Chotpatiwetchkul, Warot
Loading...
3 results
Now showing 1 - 3 of 3
- Some of the metrics are blocked by yourconsent settings
Item type:Publication, Investigation on the interactions between glucomannans and bifidobacterium protein by using molecular dynamics simulations(2018-01-01); ; Konjac glucomannan is a polysaccharide extracted from the Amorphophallus konjac K.Koch plant. It is often used as food additives due to their low toxicity, biodegradability and low calories. The unique properties of konjac glucomannan is its prebiotic activities. Konjac glucomannan which cannot be digested and absorbed in human stomachs and small intestines. Therefore, it is fermented in the large intestine and becomes food for the beneficial bacteria or probiotics especially bifidobacteria and lactobacilli in human colons. This study examined the effect of the size of konjac glucomannan on the prebiotic property. The interactions between the different degrees of polymerization of konjac glucomannan and Bifidobacterium protein were investigated in 0.15 M sodium chloride solution at 310 K by using molecular dynamics simulation. The results have shown that water molecules dramatically affect the alignment of konjac glucomannan in the system. The active site of Bifidobacterium protein that determined by the calculations are composed of ASP154, ARG49, ASN206, and ASN401. The lowest flexibility of GM5 structure shows strong interactions with Bifidobacterium protein. The most suitable size of konjac glucomannan that can bind with the protein has the degrees of polymerization no more than 8. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Exploring the catalytic mechanism of dihydropteroate synthase: Elucidating the differences between the substrate and inhibitor(2017-01-01); ; ; Gleeson, M. PaulDihydropteroate synthase (DHPS) catalyzes the condensation of 6-hydroxymethyl-7,8-dihydropterin pyrophosphate (DHPPP) with p-aminobenzoic acid (pABA) and is a well validated target for anti-malarial and anti-bacterial drugs. However, in recent years its utility as a therapeutic target has diminished considerably due to multiple mutations. As such, considerable structural biology and medicinal chemistry effort has been expended to understand and overcome this issue. To date no detailed computational analysis of the protein mechanism has been made despite the detailed crystal structures and multiple mechanistic proposals being made. In this study the mechanistic proposals for DHPS have been systematically investigated using a hybrid QM/MM method. We aimed to compare the energetics associated with S<inf>N</inf>1 and S<inf>N</inf>2 processes, whether the S<inf>N</inf>1 process involves a carbocation or neutral DHP intermediate, uncover the identity of the general base in the catalytic mechanism, and understand the differences in substrate vs. inhibitor reactivity. Our results suggest a reaction that follows an S<inf>N</inf>1 process with the rate determining step being C-O bond breaking to give a carbocation intermediate. Comparative studies on the inhibitor STZ confirm the experimental observations that it is also a DHPS substrate. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Insights into the EGFR SAR of N-phenylquinazolin-4-amine-derivatives using quantum mechanical pairwise-interaction energies(2019-08-01); Gleeson, M. PaulProtein kinases are an important class of enzymes that play an essential role in virtually all major disease areas. In addition, they account for approximately 50% of the current targets pursued in drug discovery research. In this work, we explore the generation of structure-based quantum mechanical (QM) quantitative structure–activity relationship models (QSAR) as a means to facilitate structure-guided optimization of protein kinase inhibitors. We explore whether more accurate, interpretable QSAR models can be generated for a series of 76 N-phenylquinazolin-4-amine inhibitors of epidermal growth factor receptor (EGFR) kinase by comparing and contrasting them to other standard QSAR methodologies. The QM-based method involved molecular docking of inhibitors followed by their QM optimization within a ~ 300 atom cluster model of the EGFR active site at the M062X/6-31G(d,p) level. Pairwise computations of the interaction energies with each active site residue were performed. QSAR models were generated by splitting the datasets 75:25 into a training and test set followed by modelling using partial least squares (PLS). Additional QSAR models were generated using alignment dependent CoMFA and CoMSIA methods as well as alignment independent physicochemical, e-state indices and fingerprint descriptors. The structure-based QM-QSAR model displayed good performance on the training and test sets (r<sup>2</sup> ~ 0.7) and was demonstrably more predictive than the QSAR models built using other methods. The descriptor coefficients from the QM-QSAR models allowed for a detailed rationalization of the active site SAR, which has implications for subsequent design iterations.