Motivation: Functional annotation of unknown proteins is a major goal in proteomics. A key annotation is the prediction of a protein's subcellular localization. Numerous prediction techniques have been developed, typically focusing on a single underlying biological aspect or predicting a subset of all possible localizations. An important step is taken towards emulating the protein sorting process by capturing and bringing together biologically relevant information, and addressing the clear need to improve prediction accuracy and localization coverage. Results: Here we present a novel SVM-based approach for predicting subcellular localization, which integrates N-terminal targeting sequences, amino acid composition and protein sequence motifs. We show how this approach improves the prediction based on N-terminal targeting sequences, by comparing our method TargetLoc against existing methods. Furthermore, MultiLoc performs considerably better than comparable methods predicting all major eukaryotic subcellular localizations, and shows better or comparable results to methods that are specialized on fewer localizations or for one organism. Availability: Contact:hoeglund@informatik.uni-tuebingen.de

T-cells are key players in regulating a specific immune response. Activation of cytotoxic T-cells requires recognition of specific peptides bound to Major Histocompatibility Complex (MHC) class I molecules. MHC-peptide complexes are potential tools for diagnosis and treatment of pathogens and cancer, as well as for the development of peptide vaccines. Only one in 100 to 200 potential binders actually binds to a certain MHC molecule, therefore a good prediction method for MHC class I binding peptides can reduce the number of candidate binders that need to be synthesized and tested. Here, we present a novel approach, SVMHC, based on support vector machines to predict the binding of peptides to MHC class I molecules. This method seems to perform slightly better than two profile based methods, SYFPEITHI and HLA_BIND. The implementation of SVMHC is quite simple and does not involve any manual steps, therefore as more data become available it is trivial to provide prediction for more MHC types. SVMHC currently contains prediction for 26 MHC class I types from the MHCPEP database or alternatively 6 MHC class I types from the higher quality SYFPEITHI database. The prediction models for these MHC types are implemented in a public web service available at http://www.sbc.su.se/svmhc/. Prediction of MHC class I binding peptides using Support Vector Machines, shows high performance and is easy to apply to a large number of MHC class I types. As more peptide data are put into MHC databases, SVMHC can easily be updated to give prediction for additional MHC class I types. We suggest that the number of binding peptides needed for SVM training is at least 20 sequences.

Autophagy is a pivotal cytoprotective process that secures cellular homeostasis, fulfills essential roles in development, immunity and defence against pathogens, and determines the lifespan of eukaryotic organisms. However, autophagy also crucially contributes to the development of age-related human pathologies, including cancer and neurodegeneration. Macroautophagy (hereafter referred to as autophagy) clears the cytoplasm by stochastic or specific cargo recognition and destruction, and is initiated and executed by autophagy related (ATG) proteins functioning in dynamical hierarchies to form autophagosomes. Autophagosomes sequester cytoplasmic cargo material, including proteins, lipids and organelles, and acquire acidic hydrolases from the lysosomal compartment for cargo degradation. Prerequisite and essential for autophagosome formation is the production of phosphatidylinositol 3-phosphate (PtdIns3P) by phosphatidylinositol 3-kinase class III (PI3KC3, also known as PIK3C3) in complex with beclin 1, p150 (also known as PIK3R4; Vps15 in yeast) and ATG14L. Members of the human WD-repeat protein interacting with phosphoinositides (WIPI) family play an important role in recognizing and decoding the PtdIns3P signal at the nascent autophagosome, and hence function as autophagy-specific PtdIns3P-binding effectors, similar to their ancestral yeast Atg18 homolog. The PtdIns3P effector function of human WIPI proteins appears to be compromised in cancer and neurodegeneration, and WIPI genes and proteins might present novel targets for rational therapies. Here, we summarize the current knowledge on the roles of the four human WIPI proteins, WIPI1–4, in autophagy.

In the past decade, the emergence of machine learning (ML) applications has led to significant advances towards implementation of personalised medicine approaches for improved health care, due to the exceptional performance of ML models when utilising complex big data. The immune-mediated chronic inflammatory diseases are a group of complex disorders associated with dysregulated immune responses resulting in inflammation affecting various organs and systems. The heterogeneous nature of these diseases poses great challenges for tailored disease management and addressing unmet patient needs. Applying novel ML techniques to the clinical study of chronic inflammatory diseases shows promising results and great potential for precision medicine applications in clinical research and practice. In this review, we highlight the clinical applications of various ML techniques for prediction, diagnosis and prognosis of autoimmune rheumatic diseases, inflammatory bowel disease, autoimmune chronic kidney disease, and multiple sclerosis, as well as ML applications for patient stratification and treatment selection. We highlight the use of ML in drug development, including target identification, validation and drug repurposing, as well as challenges related to data interpretation and validation, and ethical concerns related to the use of artificial intelligence in clinical research.

Cell-based models of the blood–brain barrier (BBB) are important for increasing the knowledge of BBB formation, degradation and brain exposure of drug substances. Human models are preferred over animal models because of interspecies differences in BBB structure and function. However, access to human primary BBB tissue is limited and has shown degeneration of BBB functions in vitro. Human induced pluripotent stem cells (iPSCs) can be used to generate relevant cell types to model the BBB with human tissue. We generated a human iPSC-derived model of the BBB that includes endothelial cells in coculture with pericytes, astrocytes and neurons. Evaluation of barrier properties showed that the endothelial cells in our coculture model have high transendothelial electrical resistance, functional efflux and ability to discriminate between CNS permeable and non-permeable substances. Whole genome expression profiling revealed transcriptional changes that occur in coculture, including upregulation of tight junction proteins, such as claudins and neurotransmitter transporters. Pathway analysis implicated changes in the WNT, TNF, and PI3K-Akt pathways upon coculture. Our data suggest that coculture of iPSC-derived endothelial cells promotes barrier formation on a functional and transcriptional level. The information about gene expression changes in coculture can be used to further improve iPSC-derived BBB models through selective pathway manipulation. Stem Cells 2018;36:1816–12

Functional characterization of every single protein is a major challenge of the post-genomic era. The large-scale analysis of a cell’s proteins, proteomics, seeks to provide these proteins with reliable annotations regarding their interaction partners and functions in the cellular machinery. An important step on this way is to determine the subcellular localization of each protein. Eukaryotic cells are divided into subcellular compartments, or organelles. Transport across the membrane into the organelles is a highly regulated and complex cellular process. Predicting the subcellular localization by computational means has been an area of vivid activity during recent years. The publicly available prediction methods differ mainly in four aspects: the underlying biological motivation, the computational method used, localization coverage, and reliability, which are of importance to the user. This review provides a short description of the main events in the protein sorting process and an overview of the most commonly used methods in this field.