A Profile Hidden Markov Model Framework for Modeling and Analysis of Shape

BackgroundIntrinsically Disordered Proteins (IDPs) lack an ordered three-dimensional structure and are enriched in various biological processes. The Molecular Recognition Features (MoRFs) are functional regions within IDPs that undergo a disorder-to-order transition on binding to a partner protein. Identifying MoRFs in IDPs using computational methods is a challenging task.MethodsIn this study, we introduce hidden Markov model (HMM) profiles to accurately identify the location of MoRFs in disordered protein sequences. Using windowing technique, HMM profiles are utilised to extract features from protein sequences and support vector machines (SVM) are used to calculate a propensity score for each residue. Two different SVM kernels with high noise tolerance are evaluated with a varying window size and the scores of the SVM models are combined to generate the final propensity score to predict MoRF residues. The SVM models are designed to extract maximal information between MoRF residues, its neighboring regions (Flanks) and the remainder of the sequence (Others).ResultsTo evaluate the proposed method, its performance was compared to that of other MoRF predictors; MoRFpred and ANCHOR. The results show that the proposed method outperforms these two predictors.ConclusionsUsing HMM profile as a source of feature extraction, the proposed method indicates improvement in predicting MoRFs in disordered protein sequences.

BackgroundProfile Hidden Markov Model (HMM) is a powerful statistical model to represent a family of DNA, RNA, and protein sequences. Profile HMM has been widely used in bioinformatics research such as sequence alignment, gene structure prediction, motif identification, protein structure prediction, and biological database search. However, few comprehensive, visual editing tools for profile HMM are publicly available.ResultsWe develop a visual editor for profile Hidden Markov Models (HMMEditor). HMMEditor can visualize the profile HMM architecture, transition probabilities, and emission probabilities. Moreover, it provides functions to edit and save HMM and parameters. Furthermore, HMMEditor allows users to align a sequence against the profile HMM and to visualize the corresponding Viterbi path.ConclusionHMMEditor provides a set of unique functions to visualize and edit a profile HMM. It is a useful tool for biological sequence analysis and modeling. Both HMMEditor software and web service are freely available.

The profile hidden Markov model (PHMM) is widely used to assign the protein sequences to their respective families. A major limitation of a PHMM is the assumption that given states the observations (amino acids) are independent. To overcome this limitation, the dependency between amino acids in a multiple sequence alignment (MSA) which is the representative of a PHMM can be appended to the PHMM. Due to the fact that with a MSA, the sequences of amino acids are biologically related, the one-by-one dependency between two amino acids can be considered. In other words, based on the MSA, the dependency between an amino acid and its corresponding amino acid located above can be combined with the PHMM. For this purpose, the new emission probability matrix which considers the one-by-one dependencies between amino acids is constructed. The parameters of a PHMM are of two types; transition and emission probabilities which are usually estimated using an EM algorithm called the Baum-Welch algorithm. We have generalized the Baum-Welch algorithm using similarity emission matrix constructed by integrating the new emission probability matrix with the common emission probability matrix. Then, the performance of similarity emission is discussed by applying it to the top twenty protein families in the Pfam database. We show that using the similarity emission in the Baum-Welch algorithm significantly outperforms the common Baum-Welch algorithm in the task of assigning protein sequences to protein families.

This paper proposes a novel similarity measure between vector sequences. We work in the framework of model-based approaches, where each sequence is first mapped to a Hidden Markov Model (HMM) and then a measure of similarity is computed between the HMMs. We propose to model sequences with semicontinuous HMMs (SC-HMMs). This is a particular type of HMM whose emission probabilities in each state are mixtures of shared Gaussians. This crucial constraint provides two major benefits. First, the a priori information contained in the common set of Gaussians leads to a more accurate estimate of the HMM parameters. Second, the computation of a similarity between two SC-HMMs can be simplified to a Dynamic Time Warping (DTW) between their mixture weight vectors, which significantly reduces the computational cost. Experiments are carried out on a handwritten word retrieval task in three different datasets-an in-house dataset of real handwritten letters, the George Washington dataset, and the IFN/ENIT dataset of Arabic handwritten words. These experiments show that the proposed similarity outperforms the traditional DTW between the original sequences, and the model-based approach which uses ordinary continuous HMMs. We also show that this increase in accuracy can be traded against a significant reduction of the computational cost.

Increasing the accuracy of single-molecule data analysis using tMAVEN

Regulation of gene expression influences almost all biological processes in an organism; sequence-specific DNA-binding transcription factors are critical to this control. For most genomes, the repertoire of transcription factors is only partially known. Hitherto transcription factor identification has been largely based on genome annotation pipelines that use pairwise sequence comparisons, which detect only those factors similar to known genes, or on functional classification schemes that amalgamate many types of proteins into the category of ‘transcription factor’. Using a novel transcription factor identification method, the DBD transcription factor database fills this void, providing genome-wide transcription factor predictions for organisms from across the tree of life. The prediction method behind DBD identifies sequence-specific DNA-binding transcription factors through homology using profile hidden Markov models (HMMs) of domains. Thus, it is limited to factors that are homologus to those HMMs. The collection of HMMs is taken from two existing databases (Pfam and SUPERFAMILY), and is limited to models that exclusively detect transcription factors that specifically recognize DNA sequences. It does not include basal transcription factors or chromatin-associated proteins, for instance. Based on comparison with experimentally verified annotation, the prediction procedure is between 95% and 99% accurate. Between one quarter and one-half of our genome-wide predicted transcription factors represent previously uncharacterized proteins. The DBD () consists of predicted transcription factor repertoires for 150 completely sequenced genomes, their domain assignments and the hand curated list of DNA-binding domain HMMs. Users can browse, search or download the predictions by genome, domain family or sequence identifier, view families of transcription factors based on domain architecture and receive predictions for a protein sequence.

A new type of hidden Markov model (HMM) developed based on the fuzzy clustering result is proposed for identification of human motion. By associating the human continuous movements with a series of human motion primitives, the complex human motion could be analysed as the same process as recognizing a word by alphabet. However, because the human movements can be multi-paths and inherently stochastic, it is indisputable that a more sophisticated framework must be applied to reveal the statistic relationships among the different human motion primitives. Hence, based on the human motion recognition results derived from the fuzzy clustering function, HMM is modified by changing the formulation of the emission and transition matrices to analyse the human wrist motion. According to the experimental results, the complex human wrist motion sequence can be identified by the novel HMM holistically and efficiently

In this work we created a sequence model that goes beyond simple linear patterns to model a specific type of higher-order relationship possible in biological sequences. Particularly, we seek models that can account for partially overlaid and interleaved patterns in biological sequences. Our proposed context-switching model (cswHMM) is designed as a variable-order hidden Markov model (HMM) with a specific structure that allows switching control between two or more sub-models.Tests of this approach suggest that a combination of HMMs for protein sequence analysis, such as pattern mining based HMMs or profile HMMs, with the context-switching approach can improve the descriptive ability and performance of the models.

In protein structure prediction problem, DNA-binding protein identification plays a significant role in various processes like transcription, DNA replication, DNA recombination, repair and modification. It has been a very active area of research to find an effective way to solve DNA-binding protein problem. The experimental methods that are employed for protein structure prediction are quite expensive and time consuming. Most of the methods have yielded better results in extracting evolutionary features. Recently, hidden Markov model (HMM) has been employed to extract features with remarkable increase in the efficiency for identification of DNA-binding proteins. In this study, a novel system based on HMM profile has been proposed to capture distant homology and evolutionary information. The experiments using the HMM profile model have been carried out on independent dataset with prediction accuracy 72% prediction accuracy, which is 3% better compared to the previous attempts. In this study, a comprehensive analysis of HMM profile model is conducted and the results are compared with other traditional models based on performances indices.KeywordsHidden Markov modelDNA-binding proteinProfile HMM

Motivation: One of the most successful methods to date for recognizing protein sequences that are evolutionarily related has been profile hidden Markov models (HMMs). However, these models do not capture pairwise statistical preferences of residues that are hydrogen bonded in beta sheets. These dependencies have been partially captured in the HMM setting by simulated evolution in the training phase and can be fully captured by Markov random fields (MRFs). However, the MRFs can be computationally prohibitive when beta strands are interleaved in complex topologies. We introduce SMURFLite, a method that combines both simplified MRFs and simulated evolution to substantially improve remote homology detection for beta structures. Unlike previous MRF-based methods, SMURFLite is computationally feasible on any beta-structural motif.Results: We test SMURFLite on all propeller and barrel folds in the mainly-beta class of the SCOP hierarchy in stringent cross-validation experiments. We show a mean 26% (median 16%) improvement in area under curve (AUC) for beta-structural motif recognition as compared with HMMER (a well-known HMM method) and a mean 33% (median 19%) improvement as compared with RAPTOR (a well-known threading method) and even a mean 18% (median 10%) improvement in AUC over HHPred (a profile–profile HMM method), despite HHpred's use of extensive additional training data. We demonstrate SMURFLite's ability to scale to whole genomes by running a SMURFLite library of 207 beta-structural SCOP superfamilies against the entire genome of Thermotoga maritima, and make over a 100 new fold predictions.Availability and implementaion: A webserver that runs SMURFLite is available at: http://smurf.cs.tufts.edu/smurflite/Contact: lenore.cowen@tufts.edu; bab@mit.edu

Three metagenomic libraries were constructed using surface sediment samples from the northern Adriatic Sea. Two of the samples were taken from a highly polluted and an unpolluted site respectively. The third sample from a polluted site had been enriched using crude oil. The results of the metagenome analyses were incorporated in the REDPET relational database (http://redpet.bioinfo.pbf.hr/REDPET), which was generated using the previously developed MEGGASENSE platform. The database includes taxonomic data to allow the assessment of the biodiversity of metagenomic libraries and a general functional analysis of genes using hidden Markov model (HMM) profiles based on the KEGG database. A set of 22 specialised HMM profiles was developed to detect putative genes for hydrocarbon-degrading enzymes. Use of these profiles showed that the metagenomic library generated after selection on crude oil had enriched genes for aerobic n-alkane degradation. The use of this system for bioprospecting was exemplified using potential alkB and almA genes from this library.

MotivationBiologists often wish to use their knowledge on a few experimental models of a given molecular system to identify homologs in genomic data. We developed a generic tool for this purpose.Results Macromolecular System Finder (MacSyFinder) provides a flexible framework to model the properties of molecular systems (cellular machinery or pathway) including their components, evolutionary associations with other systems and genetic architecture. Modelled features also include functional analogs, and the multiple uses of a same component by different systems. Models are used to search for molecular systems in complete genomes or in unstructured data like metagenomes. The components of the systems are searched by sequence similarity using Hidden Markov model (HMM) protein profiles. The assignment of hits to a given system is decided based on compliance with the content and organization of the system model. A graphical interface, MacSyView, facilitates the analysis of the results by showing overviews of component content and genomic context. To exemplify the use of MacSyFinder we built models to detect and class CRISPR-Cas systems following a previously established classification. We show that MacSyFinder allows to easily define an accurate “Cas-finder” using publicly available protein profiles.Availability and ImplementationMacSyFinder is a standalone application implemented in Python. It requires Python 2.7, Hmmer and makeblastdb (version 2.2.28 or higher). It is freely available with its source code under a GPLv3 license at https://github.com/gem-pasteur/macsyfinder. It is compatible with all platforms supporting Python and Hmmer/makeblastdb. The “Cas-finder” (models and HMM profiles) is distributed as a compressed tarball archive as Supporting Information.

We present RVDB-prot, a database corresponding to the protein equivalent of the nucleic acid reference virus database RVDB. Protein databases can be helpful to perform more sensitive protein sequence comparisons. Similarly to its homologous public repository, RVDB-prot aims to provide reliable and accurately annotated unique entries, while including also an Hidden Markov Model (HMM) protein profiles database for distant protein searching.

We present RVDB-prot, a database corresponding to the protein equivalent of the nucleic acid reference virus database RVDB. Protein databases can be helpful to perform more sensitive protein sequence comparisons. Similarly to its homologous public repository, RVDB-prot aims to provide reliable and accurately annotated unique entries, while including also an Hidden Markov Model (HMM) protein profiles database for distant protein searching.

BackgroundIn a previous study, we conducted a large-scale similarity-free function prediction of mitochondrion-encoded hypothetical proteins, by which the hypothetical gene murf1 (maxicircle unidentified reading frame 1) was assigned as nad2, encoding subunit 2 of NADH dehydrogenase (Complex I of the respiratory chain). This hypothetical gene occurs in the mitochondrial genome of kinetoplastids, a group of unicellular eukaryotes including the causative agents of African sleeping sickness and leishmaniasis. In the present study, we test this assignment by using bioinformatics methods that are highly sensitive in identifying remote homologs and confront the prediction with available biological knowledge.ResultsComparison of MURF1 profile Hidden Markov Model (HMM) against function-known profile HMMs in Pfam, Panther and TIGR shows that MURF1 is a Complex I protein, but without specifying the exact subunit. Therefore, we constructed profile HMMs for each individual subunit, using all available sequences clustered at various identity thresholds. HMM-HMM comparison of these individual NADH subunits against MURF1 clearly identifies this hypothetical protein as NAD2. Further, we collected the relevant experimental information about kinetoplastids, which provides additional evidence in support of this prediction.ConclusionOur in silico analyses provide convincing evidence for MURF1 being a highly divergent member of NAD2.