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At least 487 records · Page 27Linked to original sources
PubMed2003

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/.

Algorithms↗
PubMed2004

Best alpha-helical transmembrane protein topology predictions are achieved using hidden Markov models and evolutionary information.

Methods that predict the topology of helical membrane proteins are standard tools when analyzing any proteome. Therefore, it is important to improve the performance of such methods. Here we introduce a novel method, PRODIV-TMHMM, which is a profile-based hidden Markov model (HMM) that also incorporates the best features of earlier HMM methods. In our tests, PRODIV-TMHMM outperforms earlier methods both when evaluated on "low-resolution" topology data and on high-resolution 3D structures. The results presented here indicate that the topology could be correctly predicted for approximately two-thirds of all membrane proteins using PRODIV-TMHMM. The importance of evolutionary information for topology prediction is emphasized by the fact that compared with using single sequences, the performance of PRODIV-TMHMM (as well as two other methods) is increased by approximately 10 percentage units by the use of homologous sequences. On a more general level, we also show that HMM-based (or similar) methods perform superiorly to methods that focus mainly on identification of the membrane regions.

Animals↗
PubMed1991

The metabolism of mexiletine in relation to the debrisoquine/sparteine-type polymorphism of drug oxidation.

1. The relationship between the metabolism of the antiarrhythmic drug mexiletine and the debrisoquine/sparteine-type polymorphism was studied in vitro, using microsomes from six human livers, and in vivo, in nine healthy drug-free volunteers with wide variation in their ability to hydroxylate debrisoquine. 2. There was a strong and similar correlation between the formation rate of both major mexiletine metabolites, p-hydroxymexiletine (PHM) and hydroxymethylmexiletine (HMM), and the high affinity component of dextromethorphan O-demethylase activity in human liver microsomes (rs = 0.94; P less than 0.01). 3. There were marked interindividual differences in the amounts of PHM and HMM excreted in the urine over 48 h after a single 200 mg oral dose of mexiletine hydrochloride. Recoveries of both metabolites were correlated inversely with the debrisoquine/4-hydroxydebrisoquine (D/HD) urinary metabolic ratio (rs = -0.83; P = 0.006 and rs = -0.85; P = 0.004, respectively) and were lower in poor metabolisers of debrisoquine (PM) than in extensive metabolisers (EM). Moreover, PM had the highest values of mexiletine/PHM and mexiletine/HMM urinary ratios. In addition, there was a strong correlation between these two indices of mexiletine hydroxylation and the D/HD metabolic ratios (rs = 0.92; P = 0.001 and rs = 0.90; P = 0.001, respectively). 4. After mexiletine pretreatment, the values for D/HD ratio were significantly increased in EM while corresponding values in PM were similar. 5. These findings are in accordance with previous in vitro data suggesting that PHM and HMM formation is predominantly catalyzed by the genetically variable human liver cytochrome P450IID6 isoenzyme responsible for the debrisoquine/sparteine-type polymorphism of drug oxidation.(ABSTRACT TRUNCATED AT 250 WORDS)

Adult↗
PubMedDate not supplied

Actin like filaments in the peritubular cells of human testis chemical extraction and binding with heavy meromyosin.

Two types of the cytoplasmic filaments are found in human peritubular cells; thin filaments (50-80 A) and 100 A filaments. The thin filaments are numerous and tightly packed in cytoplasma of peripheral cell processes, and form bundles. These bundles are arranged in parallel with the long axis of the cells. Dense areas are occasionally seen among the filaments, or underneath the cell membrane to which the thin filaments seem to be anchored. The 100 A filaments are generally located around the perinuclear region or in the middle region of cytoplasma. The thin filaments are not altered after incubation with Hasselbach-Schneider's solution. These filaments, however, disappear in cytoplasma when extracted with 0.6 M KI solution. And also, the thin filaments form chracteristic arrowhead complexes, when incubated with HMM, while these complexes are never found, when incubated with HMM plus ATP. The 100 A filaments do not react to HMM. Thus it is suggested that the thin filaments are actin or actin like and that a contractile system exists in human peritubular cells, which may be involved in transport of nonmotile sperm or permeability of the seminiferous walls.

Adult↗
PubMed1995

Automatic recognition of syllable-final nasals preceded by /epsilon/.

In this paper, it is shown how an automatic recognition algorithm, based on hidden Markov models (HMM), can benefit by properly utilizing findings from perceptual experiments on nasals. Perceptual studies on nasal consonants have shown that both nasal murmurs and formant transitions are important in the identification of place of articulation. Thus both acoustic segments bordering the nasal release were incorporated into this HMM-based system. A 7% improvement in alveolar recognition was obtained by explicitly modeling the vowel-nasal transition segments. Further overall improvement (6%) was realized by making the HMM recognizer "focus" more on the vowel-nasal transition segments bordering the nasal release, and less on the nasal murmur and vowel portion of the /epsilon m/ and /epsilon n/ syllables. An overall average [m]-[n] recognition of 95% was obtained when testing this technique on 60 speakers outside the training set.

Humans↗
PubMed1991

Phosphate-containing proteins and glycoproteins of the cell wall of Candida albicans.

The distribution of phosphate, carbohydrate, and protein in the cell wall components extracted from intact yeast cells of Candida albicans by beta-mercaptoethanol (beta ME) at pH 8.6 was examined by analysis of the material separated by DEAE-cellulose chromatography. All protein peaks did not coincide with peaks of both carbohydrate and phosphate. Subsequent analysis was performed on material obtained from yeast cells and germ tubes which were grown in medium containing [32P]phosphate. Two extracts were obtained by treating cells with beta ME or with zymolyase following beta ME. The extracts were analyzed by sodium dodecyl sulfate-polyacrylamide gel electrophoresis, followed by autoradiography. beta ME-extracted material contained high-molecular-mass (HMM), greater than or equal to 200 kDa, polydisperse material and a major and minor band of 19 to 20 kDa, Zymolyase extracts contained (i) three components of less than or equal to 40 kDa, one of which may correspond to the major beta ME band; (ii) four bands within the HMM region which may correspond to previously reported bands; and (iii) one band of 100 to 120 kDa. After longer exposures, additional midrange bands were detected in the zymolyase extract. In extracts treated with endo-beta-N-acetylglucosaminidase H, the HMM polydisperse material increased in mobility although retaining sufficient radiolabel for detection. Western immunoblot analysis of extracts with germ tube-specific antiserum and a germ tube-specific monoclonal antibody and concanavalin A showed that not all components contained detectable phosphate, not all glycoproteins contained detectable phosphate, and at least one 19- to 20-kDa protein may be phosphorylated in the absence of carbohydrate.

Acetylglucosaminidase↗
PubMed1998

Immunological relationship between the class I epitope of streptococcal M protein and myosin.

The class I epitope of streptococcal M protein is an epidemiological marker for acute rheumatic fever (ARF)-associated serotypes of group A streptococci and is recognized by anti-M protein monoclonal antibody (MAb) 10B6. Using MAb 10B6, we determined the relationship between the class I epitope of M protein and the alpha-helical coiled-coil protein myosin. MAb 10B6 reacted by enzyme-linked immunosorbent assay and Western blotting with human cardiac myosin and rabbit skeletal myosin and its heavy meromyosin (HMM) subfragment. Overlapping synthetic peptides of M5 protein were used to identify the region of M5 protein recognized by MAb 10B6. Two C repeat peptides (C2A and C3) containing the amino acid sequence KGLRRDLDASREAK reacted with MAb 10B6. Partial sequence identity, RRDL, was found in the HMM fragment of myosin, which reacted with MAb 10B6. However, not all peptides of M5 protein and myosin containing the RRDL sequence reacted with MAb 10B6. ARF sera and sera from uncomplicated pharyngitis (UNC) reacted with C repeat region peptides of M protein, while acute glomerulonephritis sera were not as reactive. Affinity-purified human antibody to peptide C3 reacted with myosin. The data demonstrate that the class I epitope of M protein is immunologically cross-reactive with myosin and the HMM subfragment, and antibodies to peptide C3 and myosin were present in ARF and UNC sera.

Amino Acid Sequence↗
PubMed2006

Molecular characterization and phylogenetic analysis of a novel glutenin gene (Dy10.1t) from Aegilops tauschii.

A novel y-type high molecular mass glutenin subunit (HMM-GS) possessing a mobility that is slightly slower than that of the subunit Dy10 obtained by SDS-PAGE, named Dy10.1t, in the wild wheat Aegilops tauschii was identified by 1- and 2-dimensional gel electrophoresis, capillary electrophoresis, and matrix-assisted laser desorption ionization time of flight mass spectrometry (MALDI-TOF-MS). The gene encoding the HMM subunit Dy10.1t was amplified with allele-specific PCR primers, and the amplified products were cloned and sequenced. The coding domain of the Dy10.1t subunit gene consisted of 1980 bp encoding a protein of 658 residues with an M rs of 68 611 Da, which was similar to the M rs determined by MALDI-TOF-MS. The deduced amino acid sequence indicated that Dy10.1t subunit displayed a greater similarity to the Dy12 subunit, differing by only 8 amino acid substitutions. Six coding region single-nucleotide polymorphisms were discovered in the Dy10.1t gene by multiple alignments (1 per 330 bp), 1 in the N-terminal domain and the others in the central repeats. Five of them resulted in residue substitutions, whereas 3 created enzyme site changes. The homology and neighbour-joining trees constructed from code domain sequences of 20 x- and y-type glutenin genes from different Triticum species separated into 2 halves, which corresponded to the x-type and y-type HMM glutenin alleles. Phylogenetic analysis revealed that the Glu-1 gene duplication event probably occurred at about 16.83 million years ago, whereas the divergence times of A, B, and D genomes within x-type and y-type halves were before 7.047 and 10.54 million years ago, respectively.

Alleles↗
PubMed2006

Optimally-connected hidden markov models for predicting MHC-binding peptides.

Hidden Markov models (HMMs) are one of various methods that have been applied to prediction of major histo-compatibility complex (MHC) binding peptide. In terms of model topology, a fully-connected HMM (fcHMM) has the greatest potential to predict binders, at the cost of intensive computation. While a profile HMM (pHMM) performs dramatically fewer computations, it potentially merges overlapping patterns into one which results in some patterns being missed. In a profile HMM a state corresponds to a position on a peptide while in an fcHMM a state has no specific biological meaning. This work proposes optimally-connected HMMs (ocHMMs), which do not merge overlapping patterns and yet, by performing topological reductions, a model's connectivity is greatly reduced from an fcHMM. The parameters of ocHMMs are initialized using a novel amino acid grouping approach called "multiple property grouping." Each group represents a state in an ocHMM. The proposed ocHMMs are compared to a pHMM implementation using HMMER, based on performance tests on two MHC alleles HLA (Human Leukocyte Antigen)-A*0201 and HLA-B*3501. The results show that the heuristic approaches can be adjusted to make an ocHMM achieve higher predictive accuracy than HMMER. Hence, such obtained ocHMMs are worthy of trial for predicting MHC-binding peptides.

Amino Acid Sequence↗
PubMed2006

SERPINB1 upregulation is associated with in vivo complex formation with neutrophil elastase and cathepsin G in a baboon model of bronchopulmonary dysplasia.

Bronchopulmonary dysplasia (BPD) continues to be a major cause of morbidity in premature infants. An imbalance between neutrophil elastase and its inhibitors has been implicated in BPD. Serine protease inhibitor (SERPIN)B1 is an inhibitor of neutrophil proteases, including neutrophil elastase (NE) and cathepsin G (cat G). Recent studies suggest that SERPINB1 could provide protection in the airways by regulating excess protease activity associated with inflammatory lung disorders. In this study, we determined the distribution and ontogeny of SERPINB1 in the baboon lung and characterized the expression of SERPINB1 in baboon models of BPD. SERPINB1 expression was detected in the conducting airway and glandular epithelial cells in addition to neutrophils, macrophages, and mast cells. SERPINB1 mRNA and protein expression increased with advancing gestational age and in the new BPD model. In contrast, SERPINB1 expression levels were decreased in the old BPD model. Furthermore, SERPINB1 was detected as a high-molecular-mass (HMM) complex in lung tissue and bronchoalveolar lavage fluid samples from the BPD group. Analysis of the HMM complex by coimmunoprecipitation showed that these complexes were formed between SERPINB1 and NE or cat G. High-performance liquid chromatography (HPLC) ion trap mass spectrometry verified the presence of SERPINB1 in HMM complexes. Finally, NE activity level was compared between new and old baboon models of BPD and was found to be significantly lower in new BPD. Thus SERPINB1 upregulation in new BPD may be protective by contributing to the regulation of neutrophil proteases NE and cat G.

Animals↗
PubMed1972

The actin and myosin filaments of human and bovine blood platelets.

The contractility of platelets has been attributed to an actomyosin-like protein which has been well defined on a physicochemical basis. Moreover, platelets contain +/-80 A filaments which resemble actin filaments in smooth muscle. Studies were undertaken on human and bovine platelets to better define the morphologic structures which may subserve this contractile function. In order to identify actin, the ability of the filaments to react with heavy meromyosin (HMM) was tested. Accordingly, platelets were glycerinated and treated with HMM. In addition, platelet actin was extracted, reacted with HMM, and examined by negative staining. In both instances typical arrowhead structures with clearly defined polarity and a periodicity of +/-360 A formed. As is the case with purified muscle actin, the complexes were dissociable with Mg-ATP. The formation of myosin-like filaments was observed when osmotically shocked platelets were incubated with MgCl(2) and excess ATP. These "thick" filaments measured 250-300 A in width, tapered at both ends and often occurred in clumps. They resembled aggregates of thick filaments described in contracted smooth muscle. Extraction of platelets by methods suitable for the demonstration of myosin showed filaments with an average length of 0.3 mu, a smooth shaft, and frayed or bulbous ends. These appeared identical to those seen in synthetically prepared myosin of striated muscle. It is suggested that the filaments described here represent the actin and myosin of platelets.

Actins↗
PubMed2003

EasyGene--a prokaryotic gene finder that ranks ORFs by statistical significance.

BACKGROUND: Contrary to other areas of sequence analysis, a measure of statistical significance of a putative gene has not been devised to help in discriminating real genes from the masses of random Open Reading Frames (ORFs) in prokaryotic genomes. Therefore, many genomes have too many short ORFs annotated as genes. RESULTS: In this paper, we present a new automated gene-finding method, EasyGene, which estimates the statistical significance of a predicted gene. The gene finder is based on a hidden Markov model (HMM) that is automatically estimated for a new genome. Using extensions of similarities in Swiss-Prot, a high quality training set of genes is automatically extracted from the genome and used to estimate the HMM. Putative genes are then scored with the HMM, and based on score and length of an ORF, the statistical significance is calculated. The measure of statistical significance for an ORF is the expected number of ORFs in one megabase of random sequence at the same significance level or better, where the random sequence has the same statistics as the genome in the sense of a third order Markov chain. CONCLUSIONS: The result is a flexible gene finder whose overall performance matches or exceeds other methods. The entire pipeline of computer processing from the raw input of a genome or set of contigs to a list of putative genes with significance is automated, making it easy to apply EasyGene to newly sequenced organisms. EasyGene with pre-trained models can be accessed at http://www.cbs.dtu.dk/services/EasyGene.

Bacillus subtilis↗
PubMed2005

A new decoding algorithm for hidden Markov models improves the prediction of the topology of all-beta membrane proteins.

BACKGROUND: Structure prediction of membrane proteins is still a challenging computational problem. Hidden Markov models (HMM) have been successfully applied to the problem of predicting membrane protein topology. In a predictive task, the HMM is endowed with a decoding algorithm in order to assign the most probable state path, and in turn the labels, to an unknown sequence. The Viterbi and the posterior decoding algorithms are the most common. The former is very efficient when one path dominates, while the latter, even though does not guarantee to preserve the HMM grammar, is more effective when several concurring paths have similar probabilities. A third good alternative is 1-best, which was shown to perform equal or better than Viterbi. RESULTS: In this paper we introduce the posterior-Viterbi (PV) a new decoding which combines the posterior and Viterbi algorithms. PV is a two step process: first the posterior probability of each state is computed and then the best posterior allowed path through the model is evaluated by a Viterbi algorithm. CONCLUSION: We show that PV decoding performs better than other algorithms when tested on the problem of the prediction of the topology of beta-barrel membrane proteins.

Algorithms↗
PubMed2006

MCALIGN2: faster, accurate global pairwise alignment of non-coding DNA sequences based on explicit models of indel evolution.

BACKGROUND: Non-coding DNA sequences comprise a very large proportion of the total genomic content of mammals, most other vertebrates, many invertebrates, and most plants. Unraveling the functional significance of non-coding DNA depends on how well we are able to align non-coding DNA sequences. However, the alignment of non-coding DNA sequences is more difficult than aligning protein-coding sequences. RESULTS: Here we present an improved pair-hidden-Markov-Model (pair HMM) based method for performing global pairwise alignment of non-coding DNA sequences. The method uses an explicit model of indel length frequency distribution which can be specified, and allows any time reversible model of nucleotide substitution. The method uses a deterministic global optimiser to find the alignment with the highest posterior probability. We test MCALIGN2 in simulations, and compare it to a previous Monte Carlo based method (MCALIGN), to the pair HMM method of Knudsen and Miyamoto, and to a heuristic method (AVID) that performed very well in a previous simulation study. We show that the pair HMM methods have excellent performance for all combinations of parameter values we have considered. MCALIGN2 is up to ten times faster than MCALIGN. MCALIGN2 is more accurate in resolving indels given an accurate explicit model than heuristic methods, but is computationally slower. CONCLUSION: MCALIGN2 produces better quality alignments by explicitly using biological knowledge about the indel length distribution and time reversible models of nucleotide substitution. As a result, it can outperform other available sequence alignment methods for the cases we have considered to align non-coding DNA sequences.

Algorithms↗
PubMed2006

Development of novel techniques to classify physical activity mode using accelerometers.

PURPOSE: Use of accelerometers to assess physical activity (PA) is widespread in public health research, but their utility is often limited by the accuracy of data-processing techniques. We hypothesized that more sophisticated approaches to data processing could distinguish between activity types based on accelerometer data, providing a more accurate picture of PA. METHODS: Using data from MTI Actigraphs worn by six subjects during four activities (walking, walking uphill, vacuuming, working at a computer), quadratic discriminant analysis (QDA) was performed, and a hidden Markov model (HMM) was trained to recognize the activities. The ability of the new analytic techniques to accurately classify PA was assessed. RESULTS: The mean (SE) percentage of time points for which the QDA correctly identified activity mode was 70.9% (1.2%). Computer work was correctly recognized most frequently (mean (SE) percent correct = 100% (0.01%)), followed by vacuuming (67.5% (1.5%)), uphill walking (58.2% (3.5%)), and walking (53.6% (3.3%)). The mean (SE) percentage of time points for which the HMM correctly identified activity mode was 80.8% (0.9%). Vacuuming was correctly recognized most frequently (mean (SE) percent correct = 98.8% (0.05%)), followed by computer work (97.3% (0.7%)), walking (62.6% (2.3%)), and uphill walking (62.5% (2.3%)). In contrast to a traditional method of data processing that misidentified the intensity level of 100% of the time spent vacuuming and walking uphill, the QDA and HMM approaches correctly estimated the intensity of activity 99% of the time. CONCLUSION: The novel approach of estimating activity mode, rather than activity level, may allow for more accurate field-based estimates of physical activity using accelerometer data, and this approach warrants more study in a larger and more diverse population of subjects and activities.

Acceleration↗
PubMed2004

Five hierarchical levels of sequence-structure correlation in proteins.

This article reviews recent work towards modelling protein folding pathways using a bioinformatics approach. Statistical models have been developed for sequence-structure correlations in proteins at five levels of structural complexity: (i) short motifs; (ii) extended motifs; (iii) nonlocal pairs of motifs; (iv) 3-dimensional arrangements of multiple motifs; and (v) global structural homology. We review statistical models, including sequence profiles, hidden Markov models (HMMs) and interaction potentials, for the first four levels of structural detail. The I-sites (folding Initiation sites) Library models short local structure motifs. Each succeeding level has a statistical model, as follows: HMMSTR (HMM for STRucture) is an HMM for extended motifs; HMMSTR-CM (Contact Maps) is a model for pairwise interactions between motifs; and SCALI-HMM (HMMs for Structural Core ALIgnments) is a set of HMMs for the spatial arrangements of motifs. The parallels between the statistical models and theoretical models for folding pathways are discussed in this article; however, global sequence models are not discussed because they have been extensively reviewed elsewhere. The data used and algorithms presented in this article are available at http://www.bioinfo.rpi.edu/~bystrc/ (click on "servers" or "downloads") or by request to bystrc@rpi.edu .

Algorithms↗
PubMed2006

A heuristic Bayesian method for segmenting DNA sequence alignments and detecting evidence for recombination and gene conversion.

We propose a heuristic approach to the detection of evidence for recombination and gene conversion in multiple DNA sequence alignments. The proposed method consists of two stages. In the first stage, a sliding window is moved along the DNA sequence alignment, and phylogenetic trees are sampled from the conditional posterior distribution with MCMC. To reduce the noise intrinsic to inference from the limited amount of data available in the typically short sliding window, a clustering algorithm based on the Robinson-Foulds distance is applied to the trees thus sampled, and the posterior distribution over tree clusters is obtained for each window position. While changes in this posterior distribution are indicative of recombination or gene conversion events, it is difficult to decide when such a change is statistically significant. This problem is addressed in the second stage of the proposed algorithm, where the distributions obtained in the first stage are post-processed with a Bayesian hidden Markov model (HMM). The emission states of the HMM are associated with posterior distributions over phylogenetic tree topology clusters. The hidden states of the HMM indicate putative recombinant segments. Inference is done in a Bayesian sense, sampling parameters from the posterior distribution with MCMC. Of particular interest is the determination of the number of hidden states as an indication of the number of putative recombinant regions. To this end, we apply reversible jump MCMC, and sample the number of hidden states from the respective posterior distribution.

Actins↗
PubMed1986

Financial modeling for hospital materials managers. Part I.

Beginning in this issue, HMM Editorial Board member Dennis Green will explore five types of financial analysis materials managers are increasingly expected to perform. By the end of this series, HMM readers equipped with a personal computer and/or a financial calculator will have learned to make the following types of financial decisions: Whether to take advantage of a "sale"; Whether to make or buy; How to analyze the "reagent rental" alternative to purchase: How to extrapolate a series of cashflows in present value; and How to create negotiation models for cash acquisitions. As always, HMM welcomes your comments and suggestions.

Financial Management↗