(2011). iSMART: An Integrated Cloud Computing Web Server for Traditional Chinese Medicine for Online Virtual Screening, de novo Evolution and Drug Design. Journal of Biomolecular Structure and Dynamics: Vol. 29, No. 1, pp. 243-250.

Phosphodiesterase 4D (PDE4D) has been identified be a promising target which associate with stroke, which is one of the top 3 leading of death and main leading cause of adult disability in US. In this study, we applied virtual screening on the world's largest traditional Chinese medicine (TCM) database (http://tcm.cmu.edu.tw;¹ C. Y. C. Chen, PLoS One, 2011, 6, e15939) for natural compounds that inhibit PDE4D functions. Molecular docking and dynamics simulation were employed to investigate the protein–ligand interactions of compounds with top two dock scores. During the simulation, the divalent metal cations in PDE4D formed stable hydrogen bonds and electrostatic interactions between ligand and binding site residues. Furthermore, the two top TCM candidates, 2-O-caffeoyl tartaric acid and mumefural, formed additional steady hydrogen bond with binding site residue and active site residue respectively. The additional hydrogen bonds further stabilize protein-ligand interaction at the PDE4D binding site. To predict the bioactivity of the top TCM candidates, we built two prediction models using multiple linear regression (MLR) and support vector machine (SVM). The predicted pIC50 values suggest that 2-O-caffeoyl tartaric acid and mumefural are potential PDE4D inhibitors.

Aberrations in cyclic adenosine monophosphate (cAMP) signaling cascade has been linked to the allergic responses that associate with the risks of stroke or cardiovascular diseases. Phosphodiesterase 4D (PDE4D) has been shown to be highly involved in cAMP regulation and is hence implied to be a potential drug target in stroke prevention. To identify potential PDE4Dinhibitors from traditional Chinese medicine (TCM), we employed machine learning modeling techniques to screen a comprehensive TCM database. The multiple linear regression (MLR) and support vector machine (SVM) models constructed have correlation coefficients of 0.8234 and 0.7854 respectively. Three candidates from the ginger family were identified based on the prediction models. Molecular dynamics simulation further validated the binding stabilities of each candidate in comparison to the control inhibitorL-454560. The intermolecular distances suggested that the candidates could hinder PDE4D from binding to cAMP. Furthermore, the HypoGen validation suggested that top2, top3, and the control L-454560 mapped with the predicted pharmacophores. The results suggested that the 3 compounds identified from the ginger family were capable in inhibiting cAMP binding and hydrolysis by PDE4D. We further identified and characterized the ligand binding properties that are associated with the inhibition of PDE4D.

Nowadays, the occurrence of metabolic syndrome, which is characterized by obesity and clinical disorders, has been increasing rapidly over the world. It induces several serious chronic diseases such as cardiovascular disease, dyslipidemia, gall bladder disease, hypertension, osteoarthritis, sleep apnea, stroke, and type 2 diabetes mellitus. Peroxisome proliferator-activated receptors (PPARs), which have three isoforms: PPAR-α, PPAR-γ, and PPAR-δ, are key regulators of adipogenesis, lipid and carbohydrate metabolism, and are potential drug targets for treating metabolic syndrome. The traditional Chinese medicine (TCM) compounds from TCM Database@Taiwan (http://tcm.cmu.edu.tw/) were employed to virtually screen for potential PPAR agonists, and structure-based pharmacophore models were generated to identify the key interactions for each PPAR protein. In addition, molecular dynamics (MD) simulation was performed to evaluate the stability of the PPAR–ligand complexes in a dynamic state. (S)-Tryptophan-betaxanthin and berberrubine, which have higher Dock Score than controls, form stable interactions during MD, and are further supported by the structure-based pharmacophore models in each PPAR protein. Key features include stable H-bonds with Thr279 and Ala333 of PPAR-α, with Thr252, Thr253 and Lys331 of PPAR-δ, and with Arg316 and Glu371 of PPAR-γ. Hence, we propose the top two TCM candidates as potential lead compounds in developing agonists targeting PPARs protein for treating metabolic syndrome.

Based on genome wide association studies (GWAS), the activities of phosphodiesterase 4D (PDE4D) and 5-Lipoxygenase activating protein (ALOX5AP) were suggested as two of the major factors involved in ischemic stroke risks. Uncontrolled PDE4D activities often lead to cAMP-induced stroke and cardiovascular diseases. Overexpression of ALOX5AP, on the other hand, had been shown to play a major role in inflammation pathway that could induce the development of atherosclerosis and stroke. To eliminate the risk factors that lead to stroke, we reported the identification and analysis of dual-targeting compounds that could reduce PDE4D and ALOX5AP activities from traditional Chinese medicine (TCM). We employed world's largest TCM database, TCM Database@Taiwan, for in silico drug identification. We also introduced machine learning predictive models, as well as pharmacophore model, for characterizing the drug-like candidates. Both myristic acid and pentadecanoic acid were identified. The follow-up analysis on molecular dynamics simulation further determined the major roles of the carboxyl group for forming stable molecular interactions. Intriguingly, the carboxyl group demonstrated different bonding patterns with PDE4D and ALOX5AP, through electrostatic interaction and hydrogen bonds, respectively. In addition, the large volume occupied by the ligand hydrophobic regions could achieve inhibition through occupying the vacant spaces in the binding site. These pharmacophores held true for both candidates against each protein targets. Hence, we proposed the presence of the carboxyl group and hydrophobic regions as potent dual targeting features that inhibit both PDE4D and ALOX5AP activities.