Carolina Brito

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Author summary Allostery in proteins is the property of highly specific responses to ligand binding at a distant site. To inform protocols of de novo drug design, it is fundamental to understand the impact of mutations on allosteric regulation and whether it can be predicted from evolutionary correlations. In this work we consider allosteric architectures artificially evolved to optimize the cooperativity of binding at allosteric and active site. We first characterize the emergent pattern of epistasis as well as the underlying mechanical phenomena, finding the four types of epistasis (Synergistic, Sign, Antagonistic, Saturation), which can be both short or long-range. The numerical evolution of these allosteric architectures allows us to benchmark Direct Coupling Analysis, a method which relies on co-evolution in sequence data to infer direct evolutionary couplings, in connection to allostery. We show that Direct Coupling Analysis predicts quantitatively point mutation costs but underestimates strong long-range epistasis. We provide an argument, based on a simplified model, illustrating the reasons for this discrepancy. Our analysis suggests neural networks as more promising tool to measure epistasis.

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Brazil’s presidential system is characterized by the existence of many political parties that are elected for the Chamber of Deputies and unite in legislative coalitions to form a majority. Since the re-democratization in 1985, Brazil has had 8 direct presidential elections, among which there were two impeachments of the elected presidents. In this work we identify clear differences between stable presidential periods and Legislative terms with an impeachment by analyzing the votes that took place in the Chamber of Deputies from 1991 to 2019. Our statistical analysis are blind to the content of the bills. We start by measuring the cohesion of the parties and the congress for each bill. We then quantify the agreement between the votes of congressmen and observe that there is a stronger polarization among congressmen during legislative periods where there was no impeachment, referred here as stable legislative periods. Using clustering algorithms, we are able to associate these polarized groups observed during the stable periods with the opposition to the government and government base. For periods with an impeachment, the data shows that the congress split up in more than two groups. To characterize the impeachment of Collor and Dilma Rousseff (in 1992 and 2016, respectively) we analyze how the agreement between congressmen and the government evolved over time and we also propose a division of the congressmen in three groups. We identified that, in periods with an impeachment, the third group aligns itself against the president.