Enhancing microbial superoxide generation and conversion to hydroxyl radicals for enhanced bioremediation using iron-binding ligands.

Harnessing bacteria for superoxide production in bioremediation holds immense promise, yet its practical application is hindered by slow production rates and the relatively weak redox potential of superoxide. This study delves into a cost-effective approach to amplify superoxide production using an Arthrobacter strain, a prevalent soil bacterial genus. Our research reveals that introducing a carbon source along with specific iron-binding ligands, including deferoxamine (DFO), diethylenetriamine pentaacetate (DTPA), citrate, and oxalate, robustly augments microbial superoxide generation. Moreover, our findings suggest that these iron-binding ligands play a pivotal role in converting superoxide into hydroxyl radicals by modulating the electron transfer rate between Fe(III)/Fe(II) and superoxide. Remarkably, among the tested ligands, only DTPA emerges as a potent promoter of this conversion process when complexed with Fe(III). We identify an optimal Fe(III) to DTPA ratio of approximately 1:1 for enhancing hydroxyl radical production within the Arthrobacter culture. This research underscores the efficacy of simultaneously introducing carbon sources and DTPA in facilitating superoxide production and its subsequent conversion to hydroxyl radicals, significantly elevating bioremediation performance. Furthermore, our study reveals that DTPA augments superoxide production in cultures of diverse soils, with various soil microorganisms beyond Arthrobacter identified as contributors to superoxide generation. This emphasizes the universal applicability of DTPA across multiple bacterial genera. In conclusion, our study introduces a promising methodology for enhancing microbial superoxide production and its conversion into hydroxyl radicals. These findings hold substantial implications for the deployment of microbial reactive oxygen species in bioremediation, offering innovative solutions for addressing environmental contamination challenges.

GEMF: a novel geometry-enhanced mid-fusion network for PLA prediction.

Accurate prediction of protein-ligand binding affinity (PLA) is important for drug discovery. Recent advances in applying graph neural networks have shown great potential for PLA prediction. However, existing methods usually neglect the geometric information (i.e. bond angles), leading to difficulties in accurately distinguishing different molecular structures. In addition, these methods also pose limitations in representing the binding process of protein-ligand complexes. To address these issues, we propose a novel geometry-enhanced mid-fusion network, named GEMF, to learn comprehensive molecular geometry and interaction patterns. Specifically, the GEMF consists of a graph embedding layer, a message passing phase, and a multi-scale fusion module. GEMF can effectively represent protein-ligand complexes as graphs, with graph embeddings based on physicochemical and geometric properties. Moreover, our dual-stream message passing framework models both covalent and non-covalent interactions. In particular, the edge-update mechanism, which is based on line graphs, can fuse both distance and angle information in the covalent branch. In addition, the communication branch consisting of multiple heterogeneous interaction modules is developed to learn intricate interaction patterns. Finally, we fuse the multi-scale features from the covalent, non-covalent, and heterogeneous interaction branches. The extensive experimental results on several benchmarks demonstrate the superiority of GEMF compared with other state-of-the-art methods.

Computational Investigation of BMAA and Its Carbamate Adducts as Potential GluR2 Modulators.

Beta-N-methylamino-l-alanine (BMAA) is a potential neurotoxic nonprotein amino acid, which can reach the human body through the food chain. When BMAA interacts with bicarbonate in the human body, carbamate adducts are produced, which share a high structural similarity with the neurotransmitter glutamate. It is believed that BMAA and its l-carbamate adducts bind in the glutamate binding site of ionotropic glutamate receptor 2 (GluR2). Chronic exposure to BMAA and its adducts could cause neurological illness such as neurodegenerative diseases. However, the mechanism of BMAA action and its carbamate adducts bound to GluR2 has not yet been elucidated. Here, we investigate the binding modes and the affinity of BMAA and its carbamate adducts to GluR2 in comparison to the natural agonist, glutamate, to understand whether these can act as GluR2 modulators. Initially, we perform molecular dynamics simulations of BMAA and its carbamate adducts bound to GluR2 to examine the stability of the ligands in the S1/S2 ligand-binding core of the receptor. In addition, we utilize alchemical free energy calculations to compute the difference in the free energy of binding of the beta-carbamate adduct of BMAA to GluR2 compared to that of glutamate. Our findings indicate that carbamate adducts of BMAA and glutamate remain stable in the binding site of the GluR2 compared to BMAA. Additionally, alchemical free energy results reveal that glutamate and the beta-carbamate adduct of BMAA have comparable binding affinity to the GluR2. These results provide a rationale that BMAA carbamate adducts may be, in fact, the modulators of GluR2 and not BMAA itself.

Multireceptor Analysis for Evaluating the Antidiabetic Efficacy of Karanjin: A Computational Approach.

BACKGROUND: Diabetes mellitus, notably type 2, is a rising global health challenge, prompting the need for effective management strategies. Common medications such as metformin, insulin, repaglinide and sitagliptin can induce side effects like gastrointestinal disturbances, hypoglycemia, weight gain and specific organ risks. Plant-derived therapies like Karanjin from Pongamia pinnata present promising alternatives due to their historical use, holistic health benefits and potentially fewer adverse effects. This study employs in silico analysis to explore Karanjin's interactions with diabetes-associated receptors, aiming to unveil its therapeutic potential while addressing the limitations and side effects associated with conventional medications. METHODOLOGY: The research encompassed the selection of proteins from the Protein Data Bank (PDB), followed by structural refinement processes and optimization. Ligands such as Karanjin and standard drugs were retrieved from PubChem, followed by a comprehensive analysis of their ADMET profiling and pharmacokinetic properties. Protein-ligand interactions were evaluated through molecular docking using AutoDockTools 1.5.7, followed by the analysis of structural stability using coarse-grained simulations with CABS Flex 2.0. Molecular dynamics simulations were performed using Desmond 7.2 and the OPLS4 force field to explore how Karanjin interacts with proteins over 100 nanoseconds, focusing on the dynamics and structural stability. RESULTS: Karanjin, a phytochemical from Pongamia pinnata, shows superior drug candidate potential compared to common medications, offering advantages in efficacy and reduced side effects. It adheres to drug-likeness criteria and exhibits optimal ADMET properties, including moderate solubility, high gastrointestinal absorption and blood-brain barrier penetration. Molecular docking revealed Karanjin's highest binding energy against receptor 3L2M (Pig pancreatic alpha-amylase) at -9.1 kcal/mol, indicating strong efficacy potential. Molecular dynamics simulations confirmed stable ligand-protein complexes with minor fluctuations in RMSD and RMSF, suggesting robust interactions with receptors 3L2M. CONCLUSION: Karanjin demonstrates potential in pharmaceutical expansion for treating metabolic disorders such as diabetes, as supported by computational analysis. Prospects for Karanjin in pharmaceutical development include structural modifications for enhanced efficacy and safety. Nanoencapsulation may improve bioavailability and targeted delivery to pancreatic cells, while combination therapies could optimize treatment outcomes in diabetes management. Clinical trials and experimental studies are crucial to validate its potential as a novel therapeutic agent.

From Multiple Protein Docking to Protein-Protein Docking at Interactome Level.

Molecular docking is used to anticipate the optimal orientation of a particular molecule to a target to form a stable complex. It makes predictions about the 3D structure of any complex based on the binding characteristics of the ligand and the target receptor usually a protein. It is an exceptionally useful tool, which is used as a model to study how ligands attach to proteins. Docking can also be used for studying the interaction of ligands and proteins to analyze inhibitory efficacy. The ligand may also be a protein, making it possible to study interactions between two different proteins using the numerous docking tools available for basic research on protein interactions. The protein-protein docking is a crucial approach to understanding the protein interactions and predicting the structure of protein complexes that have not yet been experimentally determined. Moreover, the protein-protein interactions can predict the function of target proteins and the drug-like properties of molecules. Therefore, protein docking assists in uncovering insights into protein interactions and also aids in a better understanding of molecular pathways/mechanisms. This chapter comprehends the various tools for protein-protein docking (pairwise and multiple), including their methodologies and analysis of output as results.

Assessing Protein-Protein Docking Protocols: Case Studies of G-Protein-Coupled Receptor Interactions.

The interactions of G-protein-coupled receptors (GPCRs) with other proteins are critical in several cellular processes but resolving their structural dynamics remains challenging. An increasing number of GPCR complexes have been experimentally resolved but others including receptor variants are yet to be characterized, necessitating computational predictions of their interactions. Although integrative approaches with multi-scale simulations would provide rigorous estimates of their conformational dynamics, protein-protein docking remains a first tool of choice of many researchers due to the availability of open-source programs and easy to use web servers with reasonable predictive power. Protein-protein docking algorithms have limited ability to consider protein flexibility, environment effects, and entropy contributions and are usually a first step towards more integrative approaches. The two critical steps of docking: the sampling and scoring algorithms have improved considerably and their performance has been validated against experimental data. In this chapter, we provide an overview and generalized protocol of a few docking protocols using GPCRs as test cases. In particular, we demonstrate the interactions of GPCRs with extracellular protein ligands and an intracellular protein effectors (G-protein) predicted from docking approaches and test their limitations. The current chapter will help researchers critically assess docking protocols and predict experimentally testable structures of GPCR complexes.

MAGPIE: An interactive tool for visualizing and analyzing protein-ligand interactions.

Quantitative tools to compile and analyze biomolecular interactions among chemically diverse binding partners would improve therapeutic design and aid in studying molecular evolution. Here we present Mapping Areas of Genetic Parsimony In Epitopes (MAGPIE), a publicly available software package for simultaneously visualizing and analyzing thousands of interactions between a single protein or small molecule ligand (the "target") and all of its protein binding partners ("binders"). MAGPIE generates an interactive three-dimensional visualization from a set of protein complex structures that share the target ligand, as well as sequence logo-style amino acid frequency graphs that show all the amino acids from the set of protein binders that interact with user-defined target ligand positions or chemical groups. MAGPIE highlights all the salt bridge and hydrogen bond interactions made by the target in the visualization and as separate amino acid frequency graphs. Finally, MAGPIE collates the most common target-binder interactions as a list of "hotspots," which can be used to analyze trends or guide the de novo design of protein binders. As an example of the utility of the program, we used MAGPIE to probe how different antibody fragments bind a viral antigen; how a common metabolite binds diverse protein partners; and how two ligands bind orthologs of a well-conserved glycolytic enzyme for a detailed understanding of evolutionarily conserved interactions involved in its activation and inhibition. MAGPIE is implemented in Python 3 and freely available at https://github.com/glasgowlab/MAGPIE, along with sample datasets, usage examples, and helper scripts to prepare input structures.

Pills of Multi-Target H2S Donating Molecules for Complex Diseases.

Among the various drug discovery methods, a very promising modern approach consists in designing multi-target-directed ligands (MTDLs) able to modulate multiple targets of interest, including the pathways where hydrogen sulfide (H2S) is involved. By incorporating an H2S donor moiety into a native drug, researchers have been able to simultaneously target multiple therapeutic pathways, resulting in improved treatment outcomes. This review gives the reader some pills of successful multi-target H2S-donating molecules as worthwhile tools to combat the multifactorial nature of complex disorders, such as inflammatory-based diseases and cancer, as well as cardiovascular, metabolic, and neurodegenerative disorders.

Actinium chelation and crystallization in a macromolecular scaffold.

Targeted alpha therapy (TAT) pairs the specificity of antigen targeting with the lethality of alpha particles to eradicate cancerous cells. Actinium-225 [225Ac; t1/2 = 9.920(3) days] is an alpha-emitting radioisotope driving the next generation of TAT radiopharmaceuticals. Despite promising clinical results, a fundamental understanding of Ac coordination chemistry lags behind the rest of the Periodic Table due to its limited availability, lack of stable isotopes, and inadequate systems poised to probe the chemical behavior of this radionuclide. In this work, we demonstrate a platform that combines an 8-coordinate synthetic ligand and a mammalian protein to characterize the solution and solid-state behavior of the longest-lived Ac isotope, 227Ac [t1/2 = 21.772(3) years]. We expect these results to direct renewed efforts for 225Ac-TAT development, aid in understanding Ac coordination behavior relative to other +3 lanthanides and actinides, and more broadly inform this element's position on the Periodic Table.

A µ-opioid receptor modulator that works cooperatively with naloxone.

The µ-opioid receptor (µOR) is a well-established target for analgesia1, yet conventional opioid receptor agonists cause serious adverse effects, notably addiction and respiratory depression. These factors have contributed to the current opioid overdose epidemic driven by fentanyl2, a highly potent synthetic opioid. µOR negative allosteric modulators (NAMs) may serve as useful tools in preventing opioid overdose deaths, but promising chemical scaffolds remain elusive. Here we screened a large DNA-encoded chemical library against inactive µOR, counter-screening with active, G-protein and agonist-bound receptor to 'steer' hits towards conformationally selective modulators. We discovered a NAM compound with high and selective enrichment to inactive µOR that enhances the affinity of the key opioid overdose reversal molecule, naloxone. The NAM works cooperatively with naloxone to potently block opioid agonist signalling. Using cryogenic electron microscopy, we demonstrate that the NAM accomplishes this effect by binding a site on the extracellular vestibule in direct contact with naloxone while stabilizing a distinct inactive conformation of the extracellular portions of the second and seventh transmembrane helices. The NAM alters orthosteric ligand kinetics in therapeutically desirable ways and works cooperatively with low doses of naloxone to effectively inhibit various morphine-induced and fentanyl-induced behavioural effects in vivo while minimizing withdrawal behaviours. Our results provide detailed structural insights into the mechanism of negative allosteric modulation of the µOR and demonstrate how this can be exploited in vivo.

Rapid Protein-Ligand Affinity Determination by Photoinduced Hyperpolarized NMR.

The binding affinity determination of protein-ligand complexes is a cornerstone of drug design. State-of-the-art techniques are limited by lengthy and expensive processes. Building upon our recently introduced novel screening method utilizing photochemically induced dynamic nuclear polarization (photo-CIDNP) NMR, we provide the methodological framework to determine binding affinities within 5-15 min using 0.1 mg of protein. The accuracy of our method is demonstrated for the affinity constants of peptides binding to a PDZ domain and fragment ligands binding to the protein PIN1. The method can also be extended to measure the affinity of nonphoto-CIDNP-polarizable ligands in competition binding experiments. Finally, we demonstrate a strong correlation between the ligand-reduced signals in photo-CIDNP-based NMR fragment screening and the well-established saturation transfer difference (STD) NMR. Thus, our methodology measures protein-ligand affinities in the micro- to millimolar range in only a few minutes and informs on the binding epitope in a single-scan experiment, opening new avenues for early stage drug discovery approaches.

Exploring the role of neutral ligands in modulating the photoluminescence of samarium complexes with 1,1,1,5,5,5-hexafluoro-2,4-pentanedione.

Four eight-coordinated luminescent samarium complexes of type [Sm(hfpd)3L2] and [Sm(hfpd)3L'] [where hfpd = 1,1,1,5,5,5-Hexafluoro-2,4-pentanedione L = tri-octyl-phosphine oxide (TOPO) and L' = 1,10-phenanthroline (phen), neocuproine (neoc) and bathocuproine (bathoc) were synthesized via a stoichiometrically controlled approach. This allows for precise control over the stoichiometry of the complexes, leading to reproducible properties. This investigation focuses on understanding the impact of secondary ligands on the luminescent properties of these complexes. Infrared (IR) spectra provided information about the molecular structures, whereas 1H and 13C nuclear magnetic resonance (NMR) spectra confirmed these structural details along with the coordination of ligands to trivalent Sm ion. The UV-vis spectra revealed the molar absorption coefficient and absorption bands associated with the hfpd ligand and photoluminescence (PL) spectroscopy demonstrated intense orange-red emission (648 nm relative to 4G5/2 → 6H9/2) from the complexes. The Commission Internationale de l'Éclairage (CIE) triangles indicated that the complexes emitted warm orange red light with color coordinates (x, y) ranging from (0.62, 0.36) to (0.40, 0.27). The investigation of the band gap as well as color parameters confirms the utility of these complexes in displays and LEDs.

In vivo photocontrol of orexin receptors with a nanomolar light-regulated analogue of orexin-B.

Orexinergic neurons are critically involved in regulating arousal, wakefulness, and appetite. Their dysfunction has been associated with sleeping disorders, and non-peptide drugs are currently being developed to treat insomnia and narcolepsy. Yet, no light-regulated agents are available to reversibly control their activity. To meet this need, a photoswitchable peptide analogue of the endogenous neuroexcitatory peptide orexin-B was designed, synthesized, and tested in vitro and in vivo. This compound - photorexin - is the first photo-reversible ligand reported for orexin receptors. It allows dynamic control of activity in vitro (including almost the same efficacy as orexin-B, high nanomolar potency, and subtype selectivity to human OX2 receptors) and in vivo in zebrafish larvae by direct application in water. Photorexin induces dose- and light-dependent changes in locomotion and a reduction in the successive induction reflex that is associated with sleep behavior. Molecular dynamics calculations indicate that trans and cis photorexin adopt similar bent conformations and that the only discriminant between their structures and activities is the positioning of the N-terminus. This, in the case of the more active trans isomer, points towards the OX2 N-terminus and extra-cellular loop 2, a region of the receptor known to be involved in ligand binding and recognition consistent with a "message-address" system. Thus, our approach could be extended to several important families of endogenous peptides, such as endothelins, nociceptin, and dynorphins among others, that bind to their cognate receptors through a similar mechanism: a "message" domain involved in receptor activation and signal transduction, and an "address" sequence for receptor occupation and improved binding affinity.

Study of the antidiabetic mechanism of berberine compound on FOXO1 transcription factor through molecular docking and molecular dynamics simulations.

CONTEXT: Diabetes mellitus (DM) is a metabolic disorder disease that causes hyperglycemia conditions and associated with various chronic complications leading to mortality. Due to high toxicity of conventional diabetic drugs, the exploration of natural compounds as alternative diabetes treatments has been widely carried out. Previous in silico studies have highlighted berberine, a natural compound, as a promising alternative in antidiabetic therapy, potentially acting through various pathways, including the inhibition of the FOXO1 transcription factor in the gluconeogenesis pathway. However, the specific mechanism by which berberine interacts with FOXO1 remains unclear, and research in this area is relatively limited. Therefore, this study aims to determine the stability of berberine structure with FOXO1 based on RMSD, RMSF, binding energy, and trajectory analysis to determine the potential of berberine to inhibit the gluconeogenesis pathway. This research was conducted by in silico method with molecular docking using AutoDock4.2 and molecular dynamics study using Amber20, then visualized by VMD. METHODS: Docking between ligand and FOXO1 receptor was carried out with Autodock4.2. For molecular dynamics simulations, the force fields of DNA.OL15, protein.ff14SB, gaff2, and tip3p were used.

tracerDB: a crowdsourced fluorescent tracer database for target engagement analysis.

Investigating ligand-protein complexes is essential in the areas of chemical biology and drug discovery. However, detailed information on key reagents such as fluorescent tracers and associated data for the development of widely used bioluminescence resonance energy transfer (BRET) assays including NanoBRET, time-resolved Förster resonance energy transfer (TR-FRET) and fluorescence polarization (FP) assays are not easily accessible to the research community. We created tracerDB, a curated database of validated tracers. This resource provides an open access knowledge base and a unified system for tracer and assay validation. The database is freely available at https://www.tracerdb.org/ .

Insight into binding of endogenous neurosteroid ligands to the sigma-1 receptor.

The sigma-1 receptor (σ1R) is a non-opioid membrane receptor, which responds to a diverse array of synthetic ligands to exert various pharmacological effects. Meanwhile, candidates for endogenous ligands of σ1R have also been identified. However, how endogenous ligands bind to σ1R remains unknown. Here, we present crystal structures of σ1R from Xenopus laevis (xlσ1R) bound to two endogenous neurosteroid ligands, progesterone (a putative antagonist) and dehydroepiandrosterone sulfate (DHEAS) (a putative agonist), at 2.15-3.09 Å resolutions. Both neurosteroids bind to a similar location in xlσ1R mainly through hydrophobic interactions, but surprisingly, with opposite binding orientations. DHEAS also forms hydrogen bonds with xlσ1R, whereas progesterone interacts indirectly with the receptor through water molecules near the binding site. Binding analyses are consistent with the xlσ1R-neurosteroid complex structures. Furthermore, molecular dynamics simulations and structural data reveal a potential water entry pathway. Our results provide insight into binding of two endogenous neurosteroid ligands to σ1R.

In silico studies of the open form of human tissue transglutaminase.

Human tissue transglutaminase (tTG) is an intriguing multifunctional enzyme involved in various diseases, including celiac disease and neurological disorders. Although a number of tTG inhibitors have been developed, the molecular determinants governing ligand binding remain incomplete due to the lack of high-resolution structural data in the vicinity of its active site. In this study, we obtained the complete high-resolution model of tTG by in silico methods based on available PDB structures. We discovered significant differences in the active site architecture between our and known tTG models, revealing an additional loop which affects the ligand binding affinity. We assembled a library of new potential tTG inhibitors based on the obtained complete model of the enzyme. Our library substantially expands the spectrum of possible drug candidates targeting tTG and encompasses twelve molecular scaffolds, eleven of which are novel and exhibit higher binding affinity then already known ones, according to our in silico studies. The results of this study open new directions for structure-based drug design of tTG inhibitors, offering the complete protein model and suggesting a wide range of new compounds for further experimental validation.

Descriptive molecular pharmacology of the δ opioid receptor (DOR): A computational study with structural approach.

This work focuses on the δ receptor (DOR), a G protein-coupled receptor (GPCR) belonging to the opioid receptor group. DOR is expressed in numerous tissues, particularly within the nervous system. Our study explores computationally the receptor's interactions with various ligands, including opiates and opioid peptides. It elucidates how these interactions influence the δ receptor response, relevant in a wide range of health and pathological processes. Thus, our investigation aims to explore the significance of DOR as an incoming drug target for pain relief and neurodegenerative diseases and as a source for novel opioid non-narcotic analgesic alternatives. We analyze the receptor's structural properties and interactions using Molecular Dynamics (MD) simulations and Gaussian-accelerated MD across different functional states. To thoroughly assess the primary differences in the structural and conformational ensembles across our different simulated systems, we initiated our study with 1 µs of conventional Molecular Dynamics. The strategy was chosen to encompass the full activation cycle of GPCRs, as activation processes typically occur within this microsecond range. Following the cMD, we extended our study with an additional 100 ns of Gaussian accelerated Molecular Dynamics (GaMD) to enhance the sampling of conformational states. This simulation approach allowed us to capture a comprehensive range of dynamic interactions and conformational changes that are crucial for GPCR activation as influenced by different ligands. Our study includes comparing agonist and antagonist complexes to uncover the collective patterns of their functional states, regarding activation, blocking, and inactivation of DOR, starting from experimental data. In addition, we also explored interactions between agonist and antagonist molecules from opiate and opioid classifications to establish robust structure-activity relationships. These interactions have been systematically quantified using a Quantitative Structure-Activity Relationships (QSAR) model. This research significantly contributes to our understanding of this significant pharmacological target, which is emerging as an attractive subject for drug development.

Metal-ligand dual-site single-atom nanozyme mimicking urate oxidase with high substrates specificity.

In nature, coenzyme-independent oxidases have evolved in selective catalysis using isolated substrate-binding pockets. Single-atom nanozymes (SAzymes), an emerging type of non-protein artificial enzymes, are promising to simulate enzyme active centers, but owing to the lack of recognition sites, realizing substrate specificity is a formidable task. Here we report a metal-ligand dual-site SAzyme (Ni-DAB) that exhibited selectivity in uric acid (UA) oxidation. Ni-DAB mimics the dual-site catalytic mechanism of urate oxidase, in which the Ni metal center and the C atom in the ligand serve as the specific UA and O2 binding sites, respectively, characterized by synchrotron soft X-ray absorption spectroscopy, in situ near ambient pressure X-ray photoelectron spectroscopy, and isotope labeling. The theoretical calculations reveal the high catalytic specificity is derived from not only the delicate interaction between UA and the Ni center but also the complementary oxygen reduction at the beta C site in the ligand. As a potential application, a Ni-DAB-based biofuel cell using human urine is constructed. This work unlocks an approach of enzyme-like isolated dual sites in boosting the selectivity of non-protein artificial enzymes.