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. We apply the RMSD metric to ensure unambiguous future 3D protein structure recovery. The selection of statistically assessed features is a key component of the proposed method. We suggest that ML input features should be created from the statistically significant predictors that are derived from the amino acids' physicochemical properties and the resolved structures' statistics. The statistical significance of the suggested features was assessed using a stepwise regression analysis that permitted the evaluation of the contribution and statistical significance of each predictor. We used the set of 380 statistically significant predictors as a learning model for the regression neural network that was trained using the PISCES30 dataset. When using the same dataset and metrics for benchmarking, our method outperformed all other methods reported in the literature for the CB513 nonredundant dataset (for the PBs, Q16 = 81.01%, and for the DSSP, Q3 = 85.99% and Q8 = 79.35%).

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. Our software also enables breeding planning for balanced male-to-female ratio or single-sex experiments. We show that, for single-sex designs, the necessary number of breedings is at least doubled compared to the use of all born animals. While the presented tool provides preset parameters for the laboratory mouse, it can be readily used for any other species.

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. To finally shed light on the function of IGF1-signaling for ODCs, we deleted insulin-like growth factor 1 receptor (IGF1R) specifically in mature ODCs of the mouse. We found that ODC survival and myelin status were unaffected by the absence of IGF1R until 15 months of age, indicating that IGF-1 signaling does not play a major role in post-mitotic ODCs during homeostasis. Notably, the absence of IGF1R did neither affect ODC survival nor myelin status upon cuprizone intoxication or induction of experimental autoimmune encephalomyelitis (EAE), models for toxic and autoimmune demyelination, respectively. Surprisingly, however, the absence of IGF1R from ODCs protected against clinical neuroinflammation in the EAE model. Together, our data indicate that IGF-1 signaling is not required for the function and survival of mature ODCs in steady-state and disease.

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. However, most classification methods rely on homologous proteins' existence, unavoidably lose information by attributing a peptide to a single class or suffer from a suboptimal choice of the representative classes. RESULTS: To alleviate the above challenges, we propose a method that constructs a peptide's structural representation from the sequence, reflecting its similarity to several basic representative structures. For 5-mer peptides and 16 representative structures, we achieved the Q16 classification accuracy of 67.9%, which is higher than what is currently reported in the literature. Our prediction method does not utilize information about protein homologues but relies only on the amino acids' physicochemical properties and the resolved structures' statistics. We also show that the 3D coordinates of a peptide can be uniquely recovered from its structural coordinates, and show the required conditions under various geometric constraints.

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. Since the optimal number of windows is unknown a priory, the proposed algorithm is more robust than the traditional one. We can choose the number of windows in excess and do not worry about the loss of efficiency. We illustrate the method's efficiency with the computation of the hydration free energies of pyridine and water.

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. This allows us to provide a detailed phenotypical and transcriptional profile of CD44-positive arterial endothelial cells from which HSPCs emerge. They are characterized with high expression of genes related to Notch signalling, TGFbeta/BMP antagonists, a downregulation of genes related to glycolysis and the TCA cycle, and a lower rate of cell cycle. Moreover, we demonstrate that by inhibiting the interaction between CD44 and its ligand hyaluronan, we can block EHT, identifying an additional regulator of HSPC development.

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. Here, using mouse models that faithfully recapitulate human disease together with organoid cultures, we have demonstrated that residual cells acquire a transcriptionally distinct state from normal epithelium and primary tumors. Gene expression changes and functional characterization revealed altered lipid metabolism and elevated ROS as hallmarks of the cells that survive tumor regression. These residual cells exhibited increased oxidative DNA damage, potentiating the acquisition of somatic mutations during hormonal-induced expansion of the mammary cell population. Inhibition of either cellular fatty acid synthesis or fatty acid transport into mitochondria reduced cellular ROS levels and DNA damage, linking these features to lipid metabolism. Direct perturbation of these hallmarks in vivo, either by scavenging ROS or by halting the cyclic mammary cell population expansion, attenuated tumor recurrence. Finally, these observations were mirrored in transcriptomic and histological signatures of residual cancer cells from neoadjuvant-treated breast cancer patients. These results highlight the potential of lipid metabolism and ROS as therapeutic targets for reducing tumor recurrence in breast cancer patients.

ELM 2016--data update and new functionality of the eukaryotic linear motif resource.

The Eukaryotic Linear Motif (ELM) resource (http://elm.eu.org) is a manually curated database of short linear motifs (SLiMs). In this update, we present the latest additions to this resource, along with more improvements to the web interface. ELM 2016 contains more than 240 different motif classes with over 2700 experimentally validated instances, manually curated from more than 2400 scientific publications. In addition, more data have been made available as individually searchable pages and are downloadable in various formats.