Ligand Binding Affinity Research Articles

Rendering Proteins Fluorescent Inconspicuously: Genetically Encoded 4-Cyanotryptophan Conserves Their Structure and Enables the Detection of Ligand Binding Sites.

Cyanotryptophans (CN-Trp) are privileged multimodal reporters on protein structure. They are similar in size to the canonical amino acid tryptophan and some of them exhibit bright fluorescence which responds sensitively to changes in the environment. We selected aminoacyl-tRNA synthetases specific for 4-, 5-, 6-, and 7-CN-Trp for high-yield in vivo production of proteins with a single, site-specifically introduced nitrile label. The absorption maximum of 4-CN-Trp is distinct from Trp, allowing the selective excitation of its intense fluorescence. 4-CN-Trp fluoresces in the visible range with an intensity rivalling that of 7-hydroxy-coumarin. Crystal structures of maltose binding protein demonstrate near-complete structural conservation when a native buried Trp residue is replaced by 4-CN-Trp. Besides presenting an inconspicuous tag for live cell microscopy, the high fluorescence of 4-CN-Trp enables measurements of subnanomolar ligand binding affinities in isotropic solution, as demonstrated by the complex between rapamycin and the peptidyl-prolyl isomerase FKBP12 furnished with a 4-CN-Trp residue in the substrate binding pocket. Furthermore, 4-CN-Trp residues positioned at different locations of a protein containing multiple tryptophan residues permits using fluorescence quenching experiments to detect the proximity of individual Trp residues to the binding site of aromatic ligands.

Design, synthesis and evaluation of benzothiazole-derived phenyl thioacetamides as dual inhibitors of monoamine oxidases and cholinesterases.

A series of rationally designed benzothiazole-derived thioacetamides was synthesized and investigated for monoamine oxidases (MAO-A and MAO-B) and cholinesterases (AChE and BChE) inhibition properties. The tested compounds 18-31 inhibited MAO-A and MAO-B in the micromolar to nanomolar range and AChE in the submicromolar range. Compound 28 was identified as the most potent MAO-A inhibitor with an IC50 = 0.030 ± 0.008 µM, whereas compound 30 showed the highest potency towards MAO-B and AChE with IC50 values of 0.015 ± 0.007 µM and 0.114 ± 0.003 µM, respectively. Further, compound 30 inhibited BChE at an IC50 value of 4.125 ± 0.143 µM. Among all screened molecules, compound 30 emerged as the lead dual MAO-B and AChE inhibitor that blocked these enzymes in a competitive-reversible and mixed-reversible mode, respectively. Selected compounds have displayed iron-chelation and antioxidant properties. Further, computational assessment of ligand binding affinity and pharmacokinetic parameters of all new compounds and molecular dynamic simulation of compound 30 with MAO-B and AChE were carried out to understand ligand efficiency, pharmacokinetic, and virtual molecular interaction profile, respectively. The in silico ADMET prediction studies revealed a few undesired pharmacokinetic attributes of our compounds. The attempted virtual lead-based library synthesis and subsequent biological investigation produced a new benzothiazole-bearing dual MAO-B and AChE inhibitor as a prospective MTDL candidate for treating neurological disorders.

Accurate prediction of protein–ligand interactions by combining physical energy functions and graph-neural networks

We introduce an advanced model for predicting protein–ligand interactions. Our approach combines the strengths of graph neural networks with physics-based scoring methods. Existing structure-based machine-learning models for protein–ligand binding prediction often fall short in practical virtual screening scenarios, hindered by the intricacies of binding poses, the chemical diversity of drug-like molecules, and the scarcity of crystallographic data for protein–ligand complexes. To overcome the limitations of existing machine learning-based prediction models, we propose a novel approach that fuses three independent neural network models. One classification model is designed to perform binary prediction of a given protein–ligand complex pose. The other two regression models are trained to predict the binding affinity and root-mean-square deviation of a ligand conformation from an input complex structure. We trained the model to account for both deviations in experimental and predicted binding affinities and pose prediction uncertainties. By effectively integrating the outputs of the triplet neural networks with a physics-based scoring function, our model showed a significantly improved performance in hit identification. The benchmark results with three independent decoy sets demonstrate that our model outperformed existing models in forward screening. Our model achieved top 1% enrichment factors of 32.7 and 23.1 with the CASF2016 and DUD-E benchmark sets, respectively. The benchmark results using the LIT-PCBA set further confirmed its higher average enrichment factors, emphasizing the model’s efficiency and generalizability. The model’s efficiency was further validated by identifying 23 active compounds from 63 candidates in experimental screening for autotaxin inhibitors, demonstrating its practical applicability in hit discovery.Scientific contributionOur work introduces a novel training strategy for a protein–ligand binding affinity prediction model by integrating the outputs of three independent sub-models and utilizing expertly crafted decoy sets. The model showcases exceptional performance across multiple benchmarks. The high enrichment factors in the LIT-PCBA benchmark demonstrate its potential to accelerate hit discovery.

ProteinReDiff: Complex-based ligand-binding proteins redesign by equivariant diffusion-based generative models

Proteins, serving as the fundamental architects of biological processes, interact with ligands to perform a myriad of functions essential for life. Designing functional ligand-binding proteins is pivotal for advancing drug development and enhancing therapeutic efficacy. In this study, we introduce ProteinReDiff, an diffusion framework targeting the redesign of ligand-binding proteins. Using equivariant diffusion-based generative models, ProteinReDiff enables the creation of high-affinity ligand-binding proteins without the need for detailed structural information, leveraging instead the potential of initial protein sequences and ligand SMILES strings. Our evaluations across sequence diversity, structural preservation, and ligand binding affinity underscore ProteinReDiff's potential to advance computational drug discovery and protein engineering.

In silico sequential mutagenesis of the Carbohydrate Binding Module Family 32 (CBM32) enhances ligand binding affinity

Alginate lyase is a promising target for genetic modification for its degrading biofilm, contributing to bacterial proliferation and antimicrobial resistance. Apart from the main enzyme, the carbohydrate binding module (CBM) component can also be modified to enhance alginate lyase’s activity. This study aimed to perform sequential in silico mutagenesis, molecular docking of selected amino acid residues of Vibrio splendidus CBM32 and performed molecular dynamics (MD) simulations of the mutated structure to validate its ligand-binding efficacy. Seven residues were selected for mutagenesis based on the predicted bonds that formed between the CBM32 and the glucuronic acid ligand (LGU9). Four of seven sequential residue substitutions increased the ligand binding affinity cumulatively from -5.4 Kcal/mol to -6.9 Kcal/mol. The mutated CBM32 had similar MolProbity scores to the original V. splendidus CBM32 structure. From the post-MD simulation analysis, the mutated CBM32 had higher structural stability in a solvent system, a greater number of hydrogen bonds formed with ligand but a lower solvent-accessible surface area than the original structure. The sequential mutagenesis process significantly increased the ligand binding affinity of CBM32 while incurring a minimal change in the overall CBM32 structure. The information on these substituted residues would be beneficial for designing subsequent in vitro mutagenesis and enzymatic assays.

A head-to-head comparison of MM/PBSA and MM/GBSA in predicting binding affinities for the CB1 cannabinoid ligands.

The substantial increase in the number of active and inactive-state CB1 receptor experimental structures has provided opportunities for CB1 drug discovery using various structure-based drug design methods, including the popular end-point methods for predicting binding free energies-Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA) and Molecular Mechanics/Generalized Born Surface Area (MM/GBSA). In this study, we have therefore evaluated the performance of MM/PBSA and MM/GBSA in calculating binding free energies for CB1 receptor. Additionally, with both MM/PBSA and MM/GBSA being known for their highly individualized performance, we have evaluated the effects of various simulation parameters including the use of energy minimized structures, choice of solute dielectric constant, inclusion of entropy, and the effects of the five GB models. Generally, MM/GBSA provided higher correlations than MM/PBSA (rMM/GBSA = 0.433 - 0.652 vs. rMM/PBSA = 0.100 - 0.486) regardless of the simulation parameters, while also offering faster calculations. Improved correlations were observed with the use of molecular dynamics ensembles compared with energyminimized structures and larger solute dielectric constants. Incorporation of entropic terms led to unfavorable results for both MM/PBSA and MM/GBSA for a majority of the dataset, while the evaluation of the various GB models exerted a varying effect on both the datasets. The findings obtained in this study demonstrate the utility of MM/PBSA and MM/GBSA in predicting binding free energies for the CB1 receptor, hence providing a useful benchmark for their applicability in the endocannabinoid system as well as other G protein-coupled receptors. The study utilized the docked dataset (Induced Fit Docking with Glide XP scoring function) from Loo et al., consisting of 46 ligands-23 agonists and 23 antagonists. The equilibrated structures from Loo et al. were subjected to 30ns production simulations using GROMACS 2018 at 300K and 1atm with the velocity rescaling thermostat and the Parinello-Rahman barostat. AMBER ff99SB*-ILDN was used for the proteins, General Amber Force Field (GAFF) was used for the ligands, and Slipids parameters were used for lipids. MM/PBSA and MM/GBSA binding free energies were then calculated using gmx_MMPBSA. The solute dielectric constant was varied between 1, 2, and 4 to study the effect of different solute dielectric constants on the performance of MM/PB(GB)SA. The effect of entropy on MM/PB(GB)SA binding free energies was evaluated using the interaction entropy module implemented in gmx_MMPBSA. Five GB models, GBHCT, GBOBC1, GBOBC2, GBNeck, and GBNeck2, were evaluated to study the effect of the choice of GB models in the performance of MM/GBSA. Pearson correlation coefficients were used to measure the correlation between experimental and predicted binding free energies.

Biological and Biophysical Methods for Evaluation of Inhibitors of Sortase A in Staphylococcus aureus: An Overview.

Staphylococcus aureus, one of the most notorious pathogens, develops antibiotic resistance by the formation of a thick layer of exopolysaccharides known as biofilms. Sortase A, a transpeptidase responsible for biofilm formation and attachment to the host surface, has emerged as an important drug target for development of anti-virulence agents. A number of sortase A inhibitors, both peptide and non-peptides are reported which involved the use of several experiments which may provide insights regarding binding affinity, specificity, safety, and efficacy of ligands. In this review, we focus on the principles, pros and cons, and the type of information obtained from biophysical (FRET assay, Microscale Thermophoresis, Surface Plasmon Resonance, CD spectroscopy etc.) and biological (cell viability assay, biofilm formation assay, CLSM, western blot analysis, in vivo characterization on mice etc.) methods for estimation of probable sortase A inhibitors, which might be helpful to the researchers who might be interested to delve into the development of sortase A inhibitors as a drug, to address the burning question of antimicrobial resistance (AMR).

Comparison of Methodologies for Absolute Binding Free Energy Calculations of Ligands to Intrinsically Disordered Proteins.

Modulating the function of Intrinsically Disordered Proteins (IDPs) with small molecules is of considerable importance given the crucial roles of IDPs in the pathophysiology of numerous diseases. Reported binding affinities for ligands to diverse IDPs vary broadly, and little is known about the detailed molecular mechanisms that underpin ligand efficacy. Molecular simulations of IDP ligand binding mechanisms can help us understand the mode of action of small molecule inhibitors of IDP function, but it is still unclear how binding energies can be modeled rigorously for such a flexible class of proteins. Here, we compare alchemical absolute binding free energy calculations (ABFE) and Markov-State Modeling (MSM) protocols to model the binding of the small molecule 10058-F4 to a disordered peptide extracted from a segment of the oncoprotein c-Myc. The ABFE results produce binding energy estimates that are sensitive to the choice of reference structure. In contrast, the MSM results produce more reproducible binding energy estimates consistent with weak mM binding affinities and transient intermolecular contacts reported in the literature.

Structure‐Based Computational Approach in Search of the Potent Molecules Targeting Phosphoglycerate Dehydrogenase (PHGDH) Enzyme for Cancer Treatment

AbstractThis work aims to target phosphoglycerate dehydrogenase (PHGDH), a promising druggable target, that is overexpressed in various types of cancer. A structure‐based approach was employed to identify novel inhibitors against the enzyme. A common five‐feature pharmacophore model (RRHDA) was constructed using the active site co‐crystalized ligands. These chemical features were responsible for showing inhibition. The generated models were subsequently subjected to the validation method using a test set, receiver‐operator characteristic analysis, enrichment factor, and Güner–Henry studies. The validated models were subjected to the screening of a dataset of natural compounds. The screened unique natural compounds (1795) were further selected for the interaction analysis and study of ligand binding affinity considering the effect of the hydrogen bonding and desolvation and hydrophobic interactions contribution to the binding. The natural compounds which exhibited good ligand binding efficiency were selected and further subjected to pharmacokinetics and pharmacodynamic study. The natural compounds which exhibited good ligand binding efficiency were selected and further subjected to pharmacokinetics and pharmacodynamic study. The finalized complexes were selected for the simulation studies and MM/PBSA‐based binding free energy calculations. This study expands the possibilities for the development of shortlisted molecules as novel anti‐cancer compounds.

R150S mutation in the human oxytocin receptor: Gain-of-function effects and implication in autism spectrum disorder

This study investigates the rs547238576 (R150S) missense variant in the oxytocin receptor (OXTR) gene, previously observed through screening of rare variants in Japanese individuals with autism spectrum disorders (ASD). Contrary to the anticipated loss-of-function, R150S exhibits gain-of-function effects, enhancing oxytocin (OXT) sensitivity, ligand-binding affinity, and OXT-induced Ca2+ mobilization in vitro. This suggests R150S may alter OXT signaling, potentially contributing to the excitatory/inhibitory imbalance seen in ASD and other psychiatric disorders. Our findings underscore the significance of genetic variations in OXTR on functional activity and highlight the necessity for population-specific genetic study and in vitro analysis to elucidate genetic susceptibilities to neuropsychiatric conditions.

Cheminformatic evaluation of the multi-protein binding potential of some diselenide derivatives: A plausible drug discovery approach for leishmaniasis

Over twelve million individuals worldwide suffer from leishmaniasis, and an additional billion people are at risk in places where the disease is prevalent. This work was motivated by the lack of vaccination against leishmaniasis and the shortcomings of current anti-leishmanial treatments. Large compound libraries can be screened using computational methods, which can also be used to analyze pharmacokinetic features, explore protein–ligand interactions, and quickly, accurately, and precisely create novel pharmacological entities. Virtual docking screening was performed to assess the multi-protein targeting capability of certain diselenides using four Leishmania protein targets—2XOX, 3SPX, 5ZWY, and 6K91. Following the Lipinski filter's screening of a subset of analogues, density functional theory (DFT), molecular dynamics (MD) simulation analysis, and ADMET profiles were used to profile the lead compounds. The overall average binding affinity of ligands to target proteins is as follows: 3SPX (− 184.998 kcal/mol) > 6K91 (− 180.114 kcal/mol) > 2XOX (− 176.581 kcal/mol) > 5ZWY (− 157.198 kcal/mol). The focus is on ligands that target all investigated proteins with binding scores greater than the average binding scores for the proteins as a whole for which seven compounds were identified that also showed higher MolDock scores than both reference drugs (Miltefosine and Pentamidine). Further screening showed four compounds (4, 7, 25, and 26) passed Lipinski’s test, making them orally bioavailable with excellent ADMET properties that compared well to both reference medications. The DFT analysis and MD simulation results indicate these compounds' favorability, stability, and reactivity in binding to the studied targets, 3SPX and 6K91 with 25_3SPX and 4_6K91 showing the highest binding free energy (MM-GBSA) of − 88.35 kcal/mol and − 90.29 kcal/mol respectively. Conclusively, the chosen diselenides could be developed as prospective anti-leishmanial pharmacological compounds or as useful scaffolds for creating more potent anti-leishmanial drugs.

Reversal of insulin resistance to combat type 2 diabetes mellitus by newer thiazolidinedione's in fructose induced insulin resistant rats

In our pursuit of discovering new antidiabetic agents to manage type 2 diabetes mellitus (T2DM), our approach aimed to identify the bioactive feature/pharmacophore responsible for PPAR-γ expression, as it is accountable for the glucose homeostasis and lipid metabolism. This was achieved by pharmacophore model generation, screening of rationally designed newer thiazolidinedione's library, identifying synthesizing and characterizing the top ten molecules (5a-5j) for their (Invitro &invivo) antidiabetic activity. Preliminary screening of all the ligands by Invitro glucose uptake assay in L6 myotubes (skeletal muscle cell line of rats) revealed compound 5b and 5f stimulated the glucose uptake with 79.29 ± 1.02 % and 74.58 ± 1.02 % respectively compared to pioglitazone with 82.36 ± 0.98 %. This was validated by PPAR-γ TF expression assay, which highlighted a dose dependent increase in transactivation of PPAR-γ. These compounds 5b and 5f were evaluated in fructose induced insulin resistance rat model. Where the treatment with 5b and 5f markedly increased the exogenous clearance of glucose and exogenous insulin via OGTT and ITT respectively, also improved the glucose utilization by significantly increasing content of glycogen and uptake of glucose in rat hemidiaphragm and reversed insulin resistance. Likewise a significant decreased in the VLDL and triglyceride levels was seen in 5b and 5f treated groups compared to insulin resistant (IR) group. It improved glycogenesis by catabolism of glucose and maintained glycaemic control. Similarly it had marked action on enzymatic oxidative biomarkers. Compound 5b displayed better, improved T1/2 (half-life) of 4.21 h and Kel (elimination constant) of 0.381 was noticed in comparison to compound 5f indicating the pharmacokinetic profile. Insilico studies like DFT calculations refined the geometry of 5b and 5f ligands, docking and molecular simulation provided the insights in binding affinity, dynamic behaviour and stability of ligands in PPAR-γ ligand binding domain. MM/GBSA provided the energetics of 5b and 5f in binding pocket. Finally network pharmacology identified ADIPOQ (adiponectin), NR1C3 (PPAR-γ), SLC2A4 (GLUT4), and LEP (leptin) proteins associate with compound 5b and 5f and enriched in Adipocytokine pathway, and PPAR-γ signaling pathway.

Unanticipated mechanisms of covalent inhibitor and synthetic ligand cobinding to PPARγ.

Peroxisome proliferator-activated receptor gamma (PPARγ) is a nuclear receptor transcription factor that regulates gene expression programs in response to ligand binding. Endogenous lipids and synthetic ligands, including covalent antagonist inhibitors such as GW9662 and T0070907, are thought to compete for the orthosteric pocket in the ligand-binding domain (LBD). However, we previously showed that synthetic PPARγ ligands can cooperatively cobind with and reposition a bound endogenous orthosteric ligand to an alternate site, synergistically regulating PPARγ structure and function (Shang et al., 2018). Here, we reveal the structural mechanism of cobinding between a synthetic covalent antagonist inhibitor with other synthetic ligands. Biochemical and NMR data show that covalent antagonist inhibitors weaken-but do not prevent-the binding of other synthetic ligands via an allosteric mechanism rather than direct ligand clashing. The covalent ligands shift the LBD ensemble toward a transcriptionally repressive conformation, which structurally clashes with and reduces the orthosteric binding affinity of non-covalent synthetic ligands. Crystal structures reveal different non-covalent synthetic ligand-specific cobinding mechanisms ranging from alternate site binding to unexpectedly adopting an orthosteric binding mode by altering the covalent ligand binding pose. Our findings not only highlight the significant flexibility of the PPARγ orthosteric pocket and its ability to accommodate multiple ligands simultaneously, but also demonstrate that GW9662 and T0070907 should not be used as reliable chemical tools to inhibit the binding of other ligands to PPARγ.

Effect of Protein-Polarized Ligand Charges on Relative Protein Ligand Binding Affinities.

A major challenge in computer-aided drug design is predicting relative binding energies of different molecules to a target protein using fast and accurate free-energy calculation methods. Free-energy calculations are primarily computed by utilizing classical molecular dynamics simulations based on all-atom force fields (FF) to model the interactions in the system. The present standard classical all-atom FFs contain fixed partial charges on the atoms, and hence electrostatic interactions are modeled between them. The parametrization process to determine these partial charges usually relies on quantum mechanics or semiempirical calculations of the molecule in the gas phase or homogeneous water surrounding. These present standard parametrization schemes of the partial charges neglect, therefore, polarization effects from the protein surrounding. The absence of protein polarization effects can lead to significant errors in free-energy calculations in proteins. We present a parametrization scheme for the partial charges of ligands, named protein-induced polarization (PIP) charges, which account for the electrostatic polarization due to the protein surrounding. The scheme involves single-point quantum mechanics/molecular mechanics calculations of the ligand charges in the protein/water surrounding. Using PIP ligand partial charges, we have calculated the relative binding free energies (RBFEs) of well-studied protein-ligand systems. We show here that RBFEs computed with PIP charges are either significantly improved or at least comparable to those computed with nonpolarized standard GAFF charges. Overall, we present a simple-to-use parametrization scheme to include protein polarization in any type of binding free-energy calculations. The parametrization scheme increases the accuracy in RBFE calculations, while it does not add significant computation time to standard parametrization procedures.

Functional Linkers Support Targeting of Multivalent Tweezers to Taspase1.

Taspase 1 is a unique protease not only pivotal for embryonic development but also implicated in leukemias and solid tumors. As such, this enzyme is a promising while still challenging therapeutic target, and with its protein structure featuring a flexible loop preceding the active site a versatile model system for drug development. Supramolecular ligands provide a promising complementary approach to traditional small-molecule inhibitors. Recently, the multivalent arrangement of molecular tweezers allowed the successful targeting of Taspase 1's surface loop. With this study we now want to take the next logic step und utilize functional linker systems that not only allow the implementation of novel properties but also engage in protein surface binding. Consequently, we chose two different linker types differing from the original divalent assembly: a backbone with aggregation-induced emission (AIE) properties to enable monitoring of binding and a calix[4]arene scaffold initially pre-positioning the supramolecular binding units. With a series of four AIE-equipped ligands with stepwise increased valency we demonstrated that the functionalized AIE linkers approach ligand binding affinities in the nanomolar range and allow efficient proteolytic inhibition of Taspase 1. Moreover, implementation of the calix[4]arene backbone further enhanced the ligands' inhibitory potential, pointing to a specific linker contribution.

Electrodeposition of Au Nanowires via Amine Ligand-Induced Active Surface Growth.

Amine ligands have been widely applied as morphology-directing reagents in nanostructure synthesis. In this work, we explored the application of the amine ligands in the active surface growth mechanism in place of the strong thiolated ligands. Despite being weaker compared to the thiols, amine ligands such as aniline were also shown to be capable of facilitating the template-less electrodeposition of Au nanowires (NWs) on the substrate via the active surface growth mechanism. Given the close binding difference between the amine-grafted substrate and the ligands, substrate functionalization becomes critically important for effective construction of the active surface and the growth of the nanowires. Additionally, the growth with the amine ligands took place at more positive reduction potentials and is less prone to splitting and bundle formation. A systematic generality study revealed that besides the aromatic amines, long-chain aliphatic amines were also capable of facilitating nanowire growth. Given the weak binding affinity of the amine ligands, the Au NWs are readily accessible for further processing to generate sophisticated one-dimensional structures. As a demonstration, tandem electrodeposition was performed to directly obtain coaxial core-shell Au@Pt NWs with adjustable length, diameter, and shell thickness.

Molecular Docking and Pharmacokinetics Prediction of Piperine and Capsaicin as Human Pancreatic Lipase Inhibitors: An In Silico Study.

Introduction Obesity is a complexmultifaceted disease, characterized by excessive body fat accumulation. It is a major public health concern globally, affecting individuals of all ages, genders, and socioeconomic backgrounds. Lipase, a key enzyme involved in lipid metabolism, plays a crucial role in the hydrolysis of dietary fats. Pancreatic lipase performs hydrolysis of nearly 50%-70% of total dietary fats. Thus, inhibition of pancreatic lipase isrecognized as one of the strategies for managing obesity. Aim To predict the effect of phytocompounds from pepper as pancreatic lipase inhibitors using computational approaches. Methodology The drug-likeness and pharmacokinetic properties of compounds were evaluated using Lipinski rule of fiveand absorption, distribution, metabolism, and excretion (ADME) analysis, respectively. The drug score value was computed using Molinspiration, while the lipase inhibitor potential of ligands was evaluated using prediction of activity spectra for substances. Molecular docking was carried out to evaluate the stability and ligand binding affinity. Results Computational approaches identified bothpiperine and capsaicin as potential candidates, exhibiting favorable affinities with binding energy values of -9.9 and -7.7 kcal/mol, respectively. Both piperine and capsaicin interacted with Ser-152 and His-263, demonstrating their binding at the substrate binding site. Conclusions Findings provide insights into the underlying anti-obesity potential of these bioactive compounds from pepper and support further experimental investigations for obesity treatment.

Molecular docking of ferulic acid, bakuchiol and niazirin on peroxisome proliferator-activated receptor gamma (PPAR-γ) as anti-diabetic agents

Background: Diabetes mellitus remains one of the most dangerous illnesses worldwide. The PPAR-gamma protein plays a crucial role in lipid and carbohydrate metabolism, making it a key target for diabetes therapy. Research into plant-derived active compounds for diabetes treatment is increasingly important. Objective: This study aims to evaluate and analyze the interaction of ferulic acid, niazirin, and bakuchiol with the peroxisome proliferator-activated receptor gamma (PPAR-γ) protein using molecular docking. Methods: Molecular docking was employed to assess the interactions between ferulic acid, niazirin, bakuchiol, and the PPAR-γ protein (PDB ID: 2PRG). The analysis focused on binding free energy values, amino acid residue interactions, the number of hydrogen bonds, and bond distances, comparing these results to those of the native ligand and pioglitazone, a known anti-diabetic drug targeting PPAR-γ. Results: The binding free energies for the ferulic acid-PPAR-γ, bakuchiol-PPAR-γ, niazirin-PPAR-γ, native ligand-PPAR-γ, and pioglitazone-PPAR-γ complexes were -5.54 kcal/mol, -8.47 kcal/mol, -6.56 kcal/mol, -10.08 kcal/mol, and -9.94 kcal/mol, respectively. The amino acid residue similarity percentages with the native ligand were 18.18% for ferulic acid, 27.27% for bakuchiol, 18.18% for niazirin, and 81.82% for pioglitazone. The native ligand-2PRG and pioglitazone-2PRG complexes exhibited superior hydrogen bond numbers and shorter bond distances compared to the other compounds. Conclusion: Although bakuchiol showed the most promising interaction among the tested compounds, none surpassed the binding affinity and stability of the native ligand or pioglitazone. Further research is needed to optimize these compounds for potential anti-diabetic therapy.

Rational synthesis of Azo ligands and copper complexes: Insights into potential therapeutic agents

In the pursuit of innovation, four distinct mono Azo dyes D1-D4 were synthesized. These dyes were crafted through a coupling reaction involving 1-naphthol, resorcinol and salicylic acid with diazonium salts derived from a diverse array of aromatic amines, including aniline and sulphanilic acid. Subsequently, they are transformed into copper Azo complexes, namely (D1CuC-D4CuC). The structural underpinnings of these mono Azo dyes and their corresponding copper complexes were unveiled using an arsenal of analytical techniques: FTIR, 1H NMR, 13C NMR, and UV-visible spectroscopy which unequivocally confirmed their synthesis. A computational protein-ligand docking approach was used to assess the binding affinity of ligands and metal complexes to an anti-oxidant receptor protein (PDB ID:2HCK) which showed excellent binding interactions. The findings from an in vitro antioxidant activity using the DPPH scavenging method exhibited: Complexes > Ligands. As an antioxidant agent, the synthesized compounds may prove to be a great therapeutic option. This research introduces a novel approach by utilizing Azo dyes as ligands, leveraging their exceptional electronic tunability and stability to form innovative copper complexes. Their pharmacological applications will be explored, potentially yielding drugs with improved efficacy and selectivity.

ProteoDockNet: Novel GNN-based ligand binding affinities prediction architecture via SMILES to key liver, kidney and brain proteins using QSAR data

Assessing the affinity between specific human proteins and drugs through docking scores is a crucial aspect of structure-based drug design. Traditionally, Quantitative Structure-Activity Relationship (QSAR) models have been utilized, but they fail to capture the complex interactions within protein-ligand complexes. This research explores the burgeoning potential of Graph Neural Networks (GNNs) to revolutionize this field, by employing QSAR models as original datasets for GNN architecture development to predict the molecular properties using simple SMILES notation of small molecules. Having selected significant proteins from three major organs – liver, kidney and brain, these were docked against MolPort database, and designed QSAR models using multiple linear regression and partial least squares via Schrodinger. The core dataset employed in this research comprises molecular information denoted by MolPort IDs alongside corresponding docking scores, essential for evaluating molecular interactions, from which the initial QSAR dataset was employed. A key focus of this study lies in data preprocessing, where each molecule, initially represented as a SMILES string, undergoes conversion into a graph structure. The GNN architecture was built with graphical convolutional network (GCN) and hybrid GCN (with attention mechanisms) to improve accuracy and to allow users to choose their algorithm to be run during protein-ligand binding prediction. All 60 generated models (10 proteins- GCN + GAT models for each), were assessed via RMSE and MAE metrics, before being deployed on Streamlit (https://dockingscoreappapp-vypudgobchyjwk4n3w2fy5.streamlit.app/). The outcomes show an immediate prediction of the docking score of any given SMILES, against the set of proteins trained in ProteoDockNet.