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Publication Open Access Elucidating the structure-activity relationships of the vasorelaxation and antioxidation properties of thionicotinic acid derivatives(2010) Supaluk Prachayasittikul; Orapin Wongsawatkul; Apilak Worachartcheewan; Chanin Nantasenamat; Somsak Ruchirawat; Virapong PrachayasittikulNicotinic acid, known as vitamin B3, is an effective lipid lowering drug and intense cutaneous vasodilator. This study reports the effect of 2-(1-adamantylthio)nicotinic acid (6) and its amide 7 and nitrile analog 8 on phenylephrine-induced contraction of rat thoracic aorta as well as antioxidative activity. It was found that the tested thionicotinic acid analogs 6-8 exerted maximal vasorelaxation in a dose-dependent manner, but their effects were less than acetylcholine (ACh)-induced nitric oxide (NO) vasorelaxation. The vasorelaxations were reduced, apparently, in both NG-nitro-L-arginine methyl ester (L-NAME) and indomethacin (INDO). Synergistic effects were observed in the presence of L-NAME plus INDO, leading to loss of vasorelaxation of both the ACh and the tested nicotinic acids. Complete loss of the vasorelaxation was noted under removal of endothelial cells. This infers that the vasorelaxations are mediated partially by endothelium-induced NO and prostacyclin. The thionicotinic acid analogs all exhibited antioxidant properties in both 2,2-diphenyl-1-picrylhydrazyl (DPPH) and superoxide dismutase (SOD) assays. Significantly, the thionicotinic acid 6 is the most potent vasorelaxant with ED50 of 21.3 nM and is the most potent antioxidant (as discerned from DPPH assay). Molecular modeling was also used to provide mechanistic insights into the vasorelaxant and antioxidative activities. The findings reveal that the thionicotinic acid analogs are a novel class of vasorelaxant and antioxidant compounds which have potential to be further developed as promising therapeutics.Publication Open Access Predicting the Oligomeric States of Fluorescent Proteins(2015) Saw Simeon; Watshara Shoombuatong; Likit Preeyanon; Virapong Prachayasittikul; Chanin Nantasenamat; Mahidol University. Faculty of Medical Technology. Center of Data Mining and Biomedical Informatics; Mahidol University. Faculty of Medical Technology. Department of Clinical Microbiology and Applied TechnologyCurrently, monomeric fluorescent proteins (FP) are ideal markers for protein tagging. The prediction of oligomeric states is helpful for enhancing live biomedical imaging. Computational prediction of FP oligomeric states can accelerate the effort of protein engineering to create monomeric FPs by saving time and money. To the best of our knowledge, this study represents the first computational model for predicting and analyzing FP oligomerization directly from their amino acid sequences. An exhaustive dataset consisting of 397 unique FP oligomeric states was compiled from the literature. FP were described by 3 classes of protein descriptors including amino acid composition, dipeptide composition and physicochemical properties. The oligomeric states of FP was predicted using decision tree (DT) algorithm and results demonstrated that DT provided robust performance with accuracies in ranges of 79.97-81.72% and 80.76-82.63% for the internal (e.g. 10-fold cross-validation) and external sets, respectively. This approach was also benchmarked with other common machine learning algorithms such as artificial neural network, support vector machine and random forest. A thorough analysis of amino acid sequence features was conducted to provide informative insights into FP oligomerization, which may aid in engineering novel monomeric fluorescent proteins. The following differentiating characteristics of monomeric and oligomeric fluorescent proteins were derived from DT: (i) substitution of any amino acid to Glu led to the reduction of aggregated proteins and (ii) oligomerization of FP appears to be stabilized by several hydrophobic contacts.