Will We Witness Enzymatic or Pd-(Oligo)Peptide Catalysis in Suzuki Cross-Coupling Reactions?

Despite the development of numerous advanced ligands for Pd-catalyzed Suzuki cross-coupling reaction, the potential of (oligo)peptides serving as ligands remains unexplored. This study demonstrates via density functional theory (DFT) modeling that (oligo)peptide ligands can drive superior activity compared to classic phosphines in these reactions. The utilization of natural amino acids such as Met, SeMet, and His leads to strong binding of the Pd center, thereby ensuring substantial stability of the system. The increasing sustainability and economic viability of (oligo)peptide synthesis open new prospects for applying Pd-(oligo)peptide systems as greener catalysts. The feasibility of de novo engineering an artificial Pd-based enzyme for Suzuki cross-coupling is discussed, laying the groundwork for future innovations in catalytic systems.

Implementation of machine learning protocols to predict the hydrolysis reaction properties of organophosphorus substrates using descriptors of electron density topology.

Prediction of catalytic reaction efficiency is one of the most intriguing and challenging applications of machine learning (ML) algorithms in chemistry. In this study, we demonstrated a strategy for utilizing ML protocols applied to Quantum Theory of Atoms In Molecules (QTAIM) parameters to predict the ability of the A17 L47K catalytic antibody to covalently capture organophosphate pesticides. We found that the novel "composite" DFT functional B97-3c could be effectively employed for fast and accurate initial geometry optimization, aligning well with the input dataset creation. QTAIM descriptors proved to be well-established in describing the examined dataset using density-based and hierarchical clustering algorithms. The obtained clusters exhibited correlations with the chemical classes of the input compounds. The precise physical interpretation of the QTAIM properties simplifies the explanation of feature impact for both supervised and unsupervised ML protocols. It also enables acceleration in the search for entries with desired properties within large databases. Furthermore, our findings indicated that Ridge Regression with Laplacian kernel and CatBoost Regressor algorithms demonstrated suitable performance in handling small datasets with non-trivial dependencies. They were able to predict the actual reaction barrier values with a high level of accuracy. Additionally, the CatBoost Classifier proved reliable in discriminating between "active" and "inactive" compounds.

Novel 1,2,4-Triazole- and Tetrazole-Containing 4H-Thiopyrano[2,3-b]quinolines: Synthesis Based on the Thio-Michael/aza-Morita-Baylis-Hillman Tandem Reaction and Investigation of Antiviral Activity.

A novel method for synthesizing 1,2,4-triazole- and tetrazole-containing 4H-thiopyrano[2,3-b]quinolines using a new combination of the thio-Michael and aza-Morita-Baylis-Hillman reactions was developed. Target compounds were evaluated for their cytotoxicities and antiviral activities against influenza A/Puerto Rico/8/34 virus in MDCK cells. The compounds showed low toxicity and some exhibited moderate antiviral activity. Molecular docking identified the M2 channel and polymerase basic protein 2 as potential targets. We observed that the antiviral activity of thiopyrano[2,3-b]quinolines is notably affected by both the nature and position of the substituent within the tetrazole ring, as well as the substituent within the benzene moiety of quinoline. These findings contribute to the further search for new antiviral agents against influenza A viruses among derivatives of thiopyrano[2,3-b]quinoline.

Naphthyl-Substituted Indole and Pyrrole Carboxylic Acids as Effective Antibiotic Potentiators-Inhibitors of Bacterial Cystathionine γ-Lyase.

Over the past decades, the problem of bacterial resistance to most antibiotics has become a serious threat to patients' survival. Nevertheless, antibiotics of a novel class have not been approved since the 1980s. The development of antibiotic potentiators is an appealing alternative to the challenging process of searching for new antimicrobials. Production of H2S-one of the leading defense mechanisms crucial for bacterial survival-can be influenced by the inhibition of relevant enzymes: bacterial cystathionine γ-lyase (bCSE), bacterial cystathionine ß-synthase (bCBS), or 3-mercaptopyruvate sulfurtransferase (MST). The first one makes the main contribution to H2S generation. Herein, we present data on the synthesis, in silico analyses, and enzymatic and microbiological assays of novel bCSE inhibitors. Combined molecular docking and molecular dynamics analyses revealed a novel binding mode of these ligands to bCSE. Lead compound 2a manifested strong potentiating activity when applied in combination with some commonly used antibiotics against multidrug-resistant Acinetobacter baumannii, Pseudomonas aeruginosa, and methicillin-resistant Staphylococcus aureus. The compound was found to have favorable in vitro absorption, distribution, metabolism, excretion, and toxicity parameters. The high effectiveness and safety of compound 2a makes it a promising candidate for enhancing the activity of antibiotics against high-priority pathogens.

Using novel click chemistry algorithm to design D3R inhibitors as blood-brain barrier permeants.

Dopamine receptor D3 (D3R) has gained attention as a promising therapeutic target for neurological disorders. In this study, an innovative in silico click reaction strategy was employed to identify potential D3R binders. The ligand template, 1-phenyl-4-[4-(1H-1,2,3-triazol-5-yl)butyl]piperazine, with substitution at the 1,2,3-triazole ring, served as the starting point. Generated compounds underwent filtration based on their brain-to-blood concentration ratio (logBB), leading to the identification of 1-{4-[1-(decahydronaphthalen-1-yl)-1H-1,2,3-triazol-5-yl]butyl}-4-phenylpiperazine as the most promising candidate, displaying superior D3R affinity and blood-brain barrier (BBB) permeability compared to the reference ligand, eticlopride. Molecular dynamics simulations further supported these findings. This study presents a novel hit for designing D3R ligands and establishes a workflow utilizing in silico click chemistry to screen compounds with BBB permeability. The proposed click reaction-based algorithm holds significant potential as a valuable tool in the development of effective antipsychotic compounds.

Exhaustive in silico design and screening of novel antipsychotic compounds with improved pharmacodynamics and blood-brain barrier permeation properties.

Antipsychotic drugs or neuroleptics are widely used in the treatment of psychosis as a manifestation of schizophrenia and bipolar disorder. However, their effectiveness largely depends on the blood-brain barrier (BBB) permeation (pharmacokinetics) and drug-receptor pharmacodynamics. Therefore, in this study, we developed and implemented the in silico pipeline to design novel compounds (n = 260) as leads using the standard drug scaffolds with improved PK/PD properties from the standard scaffolds. As a result, the best candidates (n = 3) were evaluated in molecular docking to interact with serotonin and dopamine receptors. Finally, haloperidol (HAL) derivative (1-(4-fluorophenyl)-4-(4-hydroxy-4-{4-[(2-phenyl-1,3-thiazol-4-yl)methyl]phenyl}piperidin-1-yl)butan-1-one) was identified as a "magic shotgun" lead compound with better affinity to the 5-HT2A, 5-HT1D, D2, D3, and 5-HT1B receptors than the control molecule. Additionally, this hit substance was predicted to possess similar BBB permeation properties and much lower toxicological profiles in comparison to HAL. Overall, the proposed rational drug design platform for novel antipsychotic drugs based on the BBB permeation and receptor binding might be an invaluable asset for a medicinal chemist or translational pharmacologist.Communicated by Ramaswamy H. Sarma.

Emergence of an Auxin Sensing Domain in Plant-Associated Bacteria.

Bacteria have evolved a sophisticated array of signal transduction systems that allow them to adapt their physiology and metabolism to changing environmental conditions. Typically, these systems recognize signals through dedicated ligand binding domains (LBDs) to ultimately trigger a diversity of physiological responses. Nonetheless, an increasing number of reports reveal that signal transduction receptors also bind antagonists to inhibit responses mediated by agonists. The mechanisms by which antagonists block the downstream signaling cascade remain largely unknown. To advance our knowledge in this field, we used the LysR-type transcriptional regulator AdmX as a model. AdmX activates the expression of an antibiotic biosynthetic cluster in the rhizobacterium Serratia plymuthica. AdmX specifically recognizes the auxin phytohormone indole-3-acetic acid (IAA) and its biosynthetic intermediate indole-3-pyruvic acid (IPA) as signals. However, only IAA, but not IPA, was shown to regulate antibiotic production in S. plymuthica. Here, we report the high-resolution structures of the LBD of AdmX in complex with IAA and IPA. We found that IAA and IPA compete for binding to AdmX. Although IAA and IPA binding does not alter the oligomeric state of AdmX, IPA binding causes a higher degree of compactness in the protein structure. Molecular dynamics simulations revealed significant differences in the binding modes of IAA and IPA by AdmX, and the inspection of the three-dimensional structures evidenced differential agonist- and antagonist-mediated structural changes. Key residues for auxin binding were identified and an auxin recognition motif defined. Phylogenetic clustering supports the recent evolutionary emergence of this motif specifically in plant-associated enterobacteria. IMPORTANCE Although antagonists were found to bind different bacterial signal transduction receptors, we are still at the early stages of understanding the molecular details by which these molecules exert their inhibitory effects. Here, we provide insight into the structural changes resulting from the binding of an agonist and an antagonist to a sensor protein. Our data indicate that agonist and antagonist recognition is characterized by small conformational differences in the LBDs that can be efficiently transmitted to the output domain to modulate the final response. LBDs are subject to strong selective pressures and are rapidly evolving domains. An increasing number of reports support the idea that environmental factors drive the evolution of sensor domains. Given the recent evolutionary history of AdmX homologs, as well as their narrow phyletic distribution within plant-associated bacteria, our results are in accordance with a plant-mediated evolutionary process that resulted in the emergence of receptor proteins that specifically sense auxin phytohormones.

Can Modern Molecular Modeling Methods Help Find the Area of Potential Vulnerability of Flaviviruses?

Flaviviruses are single-stranded RNA viruses that have emerged in recent decades and infect up to 400 million people annually, causing a variety of potentially severe pathophysiological processes including hepatitis, encephalitis, hemorrhagic fever, tissues and capillaries damage. The Flaviviridae family is represented by four genera comprising 89 known virus species. There are no effective therapies available against many pathogenic flaviviruses. One of the promising strategies for flavivirus infections prevention and therapy is the use of neutralizing antibodies (NAb) that can disable the virus particles from infecting the host cells. The envelope protein (E protein) of flaviviruses is a three-domain structure that mediates the fusion of viral and host membranes delivering the infectious material. We previously developed and characterized 10H10 mAb which interacts with the E protein of the tick-borne encephalitis virus (TBEV) and many other flaviviruses' E proteins. The aim of this work was to analyze the structure of E protein binding sites recognized by the 10H10 antibody, which is reactive with different flavivirus species. Here, we present experimental data and 3D modeling indicating that the 10H10 antibody recognizes the amino acid sequence between the two cysteines C92-C116 of the fusion loop (FL) region of flaviviruses' E proteins. Overall, our results indicate that the antibody-antigen complex can form a rigid or dynamic structure that provides antibody cross reactivity and efficient interaction with the fusion loop of E protein.

In Vitro and In Silico Studies of Human Tyrosyl-DNA Phosphodiesterase 1 (Tdp1) Inhibition by Stereoisomeric Forms of Lipophilic Nucleosides: The Role of Carbohydrate Stereochemistry in Ligand-Enzyme Interactions.

Inhibition of human DNA repair enzyme tyrosyl-DNA phosphodiesterase 1 (Tdp1) by different chiral lipophilic nucleoside derivatives was studied. New Tdp1 inhibitors were found in the series of the studied compounds with IC50 = 2.7-6.7 µM. It was shown that D-lipophilic nucleoside derivatives manifested higher inhibition activity than their L-analogs, and configuration of the carbohydrate moiety can influence the mechanism of Tdp1 inhibition.

Design, Synthesis and Biological Evaluation of Neogliptin, a Novel 2-Azabicyclo[2.2.1]heptane-Based Inhibitor of Dipeptidyl Peptidase-4 (DPP-4).

Compounds that contain (R)-3-amino-4-(2,4,5-trifluorophenyl)butanoic acid substituted with bicyclic amino moiety (2-aza-bicyclo[2.2.1]heptane) were designed using molecular modelling methods, synthesised, and found to be potent DPP-4 (dipeptidyl peptidase-4) inhibitors. Compound 12a (IC50 = 16.8 ± 2.2 nM), named neogliptin, is a more potent DPP-4 inhibitor than vildagliptin and sitagliptin. Neogliptin interacts with key DPP-4 residues in the active site and has pharmacophore parameters similar to vildagliptin and sitagliptin. It was found to have a low cardiotoxic effect compared to sitagliptin, and it is superior to vildagliptin in terms of ADME properties. Moreover, compound 12a is stable in aqueous solutions due to its low intramolecular cyclisation potential. These findings suggest that compound 12a has unique properties and can act as a template for further type 2 diabetes mellitus drug development.

PCSK9 as a Target for Development of a New Generation of Hypolipidemic Drugs.

PCSK9 has now become an important target to create new classes of lipid-lowering drugs. The prevention of its interaction with LDL receptors allows an increase in the number of these receptors on the surface of the cell membrane of hepatocytes, which leads to an increase in the uptake of cholesterol-rich atherogenic LDL from the bloodstream. The PCSK9 antagonists described in this review belong to different classes of compounds, may have a low molecular weight or belong to macromolecular structures, and also demonstrate different mechanisms of action. The mechanisms of action include preventing the effective binding of PCSK9 to LDLR, stimulating the degradation of PCSK9, and even blocking its transcription or transport to the plasma membrane/cell surface. Although several types of antihyperlipidemic drugs have been introduced on the market and are actively used in clinical practice, they are not without disadvantages, such as well-known side effects (statins) or high costs (monoclonal antibodies). Thus, there is still a need for effective cholesterol-lowering drugs with minimal side effects, preferably orally bioavailable. Low-molecular-weight PCSK9 inhibitors could be a worthy alternative for this purpose.

Towards Understanding the Pathogenicity of DROSHA Mutations in Oncohematology.

Myelodysplastic syndrome (MDS) refers to a heterogeneous group of closely related clonal hematopoietic disorders, which are characterized by accumulation of somatic mutations. The acquired mutation burden is suggested to define the pathway and consequent phenotype of the pathology. Recent studies have called attention to the role of miRNA biogenesis genes in MDS progression; in particular, the mutational pressure of the DROSHA gene was determined. Therefore, this highlights the importance of studying the impact of all collected missense mutations found within the DROSHA gene in oncohematology that might affect the functionality of the protein. In this study, the selected mutations were extensively examined by computational screening, and the most deleterious were subjected to a further molecular dynamic simulation in order to uncover the molecular mechanism of the structural damage to the protein altering its biological function. The most significant effect was found for variants I625K, L1047S, and H1170D, presumably affecting the endonuclease activity of DROSHA. Such alterations arisen during MDS progression should be taken into consideration as evoking certain clinical traits in the malignifying clonal evolution.

Fluorescently Labeled PLGA Nanoparticles for Visualization In Vitro and In Vivo: The Importance of Dye Properties.

Fluorescently labeled nanoparticles are widely used for evaluating their distribution in the biological environment. However, dye leakage can lead to misinterpretations of the nanoparticles' biodistribution. To better understand the interactions of dyes and nanoparticles and their biological environment, we explored PLGA nanoparticles labeled with four widely used dyes encapsulated (coumarin 6, rhodamine 123, DiI) or bound covalently to the polymer (Cy5.5.). The DiI label was stable in both aqueous and lipophilic environments, whereas the quick release of coumarin 6 was observed in model media containing albumin (42%) or liposomes (62%), which could be explained by the different affinity of these dyes to the polymer and lipophilic structures and which we also confirmed by computational modeling (log PDPPC/PLGA: DiI-2.3, Cou6-0.7). The importance of these factors was demonstrated by in vivo neuroimaging (ICON) of the rat retina using double-labeled Cy5.5/Cou6-nanoparticles: encapsulated Cou6 quickly leaked into the tissue, whereas the stably bound Cy.5.5 label remained associated with the vessels. This observation is a good example of the possible misinterpretation of imaging results because the coumarin 6 distribution creates the impression that nanoparticles effectively crossed the blood-retina barrier, whereas in fact no signal from the core material was found beyond the blood vessels.

Pipeline for Analyzing Activity of Metabolic Pathways in Planktonic Communities Using Metatranscriptomic Data.

In this article, we present our novel pipeline for analysis of metabolic activity using a microbial community's metatranscriptome sequence data set for validation. Our method is based on expectation-maximization (EM) algorithm and provides enzyme expression and pathway activity levels. Further expanding our analysis, we consider individual enzymatic activity and compute enzyme participation coefficients to approximate the metabolic pathway activity more accurately. We apply our EM pathways pipeline to a metatranscriptomic data set of a plankton community from surface waters of the Northern Gulf of Mexico. The data set consists of RNA-seq data and respective environmental parameters, which were sampled at two depths, six times a day over multiple 24-hour cycles. Furthermore, we discuss microbial dependence on day-night cycle within our findings based on a three-way correlation of the enzyme expression during antipodal times-midnight and noon. We show that the enzyme participation levels strongly affect the metabolic activity estimates: that is, marginal and multiple linear regression of enzymatic and metabolic pathway activity correlated significantly with the recorded environmental parameters. Our analysis statistically validates that EM-based methods produce meaningful results, as our method confirms statistically significant dependence of metabolic pathway activity on the environmental parameters, such as salinity, temperature, brightness, and a few others.

Accurate assembly of minority viral haplotypes from next-generation sequencing through efficient noise reduction.

Rapidly evolving RNA viruses continuously produce minority haplotypes that can become dominant if they are drug-resistant or can better evade the immune system. Therefore, early detection and identification of minority viral haplotypes may help to promptly adjust the patient's treatment plan preventing potential disease complications. Minority haplotypes can be identified using next-generation sequencing, but sequencing noise hinders accurate identification. The elimination of sequencing noise is a non-trivial task that still remains open. Here we propose CliqueSNV based on extracting pairs of statistically linked mutations from noisy reads. This effectively reduces sequencing noise and enables identifying minority haplotypes with the frequency below the sequencing error rate. We comparatively assess the performance of CliqueSNV using an in vitro mixture of nine haplotypes that were derived from the mutation profile of an existing HIV patient. We show that CliqueSNV can accurately assemble viral haplotypes with frequencies as low as 0.1% and maintains consistent performance across short and long bases sequencing platforms.

Unlocking capacities of viral genomics for the COVID-19 pandemic response.

More than any other infectious disease epidemic, the COVID-19 pandemic has been characterized by the generation of large volumes of viral genomic data at an incredible pace due to recent advances in high-throughput sequencing technologies, the rapid global spread of SARS-CoV-2, and its persistent threat to public health. However, distinguishing the most epidemiologically relevant information encoded in these vast amounts of data requires substantial effort across the research and public health communities. Studies of SARS-CoV-2 genomes have been critical in tracking the spread of variants and understanding its epidemic dynamics, and may prove crucial for controlling future epidemics and alleviating significant public health burdens. Together, genomic data and bioinformatics methods enable broad-scale investigations of the spread of SARS-CoV-2 at the local, national, and global scales and allow researchers the ability to efficiently track the emergence of novel variants, reconstruct epidemic dynamics, and provide important insights into drug and vaccine development and disease control. Here, we discuss the tremendous opportunities that genomics offers to unlock the effective use of SARS-CoV-2 genomic data for efficient public health surveillance and guiding timely responses to COVID-19.

Towards Understanding Afghanistan Pea Symbiotic Phenotype Through the Molecular Modeling of the Interaction Between LykX-Sym10 Receptor Heterodimer and Nod Factors.

The difference in symbiotic specificity between peas of Afghanistan and European phenotypes was investigated using molecular modeling. Considering segregating amino acid polymorphism, we examined interactions of pea LykX-Sym10 receptor heterodimers with four forms of Nodulation factor (NF) that varied in natural decorations (acetylation and length of the glucosamine chain). First, we showed the stability of the LykX-Sym10 dimer during molecular dynamics (MD) in solvent and in the presence of a membrane. Then, four NFs were separately docked to one European and two Afghanistan dimers, and the results of these interactions were in line with corresponding pea symbiotic phenotypes. The European variant of the LykX-Sym10 dimer effectively interacts with both acetylated and non-acetylated forms of NF, while the Afghanistan variants successfully interact with the acetylated form only. We additionally demonstrated that the length of the NF glucosamine chain contributes to controlling the effectiveness of the symbiotic interaction. The obtained results support a recent hypothesis that the LykX gene is a suitable candidate for the unidentified Sym2 allele, the determinant of pea specificity toward Rhizobium leguminosarum bv. viciae strains producing NFs with or without an acetylation decoration. The developed modeling methodology demonstrated its power in multiple searches for genetic determinants, when experimental detection of such determinants has proven extremely difficult.