diradical character depending on the dihedral angle, as quantum mechanical calculations indicate. By varying the bulkiness of substituents at the imidazole‐based NHO, it was possible to obtain different diphospha‐indenylide species with different rotation angles in the solid state and hence varying diradical character. Imidazolium‐diphospha‐indenyli­des represent a new class of NHO‐based zwitterions with diradical character. Their synthesis, structure, and activation chemistry are described, as well as the quantum mechanical description of the electronic structure in these unusual heterocycles. In addition, along the synthesis route to diphospha‐indenylide, we also succeeded in isolating a highly reactive monoradical anion, which was also fully characterized.

Computational assessment of the platinum (II) complex of imidazolidine dioximes substituted with methoxydiglycol units: from activation to DNA binding

To overcome the issue of toxicity inherent in the approved anticancer agents, many cisplatin derivatives continue to be designed and tested today. Recently, we have proposed a new series of platinum complexes with highly potent imidazolidine dioxime ligands, a combination of two active ligands targeting DNA, substituted with aliphatic carbon chains or aromatic moiety. Quantum mechanical calculations have revealed their potential activity, particularly highlighting the potentially enhanced activity of complexes with aliphatic substituents in DNA platination. Here, we extend our previous study with the inclusion of methoxydiglycol unit (2-(2-methoxyethoxy)ethyl substituents) into the imidazolidine dioxime ligands of our previously proposed complexes to possibly enhance the solubility and bioavailability. Our current investigation focuses on modeling the aquation and DNA binding reactions to assess the potential of this modification and the results are compared to cisplatin.

Computational Methods for Predicting Chemical Reactivity of Covalent Compounds.

In recent decades, covalent inhibitors have emerged as a promising strategy for therapeutic development, leveraging their unique mechanism of forming covalent bonds with target proteins. This approach offers advantages such as prolonged drug efficacy, precise targeting, and the potential to overcome resistance. However, the inherent reactivity of covalent compounds presents significant challenges, leading to off-target effects and toxicities. Accurately predicting and modulating this reactivity have become a critical focus in the field. In this work, we compiled a data set of 419 cysteine-targeted covalent compounds and their reactivity through an extensive literature review. Employing machine learning, deep learning, and quantum mechanical calculations, we evaluated the intrinsic reactivity of the covalent compounds. Our FP-Stack models demonstrated robust Pearson and Spearman correlations of approximately 0.80 and 0.75 on the test set, respectively. This empowers rapid and accurate reactivity predictions, significantly reducing computational costs and streamlining structural handling and experimental procedures. Experimental validation on acrylamide compounds underscored the predictive efficacy of our model. This study presents an efficient computational tool for the reactivity prediction of covalent compounds and is expected to offer valuable insights for guiding covalent drug discovery and development.

Unraveling the structure-property relationships in fluorescent phenylhydrazones: the role of substituents and molecular interactions.

This study investigates the structure-property relationships of a series of phenylhydrazones bearing various electron-donating and electron-withdrawing substituents, such as methoxy, dimethylamino, morpholinyl, hydroxyl, chloro, bromo, and nitro groups. The compounds were synthesized, and their structures were characterized using single-crystal X-ray diffraction, powder X-ray diffraction, FTIR spectroscopy, NMR spectroscopy, and DSC. Three-dimensional excitation-emission matrix (3D-EEM) fluorescence spectroscopy and UV-Vis spectroscopy were employed to elucidate the complex interplay between the molecular skeleton, substituents, and the resulting photophysical properties. Quantum mechanical calculations provided further insights into the electronic structure and excited-state dynamics of the investigated compounds. The phenylhydrazones exhibited emission wavelengths ranging from 438 to 482 nm, with the molecular backbone playing a crucial role in determining the emission wavelength. The incorporation of electron-donating substituents, such as methoxy and dimethylamino groups, led to enhanced fluorescence intensity, while the presence of nitro groups, resulted in complete fluorescence quenching. This comprehensive study establishes rational design principles for the development of highly emissive phenylhydrazones with tuned photophysical properties and highlights the significance of the molecular skeleton in dictating the fluorescence behavior of this class of compounds.

Self-consistent electron density with shell structure using neural network-based Pauli potential.

The orbital-free density functional theory (OF-DFT) based method is a convenient tool to carry out electronic structure calculations scaling almost linearly with the number of electrons. However, the main impediment in the application of this method is the unavailability of the accurate form for the non-interacting kinetic energy functional in terms of electron density. The Pauli kinetic energy functional is the unknown part of the kinetic energy functional, and the corresponding Pauli potential appears in the governing Euler equation. In the present study, we present a feed-forward neural network (NN) approach to represent the Pauli potential of a group of atomic systems possessing spherically symmetric ground-state densities. This NN-based representation of Pauli potential combined with the Hohenberg-Kohn variational principle yields self-consistent radial densities that accurately exhibit the correct atomic shell structure. For this approach, the electron density in the form of a grid serves as the input to the NN model. In addition, we calculated the non-interacting kinetic energy by summing the Pauli kinetic energy, derived from the NN-based Pauli potential, and the von Weizsäcker kinetic energy. Our results demonstrate high accuracy for smaller atoms, while larger atoms exhibit greater deviations when compared with smaller atoms. The method presented in this paper provides an efficient way to calculate the Pauli potential and the Pauli kinetic energy without the need for functional derivatives. Our study represents a significant step forward in the application of machine learning techniques to OF-DFT, showcasing the potential of NNs in improving the accuracy and efficiency of quantum mechanical calculations in atomic systems.

Toward Grid-Based Models for Molecular Association.

This paper presents a grid-based approach to model molecular association processes as an alternative to sampling-based Markov models. Our method discretizes the six-dimensional space of relative translation and orientation into grid cells. By discretizing the Fokker-Planck operator governing the system dynamics via the square-root approximation, we derive analytical expressions for the transition rate constants between grid cells. These expressions depend on geometric properties of the grid, such as the cell surface area and volume, which we provide. In addition, one needs only the molecular energy at the grid cell center, circumventing the need for extensive MD simulations and reducing the number of energy evaluations to the number of grid cells. The resulting rate matrix is closely related to the Markov state model transition matrix, offering insights into metastable states and association kinetics. We validate the accuracy of the model in identifying metastable states and binding mechanisms, though improvements are necessary to address limitations like ignoring bulk transitions and anisotropic rotational diffusion. The flexibility of this grid-based method makes it applicable to a variety of molecular systems and energy functions, including those derived from quantum mechanical calculations. The software package MolGri, which implements this approach, offers a systematic and computationally efficient tool for studying molecular association processes.

Evolution of electronic structure and optical properties of naphthalenediimide dithienylvinylene (NDI-TVT) polymer as a function of reduction level: a density functional theory study.

Naphthalenediimide (NDI)-based donor-acceptor co-polymers with tunable electronic, optical, mechanical, and transport properties have shown immense potential as n-type conducting polymers in organic (opto)electronics. During the operation, the polymers undergo reduction at different charged states, which alters their (opto)electronic properties mainly due to the formation of the quasiparticles, polaron/bipolaron. The theoretical study based on quantum mechanical calculations can provide us with a detailed understanding of their (opto)electronic properties, which is missing to a great extent. To date, a theoretical understanding of how these properties vary with reduction levels for NDI-based polymers is completely missing. Herein, the evolution of the electronic structure and optical properties of the naphthalenediimide dithienylvinylene (NDI-TVT) polymer with varying reduction levels (Cred) is studied using density functional theory and time-dependent density functional theory, respectively, in the gaseous phase and solvent phase. We have envisaged that at lower reduction levels, Cred ≤ 100% (i.e., up to one negative charge per NDI moiety), only radical anions, i.e., polarons, are formed. The bipolarons are observed to be formed only at higher reduction levels, Cred > 100%. We note the coexistence of polarons and bipolarons for the intermediate reduction levels (100% < Cred < 200%). Finally, at 200% reduction levels, the presence of two electrons per NDI unit leads to the completely spin-resolved bipolaronic state formation, where one bipolaron is localized at every NDI unit. This aforementioned evolution of polarons and bipolarons with varying reduction levels is also prominently reflected in the calculated UV-vis-NIR absorption spectra. The detailed theoretical insights gained from the evolution of the (opto)electronic properties of NDI-TVT with reduction levels due to the formation of polaronic/bipolaronic states can guide the systematic design of n-type NDI-TVT-based (opto)electronic devices and in their advancement.

Simulations of γ-Valerolactone Solvents and Electrolytes for Lithium Batteries Using Polarizable Molecular Dynamics.

In this paper, we present a molecular dynamics study of the structural and dynamical properties of γ-valerolactone (GVL) both as a standalone solvent and in electrolyte formulations for electrochemistry applications. This study involves developing a new parameterization of a polarizable forcefield and applying it to simulate pure GVL and selected salt solutions. The forcefield was validated with experimental bulk data and quantum mechanical calculations, with excellent agreement obtained in both cases. Specifically, two 1M electrolyte solutions of lithium bis(fluorosulfonyl)imide and lithium bis(oxalate)borate in GVL were simulated, focusing on their ionic transport and highlighting ion solvation structure. Ion pairing in the electrolytes was also investigated through enhanced sampling molecular dynamics, obtaining a detailed picture of the ion dynamics in the GVL solution.

Anti-CRISPR proteins in Gluconobacter oxydans inactivate FnCas12a by acetylation.

Gluconobacter oxydans is an important chassis cell for one-step production of vitamin C. Previous studies reported that CRISPR/Cas12a is naturally inactivated in G. oxydans, but the specific mechanism remains unclear. Here, we identified anti-CRISPR proteins AcrVA6, AcrVA7 and AcrVA8 in G. oxydans. They functioned as acetyltransferases to inactivate FnCas12a by respectively acetylating Lys671, Lys589 and Lys823 of FnCas12a. Lys671 and Lys823 were related residues that recognise the protospacer-adjacent motif, modification of AcrVA6 and AcrVA8 untangled the interaction between FnCas12a and dsDNA, while Lys589 played an important role in binding to the crRNA-target DNA heteroduplex, AcrVA7 prevented the formation of FnCas12a-crRNA binary complexes. In addition, histone deacetylase HDAC11 was found to prevent modification of FnCas12a by AcrVA6. Quantum mechanical calculations showed that ser37 of AcrVA6, as an intermediate between acetyl group and receptor protein, achieves acetylation through ping-pong transfer mechanism. Finally, the acetyltransferase AcrVA6 and the deacetylase HDAC11 served as photoswitches by writing and erasing acetyl groups, respectively, to achieve continuous on-off of FnCas12a. Our study reveals different mechanisms by which acetyltransferase inactivates Cas12a and successfully applies reversible acetylation to the regulation of gene editing tools, providing new insights into the function and application of acetylation.

Antimicrobial Properties of Monomeric and Dimeric Catanionic Surfactant System.

Cationic gemini surfactants are used due to their broad spectrum of activity, especially surface, anticorrosive and antimicrobial properties. Mixtures of cationic and anionic surfactants are also increasingly described. In order to investigate the effect of anionic additive on antimicrobial activity, experimental studies were carried out to obtain MIC (minimal inhibitory concentration) against E. coli and S. aureus bacteria. Two gemini surfactants (12-6-12 and 12-O-12) and two single quaternary ammonium salts (DTAB and DDAC) were analyzed. The most commonly used commercial compounds of this class, i.e., SDS and SL, were used as anionic additives. In addition, computer quantum mechanical studies were also carried out to confirm the relationship between the structure of the mixture and the activity. The obtained results of microbiological tests and quantum mechanical calculations are in agreement with each other and show the lack of synergism in catanionic mixtures in the case of antibacterial activity.

Evaluation of Machine Learning/Molecular Mechanics End-State Corrections with Mechanical Embedding to Calculate Relative Protein-Ligand Binding Free Energies.

The development of machine-learning (ML) potentials offers significant accuracy improvements compared to molecular mechanics (MM) because of the inclusion of quantum-mechanical effects in molecular interactions. However, ML simulations are several times more computationally demanding than MM simulations, so there is a trade-off between speed and accuracy. One possible compromise are hybrid machine learning/molecular mechanics (ML/MM) approaches with mechanical embedding that treat the intramolecular interactions of the ligand at the ML level and the protein-ligand interactions at the MM level. Recent studies have reported improved protein-ligand binding free energy results based on ML/MM using ANI-2x with mechanical embedding, arguing that intramolecular interactions like torsion potentials of the ligand are often the limiting factor for accuracy. This claim is evaluated based on 108 relative binding free energy calculations for four different benchmark systems. As an alternative strategy, we also tested a tool that fits the MM dihedral potentials to the ML level of theory. Fitting was performed with the ML potentials ANI-2x and AIMNet2, and, for the benchmark system TYK2, also with quantum-mechanical calculations using ωB97M-D3(BJ)/def2-TZVPPD. Overall, the relative binding free energy results from MM with Open Force Field 2.2.0, MM with ML-fitted torsion potentials, and the corresponding ML/MM end-state corrected simulations show no statistically significant differences in the mean absolute errors (between 0.8 and 0.9 kcal mol-1). This can probably be explained by the usage of the same MM parameters to calculate the protein-ligand interactions. Therefore, a well-parametrized force field is on a par with simple mechanical embedding ML/MM simulations for protein-ligand binding. In terms of computational costs, the reparametrization of poor torsional potentials is preferable over employing computationally intensive ML/MM simulations of protein-ligand complexes with mechanical embedding. Also, the refitting strategy leads to lower variances of the protein-ligand binding free energy results than the ML/MM end-state corrections. For free energy corrections with ML/MM, the results indicate that better convergence and more advanced ML/MM schemes will be required for applications in computer-guided drug discovery.