No abstract

Peroxisome proliferator-activated receptor ␥ (PPAR␥) functions in various biological processes, including macrophage and adipocyte differentiation. Several natural lipid metabolites have been shown to activate PPAR␥. Here, we report that some PPAR␥ ligands, including 15-deoxy-⌬ 12,14 -prostaglandin J 2 , covalently bind to a cysteine residue in the PPAR␥ ligand binding pocket through a Michael addition reaction by an ␣,␤-unsaturated ketone. Using rhodamine-maleimide as well as mass spectroscopy, we showed that the binding of these ligands is covalent and irreversible. Consistently, mutation at the cysteine residue abolished abilities of these ligands to activate PPAR␥, but not of BRL49653, a non-covalent synthetic agonist, indicating that covalent binding of the ␣,␤-unsaturated ketone in the natural ligands was required for their transcriptional activities. Screening of lipid metabolites containing the ␣,␤-unsaturated ketone revealed that several other oxidized metabolites of hydroxyeicosatetraenoic acid, hydroxyeicosadecaenoic acid, and prostaglandins can also function as novel covalent ligands for PPAR␥. We propose that PPAR␥ senses oxidation of fatty acids by recognizing such an ␣,␤-unsaturated ketone as a common moiety.

No abstract

Trivial trajectory parallelization of multicanonical molecular dynamics (TTP-McMD) explores the conformational space of a biological system with multiple short runs of McMD starting from various initial structures. This method simply connects (i.e., trivially parallelizes) the short trajectories and generates a long trajectory. First, we theoretically prove that the simple trajectory connection satisfies a detailed balance automatically. Thus, the resultant long trajectory is regarded as a single multicanonical trajectory. Second, we applied TTP-McMD to an alanine decapeptide with an all-atom model in explicit water to compute a free-energy landscape. The theory imposes two requirements on the multiple trajectories. We have demonstrated that TTP-McMD naturally satisfies the requirements. The TTP-McMD produces the free-energy landscape considerably faster than a single-run McMD does. We quantitatively showed that the accuracy of the computed landscape increases with increasing the number of multiple runs. Generally, the free-energy landscape of a large biological system is unknown a priori. The current method is suitable for conformational sampling of such a large system to reduce the waiting time to obtain a canonical ensemble statistically reliable.