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Biomedical subjects

S Brunak

Publications and source records attributed to S Brunak.

At least 19 recordsLinked to original sources
PubMed2004

Analysis and recognition of 5' UTR intron splice sites in human pre-mRNA.

Prediction of splice sites in non-coding regions of genes is one of the most challenging aspects of gene structure recognition. We perform a rigorous analysis of such splice sites embedded in human 5' untranslated regions (UTRs), and investigate correlations between this class of splice sites and other features found in the adjacent exons and introns. By restricting the training of neural network algorithms to 'pure' UTRs (not extending partially into protein coding regions), we for the first time investigate the predictive power of the splicing signal proper, in contrast to conventional splice site prediction, which typically relies on the change in sequence at the transition from protein coding to non-coding. By doing so, the algorithms were able to pick up subtler splicing signals that were otherwise masked by 'coding' noise, thus enhancing significantly the prediction of 5' UTR splice sites. For example, the non-coding splice site predicting networks pick up compositional and positional bias in the 3' ends of non-coding exons and 5' non-coding intron ends, where cytosine and guanine are over-represented. This compositional bias at the true UTR donor sites is also visible in the synaptic weights of the neural networks trained to identify UTR donor sites. Conventional splice site prediction methods perform poorly in UTRs because the reading frame pattern is absent. The NetUTR method presented here performs 2-3-fold better compared with NetGene2 and GenScan in 5' UTRs. We also tested the 5' UTR trained method on protein coding regions, and discovered, surprisingly, that it works quite well (although it cannot compete with NetGene2). This indicates that the local splicing pattern in UTRs and coding regions is largely the same. The NetUTR method is made publicly available at www.cbs.dtu.dk/services/NetUTR.

5' Untranslated Regions↗
PubMed2004

SARS CTL vaccine candidates; HLA supertype-, genome-wide scanning and biochemical validation.

An effective Severe Acute Respiratory Syndrome (SARS) vaccine is likely to include components that can induce specific cytotoxic T-lymphocyte (CTL) responses. The specificities of such responses are governed by human leukocyte antigen (HLA)-restricted presentation of SARS-derived peptide epitopes. Exact knowledge of how the immune system handles protein antigens would allow for the identification of such linear sequences directly from genomic/proteomic sequence information (Lauemoller et al., Rev Immunogenet 2001: 2: 477-91). The latter was recently established when a causative coronavirus (SARS-CoV) was isolated and full-length sequenced (Marra et al., Science 2003: 300: 1399-404). Here, we have combined advanced bioinformatics and high-throughput immunology to perform an HLA supertype-, genome-wide scan for SARS-specific CTL epitopes. The scan includes all nine human HLA supertypes in total covering >99% of all individuals of all major human populations (Sette & Sidney, Immunogenetics 1999: 50: 201-12). For each HLA supertype, we have selected the 15 top candidates for test in biochemical binding assays. At this time (approximately 6 months after the genome was established), we have tested the majority of the HLA supertypes and identified almost 100 potential vaccine candidates. These should be further validated in SARS survivors and used for vaccine formulation. We suggest that immunobioinformatics may become a fast and valuable tool in rational vaccine design.

Antigen Presentation↗
PubMed2003

Prediction of human protein function according to Gene Ontology categories.

MOTIVATION: The human genome project has led to the discovery of many human protein coding genes which were previously unknown. As a large fraction of these are functionally uncharacterized, it is of interest to develop methods for predicting their molecular function from sequence. RESULTS: We have developed a method for prediction of protein function for a subset of classes from the Gene Ontology classification scheme. This subset includes several pharmaceutically interesting categories-transcription factors, receptors, ion channels, stress and immune response proteins, hormones and growth factors can all be predicted. Although the method relies on protein sequences as the sole input, it does not rely on sequence similarity, but instead on sequence derived protein features such as predicted post translational modifications (PTMs), protein sorting signals and physical/chemical properties calculated from the amino acid composition. This allows for prediction of the function for orphan proteins where no homologs can be found. Using this method we propose two novel receptors in the human genome, and further demonstrate chromosomal clustering of related proteins.

Algorithms↗
PubMed2003

Sensitive quantitative predictions of peptide-MHC binding by a 'Query by Committee' artificial neural network approach.

We have generated Artificial Neural Networks (ANN) capable of performing sensitive, quantitative predictions of peptide binding to the MHC class I molecule, HLA-A*0204. We have shown that such quantitative ANN are superior to conventional classification ANN, that have been trained to predict binding vs non-binding peptides. Furthermore, quantitative ANN allowed a straightforward application of a 'Query by Committee' (QBC) principle whereby particularly information-rich peptides could be identified and subsequently tested experimentally. Iterative training based on QBC-selected peptides considerably increased the sensitivity without compromising the efficiency of the prediction. This suggests a general, rational and unbiased approach to the development of high quality predictions of epitopes restricted to this and other HLA molecules. Due to their quantitative nature, such predictions will cover a wide range of MHC-binding affinities of immunological interest, and they can be readily integrated with predictions of other events involved in generating immunogenic epitopes. These predictions have the capacity to perform rapid proteome-wide searches for epitopes. Finally, it is an example of an iterative feedback loop whereby advanced, computational bioinformatics optimize experimental strategy, and vice versa.

HLA-A Antigens↗
PubMed2002

Prediction of human protein function from post-translational modifications and localization features.

We have developed an entirely sequence-based method that identifies and integrates relevant features that can be used to assign proteins of unknown function to functional classes, and enzyme categories for enzymes. We show that strategies for the elucidation of protein function may benefit from a number of functional attributes that are more directly related to the linear sequence of amino acids, and hence easier to predict, than protein structure. These attributes include features associated with post-translational modifications and protein sorting, but also much simpler aspects such as the length, isoelectric point and composition of the polypeptide chain.

Computational Biology↗
PubMed2001

Protein secondary structure: category assignment and predictability.

In the last decade, the prediction of protein secondary structure has been optimized using essentially one and the same assignment scheme known as DSSP. We present here a different scheme, which is more predictable. This scheme predicts directly the hydrogen bonds, which stabilize the secondary structures. Single sequence prediction of the new three category assignment gives an overall prediction improvement of 3.1% and 5.1% compared to the DSSP assignment and schemes where the helix category consists of alpha-helix and 3(10)-helix, respectively. These results were achieved using a standard feed-forward neural network with one hidden layer on a data set identical to the one used in earlier work.

Algorithms↗
PubMed2001

Recognition of environmental and genetic effects on barley phenolic fingerprints by neural networks.

Through computational analysis of high-performance liquid chromatography (HPLC) traces we find correlations between secondary metabolites and growth conditions of six varieties of barley. Using artificial neural networks, it was possible to classify chromatograms for which the varieties were fertilized by nitrogen and treated by fungicide. For each variety of barley we could also differentiate it from the others. Surprisingly, all these classification tasks could be solved successfully by a simple network with no hidden units. When adding to the methodology pruning of the network weights, we were able to reduce the set of peaks in the chromatograms and obtain a necessary subset from which the growth conditions and differentiation may be decided. In some instances, more complex networks with hidden units could lead to a further reduction of the number of peaks used. In most cases, far more than half of the peaks are redundant. We find that it requires fewer information-rich peaks to perform the variety differentiation tasks than to recognize any of the growth conditions. Analysis of the network weights reveals correlations between weighted combinations of peaks.

Chromatography↗
PubMed2001

On the total number of genes and their length distribution in complete microbial genomes.

In sequenced microbial genomes, some of the annotated genes are actually not protein-coding genes, but rather open reading frames that occur by chance. Therefore, the number of annotated genes is higher than the actual number of genes for most of these microbes. Comparison of the length distribution of the annotated genes with the length distribution of those matching a known protein reveals that too many short genes are annotated in many genomes. Here we estimate the true number of protein-coding genes for sequenced genomes. Although it is often claimed that Escherichia coli has about 4300 genes, we show that it probably has only approximately 3800 genes, and that a similar discrepancy exists for almost all published genomes.

Databases, Factual↗
PubMed2001

Genome organisation and chromatin structure in Escherichia coli.

We have analysed the complete sequence of the Escherichia coli K12 isolate MG1655 genome for chromatin-associated protein binding sites, and compared the predicted location of predicted sites with experimental expression data from 'DNA chip' experiments. Of the dozen proteins associated with chromatin in E. coli, only three have been shown to have significant binding preferences: integration host factor (IHF) has the strongest binding site preference, and FIS sites show a weak consensus, and there is no clear consensus site for binding of the H-NS protein. Using hidden Markov models (HMMs), we predict the location of 608 IHF sites, scattered throughout the genome. A subset of the IHF sites associated with repeats tends to be clustered around the origin of replication. We estimate there could be roughly 6000 FIS sites in E. coli, and the sites tend to be localised in two regions flanking the replication termini. We also show that the regions upstream of genes regulated by H-NS are more curved and have a higher AT content than regions upstream of other genes. These regions in general would also be localised near the replication terminus.

Bacterial Proteins↗
PubMed2001

Quantitative predictions of peptide binding to MHC class I molecules using specificity matrices and anchor-stratified calibrations.

Peptides are key immune targets. They are generated by fragmentation of antigenic proteins, selected by major histocompatibility complex (MHC) molecules and subsequently presented to T cells. One of the most selective requirements is that of peptide binding to MHC. Accurate descriptions and predictions of peptide-MHC interactions are therefore important. Quantitative matrices representing MHC class I specificity can be used to search any query protein for the presence of MHC binding peptides. Assuming that each peptide residue contributes to binding in an additive and sequence independent manner, such "crude" matrix-driven predictions can be expressed as a quantitative estimates of binding strength. Crude matrix-driven predictions are reasonably uniform (i.e. precise), however, there is a general tendency towards overestimating binding (i.e. being inaccurate). To evaluate and possibly improve predictions, we have measured the MHC class I binding of a large number of peptides. In an attempt to further improve predictions and to include sequence dependency, we subdivided the panel of peptides according to whether the peptides had zero, one or two primary anchor residues. This allowed us to define unique anchor-stratified calibrations, which led to predictions of improved precision and accuracy.

Animals↗
PubMed2001

Identification and design of p53-derived HLA-A2-binding peptides with increased CTL immunogenicity.

The replacement of a suboptimal amino acid in a primary anchor position with an optimal residue improves human leucocyte antigen (HLA) binding and immunogenicity, while maintaining cytotoxic T lymphocyte (CTL) specificity. Using a neural network capable of performing quantitative predictions of peptide binding to HLA-A2 molecules, we identified three p53 protein-derived nonamer peptides with intermediate binding owing to suboptimal amino acids in the P2 anchor position. These peptides were synthesized along with the corresponding analogs, where the natural P2 residue had been replaced with the optimal leucine residue. All three modified peptides bound to and more efficiently stabilized HLA-A2 molecules than the corresponding nonmodified peptides. The HLA-A2 transgenic mice were used for immunization. Two of the epitopes were more immunogenic in their modified than in their natural versions. The CTLs raised against the modified peptides efficiently killed the target cells pulsed with the corresponding native peptide. In terms of sensitizing the targets cells for the CTL killing, the modified peptides were more efficient than native peptides. Finally, the CTLs induced by modified peptide killed HLA-A2 transgenic mouse fibrosarcoma cells transfected with human p53 DNA. The data suggest that modified self-peptides derived from overexpressed tumour-associated proteins can be used in vaccine development against cancer, and that quantitative predictions of HLA binding is of value in the rational selection and improvement of target epitopes recognized by CTLs.

Amino Acid Sequence↗
PubMed2001

Flexibility of the genetic code with respect to DNA structure.

MOTIVATION: The primary function of DNA is to carry genetic information through the genetic code. DNA, however, contains a variety of other signals related, for instance, to reading frame, codon bias, pairwise codon bias, splice sites and transcription regulation, nucleosome positioning and DNA structure. Here we study the relationship between the genetic code and DNA structure and address two questions. First, to which degree does the degeneracy of the genetic code and the acceptable amino acid substitution patterns allow for the superimposition of DNA structural signals to protein coding sequences? Second, is the origin or evolution of the genetic code likely to have been constrained by DNA structure? RESULTS: We develop an index for code flexibility with respect to DNA structure. Using five different di- or tri-nucleotide models of sequence-dependent DNA structure, we show that the standard genetic code provides a fair level of flexibility at the level of broad amino acid categories. Thus the code generally allows for the superimposition of any structural signal on any protein-coding sequence, through amino acid substitution. The flexibility observed at the level of single amino acids allows only for the superimposition of punctual and loosely positioned signals to conserved amino acid sequences. The degree of flexibility of the genetic code is low or average with respect to several classes of alternative codes. This result is consistent with the view that DNA structure is not likely to have played a significant role in the origin and evolution of the genetic code.

Amino Acids↗
PubMed2000

Prediction of protein secondary structure at 80% accuracy.

Secondary structure prediction involving up to 800 neural network predictions has been developed, by use of novel methods such as output expansion and a unique balloting procedure. An overall performance of 77.2%-80.2% (77.9%-80.6% mean per-chain) for three-state (helix, strand, coil) prediction was obtained when evaluated on a commonly used set of 126 protein chains. The method uses profiles made by position-specific scoring matrices as input, while at the output level it predicts on three consecutive residues simultaneously. The predictions arise from tenfold, cross validated training and testing of 1032 protein sequences, using a scheme with primary structure neural networks followed by structure filtering neural networks. With respect to blind prediction, this work is preliminary and awaits evaluation by CASP4.

Neural Networks, Computer↗
PubMed2000

Predicting subcellular localization of proteins based on their N-terminal amino acid sequence.

A neural network-based tool, TargetP, for large-scale subcellular location prediction of newly identified proteins has been developed. Using N-terminal sequence information only, it discriminates between proteins destined for the mitochondrion, the chloroplast, the secretory pathway, and "other" localizations with a success rate of 85% (plant) or 90% (non-plant) on redundancy-reduced test sets. From a TargetP analysis of the recently sequenced Arabidopsis thaliana chromosomes 2 and 4 and the Ensembl Homo sapiens protein set, we estimate that 10% of all plant proteins are mitochondrial and 14% chloroplastic, and that the abundance of secretory proteins, in both Arabidopsis and Homo, is around 10%. TargetP also predicts cleavage sites with levels of correctly predicted sites ranging from approximately 40% to 50% (chloroplastic and mitochondrial presequences) to above 70% (secretory signal peptides). TargetP is available as a web-server at http://www.cbs.dtu.dk/services/TargetP/.

Amino Acid Sequence↗
PubMed2000

A DNA structural atlas for Escherichia coli.

We have performed a computational analysis of DNA structural features in 18 fully sequenced prokaryotic genomes using models for DNA curvature, DNA flexibility, and DNA stability. The structural values that are computed for the Escherichia coli chromosome are significantly different from (and generally more extreme than) that expected from the nucleotide composition. To aid this analysis, we have constructed tools that plot structural measures for all positions in a long DNA sequence (e.g. an entire chromosome) in the form of color-coded wheels (http://www.cbs.dtu. dk/services/GenomeAtlas/). We find that these "structural atlases" are useful for the discovery of interesting features that may then be investigated in more depth using statistical methods. From investigation of the E. coli structural atlas, we discovered a genome-wide trend, where an extended region encompassing the terminus displays a high of level curvature, a low level of flexibility, and a low degree of helix stability. The same situation is found in the distantly related Gram-positive bacterium Bacillus subtilis, suggesting that the phenomenon is biologically relevant. Based on a search for long DNA segments where all the independent structural measures agree, we have found a set of 20 regions with identical and very extreme structural properties. Due to their strong inherent curvature, we suggest that these may function as topological domain boundaries by efficiently organizing plectonemically supercoiled DNA. Interestingly, we find that in practically all the investigated eubacterial and archaeal genomes, there is a trend for promoter DNA being more curved, less flexible, and less stable than DNA in coding regions and in intergenic DNA without promoters. This trend is present regardless of the absolute levels of the structural parameters, and we suggest that this may be related to the requirement for helix unwinding during initiation of transcription, or perhaps to the previously observed location of promoters at the apex of plectonemically supercoiled DNA. We have also analyzed the structural similarities between groups of genes by clustering all RNA and protein-encoding genes in E. coli, based on the average structural parameters. We find that most ribosomal genes (protein-encoding as well as rRNA genes) cluster together, and we suggest that DNA structure may play a role in the transcription of these highly expressed genes.

Bacterial Proteins↗
PubMed2000

Structural analysis of DNA sequence: evidence for lateral gene transfer in Thermotoga maritima.

The recently published complete DNA sequence of the bacterium Thermotoga maritima provides evidence, based on protein sequence conservation, for lateral gene transfer between Archaea and Bacteria. We introduce a new method of periodicity analysis of DNA sequences, based on structural parameters, which brings independent evidence for the lateral gene transfer in the genome of T.maritima. The structural analysis relates the Archaea-like DNA sequences to the genome of Pyrococcus horikoshii. Analysis of 24 complete genomic DNA sequences shows different periodicity patterns for organisms of different origin. The typical genomic periodicity for Bacteria is 11 bp whilst it is 10 bp for Archaea. Eukaryotes have more complex spectra but the dominant period in the yeast Saccharomyces cerevisiae is 10.2 bp. These periodicities are most likely reflective of differences in chromatin structure.

Chromatin↗
PubMed2000

Assessing the accuracy of prediction algorithms for classification: an overview.

We provide a unified overview of methods that currently are widely used to assess the accuracy of prediction algorithms, from raw percentages, quadratic error measures and other distances, and correlation coefficients, and to information theoretic measures such as relative entropy and mutual information. We briefly discuss the advantages and disadvantages of each approach. For classification tasks, we derive new learning algorithms for the design of prediction systems by directly optimising the correlation coefficient. We observe and prove several results relating sensitivity and specificity of optimal systems. While the principles are general, we illustrate the applicability on specific problems such as protein secondary structure and signal peptide prediction.

Algorithms↗