Investigation of Evolutionary History and Origin of the Tre1 Family Suggests a Role in Regulating Hemocytes Cells Infiltration of the Blood-Brain Barrier.

Understanding the evolutionary relationship between immune cells and the blood-brain barrier (BBB) is important to devise therapeutic strategies. In vertebrates, immune cells follow either a paracellular or a transcellular pathway to infiltrate the BBB. In , glial cells form the BBB that regulates the access of hemocytes to the brain.

Interleukins and Interleukin Receptors Evolutionary History and Origin in Relation to CD4+ T Cell Evolution.

Understanding the evolution of interleukins and interleukin receptors is essential to control the function of CD4+ T cells in various pathologies. Numerous aspects of CD4+ T cells' presence are controlled by interleukins including differentiation, proliferation, and plasticity. CD4+ T cells have emerged during the divergence of jawed vertebrates.

Paracellular and Transcellular Leukocytes Diapedesis Are Divergent but Interconnected Evolutionary Events.

Infiltration of the endothelial layer of the blood-brain barrier by leukocytes plays a critical role in health and disease. When passing through the endothelial layer during the diapedesis process lymphocytes can either follow a paracellular route or a transcellular one. There is a debate whether these two processes constitute one mechanism, or they form two evolutionary distinct migration pathways.

What has single-cell RNA sequencing revealed about microglial neuroimmunology?

The use of single-cell RNA sequencing (scRNA-seq) in microglial research is increasing rapidly. The basic workflow of this approach consists of isolating single cells, followed by sequencing. scRNA-seq is capable of examining microglial heterogeneity on a cellular level.

Implicit-descriptor ligand-based virtual screening by means of collaborative filtering.

Current ligand-based machine learning methods in virtual screening rely heavily on molecular fingerprinting for preprocessing, i.e., explicit description of ligands' structural and physicochemical properties in a vectorized form.

A Python tool to set up relative free energy calculations in GROMACS.

Free energy calculations based on molecular dynamics (MD) simulations have seen a tremendous growth in the last decade. However, it is still difficult and tedious to set them up in an automated manner, as the majority of the present-day MD simulation packages lack that functionality. Relative free energy calculations are a particular challenge for several reasons, including the problem of finding a common substructure and mapping the transformation to be applied.

Guidelines for the analysis of free energy calculations.

Free energy calculations based on molecular dynamics simulations show considerable promise for applications ranging from drug discovery to prediction of physical properties and structure-function studies. But these calculations are still difficult and tedious to analyze, and best practices for analysis are not well defined or propagated. Essentially, each group analyzing these calculations needs to decide how to conduct the analysis and, usually, develop its own analysis tools.

Perspective: Alchemical free energy calculations for drug discovery.

Computational techniques see widespread use in pharmaceutical drug discovery, but typically prove unreliable in predicting trends in protein-ligand binding. Alchemical free energy calculations seek to change that by providing rigorous binding free energies from molecular simulations. Given adequate sampling and an accurate enough force field, these techniques yield accurate free energy estimates.

Predicting hydration free energies using all-atom molecular dynamics simulations and multiple starting conformations.

Molecular dynamics simulations in explicit solvent were applied to predict the hydration free energies for 23 small organic molecules in blind SAMPL2 test. We found good agreement with experimental results, with an RMS error of 2.82 kcal/mol over the whole set and 1.