https://www.cbs.dtu.dk/services/SignalP-4.1/paper-3.0.pdf

Improved Prediction of Signal Peptides: SignalP 3.0

Determining the subcellular localization of a protein is an important first step toward understanding its function. Here, we describe the properties of three well-known N-terminal sequence motifs directing proteins to the secretory pathway, mitochondria and chloroplasts, and sketch a brief history of methods to predict subcellular localization based on these sorting signals and other sequence properties. We then outline how to use a number of internet-accessible tools to arrive at a reliable subcellular localization prediction for eukaryotic and prokaryotic proteins. In particular, we provide detailed step-by-step instructions for the coupled use of the amino-acid sequence-based predictors TargetP, SignalP, ChloroP and TMHMM, which are all hosted at the Center for Biological Sequence Analysis, Technical University of Denmark. In addition, we describe and provide web references to other useful subcellular localization predictors. Finally, we discuss predictive performance measures in general and the performance of TargetP and SignalP in particular.

/pdf/locating-proteins-in-the-cell-using-targetp-signalp-and-39i9pd64rh.pdf

Locating proteins in the cell using TargetP, SignalP and related tools

The brain hypothalamus contains certain secreted molecules that are important in regulating feeding behaviour. Here we show that nesfatin, corresponding to NEFA/nucleobindin2 (NUCB2), a secreted protein of unknown function, is expressed in the appetite-control hypothalamic nuclei in rats. Intracerebroventricular (i.c.v.) injection of NUCB2 reduces feeding. Rat cerebrospinal fluid contains nesfatin-1, an amino-terminal fragment derived from NUCB2, and its expression is decreased in the hypothalamic paraventricular nucleus under starved conditions. I.c.v. injection of nesfatin-1 decreases food intake in a dose-dependent manner, whereas injection of an antibody neutralizing nesfatin-1 stimulates appetite. In contrast, i.c.v. injection of other possible fragments processed from NUCB2 does not promote satiety, and conversion of NUCB2 to nesfatin-1 is necessary to induce feeding suppression. Chronic i.c.v. injection of nesfatin-1 reduces body weight, whereas rats gain body weight after chronic i.c.v. injection of antisense morpholino oligonucleotide against the gene encoding NUCB2. Nesfatin-1-induced anorexia occurs in Zucker rats with a leptin receptor mutation, and an anti-nesfatin-1 antibody does not block leptin-induced anorexia. In contrast, central injection of alpha-melanocyte-stimulating hormone elevates NUCB2 gene expression in the paraventricular nucleus, and satiety by nesfatin-1 is abolished by an antagonist of the melanocortin-3/4 receptor. We identify nesfatin-1 as a satiety molecule that is associated with melanocortin signalling in the hypothalamus.

/pdf/identification-of-nesfatin-1-as-a-satiety-molecule-in-the-2z6vquuowo.pdf

Identification of nesfatin-1 as a satiety molecule in the hypothalamus

Host cell proteases: Critical determinants of coronavirus tropism and pathogenesis

Bioinformatics — The Machine Learning Approach

Many secretory proteins and peptides are synthesized as inactive precursors that in addition to signal peptide cleavage undergo post-translational processing to become biologically active polypeptides. Precursors are usually cleaved at sites composed of single or paired basic amino acid residues by members of the subtilisin/kexin-like proprotein convertase (PC) family. In mammals, seven members have been identified, with furin being the one first discovered and best characterized. Recently, the involvement of furin in diseases ranging from Alzheimer's disease and cancer to anthrax and Ebola fever has created additional focus on proprotein processing. We have developed a method for prediction of cleavage sites for PCs based on artificial neural networks. Two different types of neural networks have been constructed: a furin-specific network based on experimental results derived from the literature, and a general PC-specific network trained on data from the Swiss-Prot protein database. The method predicts cleavage sites in independent sequences with a sensitivity of 95% for the furin neural network and 62% for the general PC network. The ProP method is made publicly available at http://www.cbs.dtu.dk/services/ProP.

/pdf/prediction-of-proprotein-convertase-cleavage-sites-191ib6hlum.pdf

Prediction of proprotein convertase cleavage sites

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Prediction of proprotein convertase cleavage sites