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PubMed1983

Structural and actin-binding properties of the trypsin-produced HMM and S1 from gizzard smooth muscle myosin.

The reaction of trypsin on the heavy chain of gizzard myosin and chymotryptic HMM was investigated under restricted fragmentation conditions. The three fragments of the head part with 29 kDa, 50 kDa and 26 kDa were isolated and identified. The 66 K heavy chain segment containing the S1-S2 junction was slowly but extensively degraded liberating a S1-like entity which lacked an intact COOH-terminal 26 kDa region; this isolated species displayed full intrinsic ATPase activities but little actin-binding ability. Tryptic HMM was also formed bearing a fragmented heavy chain and lacking the 20 kDa light chain. Its actin-activated ATPase was derepressed upon cleavage of the 66 kDa segment by papain. We propose that the integral 66 kDa heavy chain component is directly involved in the regulation of the gizzard actomyosin ATPase.

Actins↗
PubMed2000

Temperature change does not affect force between single actin filaments and HMM from rabbit muscles.

The temperature dependence of sliding force, velocity, and unbinding force was studied on actin filaments when they were placed on heavy meromyosin (HMM) attached to a glass surface. A fluorescently labeled actin filament was attached to the gelsolin-coated surface of a 1-microm polystyrene bead. The bead was trapped by optical tweezers, and HMM-actin interaction was performed at 20-35 degrees C to examine whether force is altered by the temperature change. Our experiments demonstrate that sliding force increased moderately with temperature (Q(10) = 1.6 +/- 0.2, +/-SEM, n = 9), whereas the velocity increased significantly (Q(10) = 2.9 +/- 0.4, n = 10). The moderate increase in force is caused by the increased number of available cross-bridges for actin interaction, because the cross-bridge number similarly increased with temperature (Q(10) = 1. 5 +/- 0.2, n = 3) when measured during rigor induction. We further found that unbinding force measured during the rigor condition did not differ with temperature. These results indicate that the amount of force each cross-bridge generates is fixed, and it does not change with temperature. We found that the above generalization was not modified in the presence of 1 mM MgADP or 8 mM phosphate.

Actins↗
PubMed2003

A HMM-based method to predict the transmembrane regions of beta-barrel membrane proteins.

A novel method is developed to model and predict the transmembrane regions of beta-barrel membrane proteins. It is based on a Hidden Markov model (HMM) with architecture obeying those proteins' construction principles. The HMM is trained and tested on a non-redundant set of 11 beta-barrel membrane proteins known to date at atomic resolution with a jack-knife procedure. As a result, the method correctly locates 97% of 172 transmembrane beta-strands. Out of the 11 proteins, the barrel size for ten proteins and the overall topology for seven proteins are correctly predicted. Additionally, it successfully assigns the entire topology for two new beta-barrel membrane proteins that have no significant sequence homology to the 11 proteins. Predicted topology for two candidates for beta-barrel structure of the outer mitochondrial membrane is also presented in the paper.

Animals↗
PubMed2002

A sequence-profile-based HMM for predicting and discriminating beta barrel membrane proteins.

MOTIVATION: Membrane proteins are an abundant and functionally relevant subset of proteins that putatively include from about 15 up to 30% of the proteome of organisms fully sequenced. These estimates are mainly computed on the basis of sequence comparison and membrane protein prediction. It is therefore urgent to develop methods capable of selecting membrane proteins especially in the case of outer membrane proteins, barely taken into consideration when proteome wide analysis is performed. This will also help protein annotation when no homologous sequence is found in the database. Outer membrane proteins solved so far at atomic resolution interact with the external membrane of bacteria with a characteristic beta barrel structure comprising different even numbers of beta strands (beta barrel membrane proteins). In this they differ from the membrane proteins of the cytoplasmic membrane endowed with alpha helix bundles (all alpha membrane proteins) and need specialised predictors. RESULTS: We develop a HMM model, which can predict the topology of beta barrel membrane proteins using, as input, evolutionary information. The model is cyclic with 6 types of states: two for the beta strand transmembrane core, one for the beta strand cap on either side of the membrane, one for the inner loop, one for the outer loop and one for the globular domain state in the middle of each loop. The development of a specific input for HMM based on multiple sequence alignment is novel. The accuracy per residue of the model is 83% when a jack knife procedure is adopted. With a model optimisation method using a dynamic programming algorithm seven topological models out of the twelve proteins included in the testing set are also correctly predicted. When used as a discriminator, the model is rather selective. At a fixed probability value, it retains 84% of a non-redundant set comprising 145 sequences of well-annotated outer membrane proteins. Concomitantly, it correctly rejects 90% of a set of globular proteins including about 1200 chains with low sequence identity (<30%) and 90% of a set of all alpha membrane proteins, including 188 chains.

Algorithms↗
PubMed2003

HMM sampling and applications to gene finding and alternative splicing.

The standard method of applying hidden Markov models to biological problems is to find a Viterbi (maximal weight) path through the HMM graph. The Viterbi algorithm reduces the problem of finding the most likely hidden state sequence that explains given observations, to a dynamic programming problem for corresponding directed acyclic graphs. For example, in the gene finding application, the HMM is used to find the most likely underlying gene structure given a DNA sequence. In this note we discuss the applications of sampling methods for HMMs. The standard sampling algorithm for HMMs is a variant of the common forward-backward and backtrack algorithms, and has already been applied in the context of Gibbs sampling methods. Nevetheless, the practice of sampling state paths from HMMs does not seem to have been widely adopted, and important applications have been overlooked. We show how sampling can be used for finding alternative splicings for genes, including alternative splicings that are conserved between genes from related organisms. We also show how sampling from the posterior distribution is a natural way to compute probabilities for predicted exons and gene structures being correct under the assumed model. Finally, we describe a new memory efficient sampling algorithm for certain classes of HMMs which provides a practical sampling alternative to the Hirschberg algorithm for optimal alignment. The ideas presented have applications not only to gene finding and HMMs but more generally to stochastic context free grammars and RNA structure prediction.

Algorithms↗
PubMed2004

Efficient approximations for learning phylogenetic HMM models from data.

MOTIVATION: We consider models useful for learning an evolutionary or phylogenetic tree from data consisting of DNA sequences corresponding to the leaves of the tree. In particular, we consider a general probabilistic model described in Siepel and Haussler that we call the phylogenetic-HMM model which generalizes the classical probabilistic models of Neyman and Felsenstein. Unfortunately, computing the likelihood of phylogenetic-HMM models is intractable. We consider several approximations for computing the likelihood of such models including an approximation introduced in Siepel and Haussler, loopy belief propagation and several variational methods. RESULTS: We demonstrate that, unlike the other approximations, variational methods are accurate and are guaranteed to lower bound the likelihood. In addition, we identify a particular variational approximation to be best-one in which the posterior distribution is variationally approximated using the classic Neyman-Felsenstein model. The application of our best approximation to data from the cystic fibrosis transmembrane conductance regulator gene region across nine eutherian mammals reveals a CpG effect.

Algorithms↗
PubMed2005

An HMM posterior decoder for sequence feature prediction that includes homology information.

MOTIVATION: When predicting sequence features like transmembrane topology, signal peptides, coil-coil structures, protein secondary structure or genes, extra support can be gained from homologs. RESULTS: We present here a general hidden Markov model (HMM) decoding algorithm that combines probabilities for sequence features of homologs by considering the average of the posterior label probability of each position in a global sequence alignment. The algorithm is an extension of the previously described 'optimal accuracy' decoder, allowing homology information to be used. It was benchmarked using an HMM for transmembrane topology and signal peptide prediction, Phobius. We found that the performance was substantially increased when incorporating information from homologs. AVAILABILITY: A prediction server for transmembrane topology and signal peptides that uses the algorithm is available at http://phobius.cgb.ki.se/poly.html. An implementation of the algorithm is available on request from the authors.

Algorithms↗
PubMed1998

Pfam: multiple sequence alignments and HMM-profiles of protein domains.

Pfam contains multiple alignments and hidden Markov model based profiles (HMM-profiles) of complete protein domains. The definition of domain boundaries, family members and alignment is done semi-automatically based on expert knowledge, sequence similarity, other protein family databases and the ability of HMM-profiles to correctly identify and align the members. Release 2.0 of Pfam contains 527 manually verified families which are available for browsing and on-line searching via the World Wide Web in the UK at http://www.sanger.ac.uk/Pfam/ and in the US at http://genome.wustl. edu/Pfam/ Pfam 2.0 matches one or more domains in 50% of Swissprot-34 sequences, and 25% of a large sample of predicted proteins from the Caenorhabditis elegans genome.

Amino Acid Sequence↗
PubMed2004

HMM Logos for visualization of protein families.

BACKGROUND: Profile Hidden Markov Models (pHMMs) are a widely used tool for protein family research. Up to now, however, there exists no method to visualize all of their central aspects graphically in an intuitively understandable way. RESULTS: We present a visualization method that incorporates both emission and transition probabilities of the pHMM, thus extending sequence logos introduced by Schneider and Stephens. For each emitting state of the pHMM, we display a stack of letters. The stack height is determined by the deviation of the position's letter emission frequencies from the background frequencies. The stack width visualizes both the probability of reaching the state (the hitting probability) and the expected number of letters the state emits during a pass through the model (the state's expected contribution).A web interface offering online creation of HMM Logos and the corresponding source code can be found at the Logos web server of the Max Planck Institute for Molecular Genetics http://logos.molgen.mpg.de. CONCLUSIONS: We demonstrate that HMM Logos can be a useful tool for the biologist: We use them to highlight differences between two homologous subfamilies of GTPases, Rab and Ras, and we show that they are able to indicate structural elements of Ras.

Amino Acid Motifs↗
PubMed2005

Skeletal regulatory proteins enhance thin filament sliding speed and force by skeletal HMM.

At saturating calcium and nucleotide concentrations, troponin (Tn) and tropomyosin (Tm) enhance the in vitro motility speed of individual actin filaments, suggesting the roles of these thin filament proteins in regulating contraction may include a modulation of crossbridge kinetics. Using a homogeneous complement of fast rabbit skeletal proteins, we examined if Tn and Tm modify specific transitions in the crossbridge cycle by varying skeletal muscle crossbridge kinetics and measuring actin filament sliding speed and steady-state force using the in vitro motility and microneedle assays, respectively. Skeletal regulatory proteins increased the force and sliding speed of actin filaments sliding on skeletal HMM. Faster crossbridge cycling with increased temperature or with substitution of dATP as the contractile substrate resulted in both increased sliding speed and force of unregulated filaments, while the addition of regulatory proteins diminished or eliminated this increase. In contrast, regulatory proteins did not influence filament mechanics when crossbridge cycling was slowed with lowered ATP concentration. The results are most simply explained if addition of the Tn and Tm complex to actin enhances both the transition rate of the force-generating actomyosin isomerization (or the preceding transition) and the apparent crossbridge detachment rate, but that the relative influence of Tn and Tm is dependent on the external load.

Actins↗
PubMed2004

Enhancing HMM-based biomedical named entity recognition by studying special phenomena.

The purpose of this research is to enhance an HMM-based named entity recognizer in the biomedical domain. First, we analyze the characteristics of biomedical named entities. Then, we propose a rich set of features, including orthographic, morphological, part-of-speech, and semantic trigger features. All these features are integrated via a Hidden Markov Model with back-off modeling. Furthermore, we propose a method for biomedical abbreviation recognition and two methods for cascaded named entity recognition. Evaluation on the GENIA V3.02 and V1.1 shows that our system achieves 66.5 and 62.5 F-measure, respectively, and outperforms the previous best published system by 8.1 F-measure on the same experimental setting. The major contribution of this paper lies in its rich feature set specially designed for biomedical domain and the effective methods for abbreviation and cascaded named entity recognition. To our best knowledge, our system is the first one that copes with the cascaded phenomena.

Abbreviations as Topic↗
PubMed2003

Does the S2 rod of myosin II uncoil upon two-headed binding to actin? A leucine-zippered HMM study.

Myosin II, like many molecular motors, is a two-headed dimer held together by a coiled-coil rod. The stability of the (S2) rod has implications for head-head interactions, force generation, and possibly regulation. Whether S2 uncoils has been controversial. To test the stability of S2, we constructed a series of "zippered" dimeric smooth muscle myosin II compounds, containing a high-melting temperature 32-amino acid GCN4 leucine zipper in the S2 rod beginning 0, 1, 2, or 15 heptads from the head-rod junction. We then assessed the ability of these and wild-type myosin to bind strongly via two heads to an actin filament by measuring the fluorescence quenching of pyrene-labeled actin induced by myosin binding. Such two-headed binding is expected to exert a large strain that tends to uncoil S2, and hence provide a robust test of S2 stability. We find that wild-type and zippered heavy meromyosin (HMM) are able to bind by both heads to actin under both nucleotide-free and saturating ADP conditions. In addition, we compared the actin affinity and rates for the 0- and 15-zippered HMMs in the phosphorylated "on" state and found them to be very similar. These results strongly suggest that S2 uncoiling is not necessary for two-headed binding of myosin to actin, presumably due to a compliant point in the myosin head(s). We conclude that S2 likely remains intact during the catalytic cycle.

Actins↗
PubMedDate not supplied

Studies of the effect of supplementing protected methionine (HMM-Ca) to dairy cows on milk yield and fertility.

A trial with 90 cows was conducted to examine the effect of supplementing 30 g N-hydroxymethyl-DL-methionine-calcium (HMM-Ca) from 14 days pre calving till 100 days in lactation on the performance and some physiological traits. Cows were kept on four energy levels by feeding different amounts of concentrate. At each energy level, half of the cows were given a methionine supplement. Forage consisted of 1/3 corn- and/or beet pulp silage and 2/3 grass silage ad libitum. The amount of daily milk yield differed not significantly from 31.5 kg for the supplement group to 30.6 kg for the control group. No interaction between the methionine supplementation and energy level could be detected. Mean methionine content in blood plasma differed from 22.7 mmol/l for the supplement group to 16.8 mmol/l for the control group. GOT, tau-GT, bilirubin and urea in blood serum and milk urea content did not indicate a positive effect of the methionine supplementation on the metabolism. Conception rate differed slightly from 87% in the supplement group to 73% in the control group.

Animals↗
PubMed2006

Integrating copy number polymorphisms into array CGH analysis using a robust HMM.

MOTIVATION: Array comparative genomic hybridization (aCGH) is a pervasive technique used to identify chromosomal aberrations in human diseases, including cancer. Aberrations are defined as regions of increased or decreased DNA copy number, relative to a normal sample. Accurately identifying the locations of these aberrations has many important medical applications. Unfortunately, the observed copy number changes are often corrupted by various sources of noise, making the boundaries hard to detect. One popular current technique uses hidden Markov models (HMMs) to divide the signal into regions of constant copy number called segments; a subsequent classification phase labels each segment as a gain, a loss or neutral. Unfortunately, standard HMMs are sensitive to outliers, causing over-segmentation, where segments erroneously span very short regions. RESULTS: We propose a simple modification that makes the HMM robust to such outliers. More importantly, this modification allows us to exploit prior knowledge about the likely location of "outliers", which are often due to copy number polymorphisms (CNPs). By "explaining away" these outliers with prior knowledge about the locations of CNPs, we can focus attention on the more clinically relevant aberrated regions. We show significant improvements over the current state of the art technique (DNAcopy with MergeLevels) on previously published data from mantle cell lymphoma cell lines, and on published benchmark synthetic data augmented with outliers. AVAILABILITY: Source code written in Matlab is available from http://www.cs.ubc.ca/~sshah/acgh.

Artificial Intelligence↗
PubMed2005

A parallel-line detection algorithm based on HMM decoding.

The detection of groups of parallel lines is important in applications such as form processing and text (handwriting) extraction from rule lined paper. These tasks can be very challenging in degraded documents where the lines are severely broken. In this paper, we propose a novel model-based method which incorporates high-level context to detect these lines. After preprocessing (such as skew correction and text filtering), we use trained Hidden Markov Models (HMM) to locate the optimal positions of all lines simultaneously on the horizontal or vertical projection profiles, based on the Viterbi decoding. The algorithm is trainable so it can be easily adapted to different application scenarios. The experiments conducted on known form processing and rule line detection show our method is robust, and achieves better results than other widely used line detection methods.

Algorithms↗
PubMed2006

Multicue HMM-UKF for real-time contour tracking.

We propose an HMM model for contour detection based on multiple visual cues in spatial domain and improve it by joint probabilistic matching to reduce background clutter. It is further integrated with unscented Kalman filter to exploit object dynamics in nonlinear systems for robust contour tracking.

Algorithms↗
PubMed1979

HMM-decorated filaments in leukocyte nucleus.

HMM-decorated filaments could be observed in the leukocyte nucleus under an electron microscope. The filaments were distributed in parallel or randomly among chromatin granules and showed "arrowhead" structures similar to those of cytoplasmic filaments. These observations suggest that the leukocyte nucleus may possess contractile filaments containing actins which probably function in the structural transformation of the nucleus.

Actins↗