Publications by authors named "E Brini"

Family formation is fostered by circumstances of plannability and economic and social stability. Conversely, as documented in previous literature, employment instability can hamper fertility decisions. Based on data from the Italian Labour Force Survey, this paper examines the association between employment-related instability and the likelihood of having a first or additional child from 2000 to 2020 in Italy, covering a period characterised by increasing labour market deregulation.

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Some proteins are conformational switches, able to transition between relatively different conformations. To understand what drives them requires computing the free-energy difference Δ between their stable states, and . Molecular dynamics (MD) simulations alone are often slow because they require a reaction coordinate and must sample many transitions in between.

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Recently, predicting the native structures of proteins has become possible using computational molecular physics (CMP)─physics-based force fields sampled with proper statistics─but only for small proteins. Algorithms with better scaling are needed. We describe ML x MELD x MD, a molecular dynamics (MD) method that inputs residue contacts derived from machine learning (ML) servers into MELD, a Bayesian accelerator that preserves detailed-balance statistics.

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Molecular docking algorithms are used to seek the most active compounds from a pool of ligands. In principle, molecular dynamics (MD) simulations with accurate physical potentials and sampling could yield better enrichments, but they are computationally expensive. Here, we describe a method called that utilizes biased replica exchange ladders in MD in order to compete different ligands against each other within a fast bracket style "binding tournament".

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Every protein has a story-how it folds, what it binds, its biological actions, and how it misbehaves in aging or disease. Stories are often inferred from a protein's shape (i.e.

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