The role of folic acid in inducing of apoptosis by zinc(II) complex in ovary and cervix cancer cells.

Herein, the synthesis, structure, binding affinity, cytotoxicity, and apoptotic properties of the new Zn(II) complex composed of folic acid and bipyridine ligands are reported. Because folic acid has the ability to target cancer cells directly, so it can play a role in targeted drug delivery of the complex and be useful to distinguish normal cells from cancerous. After characterization of Zinc complex utilizing FTIR, EA, and NMR, the results of MTT assay were shown that viability levels of two FR-positive cell lines (HeLa and Ovcar-3) are dependent on time and concentration of [Zn(bpy)FA], whereas, did not show a significant effect on FR-negative cell lines (A549). Also, Real-time PCR revealed that the presence of FA can influence the expression of apoptosis in cervical carcinoma HeLa cells while cisplatin alone doesn't have the ability to trigger apoptosis. Furthermore, the experimental results were evaluated using pharmacophore modeling and molecular docking analysis. Finally, the stability of the Zn(II) complex was surveyed using quantum mechanical studies.

Predicting stability constants of transition metals; Y3+, La3+, and UO2 2+ with organic ligands using the 3D-QSPR methodology.

Stability constants prediction plays a critical role in the identification and optimization of ligand design for selective complexation of metal ions in solution. Thus, it is important to assess the potential of metal-binding ligand organic in the complex formation process. However, quantitative structure-activity/property relationships (QSAR/QSPR) provide a time-and cost-efficient approach to predict the stability constants of compounds. To this end, we applied a free alignment three-dimensional QSPR technique by generating GRid-INdependent Descriptors (GRINDs) to rationalize the underlying factors effecting on stability constants of transition metals; 105 (Y3+), 186 (La3+), and 66 (UO2 2+) with diverse organic ligands in aqueous solutions at 298 K and an ionic strength of 0.1 M. Kennard- Stone algorithm was employed to split data set to a training set of 75% molecules and a test set of 25% molecules. Fractional factorial design (FFD) and genetic algorithm (GA) applied to derive the most relevant and optimal 3 D molecular descriptors. The selected descriptors using various feature selection were correlated with stability constants by partial least squares (PLS). GA-PLS models were statistically validated ( R 2 = 0.96, q2 = 0.82 and R2 pred=0.81 for Y3+; R 2 = 0.90, q2 = 0.73 and R2 pred=0.82 for La3+ and R 2 = 0.95, q2 = 0.81 and R2 pred=0.88 for UO2 2+), and from the information derived from the graphical results confirmed that hydrogen bonding properties, shape, size, and steric effects are the main parameters influencing stability constant of metal complexation. The provided information in this research can predict the stability constant of the new organic ligand with the transition metals without experimental processes.

Exploring 3D-QSPR models of human skin permeability for a diverse dataset of chemical compounds.

The control of permeation is vital not only for the topical application of lotions, creams, and ointments but also for the toxicological and risk assessment of materials from environmental and occupational hazards. To understand the effects of physicochemical properties of a variety of 211 compounds on skin permeability, we developed a three-dimensional quantitative structure-property relationship (3 D-QSPR) model. Alignment free GRid-INdependent Descriptors (GRINDs), which were derived from molecular interaction fields (MIFs) contributed to the regression models. Kennard-Stone algorithm was employed to split data set to a training set of 159 molecules and a test set of 52 molecules. Fractional factorial design (FFD), genetic algorithm (GA) and successive projection algorithm (SPA) were applied to select the most relevant 3 D molecular descriptors. The descriptors selected using various feature selection were correlated with skin permeability constants by partial least squares (PLS) and support vector machine (SVM). SPA-SVM model gave prominent statistical values with the correlation coefficient of [Formula: see text]= 0.96, Q2= 0.73 and R2pred=0.76. According to the analysis results, the hydrogen bonding donor and acceptor properties of the investigated compounds can influence the penetration into the human skin. Furthermore, it was found that permeability was enhanced by increasing the hydrophobicity and was diminished by increasing the molecular weight. In addition, the presence of hydrophobic groups in the target molecule, as well as their shape and position, can affect the skin permeability.

Identification of new potential HIV-1 reverse transcriptase inhibitors by QSAR modeling and structure-based virtual screening.

Non-nucleoside reverse transcriptase inhibitors (NNRTIs) have gained a definitive place due to their unique antiviral potency, high specificity and low toxicity in antiretroviral combination therapies which are used to treat HIV. To design more specific HIV-1 inhibitors, 218 diverse non-nucleoside reverse transcriptase inhibitors with their EC50 values were collected. Then, different types of molecular descriptors were calculated. Also, genetic algorithm (GA) and enhanced replacement methods (ERM) were used as the variable selection approaches to choose more relevant features. Based on selected descriptors, a classification support vector machine (SVM) model was constructed to categorize compounds into two groups of active and inactive ones. The most active compound in the set was docked and was used as the input to the Pharmit server to screen the Molport and PubChem libraries by constructing a structure-based pharmacophore model. Shape filters for the protein and ligand as well as Lipinski's rule of five have been applied to filter out the output of virtual screening from pharmacophore search. Three hundred and thirty-four compounds were finally retrieved from the virtual screening and were fed to the previously constructed SVM model. Among them, the SVM model rendered seven active compounds and they were also analyzed by docking calculations and ADME/Tox parameters.

An explorative study on potent Gram-negative specific LpxC inhibitors: CoMFA, CoMSIA, HQSAR and molecular docking.

Pathogenic Gram-negative bacteria are responsible for nearly half of the serious human infections. Hologram quantitative structure-activity relationships (HQSAR), comparative molecular field analysis (CoMFA), and comparative molecular similarity index analysis (CoMSIA) were implemented on a group of 32 of potent Gram-negative LpxC inhibitors. The most effective HQSAR model was obtained using atoms, bonds, donor, and acceptor as fragment distinction. The cross-validated correlation coefficient (q2), non-cross-validated correlation coefficient (r2), and predictive correlation coefficient (r2Pred) for test set of HQSAR model were 0.937, 0.993, and 0.892, respectively. The generated models were found to be statistically significant as the CoMFA model had (r2 = 0.967, q2 = 0.804, r2Pred = 0.827); the CoMSIA model had (r2 = 0.963, q2 = 0.752, r2Pred = 0.857). Molecular docking was employed to validate the results of the HQSAR, CoMFA, and CoMSIA models. Based on the obtained information, six new LpxC inhibitors have been designed.

Alignment independent 3D-QSAR, quantum calculations and molecular docking of Mer specific tyrosine kinase inhibitors as anticancer drugs.

Mer receptor tyrosine kinase is a promising novel cancer therapeutic target in many human cancers, because abnormal activation of Mer has been implicated in survival signaling and chemoresistance. 3D-QSAR analyses based on alignment independent descriptors were performed on a series of 81 Mer specific tyrosine kinase inhibitors. The fractional factorial design (FFD) and the enhanced replacement method (ERM) were applied and tested as variable selection algorithms for the selection of optimal subsets of molecular descriptors from a much greater pool of such regression variables. The data set was split into 65 molecules as the training set and 16 compounds as the test set. All descriptors were generated by using the GRid INdependent descriptors (GRIND) approach. After variable selection, GRIND were correlated with activity values (pIC50) by PLS regression. Of the two applied variable selection methods, ERM had a noticeable improvement on the statistical parameters of PLS model, and yielded a q (2) value of 0.77, an [Formula: see text] of 0.94, and a low RMSEP value of 0.25. The GRIND information contents influencing the affinity on Mer specific tyrosine kinase were also confirmed by docking studies. In a quantum calculation study, the energy difference between HOMO and LUMO (gap) implied the high interaction of the most active molecule in the active site of the protein. In addition, the molecular electrostatic potential energy at DFT level confirmed results obtained from the molecular docking. The identified key features obtained from the molecular modeling, enabled us to design novel kinase inhibitors.

Experimental and Theoretical Approaches to Monitor the Behavior of Bovine Liver Catalase in Interaction with a Binuclear Bismuth Complex.

Here, the antioxidant potency of a binuclear Bi(III) complex {[Bi2(µ-ox)(dipic)2(H2O)2 (taa)2].H2O, where ox2- = oxalato, dipic2- = pyridine 2,6-dicarboxylato, and taa = thiourea} was evaluated using the •DPPH assay. It was demonstrated that the Bi complex exhibited a high ability to inhibit DPPH free radicals. The binding mechanism of the complex with bovine liver catalase (BLC) was also investigated, revealing structural and activity changes in the enzyme in the presence of the complex. The catalase activity in the decomposition of hydrogen peroxide increased in the presence of the Bi complex, reaching 39.8% higher than its initial activity at a concentration of 7.77 × 10-6 M. The complex exhibited a relatively high affinity for BLC, with K b values of 3.98, 0.13, and 0.09 × 105 M-1 at 303, 310, and 317 K, respectively. The mechanisms involved in the interaction were hydrogen bonding and van der Waals interactions, as validated through molecular docking simulations. Synchronous fluorescence showed that tryptophan was more affected by enzyme-complex interactions than tyrosine. In addition, a cell viability test using the MTT method revealed that at its highest concentration, the Bi complex caused a decrease in the number of cells below 50% compared to the control, while cisplatin showed negative effects at all concentrations. These findings suggest that the Bi complex has the potential to be developed as a promising candidate for BLC-related therapeutic target therapy.

Hit discovery of potential CDK8 inhibitors and analysis of amino acid mutations for cancer therapy through computer-aided drug discovery.

Cyclin-dependent kinase 8 (CDK8) has emerged as a promising target for inhibiting cancer cell function, intensifying efforts towards the development of CDK8 inhibitors as potential cancer therapeutics. Mutations in CDK8, a protein kinase, are also implicated as a primary factor associated with tumor formation. In this study, we identified potential inhibitors through virtual screening for CDK8 and single amino acid mutations in CDK8, namely D173A (Aspartate 173 mutate to Alanine), D189N (Aspartate 189 mutate to Asparagine), T196A (Threonine 196 mutate to Alanine) and T196D (Threonine 196 mutate to Aspartate). Four databases (CHEMBEL, ZINC, MCULE, and MolPort) containing 65,209,131 molecules have been searched to identify new inhibitors for CDK8 and its single mutations. In the first step, structure-based pharmacophore modeling in the Pharmit server was used to select the compounds to know the inhibitors. Then molecules with better predicted drug-like molecule properties were selected. The final filter used to select more effective inhibitors among the previously selected molecules was molecular docking. Finally, 13 hits for CDK8, 11 hits for D173A, 11 hits for D189N, 15 hits for T196A, and 12 hits for T196D were considered potential inhibitors. A majority of the virtual screening hits exhibited satisfactorily predict pharmacokinetic characteristics and toxicity properties.

Improving Tamoxifen Performance in Inducing Apoptosis and Hepatoprotection by Loading on a Dual Nanomagnetic Targeting System.

BACKGROUND: Although tamoxifen (TMX) belongs to selective estrogen receptor modulators (SERMs) and selectively binds to estrogen receptors, it affects other estrogen-producing tissues due to passive diffusion and non-differentiation of normal and cancerous cells and leads to side effects. METHODS: The problems expressed about tamoxifen (TMX) encouraged us to design a new drug delivery system based on magnetic nanoparticles (MNPs) to simultaneously target two receptors on cancer cells through folic acid (FA) and hyaluronic acid (HA) groups. The mediator of binding of two targeting agents to MNPs is a polymer linker, including dopamine, polyethylene glycol, and terminal amine (DPN). RESULTS: Zeta potential, dynamic light scattering (DLS), and Field emission scanning electron microscopy (FESEM) methods confirmed that MNPs-DPN-HA-FA has a suitable size of ~105 nm and a surface charge of -41 mV, and therefore, it can be a suitable option for carrying TMX and increasing its solubility. The cytotoxic test showed that the highest concentration of MNPs-DPN-HA-FA-TMX decreased cell viability to about 11% after 72 h of exposure compared to the control. While the protective effect of modified MNPs on normal cells was evident, unlike tamoxifen, the survival rate of liver cells, even after 180 min of treatment, was not significantly different from the control group. The protective effect of MNPs was also confirmed by examining the amount of malondialdehyde, and no significant difference was observed in the amount of lipid peroxidation caused by modified MNPs compared to the control. Flow cytometry proved that TMX loaded onto modified MNPs can induce apoptosis by targeting the overexpressed receptors on cancer cells. Real-time PCR showed that the modified MNPs activated the intrinsic and extrinsic mitochondrial pathways of apoptosis, so the Bak1/Bclx ratio for MNPs-DPN-HAFA- TMX and free TMX was 70.82 and 0.38, respectively. Also, the expression of the caspase-3 gene increased 430 times compared to the control. On the other hand, only TNF gene expression, which is responsible for metastasis in some tumors, was decreased by both free TMX and MNPs-DPN-HA-FA-TMX. Finally, molecular docking proved that MNPs-DPN-HA-FA-TMX could provide a very stable interaction with both CD44 and folate receptors, induce apoptosis in cancer cells, and reduce hepatotoxicity. CONCLUSION: All the results showed that MNPs-DPN-HA-FA-TMX can show good affinity to cancer cells using targeting agents and induce apoptosis in metastatic breast ductal carcinoma T-47D cell lines. Also, the protective effects of MNPs on hepatocytes are quite evident, and they can reduce the side effects of TMX.

SMILES-based QSAR virtual screening to identify potential therapeutics for COVID-19 by targeting 3CLpro and RdRp viral proteins.

The COVID-19 pandemic has prompted the medical systems of many countries to develop effective treatments to combat the high rate of infection and death caused by the disease. Within the array of proteins found in SARS-CoV-2, the 3 chymotrypsin-like protease (3CLpro) holds significance as it plays a crucial role in cleaving polyprotein peptides into distinct functional nonstructural proteins. Meanwhile, RNA-dependent RNA polymerase (RdRp) takes center stage as the key enzyme tasked with replicating the viral genomic RNA within host cells. These proteins, 3CLpro and RdRp, are deemed optimal subjects for QSAR modeling due to their pivotal functions in the viral lifecycle. In this study, SMILES-based QSAR classification models were developed for a dataset of 2377 compounds that were defined as either active or inactive against 3CLpro and RdRp. Pharmacophore (PH4) and QSAR modeling were used for the virtual screening on 60.2 million compounds including ZINC, ChEMBL, Molport, and MCULE databases to identify new potent inhibitors against 3CLpro and RdRp. Then, a filter was established based on typical molecular characteristics to identify drug-like molecules. The molecular docking was also performed to evaluate the binding affinity of 156 AND 51 potential inhibitors to 3CLpro and RdRp, respectively. Among the 15 hits identified based on molecular docking scores, M3, N2, and N4 were identified as promising inhibitors due to their good synthetic accessibility scores (3.07, 3.11, and 3.29 out of 10 for M3, N2, and N4 respectively). These compounds contain amine functional groups, which are known for their crucial role in the binding interactions between drugs and their targets. Consequently, these hits have been chosen for further biological assay studies to validate their activity. They may represent novel 3CLpro and RdRp inhibitors possessing drug-like properties suitable for COVID-19 therapy.

QSAR and molecular docking studies of isatin and indole derivatives as SARS 3CLpro inhibitors.

The 3C-like protease (3CLpro), known as the main protease of SARS-COV, plays a vital role in the viral replication cycle and is a critical target for the development of SARS inhibitor. Comparative sequence analysis has shown that the 3CLpro of two coronaviruses, SARS-CoV-2 and SARS-CoV, show high structural similarity, and several common features are shared among the substrates of 3CLpro in different coronaviruses. The goal of this study is the development of validated QSAR models by CORAL software and Monte Carlo optimization to predict the inhibitory activity of 81 isatin and indole-based compounds against SARS CoV 3CLpro. The models were built using a newer objective function optimization of this software, known as the index of ideality correlation (IIC), which provides favorable results. The entire set of molecules was randomly divided into four sets including: active training, passive training, calibration and validation sets. The optimal descriptors were selected from the hybrid model by combining SMILES and hydrogen suppressed graph (HSG) based on the objective function. According to the model interpretation results, eight synthesized compounds were extracted and introduced from the ChEMBL database as good SARS CoV 3CLpro inhibitor. Also, the activity of the introduced molecules further was supported by docking studies using 3CLpro of both SARS-COV-1 and SARS-COV-2. Based on the results of ADMET and OPE study, compounds CHEMBL4458417 and CHEMBL4565907 both containing an indole scaffold with the positive values of drug-likeness and the highest drug-score can be introduced as selected leads.

A study of the interaction space of two lactate dehydrogenase isoforms (LDHA and LDHB) and some of their inhibitors using proteochemometrics modeling.

Lactate dehydrogenase (LDH) is a tetramer enzyme that converts pyruvate to lactate reversibly. This enzyme becomes important because it is associated with diseases such as cancers, heart disease, liver problems, and most importantly, corona disease. As a system-based method, proteochemometrics does not require knowledge of the protein's three-dimensional structure, but rather depends on the amino acid sequence and protein descriptors. Here, we applied this methodology to model a set of LDHA and LDHB isoenzyme inhibitors. To implement the proteochemetrics method, the camb package in the R Studio Server programming environment was used. The activity of 312 compounds of LDHA and LDHB isoenzyme inhibitors from the valid Binding DB database was retrieved. The proteochemometrics method was applied to three machine learning algorithms gradient amplification model, random forest, and support vector machine as regression methods to find the best model. Through the combination of different models into an ensemble (greedy and stacking optimization), we explored the possibility of improving the performance of models. For the RF best ensemble model of inhibitors of LDHA and LDHB isoenzymes, and were 0.66 and 0.62, respectively. LDH inhibitory activation is influenced by Morgan fingerprints and topological structure descriptors.

Discovery and Design of Radiopharmaceuticals by In silico Methods.

There has been impressive growth in the use of radiopharmaceuticals for therapy, selective toxic payload delivery, and noninvasive diagnostic imaging of disease. The increasing timeframes and costs involved in the discovery and development of new radiopharmaceuticals have driven the development of more efficient strategies for this process. Computer-Aided Drug Design (CADD) methods and Machine Learning (ML) have become more effective over the last two decades for drug and materials discovery and optimization. They are now fast, flexible, and sufficiently accurate to accelerate the discovery of new molecules and materials. Radiopharmaceuticals have also started to benefit from rapid developments in computational methods. Here, we review the types of computational molecular design techniques that have been used for radiopharmaceuticals design. We also provide a thorough examination of success stories in the design of radiopharmaceuticals, and the strengths and weaknesses of the computational methods. We begin by providing a brief overview of therapeutic and diagnostic radiopharmaceuticals and the steps involved in radiopharmaceuticals design and development. We then review the computational design methods used in radiopharmaceutical studies, including molecular mechanics, quantum mechanics, molecular dynamics, molecular docking, pharmacophore modelling, and datadriven ML. Finally, the difficulties and opportunities presented by radiopharmaceutical modelling are highlighted. The review emphasizes the potential of computational design methods to accelerate the production of these very useful clinical radiopharmaceutical agents and aims to raise awareness among radiopharmaceutical researchers about computational modelling and simulation methods that can be of benefit to this field.

Open3DALIGN: an open-source software aimed at unsupervised ligand alignment.

An open-source, cross-platform software aimed at conformer generation and unsupervised rigid-body molecular alignment is presented. Different algorithms have been implemented to perform single and multi-conformation superimpositions on one or more templates. Alignments can be accomplished by matching pharmacophores, heavy atoms or a combination of the two. All methods have been successfully validated on eight comprehensive datasets previously gathered by Sutherland and co-workers. High computational performance has been attained through efficient parallelization of the code. The unsupervised nature of the alignment algorithms, together with its scriptable interface, make Open3DALIGN an ideal component of high-throughput, automated cheminformatics workflows.

A mechanistic explanation of two novel Zn(II) dithiocarbamate complexes with ß-lactoglobulin.

The synthesis, characterization, antioxidant activity and ß-LG interaction of two Zn(II) complexes formulated as [(N-N)Zn(µ-pr-dtc)Zn(N-N)](NO3)2] (where pr-dtc is propylenbisdithiocarbamate, N-N are 2,2'-bipyridine for complex a, and 1,10 phenanthroline for complex b) were reported. The in vitro antioxidant activity of the Zn complexes was evaluated against 1,1-diphenyl-2-picrylhydrazyl radicals (DPPH). Both complexes presented moderate antioxidant activity (IC50 = 231.0 ± 5.7 mg L-1 for complex a and 250.0 ± 6.1 mg L-1 for complex b). Fluorescence studies showed that the intrinsic fluorescence of ß-LG was statically quenched by the prepared complexes mainly through Van der Waals interaction and hydrogen bond. The fluorescence results showed that the above complexes could bind with ß-LG with a relatively strong affinity (complex a:Kb(27 °C) = 0.16 × 104 M-1, Kb(37 °C) = 0.06 × 104 M-1, complex b:Kb(27 °C) = 1.94 × 104 M-1, Kb(37 °C) = 0.11 × 104 M-1). The secondary structure of ß-LG was changed in the presence of these Zn complexes and the decrease in α-helix (0.41% and 0.55% for complex a and complex b, respectively) and ß-sheet (1.19% and 1.44% for complex a and complex b, respectively) contents confirmed the protein instability during the interaction. Molecular dynamics simulation was used during the preparation of the protein receptor before docking to find the best fit of the complexes to ß-LG. Some details about molecular docking simulations describe Van der Waals interactions and hydrogen bonding, and this is in agreement with the thermodynamics data derived from fluorescence spectroscopy experiment.Communicated by Ramaswamy H. Sarma.

2D Quantitative structure-property relationship study of mycotoxins by multiple linear regression and support vector machine.

In the present work, support vector machines (SVMs) and multiple linear regression (MLR) techniques were used for quantitative structure-property relationship (QSPR) studies of retention time (t(R)) in standardized liquid chromatography-UV-mass spectrometry of 67 mycotoxins (aflatoxins, trichothecenes, roquefortines and ochratoxins) based on molecular descriptors calculated from the optimized 3D structures. By applying missing value, zero and multicollinearity tests with a cutoff value of 0.95, and genetic algorithm method of variable selection, the most relevant descriptors were selected to build QSPR models. MLR and SVMs methods were employed to build QSPR models. The robustness of the QSPR models was characterized by the statistical validation and applicability domain (AD). The prediction results from the MLR and SVM models are in good agreement with the experimental values. The correlation and predictability measure by r(2) and q(2) are 0.931 and 0.932, repectively, for SVM and 0.923 and 0.915, respectively, for MLR. The applicability domain of the model was investigated using William's plot. The effects of different descriptors on the retention times are described.

Dynamic structure based pharmacophore modeling of the Acetylcholinesterase reveals several potential inhibitors.

Acetylcholinesterase is a critical enzyme that regulates neurotransmission by catalyzing the breakdown of neurotransmitter acetylcholine in synapses of the nervous system. It is an important target for therapeutic drugs that treat Alzheimer's disease. Since, the degree of flexibility of the side chains of the residues in the active-site gorge of Acetylcholinesterase is diverse it results in different bound ligand conformations. The side-chain conformations of Ser293, Tyr341, Leu76, and Val73 are flexible, while the side-chain conformations of Tyr72, Tyr 124, Ser125, Phe295, and Arg296 appear to be fixed. In this study, multi-conformation dynamic pharmacophore models from the donepezyl-binding pocket based on highly populated structures chosen from molecular dynamics simulations were used for screening compounds that can properly bind acetylcholinesterase. Based on these structures, three pharmacophore models were generated. Consequently, 14 hits were retrieved as final candidates by utilizing virtual screening of ZINC database and molecular docking.

Anti-cancer study and whey protein complexation of new lanthanum(III) complex with the aim of achieving bioactive anticancer metal-based drugs.

In this study, a new lanthanum (III)-amino acid complex utilizing cysteine has been synthesized and characterized. The anticancer activities of the prepared La(III) complex against MCF-7 cell lines were studied. Results of MTT assay showed that at all three incubation times, the cytotoxic effect of prepared La(III) complex on MCF-7 breast cancer cell lines displays a time- and dose-dependent inhibitory effects. The interactions of the La(III) complex with two whey proteins (bovine serum albumin, BSA, and Bovine ß-lactoglobulin, ßLG) have been explored by using spectroscopic and molecular dicking methods. The obtained results indicated that La(III) complex strongly quenched the fluorescence of two carrier proteins in static quenching mode and also, BSA hah stronger binding affinity toward studied complex than ßLG whit binding constant values of KBSA-La Complex ∼ 0.11 × 104 M-1 and KßLG-La Complex ∼ 0.63 × 103 M-1 at 300 K. The thermodynamic parameters revealed the contribution of hydrogen bond and Vander Waals interactions in both systems. The distances of the La(III) complex whit whey proteins were calculated using Förster energy transfer theory and proved existence of the energy transfer between two proteins and prepared La(III) complex with a high probability. FT-IR and UV-Vis absorption measurements indicated that the binding of the La(III) to BSA and ßLG may induce conformational and micro-environmental changes of the proteins. The docking results indicate that the La(III) complex bind to residues located in the site II of BSA and second site of ßLG. Communicated by Ramaswamy H. Sarma.

Binding interaction of isoxsuprine hydrochloride and levothyroxine to milk ß-lactoglobulin; from the perspective of comparison.

Isoxsuprine hydrochloride (ISO) and levothyroxine (LEV) are medicines which can be utilized alone or simultaneously by pregnant women. The purpose of this work is to investigate the separate and simultaneous interaction of ISO and LEV with ß-LG. The results showed that both drugs can bind to ß-LG; the static quenching was suggested for fluorescence quenching mechanism of ß-LG.The values of binding constants (Kß-LG-ISO = 2.69 × 104 M-1, Kß-LG-LEV = 0.54 × 103 M-1 and Kß-LG-ISO-LEV = 2.18 × 103 M-1 at 310 K) suggested that ISO has stronger binding affinity toward ß-LG than LEV and affinity of ß-LG to LEV is increased in the presence of ISO while the presence of LEV has no significant effect on the affinity of protein to ISO. Thermodynamic parameters showed that the binding of LEV to ß-LG are hydrogen bonding and Van der Waals forces but the formation of ß-LG-ISO is hydrophobic associations. The results of FT-IR and UV-visible measurements indicated that the binding of both drugs to ß-LG may induce conformational changes of protein. In silico molecular docking analyses confirmed that ISO and LEV binds to residues located at site I and site II of ß-LG, respectively.

Synthesis, characterization, in silico ADMET prediction, and protein binding analysis of a novel zinc(II) Schiff-base complex: Application of multi-spectroscopic and computational techniques.

By reaction of 1,2-diaminocyclohexane with the 2,3-butanedione monoxime in the presence of ZnCl2, a new Schiff base complex was obtained. This complex was characterized by elemental analyses, FT-IR, 1H NMR, UV-Vis, and conductivity measurements. The reactivity of this complex to human serum albumin (HSA) under simulative physiological conditions was studied by spectroscopic and molecular docking analysis. Experimental results at various temperatures indicated that the intrinsic fluorescence of protein was quenched through a static quenching mechanism. The negative value of enthalpy change and positive value of entropy change indicated that both hydrogen bonding and hydrophobic forces played a major role in the binding of Zn(II) complex to HSA. FT-IR, three-dimensional fluorescence, and UV-Vis absorption results showed that the secondary structure of HSA changed after Zn(II) complex bound to protein. The binding distance was calculated to be 4.96 nm, according to fluorescence resonance energy transfer. Molecular docking results confirmed the spectroscopic results and showed that above complex is embedded into subdomain IIA of protein. All these experimental and computational results clarified that Zn(II) complex could bind with HSA effectively, which could be a useful guideline for efficient Schiff-base drug design.