MolADI: A Web Server for Automatic Analysis of Protein–Small Molecule Dynamic Interactions

Protein–ligand interaction analysis is important for drug discovery and rational protein design. The existing online tools adopt only a single conformation of the complex structure for calculating and displaying the interactions, whereas both protein residues and ligand molecules are flexible to some extent. The interactions evolved with time in the trajectories are of greater interest. MolADI is a user-friendly online tool which analyzes the protein–ligand interactions in detail for either a single structure or a trajectory. Interactions can be viewed easily with both 2D graphs and 3D representations. MolADI is available as a web application.     

Pressure-Dependent Structure of Methanol–Water Mixtures up to 1.2 GPa: Neutron Diffraction Experiments and Molecular Dynamics Simulations

Total scattering structure factors of per-deuterated methanol and heavy water, CD₃OD and D₂O, have been determined across the entire composition range as a function of pressure up to 1.2 GPa, by neutron diffraction. The largest variations due to increasing pressure were observed below a scattering variable value of 5 Å⁻¹, mostly as shifts in terms of the positions of the first and second maxima. Molecular dynamics computer simulations, using combinations of all-atom potentials for methanol and various water force fields, were conducted at the experimental pressures with the aim of interpreting neutron diffraction results. The peak-position shifts mentioned above could be qualitatively reproduced by simulations, although in terms of peak intensities, the accord between neutron diffraction and molecular dynamics was much less satisfactory. However, bearing in mind that increasing pressure must have a profound effect on repulsive forces between neighboring molecules, the agreement between experiment and computer simulation can certainly be termed as satisfactory. In order to reveal the influence of changing pressure on local intermolecular structure in these “simplest of complex” hydrogen-bonded liquid mixtures, simulated structures were analyzed in terms of hydrogen bond-related partial radial distribution functions and size distributions of hydrogen-bonded cyclic entities. Distinct differences between pressure-dependent structures of water-rich and methanol-rich composition regions were revealed.     

Halogen Bonding in Haspin-Halogenated Tubercidin Complexes: Molecular Dynamics and Quantum Chemical Calculations

Haspin, an atypical serine/threonine protein kinase, is a potential target for cancer therapy. 5-iodotubercidin (5-iTU), an adenosine derivative, has been identified as a potent Haspin inhibitor in vitro. In this paper, quantum chemical calculations and molecular dynamics (MD) simulations were employed to identify and quantitatively confirm the presence of halogen bonding (XB), specifically halogen∙∙∙π (aromatic) interaction between halogenated tubercidin ligands with Haspin. Consistent with previous theoretical finding, the site specificity of the XB binding over the ortho-carbon is identified in all cases. A systematic increase of the interaction energy down Group 17, based on both quantum chemical and MD results, supports the important role of halogen bonding in this series of inhibitors. The observed trend is consistent with the experimental observation of the trend of activity within the halogenated tubercidin ligands (F < Cl < Br < I). Furthermore, non-covalent interaction (NCI) plots show that cooperative non-covalent interactions, namely, hydrogen and halogen bonds, contribute to the binding of tubercidin ligands toward Haspin. The understanding of the role of halogen bonding interaction in the ligand–protein complexes may shed light on rational design of potent ligands in the future.     

Molecular Dynamics Scoring of Protein–Peptide Models Derived from Coarse-Grained Docking

One of the major challenges in the computational prediction of protein–peptide complexes is the scoring of predicted models. Usually, it is very difficult to find the most accurate solutions out of the vast number of sometimes very different and potentially plausible predictions. In this work, we tested the protocol for Molecular Dynamics (MD)-based scoring of protein–peptide complex models obtained from coarse-grained (CG) docking simulations. In the first step of the scoring procedure, all models generated by CABS-dock were reconstructed starting from their original C-alpha trace representations to all-atom (AA) structures. The second step included geometry optimization of the reconstructed complexes followed by model scoring based on receptor–ligand interaction energy estimated from short MD simulations in explicit water. We used two well-known AA MD force fields, CHARMM and AMBER, and a CG MARTINI force field. Scoring results for 66 different protein–peptide complexes show that the proposed MD-based scoring approach can be used to identify protein–peptide models of high accuracy. The results also indicate that the scoring accuracy may be significantly affected by the quality of the reconstructed protein receptor structures.     

Machine Learning Models to Predict Protein–Protein Interaction Inhibitors

Protein–protein interaction (PPI) inhibitors have an increasing role in drug discovery. It is hypothesized that machine learning (ML) algorithms can classify or identify PPI inhibitors. This work describes the performance of different algorithms and molecular fingerprints used in chemoinformatics to develop a classification model to identify PPI inhibitors making the codes freely available to the community, particularly the medicinal chemistry research groups working with PPI inhibitors. We found that classification algorithms have different performances according to various features employed in the training process. Random forest (RF) models with the extended connectivity fingerprint radius 2 (ECFP4) had the best classification abilities compared to those models trained with ECFP6 o MACCS keys (166-bits). In general, logistic regression (LR) models had lower performance metrics than RF models, but ECFP4 was the representation most appropriate for LR. ECFP4 also generated models with high-performance metrics with support vector machines (SVM). We also constructed ensemble models based on the top-performing models. As part of this work and to help non-computational experts, we developed a pipeline code freely available.     

In Vitro Investigation of the Impact of Bacterial–Fungal Interaction on Carbapenem-Resistant Klebsiella pneumoniae

Fungal–bacterial co-culturing is a potential technique for the production of secondary metabolites with antibacterial activity. Twenty-nine fungal species were screened in a co-culture with carbapenem-resistant Klebsiella pneumoniae at different temperatures. A temperature of 37 ° showed inhibition of bacterial growth. Antimicrobial susceptibility testing for K. pneumoniae was conducted to compare antibiotic resistance patterns before and after the co-culture. Genotypic comparison of the K. pneumonia was performed using next generation sequencing (NGS). It was shown that two out of five K. pneumoniae, with sequence type ST 101 isolates, lost bla-_OXA48, bla-_CTX-M-14, tir, strA and strB genes after the co-culture with Scopulariopsis brevicaulis fungus. The other three isolates (ST 383 and 147) were inhibited in the co-culture but did not show any changes in resistance. The total ethyl acetate extract of the fungal–bacterial co-culture was tested against K. pneumoniae using a disc diffusion method. The concentration of the crude extract was 0.97 mg/µL which resulted in total inhibition of the bacteria. Using chromatographic techniques, the purified compounds were identified as 11-octadecenoic acid, 2,4-Di-tert-butylphenol, 2,3-Butanediol and 9-octadecenamide. These were tested against K. pneumoniae using the well diffusion method at a concentration of 85 µg/µL which resulted in total inhibition of bacteria. The co-culture results indicated that bacteria under chemical stress showed variable responses and induced fungal secondary metabolites with antibacterial activities.     

Ab-Initio Molecular Dynamics Simulation of Condensed-Phase Reactivity: The Electrolysis of Amino Acids and Peptides

Electrolysis is a potential candidate for a quick method of wastewater cleansing. However, it is necessary to know what compounds might be formed from bioorganic matter. We want to know if there are toxic intermediates and if it is possible to influence the product formation by the variation in initial conditions. In the present study, we use Car–Parrinello molecular dynamics to simulate the fastest reaction steps under such circumstances. We investigate the behavior of amino acids and peptides under anodic conditions. Such highly reactive situations lead to chemical reactions within picoseconds, and we can model the reaction mechanisms in full detail. The role of the electric current is to discharge charged species and, hence, to produce radicals from ions. This leads to ultra-fast radical reactions in a bulk environment, which can also be seen as redox reactions as the oxidation states change. In the case of amino acids, the educts can be zwitterionic, so we also observe complex acid–base chemistry. Hence, we obtain the full spectrum of condensed-phase chemistry.     

Copper(II) Complexes of 5–Fluoro–Salicylaldehyde: Synthesis,Characterization, Antioxidant Properties,Interaction with DNA and Serum Albumins

The synthesis, characterization and biological profile (antioxidant capacity, interaction with calf-thymus DNA and serum albumins) of five neutral copper(II) complexes of 5–fluoro–salicylaldehyde in the absence or presence of the N,N’–donor co–ligands 2,2′–bipyridylamine, 2,9–dimethyl–1,10–phenanthroline, 1,10–phenanthroline and 2,2′–bipyridine are presented herein. The compounds were characterized by physicochemical and spectroscopic techniques. The crystal structures of four complexes were determined by single-crystal X-ray crystallography. The ability of the complexes to scavenge 1,1–diphenyl–picrylhydrazyl and 2,2′–azinobis(3–ethylbenzothiazoline–6–sulfonic acid) radicals and to reduce H₂O₂ was investigated in order to evaluate their antioxidant activity. The interaction of the compounds with calf-thymus DNA possibly takes place via intercalation as suggested by UV–vis spectroscopy and DNA–viscosity titration studies and via competitive studies with ethidium bromide. The affinity of the complexes with bovine and human serum albumins was examined by fluorescence emission spectroscopy revealing the tight and reversible binding of the complexes with the albumins.     

Graph Neural Network for Protein–Protein Interaction Prediction: A Comparative Study

Proteins are the fundamental biological macromolecules which underline practically all biological activities. Protein–protein interactions (PPIs), as they are known, are how proteins interact with other proteins in their environment to perform biological functions. Understanding PPIs reveals how cells behave and operate, such as the antigen recognition and signal transduction in the immune system. In the past decades, many computational methods have been developed to predict PPIs automatically, requiring less time and resources than experimental techniques. In this paper, we present a comparative study of various graph neural networks for protein–protein interaction prediction. Five network models are analyzed and compared, including neural networks (NN), graph convolutional neural networks (GCN), graph attention networks (GAT), hyperbolic neural networks (HNN), and hyperbolic graph convolutions (HGCN). By utilizing the protein sequence information, all of these models can predict the interaction between proteins. Fourteen PPI datasets are extracted and utilized to compare the prediction performance of all these methods. The experimental results show that hyperbolic graph neural networks tend to have a better performance than the other methods on the protein-related datasets.     

Anticancer Profile of Rhodanines: Structure–Activity Relationship (SAR) and Molecular Targets—A Review

The rhodanine core is a well-known privileged heterocycle in medicinal chemistry. The rhodanines, as subtypes of thiazolidin-4-ones, show a broad spectrum of biological activity, including anticancer properties. This review aims to analyze the anticancer features of the rhodanines described over the last decade in the scientific literature. The structure–activity relationship of rhodanine derivatives, as well as some of the molecular targets, were discussed. The information contained in this review could be of benefit to the design of new, effective small molecules with anticancer potential among rhodanine derivatives or their related heterocycles.     

Supramolecular Chemistry: Host–Guest Molecular Complexes

In recent times, researchers have emphasized practical approaches for capturing coordinated and selective guest entrap. The physisorbed nanoporous supramolecular complexes have been widely used to restrain various guest species on compact supporting surfaces. The host–guest (HG) interactions in two-dimensional (2D) permeable porous linkages are growing expeditiously due to their future applications in biocatalysis, separation technology, or nanoscale patterning. The different crystal-like nanoporous network has been acquired to enclose and trap guest molecules of various dimensions and contours. The host centers have been lumped together via noncovalent interactions (such as hydrogen bonds, van der Waals (vdW) interactions, or coordinate bonds). In this review article, we enlighten and elucidate recent progress in HG chemistry, explored via scanning tunneling microscopy (STM). We summarize the synthesis, design, and characterization of typical HG structural design examined on various substrates, under ambient surroundings at the liquid-solid (LS) interface, or during ultrahigh vacuum (UHV). We emphasize isoreticular complexes, vibrant HG coordination, or hosts functional cavities responsive to the applied stimulus. Finally, we critically discuss the significant challenges in advancing this developing electrochemical field.     

Targeting Microglia in Alzheimer’s Disease: From Molecular Mechanisms to Potential Therapeutic Targets for Small Molecules

Alzheimer’s disease (AD) is a common, progressive, and devastating neurodegenerative disorder that mainly affects the elderly. Microglial dysregulation, amyloid-beta (Aβ) plaques, and intracellular neurofibrillary tangles play crucial roles in the pathogenesis of AD. In the brain, microglia play roles as immune cells to provide protection against virus injuries and diseases. They have significant contributions in the development of the brain, cognition, homeostasis of the brain, and plasticity. Multiple studies have confirmed that uncontrolled microglial function can result in impaired microglial mitophagy, induced Aβ accumulation and tau pathology, and a chronic neuroinflammatory environment. In the brain, most of the genes that are associated with AD risk are highly expressed by microglia. Although it was initially regarded that microglia reaction is incidental and induced by dystrophic neurites and Aβ plaques. Nonetheless, it has been reported by genome-wide association studies that most of the risk loci for AD are located in genes that are occasionally uniquely and highly expressed in microglia. This finding further suggests that microglia play significant roles in early AD stages and they be targeted for the development of novel therapeutics. In this review, we have summarized the molecular pathogenesis of AD, microglial activities in the adult brain, the role of microglia in the aging brain, and the role of microglia in AD. We have also particularly focused on the significance of targeting microglia for the treatment of AD.     

Kinetic Analysis and Structural Interpretation of Competitive Ligand Binding for NO Dioxygenation in Truncated Hemoglobin N

The conversion of nitric oxide (NO) into nitrate (NO₃^?) by dioxygenation protects cells from lethal NO. Starting from NO‐bound heme, the first step in converting NO into benign NO₃^? is the ligand exchange reaction FeNO+O₂→FeO₂+NO, which is still poorly understood at a molecular level. For wild‐type (WT) truncated hemoglobin N (trHbN) and its Y33A mutant, the calculated barriers for the exchange reaction differ by 1.5 kcal mol^?1, compared with 1.7 kcal mol^?1 from experiment. It is directly confirmed that the ligand exchange reaction is rate‐limiting in trHbN and that entropic contributions account for 75 % of the difference between the WT and the mutant. Residues Tyr 33, Phe 46, Val 80, His 81, and Gln 82 surrounding the active site are expected to control the reaction path. By comparison with electronic structure calculations, the transition state separating the two ligand‐bound states was assigned to a ²A state.     

Molecular Dynamics Simulation of the Binding Interaction between Hormone Glucagon Protein and Self-Assembled Monolayer Molecules

Restrained molecular dynamics simulations were performed to study the binding affinity of the peptide with alkanethiols of different tail-groups, S（CH2）7CH3, S（CH2）7OH and S（CH2）7COOH, which self-assembled on Au（111） surface in the presence of water molecules. The curves of binding affinity were calculated by fixing the center of mass of the peptide at various distances from the assembling surface. Simulation results show that the binding affin- ity is in the order as COOH-SAMs〉OH-SAMs〉CH3-SAMs, while 100% COOH-SAMs〉5% COOH-SAMS in concentration. The effects on binding affinity by different tail-groups were also studied. Results show that the binding affinity between COOH-SAMs and the peptide is bigger than those of the others and increasing the acidity of COOH-SAMs will result in stronger attractive power.     

Synthesis,Characterization and Biological Evaluation of Benzothiazole–Isoquinoline Derivative

Currently, no suitable clinical drugs are available for patients with neurodegenerative diseases complicated by depression. Based on a fusion technique to create effective multi–target–directed ligands (MTDLs), we synthesized a series of (R)–N–(benzo[d]thiazol–2–yl)–2–(1–phenyl–3,4–dihydroisoquinolin–2(1H)–yl) acetamides with substituted benzothiazoles and (S)–1–phenyl–1,2,3,4–tetrahydroisoquinoline. All compounds were tested for their inhibitory potency against monoamine oxidase (MAO) and cholinesterase (ChE) by in vitro enzyme activity assays, and further tested for their specific inhibitory potency against monoamine oxidase B (MAO–B) and butyrylcholinesterase (BuChE). Among them, six compounds (4b–4d, 4f, 4g and 4i) displayed excellent activity. The classical antidepressant forced swim test (FST) was used to verify the in vitro results, revealing that six compounds reduced the immobility time significantly, especially compound 4g. The cytotoxicity of the compounds was assessed by the MTT method and Acridine Orange (AO) staining, with cell viability found to be above 90% at effective compound concentrations, and not toxic to L929 cells reversibility, kinetics and molecular docking studies were also performed using compound 4g, which showed the highest MAO–B and BuChE inhibitory activities. The results of these studies showed that compound 4g binds to the primary interaction sites of both enzymes and has good blood–brain barrier (BBB) penetration. This study provides new strategies for future research on neurodegenerative diseases complicated by depression.     

Identification of MiRNA–Disease Associations Based on Information of Multi-Module and Meta-Path

Cumulative research reveals that microRNAs (miRNAs) are involved in many critical biological processes including cell proliferation, differentiation and apoptosis. It is of great significance to figure out the associations between miRNAs and human diseases that are the basis for finding biomarkers for diagnosis and targets for treatment. To overcome the time-consuming and labor-intensive problems faced by traditional experiments, a computational method was developed to identify potential associations between miRNAs and diseases based on the graph attention network (GAT) with different meta-path mode and support vector (SVM). Firstly, we constructed a multi-module heterogeneous network based on the meta-path and learned the latent features of different modules by GAT. Secondly, we found the average of the latent features with weight to obtain a final node representation. Finally, we characterized miRNA–disease-association pairs with the node representation and trained an SVM to recognize potential associations. Based on the five-fold cross-validation and benchmark datasets, the proposed method achieved an area under the precision–recall curve (AUPR) of 0.9379 and an area under the receiver–operating characteristic curve (AUC) of 0.9472. The results demonstrate that our method has an outstanding practical application performance and can provide a reference for the discovery of new biomarkers and therapeutic targets.     

Molecular and Crystal Magnetic Engineering of Polymetallic Coupling System: From Magnetic Molecules to Molecular Magnets

One of the main challenges in the field of molecular materials is the design of molecular ferromagnets. General design strategy includes two steps, that is molecular magnetic engineering and crystal magnetic engineering. The first step is the synthesis of ferromagnetically coupled polymetallic systems. The second step is the assembly of polymetallic systems with muti‐dimensional structure and exhibiting a ferromagnetic transition. This paper summarized the strategies of molecular design and crystal engineering allowed to obtain such systems and our efforts in the fields of molecular magnetism and molecular‐based magnets.     

Doxorubicin Encapsulation in Carbon Nanotubes Having Haeckelite or Stone–Wales Defects as Drug Carriers: A Molecular Dynamics Approach

Doxorubicin (DOX), a recognized anticancer drug, forms stable associations with carbon nanotubes (CNTs). CNTs when properly functionalized have the ability to anchor directly in cancerous tumors where the release of the drug occurs thanks to the tumor slightly acidic pH. Herein, we study the armchair and zigzag CNTs with Stone–Wales (SW) defects to rank their ability to encapsulate DOX by determining the DOX-CNT binding free energies using the MM/PBSA and MM/GBSA methods implemented in AMBER16. We investigate also the chiral CNTs with haeckelite defects. Each haeckelite defect consists of a pair of square and octagonal rings. The armchair and zigzag CNT with SW defects and chiral nanotubes with haeckelite defects predict DOX-CNT interactions that depend on the length of the nanotube, the number of present defects and nitrogen doping. Chiral nanotubes having two haeckelite defects reveal a clear dependence on the nitrogen content with DOX-CNT interaction forces decreasing in the order 0N > 4N > 8N. These results contribute to a further understanding of drug-nanotube interactions and to the design of new drug delivery systems based on CNTs.