Chanin Nantasenamat

Copper complexes of pyridine derivatives with superoxide scavenging and antimicrobial activities

Superoxide anions are reactive oxygen species that can attack biomolecules such as DNA, lipids and proteins to cause many serious diseases. This study reports the synthesis of copper complexes of nicotinic acid with related pyridine derivatives. The copper complexes were shown to possess superoxide dismutase (SOD) and antimicrobial activities. The copper complexes exerted SOD activity in range of 49.07-130.23 microM. Particularly, copper complex of nicotinic acid with 2-hydroxypyridine was the most potent SOD mimic with an IC(50) of 49.07 microM. In addition, the complexes exhibited antimicrobial activity against Bacillus subtilis ATCC 6633 and Candida albicans ATCC 90028 with MIC range of 128-256 microg/mL. The SOD activities were well correlated with the theoretical parameters as calculated by density functional theory at the B3LYP/LANL2DZ level of theory. Interestingly, the SOD activity of the copper complexes was demonstrated to be inversely correlated with the electron affinity, but was well correlated with both HOMO and LUMO energies. The vitamin-metal complexes described in this report are great examples of the value-added benefits of vitamins for medicinal applications.

A practical overview of quantitative structure-activity relationship

Quantitative structure-activity relationship (QSAR) modeling pertains to the construction of predictive models of biological activities as a function of structural and molecular information of a compound library. The concept of QSAR has typically been used for drug discovery and development and has gained wide applicability for correlating molecular information with not only biological activities but also with other physicochemical properties, which has therefore been termed quantitative structure-property relationship (QSPR). Typical molecular parameters that are used to account for electronic properties, hydrophobicity, steric effects, and topology can be determined empirically through experimentation or theoretically via computational chemistry. A given compilation of data sets is then subjected to data pre-processing and data modeling through the use of statistical and/or machine learning techniques. This review aims to cover the essential concepts and techniques that are relevant for performing QSAR/QSPR studies through the use of selected examples from our previous work.

Advances in computational methods to predict the biological activity of compounds

Importance of the field: The past decade had witnessed remarkable advances in computer science which had given rise to many new possibilities including the ability to simulate and model lifes phenomena. Among one of the greatest gifts computer science had contributed to drug discovery is the ability to predict the biological activity of compounds and in doing so drives new prospects and possibilities for the development of novel drugs with robust properties. Areas covered in this review: This review presents an overview of the advances in the computational methods utilized for predicting the biological activity of compounds. What the reader will gain: The reader will gain a conceptual view of the quantitative structure–activity relationship paradigm and the methodological overview of commonly used machine learning algorithms. Take home message: Great advancements in computational methods have now made it possible to model the biological activity of compounds in an accurate manner. To obtain such a feat, it is often necessary to forgo several data pre-processing and post-processing procedures. A wide range of tools are available to perform such tasks; however, the proper selection and piecing together of complementary components in the prediction workflow remains a challenging and highly subjective task that heavily relies on the experience and judgment of the practitioner.

Copper Complexes of Nicotinic-Aromatic Carboxylic Acids as Superoxide Dismutase Mimetics

Nicotinic acid (also known as vitamin B3) is a dietary element essential for physiological and antihyperlipidemic functions. This study reports the synthesis of novel mixed ligand complexes of copper with nicotinic and other select carboxylic acids (phthalic, salicylic and anthranilic acids). The tested copper complexes exhibited superoxide dismutase (SOD) mimetic activity and antimicrobial activity against Bacillus subtilis ATCC 6633, with a minimum inhibition concentration of 256 µg/mL. Copper complex of nicotinic-phthalic acids (CuNA/Ph) was the most potent with a SOD mimetic activity of IC50 34.42 µM. The SOD activities were observed to correlate well with the theoretical parameters as calculated using density functional theory (DFT) at the B3LYP/LANL2DZ level of theory. Interestingly, the SOD activity of the copper complex CuNA/Ph was positively correlated with the electron affinity (EA) value. The two quantum chemical parameters, highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO), were shown to be appropriate for understanding the mechanism of the metal complexes as their calculated energies show good correlation with the SOD activity. Moreover, copper complex with the highest SOD activity were shown to possess the lowest HOMO energy. These findings demonstrate a great potential for the development of value-added metallovitamin-based therapeutics.

Prediction of GFP Spectral Properties Using Artificial Neural Network

The prediction of the excitation and the emission maxima of green fluorescent protein (GFP) chromophores were investigated by a quantitative structure‐property relationship study. A data set of 19 GFP color variants and an additional data set consisting of 29 synthetic GFP chromophores were collected from the literature. Artificial neural network implementing the back‐propagation algorithm was employed. The proposed computational approach reliably predicted the excitation and the emission maxima of GFP chromophores with correlation coefficient exceeding 0.9. The usefulness of quantum chemical descriptors was revealed by a comparative study with other molecular descriptors. Assignment of appropriate protonation state of the chromophore for the GFP color variants data set was shown to be necessary for good predictive performance. Results suggest that the confinement of the GFP chromophore has no significant influence on the predictive performance of the data set used. A comparative investigation with the traditional modeling methods, particularly multiple linear regression and partial least squares, reveals that artificial neural network is the most suitable modeling approach for the GFP spectral properties. It is anticipated that this methodology has great potential in accelerating the design and engineering of novel GFP color variants of scientific or industrial interest.

Quantitative prediction of imprinting factor of molecularly imprinted polymers by artificial neural network.

SummaryArtificial neural network (ANN) implementing the back-propagation algorithm was applied for the calculation of the imprinting factors (IF) of molecularly imprinted polymers (MIP) as a function of the computed molecular descriptors of template and functional monomer molecules and mobile phase descriptors. The dataset used in our study were obtained from the literature and classified into two distinctive datasets on the basis of the polymer’s morphology, irregularly sized MIP and uniformly sized MIP datasets. Results revealed that artificial neural network was able to perform well on datasets derived from uniformly sized MIP (n=23, r=0.946, RMS=2.944) while performing poorly on datasets derived from irregularly sized MIP (n=75, r=0.382, RMS=6.123). The superior performance of the uniformly sized MIP dataset over the irregularly sized MIP dataset could be attributed to its more predictable nature owing to the consistency of MIP particles, uniform number and association constant of binding sites, and minimal deviation of the imprinted polymers. The ability to predict the imprinting factor of imprinted polymer prior to performing actual experimental work provide great insights on the feasibility of the interaction between template-functional monomer pairs.

Prediction of bond dissociation enthalpy of antioxidant phenols by support vector machine

Antioxidants play crucial roles in scavenging oxidative damages arising from reactive oxygen species. Bond dissociation enthalpy (BDE) of phenolic O-H bond has well been accepted as an indicator of antioxidant activity since phenols donate the hydrogen atom to the free radicals thereby neutralizing its toxic effect. The BDEs from a data set of 39 antioxidant phenols were modeled using computationally inexpensive quantum chemical descriptors with multiple linear regression (MLR), partial least squares (PLS), and support vector machine (SVM). The molecular descriptors of the phenols were derived from calculations at the following theoretical levels: AM1, HF/3-21g(d), B3LYP/3-21g(d), and B3LYP/6-31g(d). Results indicated that when MLR and PLS were used as the regression methods, B3LYP/3-21g(d) gave the best performance with leave-one-out cross-validated correlation coefficients (r) of 0.917 and 0.921, respectively, while the semiempirical AM1 provided slightly lower r of 0.897 and 0.888, respectively. When SVM was used as the regression method no significant difference in the accuracy was observed for models using B3LYP/3-21g(d) and AM1 as indicated by r of 0.968 and 0.966, respectively. The quantitative structure-property relationship (QSPR) model of BDE discussed in this study offers great potential for the design of novel antioxidant phenols with robust properties.

Molecular Docking of Aromatase Inhibitors

Aromatase is an enzyme that plays a critical role in the development of estrogen receptor positive breast cancer. As aromatase catalyzes the aromatization of androstenedione to estrone, a naturally occurring estrogen, it is a promising drug target for therapeutic management. The undesirable effects found in aromatase inhibitors (AIs) that are in clinical use necessitate the discovery of novel AIs with higher selectivity, less toxicity and improving potency. In this study, we elucidate the binding mode of all three generations of AI drugs to the crystal structure of aromatase by means of molecular docking. It was demonstrated that the docking protocol could reliably reproduce the interaction of aromatase with its substrate with an RMSD of 1.350 Å. The docking study revealed that polar (D309, T310, S478 and M374), aromatic (F134, F221 and W224) and non-polar (A306, A307, V370, L372 and L477) residues were important for interacting with the AIs. The insights gained from the study herein have great potential for the design of novel AIs.

Modeling the activity of furin inhibitors using artificial neural network.

Quantitative structure-activity relationship (QSAR) models were constructed for predicting the inhibition of furin-dependent processing of anthrax protective antigen of substituted guanidinylated aryl 2,5-dideoxystreptamines. Molecular descriptors calculated by E-Dragon and RECON were subjected to variable reduction using the Unsupervised Forward Selection (UFS) algorithm. The variables were then used as input for QSAR model generation using partial least squares and back-propagation neural network. Prediction was performed via a two-step approach: (i) perform classification to determine whether the molecule is active or inactive, (ii) develop a QSAR regression model of active molecules. Both classification and regression models yielded good results with RECON providing higher accuracy than that of E-DRAGON descriptors. The performance of the regression model using E-Dragon and RECON descriptors provided a correlation coefficient of 0.807 and 0.923 and root mean square error of 0.666 and 0.304, respectively. Interestingly, it was observed that appropriate representations of the protonation states of the molecules were crucial for good prediction performance, which coincides with the fact that the inhibitors interact with furin via electrostatic forces. The results provide good prospect of using the proposed QSAR models for the rational design of novel therapeutic furin inhibitors toward anthrax and furin-dependent diseases.

Synthesis and Theoretical Study of Molecularly Imprinted Nanospheres for Recognition of Tocopherols

Molecular imprinting is a technology that facilitates the production of artificial receptors toward compounds of interest. The molecularly imprinted polymers act as artificial antibodies, artificial receptors, or artificial enzymes with the added benefit over their biological counterparts of being highly durable. In this study, we prepared molecularly imprinted polymers for the purpose of binding specifically to tocopherol (vitamin E) and its derivative, tocopherol acetate. Binding of the imprinted polymers to the template was found to be two times greater than that of the control, non-imprinted polymers, when using only 10 mg of polymers. Optimization of the rebinding solvent indicated that ethanol-water at a molar ratio of 6:4 (v/v) was the best solvent system as it enhanced the rebinding performance of the imprinted polymers toward both tocopherol and tocopherol acetate with a binding capacity of approximately 2 mg/g of polymer. Furthermore, imprinted nanospheres against tocopherol was successfully prepared by precipitation polymerization with ethanol-water at a molar ratio of 8:2 (v/v) as the optimal rebinding solvent. Computer simulation was also performed to provide mechanistic insights on the binding mode of template-monomer complexes. Such polymers show high potential for industrial and medical applications, particularly for selective separation of tocopherol and derivatives.