Gunnar von Heijne

SignalP 4.0: discriminating signal peptides from transmembrane regions

We benchmarked SignalP 4.0 against SignalP 3.0 and ten other signal peptide prediction algorithms (Fig. 1). We compared prediction performance using the Matthews correlation coefficient16, for which each sequence was counted as a true or false positive or negative. To test SignalP 4.0 performance, we did not use data that had been used in training the networks or selecting the optimal architecture, and the test data did not contain homologs to the training and optimization data (Supplementary Methods). The test set for SignalP 3.0 was also independent of the training set because we removed sequences used to construct SignalP 3.0 and their homologs from the benchmark data. For other algorithms more recent than SignalP 3.0, the benchmark data may include data used to train the methods, possibly leading to slight overestimations of their performance. Our results show that SignalP 4.0 was the best signal-peptide predictor for all three organism types (Fig. 1). This comes at a price, however, because SignalP 4.0 was not in all cases as good as SignalP 3.0 according to cleavage-site sensitivity or signal-peptide correlation when there are no transmembrane proteins present (Supplementary Results). An ideal method would have the best SignalP 4.0: discriminating signal peptides from transmembrane regions

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

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.

Tissue-based map of the human proteome

Protein expression across human tissues Sequencing the human genome gave new insights into human biology and disease. However, the ultimate goal is to understand the dynamic expression of each of the approximately 20,000 protein-coding genes and the function of each protein. Uhlén et al. now present a map of protein expression across 32 human tissues. They not only measured expression at an RNA level, but also used antibody profiling to precisely localize the corresponding proteins. An interactive website allows exploration of expression patterns across the human body. Science, this issue 10.1126/science.1260419 Transcriptomics and immunohistochemistry map protein expression across 32 human tissues. INTRODUCTION Resolving the molecular details of proteome variation in the different tissues and organs of the human body would greatly increase our knowledge of human biology and disease. Here, we present a map of the human tissue proteome based on quantitative transcriptomics on a tissue and organ level combined with protein profiling using microarray-based immunohistochemistry to achieve spatial localization of proteins down to the single-cell level. We provide a global analysis of the secreted and membrane proteins, as well as an analysis of the expression profiles for all proteins targeted by pharmaceutical drugs and proteins implicated in cancer. RATIONALE We have used an integrative omics approach to study the spatial human proteome. Samples representing all major tissues and organs (n = 44) in the human body have been analyzed based on 24,028 antibodies corresponding to 16,975 protein-encoding genes, complemented with RNA-sequencing data for 32 of the tissues. The antibodies have been used to produce more than 13 million tissue-based immunohistochemistry images, each annotated by pathologists for all sampled tissues. To facilitate integration with other biological resources, all data are available for download and cross-referencing. RESULTS We report a genome-wide analysis of the tissue specificity of RNA and protein expression covering more than 90% of the putative protein-coding genes, complemented with analyses of various subproteomes, such as predicted secreted proteins (n = 3171) and membrane-bound proteins (n = 5570). The analysis shows that almost half of the genes are expressed in all analyzed tissues, which suggests that the gene products are needed in all cells to maintain “housekeeping” functions such as cell growth, energy generation, and basic metabolism. Furthermore, there is enrichment in metabolism among these genes, as 60% of all metabolic enzymes are expressed in all analyzed tissues. The largest number of tissue-enriched genes is found in the testis, followed by the brain and the liver. Analysis of the 618 proteins targeted by clinically approved drugs unexpectedly showed that 30% are expressed in all analyzed tissues. An analysis of metabolic activity based on genome-scale metabolic models (GEMS) revealed liver as the most metabolically active tissue, followed by adipose tissue and skeletal muscle. CONCLUSIONS A freely available interactive resource is presented as part of the Human Protein Atlas portal (www.proteinatlas.org), offering the possibility to explore the tissue-elevated proteomes in tissues and organs and to analyze tissue profiles for specific protein classes. Comprehensive lists of proteins expressed at elevated levels in the different tissues have been compiled to provide a spatial context with localization of the proteins in the subcompartments of each tissue and organ down to the single-cell level. The human tissue–enriched proteins. All tissue-enriched proteins are shown for 13 representative tissues or groups of tissues, stratified according to their predicted subcellular localization. Enriched proteins are mainly intracellular in testis, mainly membrane bound in brain and kidney, and mainly secreted in pancreas and liver. Resolving the molecular details of proteome variation in the different tissues and organs of the human body will greatly increase our knowledge of human biology and disease. Here, we present a map of the human tissue proteome based on an integrated omics approach that involves quantitative transcriptomics at the tissue and organ level, combined with tissue microarray–based immunohistochemistry, to achieve spatial localization of proteins down to the single-cell level. Our tissue-based analysis detected more than 90% of the putative protein-coding genes. We used this approach to explore the human secretome, the membrane proteome, the druggable proteome, the cancer proteome, and the metabolic functions in 32 different tissues and organs. All the data are integrated in an interactive Web-based database that allows exploration of individual proteins, as well as navigation of global expression patterns, in all major tissues and organs in the human body.

Membrane Protein Structure Prediction Hydrophobicity Analysis and the Positive-inside Rule

A new strategy for predicting the topology of bacterial inner membrane proteins is proposed on the basis of hydrophobicity analysis, automatic generation of a set of possible topologies and ranking of these according to the positive-inside rule. A straightforward implementation with no attempts at optimization predicts the correct topology for 23 out of 24 inner membrane proteins with experimentally determined topologies, and correctly identifies 135 transmembrane segments with only one overprediction.

Recognition of transmembrane helices by the endoplasmic reticulum translocon

Membrane proteins depend on complex translocation machineries for insertion into target membranes. Although it has long been known that an abundance of nonpolar residues in transmembrane helices is the principal criterion for membrane insertion, the specific sequence-coding for transmembrane helices has not been identified. By challenging the endoplasmic reticulum Sec61 translocon with an extensive set of designed polypeptide segments, we have determined the basic features of this code, including a ‘biological’ hydrophobicity scale. We find that membrane insertion depends strongly on the position of polar residues within transmembrane segments, adding a new dimension to the problem of predicting transmembrane helices from amino acid sequences. Our results indicate that direct protein–lipid interactions are critical during translocon-mediated membrane insertion.

Prediction of lipoprotein signal peptides in Gram-negative bacteria

A method to predict lipoprotein signal peptides in Gram‐negative Eubacteria, LipoP, has been developed. The hidden Markov model (HMM) was able to distinguish between lipoproteins (SPaseII‐cleaved proteins), SPaseI‐cleaved proteins, cytoplasmic proteins, and transmembrane proteins. This predictor was able to predict 96.8% of the lipoproteins correctly with only 0.3% false positives in a set of SPaseI‐cleaved, cytoplasmic, and transmembrane proteins. The results obtained were significantly better than those of previously developed methods. Even though Gram‐positive lipoprotein signal peptides differ from Gram‐negatives, the HMM was able to identify 92.9% of the lipoproteins included in a Gram‐positive test set. A genome search was carried out for 12 Gram‐negative genomes and one Gram‐positive genome. The results for Escherichia coli K12 were compared with new experimental data, and the predictions by the HMM agree well with the experimentally verified lipoproteins. A neural network‐based predictor was developed for comparison, and it gave very similar results. LipoP is available as a Web server at www.cbs.dtu.dk/services/LipoP/.

The distribution of positively charged residues in bacterial inner membrane proteins correlates with the trans-membrane topology.

The amino acid distribution in membrane spanning segments and connecting loops in bacterial inner membrane proteins was analysed. The basic residues Arg and Lys are four times less prevalent in periplasmic as compared to cytosolic connecting loops, whereas no comparable effect is observed for the acidic residues Asp and Glu. Also, Pro is shown to be tolerated to a much larger extent in membrane spanning segments with their N‐terminus pointing towards the cytosol than in those with the opposite orientation. The significance of these findings with regard to the mechanism of biogenesis of bacterial inner membrane proteins is discussed.

A neural network method for identification of prokaryotic and eukaryotic signal peptides and prediction of their cleavage sites.

We have developed a new method for the identification of signal peptides and their cleavage sites based on neural networks trained on separate sets of prokaryotic and eukaryotic sequences. The method performs significantly better than previous prediction schemes, and can easily be applied to genome-wide data sets. Discrimination between cleaved signal peptides and uncleaved N-terminal signal-anchor sequences is also possible, though with lower precision. Predictions can be made on a publicly available WWW server: http://www.cbs.dtu.dk/services/SignalP/.

Sequence differences between glycosylated and non-glycosylated Asn-X-Thr/Ser acceptor sites: implications for protein engineering

Abstract In N-glycosylated glycoproteins, carbohydrate is attached to Asn in the sequence Asn-X-Ser/Thr, where X denotes any amino acid. However, the presence of this consensus peptide does not always lead to glycosylation. We have compiled an extensive collection of glycosylated and non-glycosylated Asn-X-Thr/Ser sites and present a statistical study based on this data set. Our results indicate that non-glycosylated sites tend to be found more frequently towards the C termini of glycoproteins, and that proline residues in positions X and Y in the consensus Asn-X-Thr/Ser-Y strongly reduce the likelihood of N-linked glycosylation. Beyond this, there are no obvious local sequence features that seem to correlate with the absence or presence of N-linked glycosylation. These findings are discussed in terms of the prediction and engineering of glycosylation sites in secretory proteins.

How proteins adapt to a membrane–water interface

Membrane proteins present a hydrophobic surface to the surrounding lipid, whereas portions protruding into the aqueous milieu expose a polar surface. But how have proteins evolved to deal with the complex environment at the membrane-water interface? Some insights have been provided by high-resolution structures of membrane proteins, and recent studies of the role of individual amino acids in mediating protein-lipid contacts have shed further light on this issue. It now appears clear that the polar-aromatic residues Trp and Tyr have a specific affinity for a region near the lipid carbonyls, whereas positively charged residues extend into the lipid phosphate region.