Quantitative Structure Activity Relationship (QSAR) study predicts small molecule binding to RNA structure

The diversity of RNA structural elements and their documented role in human diseases make RNA an attractive therapeutic target. However, progress in drug discovery and development has been hindered by challenges in the determination of high-resolution RNA structures and a limited understanding of the parameters that drive RNA recognition by small molecules, including a lack of validated quantitative structure-activity relationships (QSAR). Herein, we developed QSAR models that quantitatively predict both thermodynamic and kinetic-based binding parameters of small molecules and the HIV-1 TAR model RNA system. A set of small molecules bearing diverse scaffolds was screened against the HIV-1-TAR construct using surface plasmon resonance, which provided the binding kinetics and affinities. The data was then analyzed using multiple linear regression (MLR) combined with feature selection to afford robust models for binding of diverse RNA-targeted scaffolds. The predictivity of the model was validated on untested small molecules. The QSAR models presented herein represent the first application of validated and predictive 2D-QSAR using multiple scaffolds against an RNA target. We expect the workflow to be generally applicable to other RNA structures, ultimately providing essential insight into the small molecule descriptors that drive selective binding interactions and, consequently, providing a platform that can exponentially increase the efficiency of ligand design and optimization without the need for high-resolution RNA structures.

Comprehensive Modeling and Molecular Docking of Phosphodiesterase Inhibitors

In the present study, the biological activity of some pharmaceutical molecules was investigated and predicted by using quantitative structure-activity relationship (QSAR) studies and molecular docking. The aim of this study is to apply QSAR and molecular docking methods to predict and calculate the half maximal inhibitory concentration (IC50) of phosphodiesterase (PDE) 10A. To apply QSAR method at first multiple linear regression (MLR), genetic algorithm (GA), and successive projection algorithm (SPA) were used to select the best descriptors related to PDE 10A inhibitory activity. The selected descriptors were then applied as inputs to construct MLR and a non-linear support vector machine (SVM). Also we used molecular docking method to extract the descriptors and then by using MLR and SVM methods a linear and non-linear models developed respectively. Consequently, a comparison of the results of QSAR and docking study indicated that the non-linear SVM-SPA2 in QSAR study and SVM-MLR in molecular docking study had a much better prediction power than the other models. Finally, the Y-scrambling and cross-validation tests were used to evaluate the validity of the obtained model and the results of these tests indicates that the model is appropriate for using in prediction.

Novel Algicides against Bloom-Forming Cyanobacteria from Allelochemicals: Design, Synthesis, Bioassay, and 3D-QSAR Study

Cyanobacteria bloom caused by water eutrophication has threatened human health and become a global environmental problem. To develop green algicides with strong specificity and high efficiency, three series of ester and amide derivatives from parent allelochemicals of caffeic acid (CA), cinnamic acid (CIA), and 3-hydroxyl-2-naphthoic acid (HNA) were designed and synthesized. Their inhibitory effects on the growth of five harmful cyanobacterial species, Microcystis aeruginosa (M. aeruginosa), Microcystis wesenbergii (M. wesenbergii), Microcystis flos-aquae (M. flos-aquae), Aphanizomenon flos-aquae (Ap. flos-aquae), and Anabaena flos-aquae (An. flos-aquae), were evaluated. The results revealed that CIA esters synthesized by cinnamic acid and fatty alcohols showed the best inhibition effect, with EC50 values ranging from 0.63 to >100 µM. Moreover, some CIA esters exhibited a good selectivity in inhibiting cyanobacteria. For example, the inhibitory activity of naphthalen-2-yl cinnamate was much stronger on Ap. flos-aquae (EC50 = 0.63 µM) than other species (EC50 > 10 µM). Three-dimensional quantitative structure–activity relationship (3D-QSAR) analysis was performed and the results showed that the steric hindrance of the compounds influenced the algicidal activity. Further mechanism study found that the inhibition of CIA esters on the growth of M. aeruginosa might be related to the accumulation of malondialdehyde (MDA).

Quantative STRUCTURE Activity Relationship (QSAR) Study of a Series of Molecules Derived from 2,3-dihydro-1H- perimidine having Activity against Protein Tyrosine Phosphatase 1B

In order to study the Quantitative Structure Activity Relationship (QSAR) against protein tyrosine phosphatase 1B and descriptors, we used a series of fourteen (14) molecules derived from perimidine. The compounds were optimized at the computational level B3LYP / 6-31 G (d, p), to obtain the descriptors of the model. This study was performed using the Linear Multiple Regression (MLR) method. This tool allowed us to obtain a quantitative model from the descriptors that are, the overall softness (S), the energy of the lowest vacant (ELUMO), the bond length l (N-C1). This model has good statistical performance (R2 = 0.958; RMCE = 0.110; F = 43.870). In addition, the external validation test of Tropsha and the domain of applicability from the levers were verified.

In silico repurposing of a Novobiocin derivative for activity against latency associated Mycobacterium tuberculosis drug target nicotinate-nucleotide adenylyl transferase (Rv2421c)

Nicotinamide-nucleotide adenylyl transferase (Rv2421c) was selected as a potential drug target, because it has been shown, in vitro, to be essential for Mycobacterium tuberculosis growth. It is conserved between mycobacterium species, is up-regulated during dormancy, has a known 3D crystal structure and has no known human homologs. A model of Rv2421c in complex with nicotinic acid adenine dinucleotide and magnesium ion was constructed and subject tovirtual ligand screening against the Prestwick Chemical Library and the ZINC database, which yielded 155 potential hit molecules. Of the 155 compounds identified five were pursued further using an IC50 based 3D-QSAR study. The 3D-QSAR model validated the inhibition properties of the five compounds based on R2 value of 0.895 and Q2 value of 0.944 compared to known inhibitors of Rv2421c. Higher binding affinities was observed for the novel ZINC13544129 and two FDA approved compounds (Novobiocin sodium salt, Sulfasalazine). Similarly, the total interaction energy was found to be the highest for Cromolyn disodium system (-418.88 kJ/mol) followed by Novobiocin (-379.19 kJ/mol) and Sulfasalazine with (-330.13 kJ/mol) compared to substrate DND having (-185.52 kJ/mol). Subsequent in vitro testing of the five compounds identified Novobiocin sodium salt with activity against Mycobacterium tuberculosis at 50 μM, 25μM and weakly at 10μM concentrations. Novobiocin salt interacts with a MG ion and active site residues His20, Thr86, Gly107 and Leu164 similar to substrate DND of Mycobacterium tuberculosis Rv2421c. Additional in silico structural analysis of known Novobiocin sodium salt derivatives against Rv2421c suggest Coumermycin as a promising alternative for the treatment of Mycobacterium tuberculosis based on large number of hydrogen bond interactions with Rv2421c similar in comparison to Novobiocin salt and substrate DND.

A Comparative Study of the Lipophilicity of Metformin and Phenformin

The results presented in this paper confirm the beneficial role of an easy-to-use and low-cost thin-layer chromatography (TLC) technique for describing the retention behavior and the experimental lipophilicity parameter of two biguanide derivatives, metformin and phenformin, in both normal-phase (NP) and reversed-phase (RP) TLC systems. The retention parameters (RF, RM) obtained under different chromatographic conditions, i.e., various stationary and mobile phases in the NP-TLC and RP-TLC systems, were used to determine the lipophilicity parameter (RMW) of metformin and phenformin. This study confirms the poor lipophilicity of both metformin and phenformin. It can be stated that the optimization of chromatographic conditions, i.e., the kind of stationary phase and the composition of mobile phase, was needed to obtain the reliable value of the chromatographic lipophilicity parameter (RMW) in this study. The fewer differences in the RMW values of both biguanide derivatives were ensured by the RP-TLC system composed of RP2, RP18, and RP18W plates and the mixture composed of methanol, propan-1-ol, and acetonitrile as an organic modifier compared to the NP-TLC analysis. The new calculation procedures for logP of drugs based on topological indices 0χν, 0χ, 1χν, M, and Mν may be a certain alternative to other algorithms as well as the TLC procedure performed under optimized chromatographic conditions. The knowledge of different lipophilicity parameters of the studied biguanides can be useful in the future design of novel and more therapeutically effective metformin and phenformin formulations for antidiabetic and possible anticancer treatment. Moreover, the topological indices presented in this work may be further used in the QSAR study of the examined biguanides.

The inhibitory Effects of Some Artificial Food Colorings on α-amylase and α-glucosidase: In Vitro and In Silico Studies

Background: Inhibition of α-amylase and α-glucosidase is considered as an important therapeutic target to manage type 2 diabetes mellitus (T2DM), reducing postprandial hyperglycemia (PPHG). Objective: The present work explored the antidiabetic activities of five artificial food colorings by α-amylase and α-glucosidase enzyme inhibition in vitro and in Silico. Methods: In this study, inhibition of α-amylase and α-glucosidase were evaluated. Further, the interaction between enzymes (α-amylase and α-glucosidase) and ligands (food colorings) was followed by QSAR and molecular docking studies. Results: The in vitro results obtained show that the blue patent (SIN131) exhibited more potent inhibition with IC50 values of 0.03± 0.01 mM and 0.014±0.001 mM against α-amylase and α-glucosidase inhibition respectively compared to acarbose. The QSAR study found a strong correlation between IC50 values with four molecular descriptors. This linear regression confirms that a strong polarity (Apol) and a low hydrophobia (ALogP) favor the inhibitory effect of these colorings toward both enzymes. Also, a negative role of the number of heavy atoms has been demonstrated in the phenomenon of inhibition of this enzyme. Finally, the descriptor εlumo (electronic affinity) plays a crucial role on the inhibitory power of these dyes toward both enzymes by electron transfer. The virtual screening of the inhibition of α-amylase and α-glucosidase by these colorings, using Molegro Virtual Docker (MVD), allowed us to obtain stable complexes with interaction energies resulting from the place of hydrogen bonds and several hydrophobic interactions. However, the sulfonate groups of these colorings can be the major factors in the inhibition of these enzymes. On the other hand, Rerank Score with the pose are perfectly correlated (R2> 0.76) to the inhibitory activity of these food colorings measured experimentally. Conclusion: The present study suggests that the Blue Patent V (SIN131) effectively act as α-amylase and α-glucosidase inhibitor leading to a reduction in starch hydrolysis and hence eventually to lowered glucose levels.

In silico Prediction of Novel SARS-CoV 3CL pro Inhibitors: a Combination of 3D-QSAR, Molecular Docking, ADMET Prediction, and Molecular Dynamics Simulation

The Chymotrypsin-like protease (3CLpro) is a drug target in the coronavirus because of its role in processing the polyproteins that are translated from the viral RNA. This study applied 3D quantitative structure-activity relationship (3D-QSAR), molecular docking, and ADMET prediction on a series of SARS-CoV 3CLpro inhibitors. The 3D-QSAR study was applied using Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) methods, which gave the cross-validation coefficient (Q2) values of 0.64 and 0.80, the determination coefficient (R2) values of 0.998 and 0.993 and the standard error of the estimate (SEE) values of 0.046 and 0.091, respectively. The acceptable values of the determination coefficient (R2 test) to CoMFA and CoMSIA respectively corresponding to values of 0.725 and 0.690 utilizing a test set of seven molecules prove the high predictive ability of this model. Molecular docking analysis was utilized to validate 3D-QSAR methods and explain the binding site interactions and affinity between the most active ligands and the SARS-CoV 3CLpro receptor. Based on these results, a novel series of compounds were predicted, and their pharmacokinetic properties were verified using drug-likeness and ADMET prediction. Finally, the best-docked candidate molecules were subjected to molecular dynamics (MD) simulation to affirm their dynamic behavior and stability.