Prediction of human serum albumin–drug binding affinity without albumin

New insights into the inhibitory effect of phenol carboxylic acid antioxidants on mushroom tyrosinase by molecular dynamic studies and experimental assessment

The inhibitory effects of ferulic and chlorogenic acids on tyrosinase activity were investigated through multi-spectroscopic and molecular docking techniques. Ferulic and chlorogenic acids, flavonoid compounds, demonstrated inhibitory monophenolase activities of tyrosinase. The inhibitor effects against monophenolase activity were in a reversible and competitive manner with ki value equal to 6.8 and 7.5 µM respectively. The affinity between tyrosinase and L-DOPA decreased when fatty acids were added to the solution. The multi-spectroscopic techniques like UV-vis, fluorescence, and isothermal calorimetry are employed to investigate changes. Intrinsic fluorescence quenching and conformational changes of tyrosinase by hydrophobic interaction were confirmed. Tyrosinase had two and three binding sites for ferulic and chlorogenic acids with a binding constant in the order of magnitude of -6.8 and -7.2 kcal/mol. In addition, the secondary structural changes with Circular dichroism (CD) analysis, secondary structure (DSSP), radius of gyration (Rg) and analysis of hydrogen bonds (H-bonds) confirmed. Ferulic acid effect can be observed obviously and also content of α-helix decreased. Thermodynamic parameters indicated that the interaction between enzyme and ferulic and chlorogenic acids followed a spontaneous reaction dynamic manner with ΔG = -14.78 kJ/mol and ΔG = -14.61 kJ/mol (298k). The findings highlighted the potential applications of ferulic acid and chlorogenic acids in food and drug industries as potent inhibitors of tyrosinase.Communicated by Ramaswamy H. Sarma.

Natural Products-Based Drug Design against SARS-CoV-2 Mpro 3CLpro

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has received global attention due to the serious threat it poses to public health. Since the outbreak in December 2019, millions of people have been affected and its rapid global spread has led to an upsurge in the search for treatment. To discover hit compounds that can be used alone or in combination with repositioned drugs, we first analyzed the pharmacokinetic and toxicological properties of natural products from Brazil's semiarid region. After, we analyzed the site prediction and druggability of the SARS-CoV-2 main protease (Mpro), followed by docking and molecular dynamics simulation. The best SARS-CoV-2 Mpro complexes revealed that other sites were accessed, confirming that our approach could be employed as a suitable starting protocol for ligand prioritization, reinforcing the importance of catalytic cysteine-histidine residues and providing new structural data that could increase the antiviral development mainly against SARS-CoV-2. Here, we selected 10 molecules that could be in vitro assayed in response to COVID-19. Two compounds (b01 and b02) suggest a better potential for interaction with SARS-CoV-2 Mpro and could be further studied.

Quantitative analysis of selective glycosylation of saccharides with aromatic amines

Glycosylation, a part of the Maillard reaction, occurs non-enzymatically in food and biological processes. The selectivity of N-glycosylation was analyzed based on the reactivity of monosaccharides with aromatic amines, including aromatic amino acids, and the degree of molecular interaction (MI) measured using liquid chromatography. Furthermore, the chemical structures of reaction products were determined using X-ray crystallography and/or NMR. The possible reaction products were estimated in silico using the optimized energy values of different conformations. The MI energy values of amino groups and saccharides were calculated using in silico analysis using a model phase. Saccharides having larger MI values easily produced stable crystals of N-glycosides. The reaction rate of glucose (an energy saccharide) was slow, and it easily produced the Amadori compounds. The study of the reactivity of aromatic amines with saccharides, the measurement of the retention of monosaccharides on amino phase in chromatography, and the synthesis of N-glycosides for the determination of their structures will provide useful information about selective glycosylation for the modification of drug candidates to improve their water solubility.

Stereoselective binding of chiral drugs to plasma proteins

Chiral drugs show distinct biochemical and pharmacological behaviors in the human body. The binding of chiral drugs to plasma proteins usually exhibits stereoselectivity, which has a far-reaching influence on their pharmacological activities and pharmacokinetic profiles. In this review, the stereoselective binding of chiral drugs to human serum albumin (HSA), α1-acid glycoprotein (AGP) and lipoprotein, three most important proteins in human plasma, are detailed. Furthermore, the application of AGP variants and recombinant fragments of HSA for studying enantiomer binding properties is also discussed. Apart from the stereoselectivity of enantiomer-protein binding, enantiomer-enantiomer interactions that may induce allosteric effects are also described. Additionally, the techniques and methods used to determine drug-protein binding parameters are briefly reviewed.

Evaluation of drug-human serum albumin binding interactions with support vector machine aided online automated docking

MOTIVATION

Human serum albumin (HSA), the most abundant plasma protein is well known for its extraordinary binding capacity for both endogenous and exogenous substances, including a wide range of drugs. Interaction with the two principal binding sites of HSA in subdomain IIA (site 1) and in subdomain IIIA (site 2) controls the free, active concentration of a drug, provides a reservoir for a long duration of action and ultimately affects the ADME (absorption, distribution, metabolism, and excretion) profile. Due to the continuous demand to investigate HSA binding properties of novel drugs, drug candidates and drug-like compounds, a support vector machine (SVM) model was developed that efficiently predicts albumin binding. Our SVM model was integrated to a free, web-based prediction platform (http://albumin.althotas.com). Automated molecular docking calculations for prediction of complex geometry are also integrated into the web service. The platform enables the users (i) to predict if albumin binds the query ligand, (ii) to determine the probable ligand binding site (site 1 or site 2), (iii) to select the albumin X-ray structure which is complexed with the most similar ligand and (iv) to calculate complex geometry using molecular docking calculations. Our SVM model and the potential offered by the combined use of in silico calculation methods and experimental binding data is illustrated.

Computational study of ligand binding to protein receptors

We have determined, for the first time, the enthalpic contributions to the energy change associated with ligand reorganization (LR) upon the binding of the same ligand to multiple sites within human serum albumine (HSA). Quantum mechanics based density functional theory (DFT) has been used for the LR calculations, which provides much better accuracy than previously used molecular mechanics methods (MM). Our findings show that for some ligands these enthalpic contributions can be attributed to specific structural and conformational changes.

In silico ADME modelling 2: Computational models to predict human serum albumin binding affinity using ant colony systems

Modelling of in vitro human serum albumin (HSA) binding data of 94 diverse drugs and drug-like compounds is performed to develop global predictive models that are applicable to the whole medicinal chemistry space. For this aim, ant colony systems, a stochastic method along with multiple linear regression (MLR), is employed to exhaustively search and select multivariate linear equations, from a pool of 327 molecular descriptors. This methodology helped us to derive optimal quantitative structure-property relationship (QSPR) models based on five and six descriptors with excellent predictive power. The best five-descriptor model is based on Kier and Hall valence connectivity index--Order 5 (path), Auto-correlation descriptor (Broto-Moreau) weighted by atomic masses--Order 4, Auto-correlation descriptor (Broto-Moreau) weighted by atomic polarizabilities--Order 5, AlogP98, SklogS (calculated buffer water solubility) [R=0.8942, Q=0.86790, F=62.24 and SE=0.2626]; the best six-variable model is based on Kier and Hall valence connectivity index of Order 3 (cluster), Auto-correlation descriptor (Broto-Moreau) weighted by atomic masses--Order 4, Auto-correlation descriptor (Broto-Moreau) weighted by atomic polarizabilities--Order 5, Atomic-Level-Based AI topological descriptors--AIdsCH, AlogP98, SklogS (calculated buffer water solubility) [R=0.9128, Q=0.89220, F=64.09 and SE=0.2411]. From the analysis of the physical meaning of the selected descriptors, it is inferred that the binding affinity of small organic compounds to human serum albumin is principally dependent on the following fundamental properties: (1) hydrophobic interactions, (2) solubility, (3) size and (4) shape. Finally, as the models reported herein are based on computed properties, they appear to be a valuable tool in virtual screening, where selection and prioritisation of candidates is required.

Simulation of chromatography of phenolic compounds with a computational chemical method

An ab initio simulation of reversed-phase liquid chromatography for phenolic compounds was achieved based on molecular interaction energy values calculated using molecular mechanics calculations (MM2) of the CAChe program. The precision of the predicted retention factors from the molecular interaction energy values was equivalent to the predicted retention factors based on octanol-water partition coefficients (log P) calculated using the molecular orbital package (MOPAC). The prediction of retention factors of phenolic compounds in reversed-phase liquid chromatography in a given pH eluent was possible using the predicted dissociation constant (pKa) from the atomic partial charge without a chemical experiment if the organic modifier effect was known.

An NMR spectroscopy study of bendaline-albumin interactions

The complete assignment of the 1H and 13C NMR spectra of bendaline (BNDL) was performed by mono-dimensional and homo- and hetero-correlated two-dimensional NMR experiments. The interaction between bendaline and albumin was also studied by the analysis of the motional parameters spin-lattice relaxation times, allowing the motional state of the BNDL free and bound with albumin to be defined. In absence of albumin the indazolacetic and benzylic moieties are characterized by roughly the same mobility and by positive sigma (cross-relaxation rates) values. In the presence of the macromolecule, the indazolacetic and benzylic moieties and the lysine change their motional behaviour to different extents, as indicated by correlation times. Data obtained in absence and in presence of the protein show that the molecular moiety of the bendaline most involved in the binding with albumin is the fragment H-4 H-5. The binding constant was evaluated at 2.4x10(3)M(-1).

In silico prediction of drug-binding strengths to human serum albumin

Drug binding to Human Serum Albumin (HSA) is an area of intense research. The pharmacokinetics and pharmacodynamics of drugs are strongly affected by their binding to this protein. In this article, the field is reviewed, as well as our models to predict drug-binding affinities to HSA from drug structure. The physiological role of HSA is described, as well as its influence in drug action. The crystal structures of this protein are discussed, emphasizing the two drug-binding sites and the fatty acids binding sites observed therein. The advantages of using high-performance affinity chromatography to rapidly screen drugs for HSA binding are explained. The different QSAR models for HSA binding of restricted families of drugs (both from other groups and our group) are enumerated. Finally, a detailed description of our general models to predict drug-binding strengths to HSA from structure is given. It is expected for these models to be useful in drug design and pharmaceutical research.