Tanawut Tantimongcolwat

Modeling the LPS Neutralization Activity of Anti-Endotoxins

Bacterial lipopolysaccharides (LPS), also known as endotoxins, are major structural components of the outer membrane of Gram-negative bacteria that serve as a barrier and protective shield between them and their surrounding environment. LPS is considered to be a major virulence factor as it strongly stimulates the secretion of pro-inflammatory cytokines which mediate the host immune response and culminating in septic shock. Quantitative structure-activity relationship studies of the LPS neutralization activities of anti-endotoxins were performed using charge and quantum chemical descriptors. Artificial neural network implementing the back-propagation algorithm was selected for the multivariate analysis. The predicted activities from leave-one-out cross-validation were well correlated with the experimental values as observed from the correlation coefficient and root mean square error of 0.930 and 0.162, respectively. Similarly, the external testing set also yielded good predictivity with correlation coefficient and root mean square error of 0.983 and 0.130. The model holds great potential for the rational design of novel and robust compounds with enhanced neutralization activity.

Antioxidant, cytotoxicity, and QSAR study of 1-adamantylthio derivatives of 3-picoline and phenylpyridines

A series of isomeric α- and β-(1-adamantylthio)pyridines were previously documented to possess interesting antimicrobial and antimalarial activities. In this study, the antioxidant and cytotoxic potentials of 1-adamantylthio-3-methyl and 2-,3-,4-phenylpyridines (1–10) were investigated. The tested compounds were shown to exhibit interesting superoxide (SOD)- and free radical (DPPH)-scavenging activities as well as cytotoxic activities. Particularly, β-(1-adamantylthio)-4-phenylpyridine (8) was shown to be the most potent antioxidant and cytotoxic compound. QSAR studies revealed that dipole moment (μ) and electrophilic index (ωi) were the most important descriptors accounting for the observed SOD activities. Compounds with high μ and ωi values were observed to display high SOD activity. Inversely, compounds with the lowest atomic polarizability (MATS4p) exhibited the highest DPPH activity. Other quantum chemical descriptors such as atomic masses (GATS4m), ωi, and LUMO energy were also well correlated with cytotoxicity. The findings demonstrated that thiopyridine 8 is a potential lead compound that should be further investigated in drug discovery efforts. The QSAR results offer good prospect for the rational design of novel compounds with robust bioactivities.

QSAR MODEL OF THE QUORUM-QUENCHING N-ACYL-HOMOSERINE LACTONE LACTONASE ACTIVITY

A quantitative structure-activity relationship (QSAR) study was performed to model the lactonolysis activity of N-acyl-homoserine lactone lactonase. A data set comprising of 20 homoserine lactones and related compounds was taken from the work of Wang et al. Quantum chemical descriptors were calculated using the semiempirical AM1 method. Partial least squares regression was utilized to construct a predictive model. This computational approach reliably reproduced the lactonolysis activity with high accuracy as illustrated by the correlation coefficient in excess of 0.9. It is demonstrated that the combined use of quantum chemical descriptors with partial least squares regression are suitable for modeling the AHL lactonolysis activity.

Identification of ischemic heart disease via machine learning analysis on magnetocardiograms

Ischemic heart disease (IHD) is predominantly the leading cause of death worldwide. Early detection of IHD may effectively prevent severity and reduce mortality rate. Recently, magnetocardiography (MCG) has been developed for the detection of heart malfunction. Although MCG is capable of monitoring the abnormal patterns of magnetic field as emitted by physiologically defective heart, data interpretation is time-consuming and requires highly trained professional. Hence, we propose an automatic method for the interpretation of IHD pattern of MCG recordings using machine learning approaches. Two types of machine learning techniques, namely back-propagation neural network (BNN) and direct kernel self-organizing map (DK-SOM), were applied to explore the IHD pattern recorded by MCG. Data sets were obtained by sequential measurement of magnetic field emitted by cardiac muscle of 125 individuals. Data were divided into training set and testing set of 74 cases and 51 cases, respectively. Predictive performance was obtained by both machine learning approaches. The BNN exhibited sensitivity of 89.7%, specificity of 54.5% and accuracy of 74.5%, while the DK-SOM provided relatively higher prediction performance with a sensitivity, specificity and accuracy of 86.2%, 72.7% and 80.4%, respectively. This finding suggests a high potential of applying machine learning approaches for high-throughput detection of IHD from MCG data.

Data mining of magnetocardiograms for prediction of ischemic heart disease

Ischemic Heart Disease (IHD) is a major cause of death. Early and accurate detection of IHD along with rapid diagnosis are important for reducing the mortality rate. Magnetocardiogram (MCG) is a tool for detecting electro-physiological activity of the myocardium. MCG is a fully non-contact method, which avoids the problems of skin-electrode contact in the Electrocardiogram (ECG) method. However, the interpretation of MCG recordings is time-consuming and requires analysis by an expert. Therefore, we propose the use of machine learning for identification of IHD patients. Back-propagation neural network (BPNN), the Bayesian neural network (BNN), the probabilistic neural network (PNN) and the support vector machine (SVM) were applied to develop classification models for identifying IHD patients. MCG data was acquired by sequential measurement, above the torso, of the magnetic field emitted by the myocardium using a J-T interval of 125 cases. The training and validation data of 74 cases employed 10-fold cross-validation methods to optimize support vector machine and neural network parameters. The predictive performance was assessed on the testing data of 51 cases using the following metrics: accuracy, sensitivity, and specificity and area under the receiver operating characteristic (ROC) curve. The results demonstrated that both BPNN and BNN displayed the highest and the same level of accuracy at 78.43 %. Furthermore, the decision threshold and the area under the ROC curve was -0.2774 and 0.9059, respectively, for BPNN and 0.0470 and 0.8495, respectively, for BNN. This indicated that BPNN was the best classification model, BNN was the best performing model with sensitivity of 96.65 %, and SVM employing the radial basis function kernel displayed the highest specificity of 86.36 %.

Paper-based acetylcholinesterase inhibition assay combining a wet system for organophosphate and carbamate pesticides detection

A dramatic increase in pesticide usage in agriculture highlights the need for on-site monitoring for public health and safety. Here, a paper-based sensor combined with a wet system was developed for the simple and rapid screening of organophosphate (OP) and carbamate (CM) pesticides based on the inhibition of acetylcholinesterase (AChE). The paper-based sensor was designed as a foldable device consisting of a cover and detection sheets pre-prepared with indoxyl acetate and AChE, respectively. The paper-based sensor requires only the incubation of a sample on the test zone for 10 minutes, followed by closing of the foldable sheet to initiate the enzymatic reaction. Importantly, the buffer loading hole was additionally designed on the cover sheet to facilitate the interaction of the coated substrate and the immobilized enzyme. This subsequently facilitates the mixing of indoxyl acetate with AChE, resulting in the improved analytical performance of the sensor. The absence or decrease in blue color produced by the AChE hydrolysis of indoxyl acetate can be observed in the presence of OPs and CMs. Under optimized conditions and using image analysis, the limit of detection (LOD) of carbofuran, dichlorvos, carbaryl, paraoxon, and pirimicarb are 0.003, 0.3, 0.5, 0.6, and 0.6 ppm, respectively. The assay could be applied to determine OP and CM residues in spiked food samples. Visual interpretation of the color signal was clearly observed at the concentration of 5 mg/kg. Furthermore, a self-contained sample pre-concentration approach greatly enhanced the detection sensitivity. The paper-based device developed here is low-cost, requires minimal reagents and is easy to handle. As such, it would be practically useful for pesticide screening by non-professional end-users.

Prediction of selectivity index of pentachlorophenol-imprinted polymers

A data set comprising of the selectivity index of pentachlorophenol-imprinted polymers against 53 pentachlorophenol and related compounds was obtained from the excellent work of Baggiani et al. Molecular descriptors of the phenol compounds were calculated with EDRAGON to obtain a total of 1,666 descriptors spanning 20 categories of molecular properties. Multivariate analysis of the data set was performed using multiple linear regression, partial least squares regression, and principal component regression. Partial least squares regression was found to deliver an excellent predictive model and was chosen for further investigation. The descriptor dimension was reduced by the combined use of partial least squares and Unsupervised Forward Selection algorithm. The obtained Quantitative Structure-Property Relationship (QSPR) model based on the smaller subset of the molecular descriptors displayed substantial gain in predictive ability when compared to the model of Baggiani et al. Such QSPR model can help in the computational design of MIPs with predefined selectivity toward template molecules of interest.

Native and chimeric metal-binding lactate dehydrogenase as detection and protection tools for oxidative stress induced by Fenton's reaction

In the present study, a simple and reliable antioxidant screening technique based on lactate dehydrogenase (LDH) oxidation by Cu2+-mediated Fentons reaction has successfully been developed. Oxidation of LDH by hydroxyl radical consequently leads to enzymatic inactivation, while addition of antioxidants can protect and regain enzyme activity. This method demonstrated a high feasibility on detecting of antioxidative activity of lipophilic (e.g. alpha-Tocopherol and beta-Carotene) and hydrophilic compounds (e. g. glutathione, mannitol and thiourea) in a single assay. Results from linear correlation curves revealed that the IC50 were in the order of beta-carotene (3.45 mu g/ml) > alpha-Tocopherol (52.31 mu g/ml) > Mn(II)-bacitracin (109.37 mu g/ml) > glutathione (122.63 mu g/ml). Detailed investigations revealed that oxidation of LDH resulted in enzyme degradation, which was metal- and time-dependent mechanism. Therefore, further experiments were conducted to determine whether extension of the N-terminus of LDH with metal- binding regions possesses protective effect against the inactivation process. Genetic construction of chimeric LDH carrying two and four repetitive sequences of cadmium binding peptide (CdBP), designated as CdBP2LDH and CdBP4LDH, has been carried out. From our findings, the CdBP2LDH and the CdBP4LDH exhibited protective action and enzyme activity regained 20-30 % and 70 % higher than that of the native LDH, respectively. Two possible mechanisms have been proposed to play important role in protection against metal- mediated Fentons reaction: i) changing in redox potential of Cu2+ in metal-peptide complex, and ii) taking away of Cu2+ ion from the crucial amino acids by metal saturation at the cadmium-binding peptides.

Engineering of a novel chimera of superoxide dismutase and Vitreoscilla hemoglobin for rapid detoxification of reactive oxygen species

The genes encoding human manganese superoxide dismutase (MnSOD) and Vitreoscilla hemoglobin (VHb) were fused in-frame to generate a bifunctional enzyme that possessed MnSOD and peroxidase-like activities. At neutral pH, the coupling of the SOD and peroxidase reactions revealed that the bifunctional enzyme exhibited a 2.5 times shorter transient period and a 1.67 times higher reaction rate at steady-state conditions. Furthermore, the catalytic rate of the bifunctional enzyme was not affected as much by the external H₂O₂ scavenger catalase. This indicates that the bifunctional protein possesses a greater antioxidant capability, which is possibly due to the close proximity between the active site of MnSOD and the heme moiety of VHb. Our findings not only provide insight into the synergistic functions of SOD and peroxidase but also could potentially be used to develop novel therapeutic agents with more efficient O₂ carrying capability.

Exploring the chemical space of influenza neuraminidase inhibitors

The fight against the emergence of mutant influenza strains has led to the screening of an increasing number of compounds for inhibitory activity against influenza neuraminidase. This study explores the chemical space of neuraminidase inhibitors (NAIs), which provides an opportunity to obtain further molecular insights regarding the underlying basis of their bioactivity. In particular, a large set of 347 and 175 NAIs against influenza A and B, respectively, was compiled from the literature. Molecular and quantum chemical descriptors were obtained from low-energy conformational structures geometrically optimized at the PM6 level. The bioactivities of NAIs were classified as active or inactive according to their half maximum inhibitory concentration (IC50) value in which IC50 < 1µM and ≥ 10µM were defined as active and inactive compounds, respectively. Interpretable decision rules were derived from a quantitative structure–activity relationship (QSAR) model established using a set of substructure descriptors via decision tree analysis. Univariate analysis, feature importance analysis from decision tree modeling and molecular scaffold analysis were performed on both data sets for discriminating important structural features amongst active and inactive NAIs. Good predictive performance was achieved as deduced from accuracy and Matthews correlation coefficient values in excess of 81% and 0.58, respectively, for both influenza A and B NAIs. Furthermore, molecular docking was employed to investigate the binding modes and their moiety preferences of active NAIs against both influenza A and B neuraminidases. Moreover, novel NAIs with robust binding fitness towards influenza A and B neuraminidase were generated via combinatorial library enumeration and their binding fitness was on par or better than FDA-approved drugs. The results from this study are anticipated to be beneficial for guiding the rational drug design of novel NAIs for treating influenza infections.