Biomedical subjects

A Krogh

Publications and source records attributed to A Krogh.

At least 19 recordsLinked to original sources

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

Complete genome sequence of a multiple drug resistant Salmonella enterica serovar Typhi CT18.

Salmonella enterica serovar Typhi (S. typhi) is the aetiological agent of typhoid fever, a serious invasive bacterial disease of humans with an annual global burden of approximately 16 million cases, leading to 600,000 fatalities. Many S. enterica serovars actively invade the mucosal surface of the intestine but are normally contained in healthy individuals by the local immune defence mechanisms. However, S. typhi has evolved the ability to spread to the deeper tissues of humans, including liver, spleen and bone marrow. Here we have sequenced the 4,809,037-base pair (bp) genome of a S. typhi (CT18) that is resistant to multiple drugs, revealing the presence of hundreds of insertions and deletions compared with the Escherichia coli genome, ranging in size from single genes to large islands. Notably, the genome sequence identifies over two hundred pseudogenes, several corresponding to genes that are known to contribute to virulence in Salmonella typhimurium. This genetic degradation may contribute to the human-restricted host range for S. typhi. CT18 harbours a 218,150-bp multiple-drug-resistance incH1 plasmid (pHCM1), and a 106,516-bp cryptic plasmid (pHCM2), which shows recent common ancestry with a virulence plasmid of Yersinia pestis.

Chromosome Mapping↗

PubMed2001

Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes.

We describe and validate a new membrane protein topology prediction method, TMHMM, based on a hidden Markov model. We present a detailed analysis of TMHMM's performance, and show that it correctly predicts 97-98 % of the transmembrane helices. Additionally, TMHMM can discriminate between soluble and membrane proteins with both specificity and sensitivity better than 99 %, although the accuracy drops when signal peptides are present. This high degree of accuracy allowed us to predict reliably integral membrane proteins in a large collection of genomes. Based on these predictions, we estimate that 20-30 % of all genes in most genomes encode membrane proteins, which is in agreement with previous estimates. We further discovered that proteins with N(in)-C(in) topologies are strongly preferred in all examined organisms, except Caenorhabditis elegans, where the large number of 7TM receptors increases the counts for N(out)-C(in) topologies. We discuss the possible relevance of this finding for our understanding of membrane protein assembly mechanisms. A TMHMM prediction service is available at http://www.cbs.dtu.dk/services/TMHMM/.

Animals↗

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↗

PubMed2000

Using database matches with for HMMGene for automated gene detection in Drosophila.

The application of the gene finder HMMGene to the Adh region of the Drosophila melanogaster is described, and the prediction results are analyzed. HMMGene is based on a probabilistic model called a hidden Markov model, and the probabilistic framework facilitates the inclusion of database matches of varying degrees of certainty. It is shown that database matches clearly improve the performance of the gene finder. For instance, the sensitivity for coding exons predicted with both ends correct grows from 62% to 70% on a high-quality test set, when matches to proteins, cDNAs, repeats, and transposons are included. The specificity drops more than the sensitivity increases when ESTs are used. This is due to the high noise level in EST matches, and it is discussed in more detail why this is and how it might be improved.

Alcohol Dehydrogenase↗

PubMed1999

No evidence that mRNAs have lower folding free energies than random sequences with the same dinucleotide distribution.

This work investigates whether mRNA has a lower estimated folding free energy than random sequences. The free energy estimates are calculated by the mfold program for prediction of RNA secondary structures. For a set of 46 mRNAs it is shown that the predicted free energy is not significantly different from random sequences with the same dinucleotide distribution. For random sequences with the same mononucleotide distribution it has previously been shown that the native mRNA sequences have a lower predicted free energy, which indicates a more stable structure than random sequences. However, dinucleotide content is important when assessing the significance of predicted free energy as the physical stability of RNA secondary structure is known to depend on dinucleotide base stacking energies. Even known RNA secondary structures, like tRNAs, can be shown to have predicted free energies indistinguishable from randomized sequences. This suggests that the predicted free energy is not always a good determinant for RNA folding.

Animals↗

PubMed1999

Hidden neural networks.

A general framework for hybrids of hidden Markov models (HMMs) and neural networks (NNs) called hidden neural networks (HNNs) is described. The article begins by reviewing standard HMMs and estimation by conditional maximum likelihood, which is used by the HNN. In the HNN, the usual HMM probability parameters are replaced by the outputs of state-specific neural networks. As opposed to many other hybrids, the HNN is normalized globally and therefore has a valid probabilistic interpretation. All parameters in the HNN are estimated simultaneously according to the discriminative conditional maximum likelihood criterion. The HNN can be viewed as an undirected probabilistic independence network (a graphical model), where the neural networks provide a compact representation of the clique functions. An evaluation of the HNN on the task of recognizing broad phoneme classes in the TIMIT database shows clear performance gains compared to standard HMMs tested on the same task.

Databases as Topic↗

PubMed1998

Deciphering the biology of Mycobacterium tuberculosis from the complete genome sequence.

Countless millions of people have died from tuberculosis, a chronic infectious disease caused by the tubercle bacillus. The complete genome sequence of the best-characterized strain of Mycobacterium tuberculosis, H37Rv, has been determined and analysed in order to improve our understanding of the biology of this slow-growing pathogen and to help the conception of new prophylactic and therapeutic interventions. The genome comprises 4,411,529 base pairs, contains around 4,000 genes, and has a very high guanine + cytosine content that is reflected in the biased amino-acid content of the proteins. M. tuberculosis differs radically from other bacteria in that a very large portion of its coding capacity is devoted to the production of enzymes involved in lipogenesis and lipolysis, and to two new families of glycine-rich proteins with a repetitive structure that may represent a source of antigenic variation.

Chromosome Mapping↗

PubMed1998

Prediction of signal peptides and signal anchors by a hidden Markov model.

A hidden Markov model of signal peptides has been developed. It contains submodels for the N-terminal part, the hydrophobic region, and the region around the cleavage site. For known signal peptides, the model can be used to assign objective boundaries between these three regions. Applied to our data, the length distributions for the three regions are significantly different from expectations. For instance, the assigned hydrophobic region is between 8 and 12 residues long in almost all eukaryotic signal peptides. This analysis also makes obvious the difference between eukaryotes, Gram-positive bacteria, and Gram-negative bacteria. The model can be used to predict the location of the cleavage site, which it finds correctly in nearly 70% of signal peptides in a cross-validated test--almost the same accuracy as the best previous method. One of the problems for existing prediction methods is the poor discrimination between signal peptides and uncleaved signal anchors, but this is substantially improved by the hidden Markov model when expanding it with a very simple signal anchor model.

Artificial Intelligence↗

PubMed1998

A hidden Markov model for predicting transmembrane helices in protein sequences.

A novel method to model and predict the location and orientation of alpha helices in membrane-spanning proteins is presented. It is based on a hidden Markov model (HMM) with an architecture that corresponds closely to the biological system. The model is cyclic with 7 types of states for helix core, helix caps on either side, loop on the cytoplasmic side, two loops for the non-cytoplasmic side, and a globular domain state in the middle of each loop. The two loop paths on the non-cytoplasmic side are used to model short and long loops separately, which corresponds biologically to the two known different membrane insertions mechanisms. The close mapping between the biological and computational states allows us to infer which parts of the model architecture are important to capture the information that encodes the membrane topology, and to gain a better understanding of the mechanisms and constraints involved. Models were estimated both by maximum likelihood and a discriminative method, and a method for reassignment of the membrane helix boundaries were developed. In a cross validated test on single sequences, our transmembrane HMM, TMHMM, correctly predicts the entire topology for 77% of the sequences in a standard dataset of 83 proteins with known topology. The same accuracy was achieved on a larger dataset of 160 proteins. These results compare favourably with existing methods.

Amino Acid Sequence↗

PubMed1997

Two methods for improving performance of an HMM and their application for gene finding.

A hidden Markov model for gene finding consists of submodels for coding regions, splice sites, introns, intergenic regions and possibly more. It is described how to estimate the model as a whole from labeled sequences instead of estimating the individual parts independently from subsequences. It is argued that the standard maximum likelihood estimation criterion is not optimal for training such a model. Instead of maximizing the probability of the DNA sequence, one should maximize the probability of the correct prediction. Such a criterion, called conditional maximum likelihood, is used for the gene finder 'HMM-gene'. A new (approximative) algorithm is described, which finds the most probable prediction summed over all paths yielding the same prediction. We show that these methods contribute significantly to the high performance of HMMgene.

Algorithms↗

PubMed1996

Naturally occurring nucleosome positioning signals in human exons and introns.

We describe the structural implications of a periodic pattern found in human exons and introns by hidden Markov models. We show that exons (besides the reading frame) have a specific sequential structure in the form of a pattern with triplet consensus non-T(A/T)G, and a minimal periodicity of roughly ten nucleotides. The periodic pattern is also present in intron sequences, although the strength per nucleotide is weaker. Using two independent profile methods based on triplet bendability parameters from DNase I experiments and nucleosome positioning data, we show that the pattern in multiple alignments of internal exon and intron sequences corresponds to a periodic "in phase" bending potential towards the major groove of the DNA. The nucleosome positioning data show that the consensus triplets (and their complements) have a preference for locations on a bent double helix where the major groove faces inward and is compressed. The in-phase triplets are located adjacent to GCC/GGC triplets known to have the strongest bias in their positioning on the nuclesome. Analysis of mRNA sequences encoding proteins with known tertiary structure exclude the possibility that the pattern is a consequence of the previously well-known periodicity caused by the encoding of alpha-helices in proteins. Finally, we discuss the relation between the bending potential of coding and non-coding regions and its impact on the translational positioning of nucleosomes and the recognition of genes by the transcriptional machinery.

Base Sequence↗

PubMed1996

Improving prediction of protein secondary structure using structured neural networks and multiple sequence alignments.

The prediction of protein secondary structure by use of carefully structured neural networks and multiple sequence alignments has been investigated. Separate networks are used for predicting the three secondary structures alpha-helix, beta-strand, and coil. The networks are designed using a priori knowledge of amino acid properties with respect to the secondary structure and the characteristic periodicity in alpha-helices. Since these single-structure networks all have less than 600 adjustable weights, overfitting is avoided. To obtain a three-state prediction of alpha-helix, beta-strand, or coil, ensembles of single-structure networks are combined with another neural network. This method gives an overall prediction accuracy of 66.3% when using 7-fold cross-validation on a database of 126 nonhomologous globular proteins. Applying the method to multiple sequence alignments of homologous proteins increases the prediction accuracy significantly to 71.3% with corresponding Matthew's correlation coefficients C alpha = 0.59, C beta = 0.52, and Cc = 0.50. More than 72% of the residues in the database are predicted with an accuracy of 80%. It is shown that the network outputs can be interpreted as estimated probabilities of correct prediction, and, therefore, these numbers indicate which residues are predicted with high confidence.

Amino Acid Sequence↗

PubMed1996

Hidden Markov models for sequence analysis: extension and analysis of the basic method.

Hidden Markov models (HMMs) are a highly effective means of modeling a family of unaligned sequences or a common motif within a set of unaligned sequences. The trained HMM can then be used for discrimination or multiple alignment. The basic mathematical description of an HMM and its expectation-maximization training procedure is relatively straightforward. In this paper, we review the mathematical extensions and heuristics that move the method from the theoretical to the practical. We then experimentally analyze the effectiveness of model regularization, dynamic model modification and optimization strategies. Finally it is demonstrated on the SH2 domain how a domain can be found from unaligned sequences using a special model type. The experimental work was completed with the aid of the Sequence Alignment and Modeling software suite.

Algorithms↗

PubMed1996

Dirichlet mixtures: a method for improved detection of weak but significant protein sequence homology.

We present a method for condensing the information in multiple alignments of proteins into a mixture of Dirichlet densities over amino acid distributions. Dirichlet mixture densities are designed to be combined with observed amino acid frequencies to form estimates of expected amino acid probabilities at each position in a profile, hidden Markov model or other statistical model. These estimates give a statistical model greater generalization capacity, so that remotely related family members can be more reliably recognized by the model. This paper corrects the previously published formula for estimating these expected probabilities, and contains complete derivations of the Dirichlet mixture formulas, methods for optimizing the mixtures to match particular databases, and suggestions for efficient implementation.

Algorithms↗

PubMed1995

Maximum entropy weighting of aligned sequences of proteins or DNA.

In a family of proteins or other biological sequences like DNA the various subfamilies are often very unevenly represented. For this reason a scheme for assigning weights to each sequence can greatly improve performance at tasks such as database searching with profiles or other consensus models based on multiple alignments. A new weighting scheme for this type of database search is proposed. In a statistical description of the searching problem it is derived from the maximum entropy principle. It can be proved that, in a certain sense, it corrects for uneven representation. It is shown that finding the maximum entropy weights is an easy optimization problem for which standard techniques are applicable.

Amino Acid Sequence↗

PubMed1995

Periodic sequence patterns in human exons.

We analyse the sequential structure of human exons and their flanking introns by hidden Markov models. Together, models of donor site regions, acceptor site regions and flanked internal exons, show that exons--besides the reading frame--hold a specific periodic pattern. The pattern, which has the consensus: non-T(A/T)G and a minimal periodicity of roughly 10 nucleotides, is not a consequence of the nucleotide statistics in the three codon positions, nor of the well known nucleosome positioning signal. We discuss the relation between the pattern and other known sequence elements responsible for the intrinsic bending or curvature of DNA.

Base Sequence↗