Biomedically important histone lysine acetyltransferase KAT8 catalyses the acetyl coenzyme A-dependent acetylation of lysine on histone and other proteins. Here, we explore the ability of human KAT8 to catalyse the acetylation of histone H4 peptides possessing lysine and its analogues at position 16 (H4K16). Our synthetic and enzymatic studies on chemically and structurally diverse lysine mimics demonstrate that KAT8 also has a capacity to acetylate selected lysine analogues that possess subtle changes on the side chain and main chain. Overall, this work highlights that KAT8 has a broader substrate scope beyond natural lysine, and contributes to the design of new chemical probes targeting KAT8 and other members of the histone lysine acetyltransferase (KAT) family.
In the present study, we investigated a new approach for studying the interaction between p53 and MDM2/X (where MDM is murine double minute protein). The method is based on the different mobility between the interacting domains of the oncosuppressor p53 and its protein ligands MDM2/X on polyacrylamide gels under native conditions. While the two proteins MDM2/X alone were able to enter the gel, the formation of a binary complex between p53 and MDM2/X prevented the gel entry. The novel technique is reliable for determining the different affinity elicited by MDM2 or MDMX toward p53, and can be useful for analyzing the dissociation power exerted by other molecules on the p53-MDM2/X complex.
Bayesian methods for graphical log-linear marginal models has not been developed in the same extend as traditional frequentist approaches. In this work, we introduce a novel Bayesian approach for quantitative learning for such models. They belong to curved exponential families that are difficult to handle from a Bayesian perspective. Furthermore, the likelihood cannot be analytically expressed as a function of the marginal log-linear interactions, but only in terms of cell counts or probabilities. Posterior distributions cannot be directly obtained, and MCMC methods are needed. Finally, a welldefined model requires parameter values that lead to compatible marginal probabilities. Hence, any MCMC should account for this important restriction. We construct a fully automatic and efficient MCMC strategy for quantitative learning for graphical log-linear marginal models that handles these problems. While the prior is expressed in terms of the marginal log-linear interactions, we build an MCMC algorithm which employs a proposal on the probability parameter space. The corresponding proposal on the marginal log-linear interactions is obtained via parameter transformations. By this strategy, we achieve to move within the desired target space. At each step we directly work with welldefined probability distributions. Moreover, we can exploit a conditional conjugate setup to build an
We analyze the redistribution channel of a money‐financed fiscal stimulus (MFFS) versus debt‐financed fiscal stimulus (DFFS) in a Borrower–Saver framework. The redistribution channel is larger when we consider an MFFS and borrowers are the main beneficiaries. A liquidity trap scenario amplifies the differences between an MFFS and a DFFS. The redistribution channel makes an MFFS effective at having an expansionary effect in the medium run, despite the adverse scenario. We show, however, that an MFFS increases the consumption gap between the two agents by redistributing income from savers to borrowers. Thus, an MFFS results detrimental for welfare when the welfare function is approximated around the efficient steady state.
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