Applications In Drug Design Research Articles

LogD7.4 prediction enhanced by transferring knowledge from chromatographic retention time, microscopic pKa and logP

Lipophilicity is a fundamental physical property that significantly affects various aspects of drug behavior, including solubility, permeability, metabolism, distribution, protein binding, and toxicity. Accurate prediction of lipophilicity, measured by the logD7.4 value (the distribution coefficient between n-octanol and buffer at physiological pH 7.4), is crucial for successful drug discovery and design. However, the limited availability of data for logD modeling poses a significant challenge to achieving satisfactory generalization capability. To address this challenge, we have developed a novel logD7.4 prediction model called RTlogD, which leverages knowledge from multiple sources. RTlogD combines pre-training on a chromatographic retention time (RT) dataset since the RT is influenced by lipophilicity. Additionally, microscopic pKa values are incorporated as atomic features, providing valuable insights into ionizable sites and ionization capacity. Furthermore, logP is integrated as an auxiliary task within a multitask learning framework. We conducted ablation studies and presented a detailed analysis, showcasing the effectiveness and interpretability of RT, pKa, and logP in the RTlogD model. Notably, our RTlogD model demonstrated superior performance compared to commonly used algorithms and prediction tools. These results underscore the potential of the RTlogD model to improve the accuracy and generalization of logD prediction in drug discovery and design. In summary, the RTlogD model addresses the challenge of limited data availability in logD modeling by leveraging knowledge from RT, microscopic pKa, and logP. Incorporating these factors enhances the predictive capabilities of our model, and it holds promise for real-world applications in drug discovery and design scenarios.Graphical

Semi-equivariant conditional normalizing flows, with applications to target-aware molecule generation

Learning over the domain of 3D graphs has applications in a number of scientific and engineering disciplines, including molecular chemistry, high energy physics, and computer vision. We consider a specific problem in this domain, namely: given one such 3D graph, dubbed the base graph, our goal is to learn a conditional distribution over another such graph, dubbed the complement graph. Due to the three-dimensional nature of the graphs in question, there are certain natural invariances such a distribution should satisfy: it should be invariant to rigid body transformations that act jointly on the base graph and the complement graph, and it should also be invariant to permutations of the vertices of either graph. We propose a general method for learning the conditional probabilistic model, the central part of which is a continuous normalizing flow. We establish semi-equivariance conditions on the flow which guarantee the aforementioned invariance conditions on the conditional distribution. Additionally, we propose a graph neural network architecture which implements this flow, and which is designed to learn effectively despite the typical differences in size between the base graph and the complement graph. We demonstrate the utility of our technique in the molecular setting by training a conditional generative model which, given a receptor, can generate ligands which may successfully bind to that receptor. The resulting model, which has potential applications in drug design, displays high quality performance in the key ΔBinding metric.

Exploring computational tools for improved structural design and stability of helical AApeptides

Peptidomimetics have garnered increased attention due to their unique structural properties and self-assembly, as well as their promising applications in material sciences and peptidomimetic drug design. N-acetylated-N-aminoethyl amino acid oligomers (AApeptides), a novel class of helical foldamer, have been effectively utilized to imitate protein helices and regulate protein-protein interactions. While the structures of a series of AApeptides have been determined experimentally, the chemical diversity of AApeptides is impeding the advancement of peptide inhibitor development and high-level architecture design through purely experimental methods. Consequently, there's an urgent need for effective computational tools to facilitate the structural design of more complex systems using AApeptides. While a general AMBER force field (GAFF) can be applied to simulate AApeptides, it is crucial to evaluate the accuracy of such a force field and consider alternatives to enhance accuracy. In this study, we employed molecular dynamics simulations (MD) to assess the stability of a helical AApeptide. Our findings indicate that GAFF alone is insufficient to stabilize the helical structure of our AApeptide. We suggest the use of restraints derived from experimentally determined structures to guide the simulation and maintain this helical structure. Although any set of restraint definitions used in this study can simulate helical packing, a minimum of three points per AApeptide building block restraint is necessary to accurately reproduce the hydrogen bonding pattern.

Sulfation Effects of Chondroitin Sulfate to Bind a Chemokine in Aqueous Medium: Conformational Heterogeneity and Dynamics from Molecular Simulation.

The sulfation patterns and degree of sulfation of chondroitin sulfate (CS), an important class of glycosaminoglycans (GAG), and their interactions with chemokines are accountable for various diseases. To realize the underlying mechanism of such complex biological phenomena at a molecular level and their application in rational drug design, a study on conformations and dynamics of CSs is necessary. To explore this, in this study, we performed a series of atomistic molecular dynamics (MD) simulations with different sulfated variants of octadecasaccharide CS, like CS-C, CS-E, and CS-T, in their free forms and when bound to the protein chemokine CXCL8 dimer in an aqueous medium. The calculated binding free energy of CSs with the CXCL8 dimer is favorable, and the degree of sulfation favors the complexation process further with prominent hydrophobic and hydrogen-bonded interactions. We find that the recognition is associated with the configurational entropy loss of the CS molecules as calculated from the Gaussian mixture approach, which supports that the degree of sulfation regulates the process. Cluster analysis through the k-means algorithm and end-to-end distance measurement revealed that although the free CS molecules adopted linear conformations, the nonlinear conformations during binding with protein were noted. Adaptation of nonlinear forms in the bound forms is noteworthy for the less-sulfated CS-C and CS-E. Apart from favorable 4C1 conformations, the occasional appearance of skew-boat forms from the free-energy map of ring pucker for the GlcUA unit was observed, which remains unaffected by the sulfation. We find that during recognition, the average relaxation time of intra-CS and inter-CS-CXCL8 hydrogen bonds (HBs) is about a magnitude lesser than that of CS-water HBs, most prominent on the involvement of higher sulfated CS-T analogues. The translational motion of surrounded water molecules in CSs exhibited sublinear diffusion, and the degree of sublinearity increases around the heavily sulfated molecules due to the hindrance created by them as well as the presence of the chemokine and exhibited markedly slow heterogeneous diffusion.

Parallel G-quadruplex recognition by neomycin.

G-quadruplex-forming nucleic acids have evolved to have applications in biology, drug design, sensing, and nanotechnology, to name a few. Together with the structural understanding, several attempts have been made to discover and design new classes of chemical agents that target these structures in the hope of using them as future therapeutics. Here, we report the binding of aminoglycosides, in particular neomycin, to parallel G-quadruplexes that exist as G-quadruplex monomers, dimers, or compounds that have the propensity to form dimeric G-quadruplex structures. Using a combination of calorimetric and spectroscopic studies, we show that neomycin binds to the parallel G-quadruplex with affinities in the range of Ka ∼ 105-108M-1, which depends on the base composition, ability to form dimeric G-quadruplex structures, salt, and pH of the buffer used. At pH 7.0, the binding of neomycin was found to be electrostatically driven potentially through the formation of ion pairs formed with the quadruplex. Lowering the pH resulted in neomycin's association constants in the range of Ka ∼ 106-107M-1 in a salt dependent manner. Circular dichroism (CD) studies showed that neomycin's binding does not cause a change in the parallel conformation of the G-quadruplex, yet some binding-induced changes in the intensity of the CD signals were seen. A comparative binding study of neomycin and paromomycin using d(UG4T) showed paromomycin binding to be much weaker than neomycin, highlighting the importance of ring I in the recognition process. In toto, our results expanded the binding landscape of aminoglycosides where parallel G-quadruplexes have been discovered as one of the high-affinity sites. These results may offer a new understanding of some of the undesirable functions of aminoglycosides and help in the design of aminoglycoside-based G-quadruplex binders of high affinity.

Emerging Trends in Nanomaterial-Based Biomedical Aspects

Comprehending the interfacial interaction of nanomaterials (NMs) and biological systems is a significant research interest. NMs comprise various nanoparticles (NPs) like carbon nanotubes, graphene oxides, carbon dots, graphite nanopowders, etc. These NPs show a variety of interactions with biological interfaces via organic layers, therapeutic molecules, proteins, DNA, and cellular matrices. A number of biophysical and colloidal forces act at the morphological surface to regulate the biological responses of bio-nanoconjugates, imparting distinct physical properties to the NMs. The design of future-generation nano-tools is primarily based on the basic properties of NMs, such as shape, size, compositional, functionality, etc., with studies being carried out extensively. Understanding their properties promotes research in the medical and biological sciences and improves their applicability in the health management sector. In this review article, in-depth and critical analysis of the theoretical and experimental aspects involving nanoscale material, which have inspired various biological systems, is the area of focus. The main analysis involves different self-assembled synthetic materials, bio-functionalized NMs, and their probing techniques. The present review article focuses on recent emerging trends in the synthesis and applications of nanomaterials with respect to various biomedical applications. This article provides value to the literature as it summarizes the state-of-the-art nanomaterials reported, especially within the health sector. It has been observed that nanomaterial applications in drug design, diagnosis, testing, and in the research arena, as well as many fatal disease conditions like cancer and sepsis, have explored alongwith drug therapies and other options for the delivery of nanomaterials. Even the day-to-day life of the synthesis and purification of these materials is changing to provide us with a simplified process. This review article can be useful in the research sector as a single platform wherein all types of nanomaterials for biomedical aspects can be understood in detail.

Fluorescence molecular tomography for quantum yield and lifetime.

Fluorescence molecular tomography (FMT) is a promising modality for noninvasive imaging of internal fluorescence agents in biological tissues, especially in small animal models, with applications in diagnosis, therapy, and drug design. In this paper, we present a fluorescent reconstruction algorithm that combines time-resolved fluorescence imaging data with photon-counting microcomputed tomography (PCMCT) images to estimate the quantum yield and lifetime of fluorescent markers in a mouse model. By incorporating PCMCT images, a permissible region of interest of fluorescence yield and lifetime can be roughly estimated as prior knowledge, reducing the number of unknown variables in the inverse problem and improving the image reconstruction stability. Our numerical experiments demonstrate the accuracy and stability of the proposed reconstruction method in the presence of data noise, achieving a reconstruction error of 0.02ns for the fluorescence lifetime and an average relative error of 18% for quantum yield reconstruction.

Computational Investigation of Conformational Properties of Short Azapeptides: Insights from DFT Study and NBO Analysis.

Azapeptides have gained much attention due to their ability to enhance the stability and bioavailability of peptide drugs. Their structural preferences, essential to understanding their function and potential application in the peptide drug design, remain largely unknown. In this work, we systematically investigated the conformational preferences of three azaamino acid residues in tripeptide models, Ac-azaXaa-Pro-NHMe [Xaa = Asn (4), Asp (5), Ala (6)], using the popular DFT functionals, B3LYP and B3LYP-D3. A solvation model density (SMD) was used to mimic the solvation effect on the conformational behaviors of azapeptides in water. During the calculation, we considered the impact of the amide bond in the azapeptide models on the conformational preferences of models 4-6. We analyzed the effect of the HB between the side-chain main chain and main-chain main-chain on the conformational behaviors of azapeptides 4-6. We found that the predicted lowest energy conformation for the three models differs depending on the calculation methods. In the gas phase, B3LYP functional indicates that the conformers tttANP-1 and tttADP-1 of azapeptides 4 and 5 correspond to the type I of β-turn, the lowest energy conformation with all-trans amide bonds. Considering the dispersion correction, B3LYP-D3 functional predicts the conformers tctANP-2 and tctADP-3 of azapeptide 4 and 5, which contain the cis amide bond preceding the Pro residue, as the lowest energy conformation in the gas phase. The results imply that azaAsx and Pro residues may involve cis-trans isomerization in the gas phase. In water, the predicted lowest energy conformer of azapeptides 4 and 5 differs from the gas phase results and depends on the calculational method. For azapeptide 6, regardless of calculation methods and phases, tttAAP-1 (β-I turn) is predicted as the lowest energy conformer. The results imply that the effect of the side chain that can form HBs on the conformational preferences of azapeptides 4 and 5 may not be negligible. We compared the theoretical results of azaXaa-Pro models with those of Pro-azaXaa models, showing that incorporating azaamino acid residue in peptides at different positions can significantly impact the folding patterns and stability of azapeptides.

Indol-3-ylglyoxylamide as Privileged Scaffold in Medicinal Chemistry.

In recent years, indolylglyoxylamide-based derivatives have received much attention due to their application in drug design and discovery, leading to the development of a wide array of compounds that have shown a variety of pharmacological activities. Combining the indole nucleus, already validated as a "privileged structure," with the glyoxylamide function allowed for an excellent template to be obtained that is suitable to a great number of structural modifications aimed at permitting interaction with specific molecular targets and producing desirable therapeutic effects. The present review provides insight into how medicinal chemists have elegantly exploited the indolylglyoxylamide moiety to obtain potentially useful drugs, with a particular focus on compounds exhibiting activity in in vivo models or reaching clinical trials. All in all, this information provides exciting new perspectives on existing data that can be useful in further design of indolylglyoxylamide-based molecules with interesting pharmacological profiles. The aim of this report is to present an update of collection data dealing with the employment of this moiety in the rational design of compounds that are able to interact with a specific target, referring to the last 20 years.

BMaps: A Web Application for Fragment-Based Drug Design and Compound Binding Evaluation.

Fragment-based drug design uses data about where, and how strongly, small chemical fragments bind to proteins, to assemble new drug molecules. Over the past decade, we have been successfully using fragment data, derived from thermodynamically rigorous Monte Carlo fragment-protein binding simulations, in dozens of preclinical drug programs. However, this approach has not been available to the broader research community because of the cost and complexity of doing simulations and using design tools. We have developed a web application, called BMaps, to make fragment-based drug design widely available with greatly simplified user interfaces. BMaps provides access to a large repository (>550) of proteins with 100s of precomputed fragment maps, druggable hot spots, and high-quality water maps. Users can also employ their own structures or those from the Protein Data Bank and AlphaFold DB. Multigigabyte data sets are searched to find fragments in bondable orientations, ranked by a binding-free energy metric. The designers use this to select modifications that improve affinity and other properties. BMaps is unique in combining conventional tools such as docking and energy minimization with fragment-based design, in a very easy to use and automated web application. The service is available at https://www.boltzmannmaps.com.

Unraveling the Role of Hydrogen Bonds in Thrombin via Two Machine Learning Methods.

Hydrogen bonds play a critical role in the folding and stability of proteins, such as proteins and nucleic acids, by providing strong and directional interactions. They help to maintain the secondary and 3D structure of proteins, and structural changes in these molecules often result from the formation or breaking of hydrogen bonds. To gain insights into these hydrogen bonding networks, we applied two machine learning models - a logistic regression model and a decision tree model - to study four variants of thrombin: wild-type, ΔK9, E8K, and R4A. Our results showed that both models have their unique advantages. The logistic regression model highlighted potential key residues (GLU295) in thrombin's allosteric pathways, while the decision tree model identified important hydrogen bonding motifs. This information can aid in understanding the mechanisms of folding in proteins and has potential applications in drug design and other therapies. The use of these two models highlights their usefulness in studying hydrogen bonding networks in proteins.

Selective Deuteration Improves the Affinity of Adenosine A2A Receptor Ligands: A Computational Case Study with Istradefylline and Caffeine.

We used a range of computational techniques to assess the effect of selective C-H deuteration on the antagonist istradefylline affinity for the adenosine A2A receptor, which was discussed relative to its structural analogue caffeine, a well-known and likely the most widely used stimulant. The obtained results revealed that smaller caffeine shows high receptor flexibility and exchanges between two distinct poses, which agrees with crystallographic data. In contrast, the additional C8-trans-styryl fragment in istradefylline locks the ligand within a uniform binding pose, while contributing to the affinity through the C-H···π and π···π contacts with surface residues, which, together with its much lower hydration prior to binding, enhances the affinity over caffeine. In addition, the aromatic C8-unit shows a higher deuteration sensitivity over the xanthine part, so when both of its methoxy groups are d6-deuterated, the affinity improvement is -0.4 kcal mol-1, which surpasses the overall affinity gain of -0.3 kcal mol-1 in the perdeuterated d9-caffeine. Yet, the latter predicts around 1.7-fold potency increase, being relevant for its pharmaceutical implementations, and also those within the coffee and energy drink production industries. Still, the full potential of our strategy is achieved in polydeuterated d19-istradefylline, whose A2A affinity improves by -0.6 kcal mol-1, signifying a 2.8-fold potency increase that strongly promotes it as a potential synthetic target. This knowledge supports deuterium application in drug design, and while the literature already reports about over 20 deuterated drugs currently in the clinical development, it is easily foreseen that more examples will hit the market in the years to come. With this in mind, we propose that the devised computational methodology, involving the ONIOM division of the QM region for the ligand and the MM region for its environment, with an implicit quantization of nuclear motions relevant for the H/D exchange, allows fast and efficient estimates of the binding isotope effects in any biological system.

In Silico Approach to Construct the 3D Structures of Spike Glycoproteins of Novel Variants of Severe Acute Respiratory Syndrome Coronavirus 2

Objective: This study aims in constructing a three-dimensional modeled Spike glycoprotein structure of novel variants of SARS CoV-2. Methods: The protein models were constructed using SWISS-Model online tool. The constructed protein models were submitted in online database called Protein Model Database (PMDB) for public access to the structures. Results: A total of 70 protein sequences of Spike glycoprotein of novel variants of SARS CoV-2 were retrieved from NCBI virus database and were subjected for sequence similarity search and homology model construction. The constructed models were subjected for Ramachandran plot analysis to validate the quality of the structures. A total of 40 structures were considered to be of significant quality and were submitted to the online database PMDB. Conclusion: These predicted structures would help greatly in identification and drug design. This would greatly help in drug development and personalized drug treatment against different variants of the pathogen. This database would significantly support the structure-based computational drug design applications toward personalized medicine against the variants of concern of SARS CoV-2.

Reorganization Energy Predictions with Graph Neural Networks Informed by Low-Cost Conformers.

A critical bottleneck for the design of high-conductivity organic materials is finding molecules with low reorganization energy. To enable high-throughput virtual screening campaigns for many types of organic electronic materials, a fast reorganization energy prediction method compared to density functional theory is needed. However, the development of low-cost machine-learning-based models for calculating the reorganization energy has proven to be challenging. In this paper, we combine a 3D graph-based neural network (GNN) recently benchmarked for drug design applications, ChIRo, with low-cost conformational features for reorganization energy predictions. By comparing the performance of ChIRo to another 3D GNN, SchNet, we find evidence that the bond-invariant property of ChIRo enables the model to learn from low-cost conformational features more efficiently. Through an ablation study with a 2D GNN, we find that using low-cost conformational features on top of 2D features informs the model for making more accurate predictions. Our results demonstrate the feasibility of reorganization energy predictions on the benchmark QM9 data set without needing DFT-optimized geometries and demonstrate the types of features needed for robust models that work on diverse chemical spaces. Furthermore, we show that ChIRo informed with low-cost conformational features achieves comparable performance with the previously reported structure-based model on π-conjugated hydrocarbon molecules. We expect this class of methods can be applied to the high-throughput screening of high-conductivity organic electronics candidates.

Deep Learning Model for Efficient Protein-Ligand Docking with Implicit Side-Chain Flexibility.

Protein-ligand docking is an essential tool in structure-based drug design with applications ranging from virtual high-throughput screening to pose prediction for lead optimization. Most docking programs for pose prediction are optimized for redocking to an existing cocrystallized protein structure, ignoring protein flexibility. In real-world drug design applications, however, protein flexibility is an essential feature of the ligand-binding process. Flexible protein-ligand docking still remains a significant challenge to computational drug design. To target this challenge, we present a deep learning (DL) model for flexible protein-ligand docking based on the prediction of an intermolecular Euclidean distance matrix (EDM), making the typical use of iterative search algorithms obsolete. The model was trained on a large-scale data set of protein-ligand complexes and evaluated on independent test sets. Our model generates high quality poses for a diverse set of protein and ligand structures and outperforms comparable docking methods.

Room Temperature Synthesis of Bioactive 1,2,4-Oxadiazoles.

1,2,4-Oxadiazole is an essential motif in drug discovery represented in many experimental, investigational, and marketed drugs. This review covers synthetic methods that allow the conversion of different types of organic compounds into 1,2,4-oxadiazole at ambient temperature and the practical application of the latter approaches for the preparation of pharmaceutically important molecules. The discussed methods are divided into three groups. The first combines two-stage protocols requiring the preliminary preparation of O-acylamidoximes followed by cyclization under the action of organic bases. The advantages of this route are its swiftness, high efficiency of the cyclization process, and uncomplicated work-up. However, it requires the preparation and isolation of O-acylamidoximes as a separate preliminary step. The second route is a one-pot synthesis of 1,2,4-oxadiazoles directly from amidoximes and various carboxyl derivatives or aldehydes in aprotic bipolar solvents (primarily DMSO) in the presence of inorganic bases. This recently proposed pathway proved to be highly efficient in the field of medicinal chemistry. The third group of methods consists of diverse oxidative cyclizations, and these reactions have found modest application in drug design thus far. It is noteworthy that the reviewed methods allow for obtaining 1,2,4-oxadiazoles with thermosensitive functions and expand the prospects of using the oxadiazole core as an amide- or ester-like linker in the design of bioactive compounds.

Quantifying Functional-Group-like Structural Fragments in Molecules and Its Applications in Drug Design.

A functional group in a molecule is a structural fragment consisting of a few atoms or a single atom that imparts reactivity to a molecule. Hence, defining functional groups is crucial in chemistry to predict the properties and reactivities of molecules. However, there is no established method in the literature for defining functional groups based on reactivity parameters. In this work, we addressed this issue by designing a set of predefined structural fragments along with reactivity parameters like electron conjugation and ring strain. This approach uses bond orders and atom connectivities to quantify the presence of these fragments within an organic molecule based on a given input molecular coordinate. To assess the effectiveness of this approach, we performed a case study to show the benefits of using these newly designed structural fragments instead of traditional fingerprint-based methods for grouping potential COX1/COX2 inhibitors by screening an approved drug library against aspirin molecule. The structural fragment-based model for ternary classification of rat oral LD50 of chemicals showed performance similar to the fingerprint-based models. In evaluating the regression model performance for aqueous solubility, log(S), predictions, our approach outperformed the fingerprint-based model.

State of the Art of Molecular Visualization in Immersive Virtual Environments

AbstractVisualization plays a crucial role in molecular and structural biology. It has been successfully applied to a variety of tasks, including structural analysis and interactive drug design. While some of the challenges in this area can be overcome with more advanced visualization and interaction techniques, others are challenging primarily due to the limitations of the hardware devices used to interact with the visualized content. Consequently, visualization researchers are increasingly trying to take advantage of new technologies to facilitate the work of domain scientists. Some typical problems associated with classic 2D interfaces, such as regular desktop computers, are a lack of natural spatial understanding and interaction, and a limited field of view. These problems could be solved by immersive virtual environments and corresponding hardware, such as virtual reality head‐mounted displays. Thus, researchers are investigating the potential of immersive virtual environments in the field of molecular visualization. There is already a body of work ranging from educational approaches to protein visualization to applications for collaborative drug design. This review focuses on molecular visualization in immersive virtual environments as a whole, aiming to cover this area comprehensively. We divide the existing papers into different groups based on their application areas, and types of tasks performed. Furthermore, we also include a list of available software tools. We conclude the report with a discussion of potential future research on molecular visualization in immersive environments.

Repurposing of phyto-ligand molecules from the honey bee products for Alzheimer's disease as novel inhibitors of BACE-1: small molecule bioinformatics strategies as amyloid-based therapy.

Alzheimer's disease (AD) is one of the neurodegenerative diseases, manifesting dementia, spatial disorientation, language, cognitive, and functional impairment, mainly affects the elderly population with a growing concern about the financial burden on society. Repurposing can improve the traditional progress of drug design applications and could speed up the identification of innovative remedies for AD. The pursuit of potent anti-BACE-1 drugs for AD treatment has become a pot boiler topic in the recent past and to instigate the design of novel improved inhibitors from the bee products. Drug-likeness characteristics (ADMET: absorption, distribution, metabolism, excretion, and toxicity), docking (AutoDock Vina), simulation (GROMACS), and free energy interaction (MM-PBSA, molecular mechanics Poisson-Boltzmann surface area) analyses were performed to identify the lead candidates from the bee products (500 bioactives from the honey, royal jelly, propolis, bee bread, bee wax, and bee venom) for Alzheimer's disease as novel inhibitors of BACE-1 (beta-site amyloid precursor protein cleaving enzyme (1) receptor using appropriate bioinformatics tools. Forty-four bioactive lead compounds were screened from the bee products through high throughput virtual screening on the basis of their pharmacokinetic and pharmacodynamics characteristics, showing favorable intestinal and oral absorption, bioavailability, blood brain barrier penetration, less skin permeability, and no inhibition of cytochrome P450 inhibitors. The docking score of the forty-four ligand molecules was found to be between -4 and -10.3 kcal/mol, respectively, exhibiting strong binding affinity to BACE1 receptor. The highest binding affinity was observed in the rutin (-10.3 kcal/mol), 3,4-dicaffeoylquinic acid (-9.5 kcal/mol), nemorosone (-9.5 kcal/mol), and luteolin (-8.9 kcal/mol). Furthermore, these compounds demonstrated high total binding energy -73.20 to -105.85 kJ/mol), and low root mean square deviation (0.194-0.202 nm), root mean square fluctuation (0.0985-0.1136 nm), radius of gyration (2.12 nm), number of H-bonds (0.778-5.436), and eigenvector values (2.39-3.54 nm2) in the molecular dynamic simulation, signifying restricted motion of Cα atoms, proper folding and flexibility, and highly stable with compact of the BACE1 receptor with the ligands. Docking and simulation studies concluded that rutin, 3,4-dicaffeoylquinic acid, nemorosone, and luteolin are plausibly used as novel inhibitors of BACE1 to combat AD, but further in-depth experimental investigations are warranted to prove these in silico findings.

Type II Toxin-Antitoxin Systems in Pseudomonas aeruginosa.

Toxin-antitoxin (TA) systems are typically composed of a stable toxin and a labile antitoxin; the latter counteracts the toxicity of the former under suitable conditions. TA systems are classified into eight types based on the nature and molecular modes of action of the antitoxin component so far. The 10 pairs of TA systems discovered and experimentally characterised in Pseudomonas aeruginosa are type II TA systems. Type II TA systems have various physiological functions, such as virulence and biofilm formation, protection host against antibiotics, persistence, plasmid maintenance, and prophage production. Here, we review the type II TA systems of P. aeruginosa, focusing on their biological functions and regulatory mechanisms, providing potential applications for the novel drug design.