Effective Local and Secondary Protein Structure Prediction by Combining a Neural Network-Based Approach with Extensive Feature Design and Selection without Reliance on Evolutionary Information.

Protein structure prediction continues to pose multiple challenges despite outstanding progress that is largely attributable to the use of novel machine learning techniques. One of the widely used representations of local 3D structure-protein blocks (PBs)-can be treated in a similar way to secondary structure classes. Here, we present a new approach for predicting local conformation in terms of PB classes solely from amino acid sequences.

Group size planning for breedings of gene-modified mice and other organisms following Mendelian inheritance.

Colony management of gene-modified animals is time-consuming, costly and affected by random events related to Mendelian genetics, fertility and litter size. Careful planning is mandatory to ensure successful outcomes using the least number of animals, hence adhering to the 3R principles of animal welfare. Here we have developed an R package, accessible also through an interactive public website, that optimizes breeding design by providing information about the optimal number of breedings needed to obtain defined breeding outcomes, taking into account specific species, strain, or line properties and success probability.

[Method to Generate Complex Predictive Features for Machine Learning-Based Prediction of the Local Structure and Functions of Proteins].

Recently, prediction of the structure and function of a protein from its sequence underwent a rapid increase in performance. It is primarily due to the application of machine learning methods, many of which rely on the predictive features supplied to them. It is thus crucial to retrieve the information encoded in the amino acid sequence of a protein.

IGF1R expression by adult oligodendrocytes is not required in the steady-state but supports neuroinflammation.

In the central nervous system (CNS), insulin-like growth factor 1 (IGF-1) regulates myelination by oligodendrocyte (ODC) precursor cells and shows anti-apoptotic properties in neuronal cells in different in vitro and in vivo systems. Previous work also suggests that IGF-1 protects ODCs from cell death and enhances remyelination in models of toxin-induced and autoimmune demyelination. However, since evidence remains controversial, the therapeutic potential of IGF-1 in demyelinating CNS conditions is unclear.

Structural coordinates: A novel approach to predict protein backbone conformation.

Motivation: Local protein structure is usually described via classifying each peptide to a unique class from a set of pre-defined structures. These classifications may differ in the number of structural classes, the length of peptides, or class attribution criteria. Most methods that predict the local structure of a protein from its sequence first rely on some classification and only then proceed to the 3D conformation assessment.

To the fast calculation of the solvation free energy. Combining expanded ensembles with L2MC.

A more efficient version of the Expanded Ensembles method for calculation of free energy in molecular-mechanical simulations is proposed. The method is based on the Horowitz L2MC approach to accelerate movement along the "alchemical" coordinate. It is possible to achieve the same efficiency of the algorithm both with the optimal number of "windows" and with a larger number of them compared to the original algorithm.

Single-cell transcriptomics identifies CD44 as a marker and regulator of endothelial to haematopoietic transition.

The endothelial to haematopoietic transition (EHT) is the process whereby haemogenic endothelium differentiates into haematopoietic stem and progenitor cells (HSPCs). The intermediary steps of this process are unclear, in particular the identity of endothelial cells that give rise to HSPCs is unknown. Using single-cell transcriptome analysis and antibody screening, we identify CD44 as a marker of EHT enabling us to isolate robustly the different stages of EHT in the aorta-gonad-mesonephros (AGM) region.

Metabolic shifts in residual breast cancer drive tumor recurrence.

Tumor recurrence is the leading cause of breast cancer-related death. Recurrences are largely driven by cancer cells that survive therapeutic intervention. This poorly understood population is referred to as minimal residual disease.