Employment Instability and Childbirth over the Last 20 Years in Italy.

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.

Computing Free Energies of Fold-Switching Proteins Using MELD x MD.

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.

Accelerating Protein Folding Molecular Dynamics Using Inter-Residue Distances from Machine Learning Servers.

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.

Accelerating Molecular Dynamics Enrichments of High-Affinity Ligands for Proteins.

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".

Protein storytelling through physics.

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.

Computing Ligands Bound to Proteins Using MELD-Accelerated MD.

Predicting the poses of small-molecule ligands in protein binding sites is often done by virtual screening algorithms such as DOCK. In principle, molecular dynamics (MD) using atomistic force fields could give better free-energy-based pose selection, but MD is computationally expensive. Here, we ask if modeling employing limited data (MELD)-accelerated MD (MELD × MD) can pick out the best DOCK poses taken as input.

ClusPro in rounds 38 to 45 of CAPRI: Toward combining template-based methods with free docking.

Targets in the protein docking experiment CAPRI (Critical Assessment of Predicted Interactions) generally present new challenges and contribute to new developments in methodology. In rounds 38 to 45 of CAPRI, most targets could be effectively predicted using template-based methods. However, the server ClusPro required structures rather than sequences as input, and hence we had to generate and dock homology models.

Modeling beta-sheet peptide-protein interactions: Rosetta FlexPepDock in CAPRI rounds 38-45.

Peptide-protein docking is challenging due to the considerable conformational freedom of the peptide. CAPRI rounds 38-45 included two peptide-protein interactions, both characterized by a peptide forming an additional beta strand of a beta sheet in the receptor. Using the Rosetta FlexPepDock peptide docking protocol we generated top-performing, high-accuracy models for targets 134 and 135, involving an interaction between a peptide derived from L-MAG with DLC8.

Sampling and refinement protocols for template-based macrocycle docking: 2018 D3R Grand Challenge 4.

We describe a new template-based method for docking flexible ligands such as macrocycles to proteins. It combines Monte-Carlo energy minimization on the manifold, a fast manifold search method, with BRIKARD for complex flexible ligand searching, and with the MELD accelerator of Replica-Exchange Molecular Dynamics simulations for atomistic degrees of freedom. Here we test the method in the Drug Design Data Resource blind Grand Challenge competition.

NMR-assisted protein structure prediction with MELDxMD.

We describe the performance of MELD-accelerated molecular dynamics (MELDxMD) in determining protein structures in the NMR-data-assisted category in CASP13. Seeded from web server predictions, MELDxMD was found best in the NMR category, over 17 targets, outperforming the next-best groups by a factor of ~4 in z-score. MELDxMD gives ensembles, not single structures; succeeds on a 326-mer, near the current upper limit for NMR structures; and predicts structures that match experimental residual dipolar couplings even though the only NMR-derived data used in the simulations was NOE-based ambiguous atom-atom contacts and backbone dihedrals.

Predicting Protein Dimer Structures Using MELD × MD.

It is challenging to predict the docked conformations of two proteins. Current methods are susceptible to errors from treating proteins as rigid bodies and from an inability to compute relative Boltzmann populations of different docked conformations. Here, we show that by using the ClusPro server as a front end to generate possible protein-protein contacts, and using Modeling Employing Limited Data (MELD) accelerated molecular dynamics (MELD × MD) as a back end for atomistic simulations, we can find 16/20 native dimer structures of small proteins as those having the lowest free energy, starting from good-bound-backbone structures.

Author Correction: Transcriptional dysregulation of Interferome in experimental and human Multiple Sclerosis.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has been fixed in the paper.

How Water's Properties Are Encoded in Its Molecular Structure and Energies.

How are water's material properties encoded within the structure of the water molecule? This is pertinent to understanding Earth's living systems, its materials, its geochemistry and geophysics, and a broad spectrum of its industrial chemistry. Water has distinctive liquid and solid properties: It is highly cohesive. It has volumetric anomalies-water's solid (ice) floats on its liquid; pressure can melt the solid rather than freezing the liquid; heating can shrink the liquid.

Transcriptional dysregulation of Interferome in experimental and human Multiple Sclerosis.

Recent evidence indicates that single multiple sclerosis (MS) susceptibility genes involved in interferon (IFN) signaling display altered transcript levels in peripheral blood of untreated MS subjects, suggesting that responsiveness to endogenous IFN is dysregulated during neuroinflammation. To prove this hypothesis we exploited the systematic collection of IFN regulated genes (IRG) provided by the Interferome database and mapped Interferome changes in experimental and human MS. Indeed, central nervous system tissue and encephalitogenic CD4 T cells during experimental autoimmune encephalomyelitis were characterized by massive changes in Interferome transcription.

Blind protein structure prediction using accelerated free-energy simulations.

We report a key proof of principle of a new acceleration method [Modeling Employing Limited Data (MELD)] for predicting protein structures by molecular dynamics simulation. It shows that such Boltzmann-satisfying techniques are now sufficiently fast and accurate to predict native protein structures in a limited test within the Critical Assessment of Structure Prediction (CASP) community-wide blind competition.

Adapting the semi-explicit assembly solvation model for estimating water-cyclohexane partitioning with the SAMPL5 molecules.

We describe here some tests we made in the SAMPL5 communal event of 'Semi-Explicit Assembly' (SEA), a recent method for computing solvation free energies. We combined the prospective tests of SAMPL5 with followup retrospective calculations, to improve two technical aspects of the field variant of SEA. First, SEA uses an approximate analytical surface around the solute on which a water potential is computed.

Grid-based backbone correction to the ff12SB protein force field for implicit-solvent simulations.

Force fields, such as Amber's ff12SB, can be fairly accurate models of the physical forces in proteins and other biomolecules. When coupled with accurate solvation models, force fields are able to bring insight into the conformational preferences, transitions, pathways, and free energies for these biomolecules. When computational speed/cost matters, implicit solvent is often used but at the cost of accuracy.

In-depth characterization of breast cancer tumor-promoting cell transcriptome by RNA sequencing and microarrays.

Numerous studies have reported the existence of tumor-promoting cells (TPC) with self-renewal potential and a relevant role in drug resistance. However, pathways and modifications involved in the maintenance of such tumor subpopulations are still only partially understood. Sequencing-based approaches offer the opportunity for a detailed study of TPC including their transcriptome modulation.

Impact of different ChIP-Seq protocols on DNA integrity and quality of bioinformatics analysis results.

Different ChIP-Seq protocols may have a significant impact on the final outcome in terms of quality, number and distribution of called peaks. Sample DNA undergoes a long procedure before the final sequencing step, and damaged DNA can result in excessive mismatches in the alignment with reference genome. In this letter, we present the effect of well-defined modifications (timing of formaldehyde crosslink reversal, brand of the sonicator) of standard ChIP-Seq protocol on parallel samples derived from the same cell line correlating the initial DNA quality control metrics to the final bioinformatics analysis results.

Chemically transferable coarse-grained potentials from conditional reversible work calculations.

The representability and transferability of effective pair potentials used in multiscale simulations of soft matter systems is ill understood. In this paper, we study liquid state systems composed of n-alkanes, the coarse-grained (CG) potential of which may be assumed pairwise additive and has been obtained using the conditional reversible work (CRW) method. The CRW method is a free-energy-based coarse-graining procedure, which, by means of performing the coarse graining at pair level, rigorously provides a pair potential that describes the interaction free energy between two mapped atom groups (beads) embedded in their respective chemical environments.

Thermodynamic transferability of coarse-grained potentials for polymer-additive systems.

In this work we study the transferability of systematically coarse-grained (CG) potentials for polymer-additive systems. The CG nonbonded potentials between the polymer (atactic polystyrene) and three different additives (ethylbenzene, methane and neopentane) are derived using the Conditional Reversible Work (CRW) method, recently proposed by us [Brini et al., Phys.

Deep-sequencing of endothelial cells exposed to hypoxia reveals the complexity of known and novel microRNAs.

In order to understand the role of microRNAs (miRNAs) in vascular physiopathology, we took advantage of deep-sequencing techniques to accurately and comprehensively profile the entire miRNA population expressed by endothelial cells exposed to hypoxia. SOLiD sequencing of small RNAs derived from human umbilical vein endothelial cells (HUVECs) exposed to 1% O₂ or normoxia for 24 h yielded more than 22 million reads per library. A customized bioinformatic pipeline identified more than 400 annotated microRNA/microRNA* species with a broad abundance range: miR-21 and miR-126 totaled almost 40% of all miRNAs.

Conditional reversible work method for molecular coarse graining applications.

Systematically coarse grained models for complex fluids usually lack chemical and thermodynamic transferability. Efforts to improve transferability require the development of effective potentials with unequivocal physical significance. In this paper, we introduce conditional reversible work (CRW) potentials that describe nonbonded interactions in coarse grained models at the pair level.