Classification of cow milk using artificial neural network developed from the spectral data of single- and three-detector spectrophotometers.

Spectra data from two instruments (UV-Vis/NIR and FT-NIR) consisting of three and one detectors, respectively, were employed in order to discriminate the geographical origin of milk as a way to detect adulteration. Initially, principal component analysis (PCA) was used to see if clusters of milk from different origins are formed. Separation between samples of different origins were not observed with PCA, hence, feed-forward multi-layer perceptron artificial neural network (MLP-ANN) models were designed. ANN models were developed by changing the number of input variables and the best models were chosen based on high values of generalized R-square and entropy R-square, as well as small values of root mean square error (RMSE), mean absolute deviation (Mean Abs. Dev), and -loglikelihood while considering 100% classification rate. Based on the results, whether the spectra data was collected from a single or three detector instrument the same clustering was observed based on geographical origin.

Dynamics and binding interactions of peptide inhibitors of dengue virus entry.

In this study, we investigate the binding interactions of two synthetic antiviral peptides (DET2 and DET4) on type II dengue virus (DENV2) envelope protein domain III. These two antiviral peptides are designed based on the domain III of the DENV2 envelope protein, which has shown significant inhibition activity in previous studies and can be potentially modified further to be active against all dengue strains. Molecular docking was performed using AutoDock Vina and the best-ranked peptide-domain III complex was further explored using molecular dynamics simulations. Molecular mechanics-Poisson-Boltzmann surface area (MM-PBSA) was used to calculate the relative binding free energies and to locate the key residues of peptide-protein interactions. The predicted binding affinity correlated well with the previous experimental studies. DET4 outperformed DET2 and is oriented within the binding site through favorable vdW and electrostatic interactions. Pairwise residue decomposition analysis has revealed several key residues that contribute to the binding of these peptides. Residues in DET2 interact relatively lesser with the domain III compared to DET4. Dynamic cross-correlation analysis showed that both the DET2 and DET4 trigger different dynamic patterns on the domain III. Correlated motions were seen between the residue pairs of DET4 and the binding site while binding of DET2 results in anti-correlated motion on the binding site. This work showcases the use of computational study in elucidating and explaining the experiment observation on an atomic level.

Spatial and temporal air quality pattern recognition using environmetric techniques: a case study in Malaysia.

The objective of this study is to identify spatial and temporal patterns in the air quality at three selected Malaysian air monitoring stations based on an eleven-year database (January 2000-December 2010). Four statistical methods, Discriminant Analysis (DA), Hierarchical Agglomerative Cluster Analysis (HACA), Principal Component Analysis (PCA) and Artificial Neural Networks (ANNs), were selected to analyze the datasets of five air quality parameters, namely: SO2, NO2, O3, CO and particulate matter with a diameter size of below 10 µm (PM10). The three selected air monitoring stations share the characteristic of being located in highly urbanized areas and are surrounded by a number of industries. The DA results show that spatial characterizations allow successful discrimination between the three stations, while HACA shows the temporal pattern from the monthly and yearly factor analysis which correlates with severe haze episodes that have happened in this country at certain periods of time. The PCA results show that the major source of air pollution is mostly due to the combustion of fossil fuel in motor vehicles and industrial activities. The spatial pattern recognition (S-ANN) results show a better prediction performance in discriminating between the regions, with an excellent percentage of correct classification compared to DA. This study presents the necessity and usefulness of environmetric techniques for the interpretation of large datasets aiming to obtain better information about air quality patterns based on spatial and temporal characterizations at the selected air monitoring stations.

Application of receptor models on water quality data in source apportionment in Kuantan River Basin.

Recent techniques in the management of surface river water have been expanding the demand on the method that can provide more representative of multivariate data set. A proper technique of the architecture of artificial neural network (ANN) model and multiple linear regression (MLR) provides an advance tool for surface water modeling and forecasting. The development of receptor model was applied in order to determine the major sources of pollutants at Kuantan River Basin, Malaysia. Thirteen water quality parameters were used in principal component analysis (PCA) and new variables of fertilizer waste, surface runoff, anthropogenic input, chemical and mineral changes and erosion are successfully developed for modeling purposes. Two models were compared in terms of efficiency and goodness-of-fit for water quality index (WQI) prediction. The results show that APCS-ANN model gives better performance with high R2 value (0.9680) and small root mean square error (RMSE) value (2.6409) compared to APCS-MLR model. Meanwhile from the sensitivity analysis, fertilizer waste acts as the dominant pollutant contributor (59.82%) to the basin studied followed by anthropogenic input (22.48%), surface runoff (13.42%), erosion (2.33%) and lastly chemical and mineral changes (1.95%). Thus, this study concluded that receptor modeling of APCS-ANN can be used to solve various constraints in environmental problem that exist between water distribution variables toward appropriate water quality management.

Design of new competitive dengue NS2B/NS3 protease inhibitors-a computational approach.

Dengue is a serious disease which has become a global health burden in the last decade. Currently, there are no approved vaccines or antiviral therapies to combat the disease. The increasing spread and severity of the dengue virus infection emphasizes the importance of drug discovery strategies that could efficiently and cost-effectively identify antiviral drug leads for development into potent drugs. To this effect, several computational approaches were applied in this work. Initially molecular docking studies of reference ligands to the DEN2 NS2B/NS3 serine protease were carried out. These reference ligands consist of reported competitive inhibitors extracted from Boesenbergia rotunda (i.e., 4-hydroxypanduratin A and panduratin A) and three other synthesized panduratin A derivative compounds (i.e., 246DA, 2446DA and 20H46DA). The design of new lead inhibitors was carried out in two stages. In the first stage, the enzyme complexed to the reference ligands was minimized and their complexation energies (i.e., sum of interaction energy and binding energy) were computed. New compounds as potential dengue inhibitors were then designed by putting various substituents successively on the benzyl ring A of the reference molecule. These substituted benzyl compounds were then computed for their enzyme-ligand complexation energies. New enzyme-ligand complexes, exhibiting the lowest complexation energies and closest to the computed energy for the reference compounds, were then chosen for the next stage manipulation and design, which involved substituting positions 4 and 5 of the benzyl ring A (positions 3 and 4 for 2446DA) with various substituents.

Enantioselective organocatalytic Diels-Alder reactions: a density functional theory and kinetic isotope effects study.

The mechanism and enantioselectivity of the organocatalytic Diels-Alder reaction were computationally investigated by density functional theory at the B3LYP/6-31G(d) level of theory. The uncatalyzed Diels-Alder reaction was also studied to explore the effect of the organocatalyst on this reaction in terms of energetics, selectivity, and mechanism. The catalyzed reaction showed improved endo/exo selectivity, and the free energy of activation was significantly lowered in the presence of the catalyst. Both uncatalyzed and catalyzed reactions exhibited concerted asynchronous reaction mechanism with the degree of asynchronicity being more evident in the presence of the catalyst. The Corey's experimentally derived predictive selection rules for the outcome of the organocatalytic Diels-Alder reaction were also theoretically analyzed, and an excellent agreement was found between experiment and theory.

Spatial water quality assessment of Langat River Basin (Malaysia) using environmetric techniques.

This study investigates the spatial water quality pattern of seven stations located along the main Langat River. Environmetric methods, namely, the hierarchical agglomerative cluster analysis (HACA), the discriminant analysis (DA), the principal component analysis (PCA), and the factor analysis (FA), were used to study the spatial variations of the most significant water quality variables and to determine the origin of pollution sources. Twenty-three water quality parameters were initially selected and analyzed. Three spatial clusters were formed based on HACA. These clusters are designated as downstream of Langat river, middle stream of Langat river, and upstream of Langat River regions. Forward and backward stepwise DA managed to discriminate six and seven water quality variables, respectively, from the original 23 variables. PCA and FA (varimax functionality) were used to investigate the origin of each water quality variable due to land use activities based on the three clustered regions. Seven principal components (PCs) were obtained with 81% total variation for the high-pollution source (HPS) region, while six PCs with 71% and 79% total variances were obtained for the moderate-pollution source (MPS) and low-pollution source (LPS) regions, respectively. The pollution sources for the HPS and MPS are of anthropogenic sources (industrial, municipal waste, and agricultural runoff). For the LPS region, the domestic and agricultural runoffs are the main sources of pollution. From this study, we can conclude that the application of environmetric methods can reveal meaningful information on the spatial variability of a large and complex river water quality data.

Validation of quantitative structure-activity relationship (QSAR) model for photosensitizer activity prediction.

Photodynamic therapy is a relatively new treatment method for cancer which utilizes a combination of oxygen, a photosensitizer and light to generate reactive singlet oxygen that eradicates tumors via direct cell-killing, vasculature damage and engagement of the immune system. Most of photosensitizers that are in clinical and pre-clinical assessments, or those that are already approved for clinical use, are mainly based on cyclic tetrapyrroles. In an attempt to discover new effective photosensitizers, we report the use of the quantitative structure-activity relationship (QSAR) method to develop a model that could correlate the structural features of cyclic tetrapyrrole-based compounds with their photodynamic therapy (PDT) activity. In this study, a set of 36 porphyrin derivatives was used in the model development where 24 of these compounds were in the training set and the remaining 12 compounds were in the test set. The development of the QSAR model involved the use of the multiple linear regression analysis (MLRA) method. Based on the method, r(2) value, r(2) (CV) value and r(2) prediction value of 0.87, 0.71 and 0.70 were obtained. The QSAR model was also employed to predict the experimental compounds in an external test set. This external test set comprises 20 porphyrin-based compounds with experimental IC(50) values ranging from 0.39 µM to 7.04 µM. Thus the model showed good correlative and predictive ability, with a predictive correlation coefficient (r(2) prediction for external test set) of 0.52. The developed QSAR model was used to discover some compounds as new lead photosensitizers from this external test set.

Pheophorbide b ethyl ester from a chlorella vulgaris dietary supplement.

In the title compound, C(37)H(38)N(4)O(6), four five-membered nitro-gen-bearing rings are nearly coplanar. Two N atoms in two these five-membered rings have attached H atoms, which contribute to the formation of intra-molecular N-H⋯N hydrogen bonds [N⋯N = 2.713 (5)-3.033 (6) Å].

Reductive alkylation of lipase: experimental and molecular modeling approaches.

Candida rugosa lipase was modified via reductive alkylation to increase its hydrophobicity to work better in organic solvents. The free amino group of lysines was alkylated using propionaldehyde with different degrees of modification obtained (49 and 86%). Far-ultraviolet circular dichroism (CD) spectroscopy of the lipase in aqueous solvent showed that such chemical modifications at the enzyme surface caused a loss in secondary and tertiary structure that is attributed to the enzyme unfolding. Using molecular modeling, we propose that in an aqueous environment the loss in protein structure of the modified lipase is owing to disruption of stabilizing salt bridges, particularly of surface lysines. Indeed, molecular modeling and simulation of a salt bridge formed by Lys-75 to Asp-79, in a nonpolar environment, suggests the adoption of a more flexible alkylated lysine that may explain higher lipase activity in organic solvents on alkylation.