Profile Hidden Markov Models Research Articles

Insights into the evolution of enzyme substrate promiscuity after the discovery of (βα)₈ isomerase evolutionary intermediates from a diverse metagenome.

BackgroundCurrent sequence-based approaches to identify enzyme functional shifts, such as enzyme promiscuity, have proven to be highly dependent on a priori functional knowledge, hampering our ability to reconstruct evolutionary history behind these mechanisms. Hidden Markov Model (HMM) profiles, broadly used to classify enzyme families, can be useful to distinguish between closely related enzyme families with different specificities. The (βα)8-isomerase HisA/PriA enzyme family, involved in L-histidine (HisA, mono-substrate) biosynthesis in most bacteria and plants, but also in L-tryptophan (HisA/TrpF or PriA, dual-substrate) biosynthesis in most Actinobacteria, has been used as model system to explore evolutionary hypotheses and therefore has a considerable amount of evolutionary, functional and structural knowledge available. We searched for functional evolutionary intermediates between the HisA and PriA enzyme families in order to understand the functional divergence between these families.ResultsWe constructed a HMM profile that correctly classifies sequences of unknown function into the HisA and PriA enzyme sub-families. Using this HMM profile, we mined a large metagenome to identify plausible evolutionary intermediate sequences between HisA and PriA. These sequences were used to perform phylogenetic reconstructions and to identify functionally conserved amino acids. Biochemical characterization of one selected enzyme (CAM1) with a mutation within the functionally essential N-terminus phosphate-binding site, namely, an alanine instead of a glycine in HisA or a serine in PriA, showed that this evolutionary intermediate has dual-substrate specificity. Moreover, site-directed mutagenesis of this alanine residue, either backwards into a glycine or forward into a serine, revealed the robustness of this enzyme. None of these mutations, presumably upon functionally essential amino acids, significantly abolished its enzyme activities. A truncated version of this enzyme (CAM2) predicted to adopt a (βα)6-fold, and thus entirely lacking a C-terminus phosphate-binding site, was identified and shown to have HisA activity.ConclusionAs expected, reconstruction of the evolution of PriA from HisA with HMM profiles suggest that functional shifts involve mutations in evolutionarily intermediate enzymes of otherwise functionally essential residues or motifs. These results are in agreement with a link between promiscuous enzymes and intragenic epistasis. HMM provides a convenient approach for gaining insights into these evolutionary processes.Electronic supplementary materialThe online version of this article (doi:10.1186/s12862-015-0378-1) contains supplementary material, which is available to authorized users.

Discovering short linear protein motif based on selective training of profile hidden Markov models

Short linear motifs (SLiMs) in proteins are relatively conservative sequence patterns within disordered regions of proteins, typically 3–10 amino acids in length. They play an important role in mediating protein–protein interactions. Discovering SLiMs by computational methods has attracted more and more attention, most of which were based on regular expressions and profiles. In this paper, a de novo motif discovery method was proposed based on profile hidden Markov models (HMMs), which can not only provide the emission probabilities of amino acids in the defined positions of SLiMs, but also model the undefined positions. We adopted the ordered region masking and the relative local conservation (RLC) masking to improve the signal to noise ratio of the query sequences while applying evolutionary weighting to make the important sequences in evolutionary process get more attention by the selective training of profile HMMs. The experimental results show that our method and the profile-based method returned different subsets within a SLiMs dataset, and the performance of the two approaches are equivalent on a more realistic discovery dataset. Profile HMM-based motif discovery methods complement the existing methods and provide another way for SLiMs analysis.

Learning HMMs for nucleotide sequences from amino acid alignments.

Profile hidden Markov models (profile HMMs) are known to efficiently predict whether an amino acid (AA) sequence belongs to a specific protein family. Profile HMMs can also be used to search for protein domains in genome sequences. In this case, HMMs are typically learned from AA sequences and then used to search on the six-frame translation of nucleotide (NT) sequences. However, this approach demands additional processing of the original data and search results. Here, we propose an alternative and more direct method which converts an AA alignment into an NT one, after which an NT-based HMM is trained to be applied directly on a genome.

Characterization and identification of ubiquitin conjugation sites with E3 ligase recognition specificities.

BackgroundIn eukaryotes, ubiquitin-conjugation is an important mechanism underlying proteasome-mediated degradation of proteins, and as such, plays an essential role in the regulation of many cellular processes. In the ubiquitin-proteasome pathway, E3 ligases play important roles by recognizing a specific protein substrate and catalyzing the attachment of ubiquitin to a lysine (K) residue. As more and more experimental data on ubiquitin conjugation sites become available, it becomes possible to develop prediction models that can be scaled to big data. However, no development that focuses on the investigation of ubiquitinated substrate specificities has existed. Herein, we present an approach that exploits an iteratively statistical method to identify ubiquitin conjugation sites with substrate site specificities.ResultsIn this investigation, totally 6259 experimentally validated ubiquitinated proteins were obtained from dbPTM. After having filtered out homologous fragments with 40% sequence identity, the training data set contained 2658 ubiquitination sites (positive data) and 5532 non-ubiquitinated sites (negative data). Due to the difficulty in characterizing the substrate site specificities of E3 ligases by conventional sequence logo analysis, a recursively statistical method has been applied to obtain significant conserved motifs. The profile hidden Markov model (profile HMM) was adopted to construct the predictive models learned from the identified substrate motifs. A five-fold cross validation was then used to evaluate the predictive model, achieving sensitivity, specificity, and accuracy of 73.07%, 65.46%, and 67.93%, respectively. Additionally, an independent testing set, completely blind to the training data of the predictive model, was used to demonstrate that the proposed method could provide a promising accuracy (76.13%) and outperform other ubiquitination site prediction tool.ConclusionA case study demonstrated the effectiveness of the characterized substrate motifs for identifying ubiquitination sites. The proposed method presents a practical means of preliminary analysis and greatly diminishes the total number of potential targets required for further experimental confirmation. This method may help unravel their mechanisms and roles in E3 recognition and ubiquitin-mediated protein degradation.

CNL Disease Resistance Genes in Soybean and Their Evolutionary Divergence.

Disease resistance genes (R-genes) encode proteins involved in detecting pathogen attack and activating downstream defense molecules. Recent availability of soybean genome sequences makes it possible to examine the diversity of gene families including disease-resistant genes. The objectives of this study were to identify coiled-coil NBS-LRR (= CNL) R-genes in soybean, infer their evolutionary relationships, and assess structural as well as functional divergence of the R-genes. Profile hidden Markov models were used for sequence identification and model-based maximum likelihood was used for phylogenetic analysis, and variation in chromosomal positioning, gene clustering, and functional divergence were assessed. We identified 188 soybean CNL genes nested into four clades consistent to their orthologs in Arabidopsis. Gene clustering analysis revealed the presence of 41 gene clusters located on 13 different chromosomes. Analyses of the Ks-values and chromosomal positioning suggest duplication events occurring at varying timescales, and an extrapericentromeric positioning may have facilitated their rapid evolution. Each of the four CNL clades exhibited distinct patterns of gene expression. Phylogenetic analysis further supported the extrapericentromeric positioning effect on the divergence and retention of the CNL genes. The results are important for understanding the diversity and divergence of CNL genes in soybean, which would have implication in soybean crop improvement in future.

Kernel-based logistic regression model for protein sequence without vectorialization.

Protein sequence data arise more and more often in vaccine and infectious disease research. These types of data are discrete, high-dimensional, and complex. We propose to study the impact of protein sequences on binary outcomes using a kernel-based logistic regression model, which models the effect of protein through a random effect whose variance-covariance matrix is mostly determined by a kernel function. We propose a novel, biologically motivated, profile hidden Markov model (HMM)-based mutual information (MI) kernel. Hypothesis testing can be carried out using the maximum of the score statistics and a parametric bootstrap procedure. To improve the power of testing, we propose intuitive modifications to the test statistic. We show through simulation studies that the profile HMM-based MI kernel can be substantially more powerful than competing kernels, and that the modified test statistics bring incremental gains in power. We use these proposed methods to investigate two problems from HIV-1 vaccine research: (1) identifying segments of HIV-1 envelope (Env) protein that confer resistance to neutralizing antibody and (2) identifying segments of Env that are associated with attenuation of protective vaccine effect by antibodies of isotype A in the RV144 vaccine trial.

ChemInform Abstract: The Structure Elucidation and Total Synthesis of β‐Lipomycin.

AbstractThe structure of title compound (I), originally assigned by a statistical method (profile hidden Markov model) is confirmed by total synthesis.

MacSyFinder: a program to mine genomes for molecular systems with an application to CRISPR-Cas systems.

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.

An integrative computational model for large-scale identification of metalloproteins in microbial genomes: a focus on iron-sulfur cluster proteins.

Metalloproteins represent a ubiquitous group of molecules which are crucial to the survival of all living organisms. While several metal-binding motifs have been defined, it remains challenging to confidently identify metalloproteins from primary protein sequences using computational approaches alone. Here, we describe a comprehensive strategy based on a machine learning approach to design and assess a penalized generalized linear model. We used this strategy to detect members of the iron-sulfur cluster protein family. A new category of descriptors, whose profile is based on profile hidden Markov models, encoding structural information was combined with public descriptors into a linear model. The model was trained and tested on distinct datasets composed of well-characterized iron-sulfur protein sequences, and the resulting model provided higher sensitivity compared to a motif-based approach, while maintaining a good level of specificity. Analysis of this linear model allows us to detect and quantify the contribution of each descriptor, providing us with a better understanding of this complex protein family along with valuable indications for further experimental characterization. Two newly-identified proteins, YhcC and YdiJ, were functionally validated as genuine iron-sulfur proteins, confirming the prediction. The computational model was then applied to over 550 prokaryotic genomes to screen for iron-sulfur proteomes; the results are publicly available at: . This study represents a proof-of-concept for the application of a penalized linear model to identify metalloprotein superfamilies on a large-scale. The application employed here, screening for iron-sulfur proteomes, provides new candidates for further biochemical and structural analysis as well as new resources for an extensive exploration of iron-sulfuromes in the microbial world.

Identification and phylogenetic analyses of VASt, an uncharacterized protein domain associated with lipid-binding domains in Eukaryotes.

BackgroundSeveral regulators of programmed cell death (PCD) in plants encode proteins with putative lipid-binding domains. Among them, VAD1 is a regulator of PCD propagation harboring a GRAM putative lipid-binding domain. However the function of VAD1 at the subcellular level is unknown and the domain architecture of VAD1 has not been analyzed in details.ResultsWe analyzed sequence conservation across the plant kingdom in the VAD1 protein and identified an uncharacterized VASt (VAD1 Analog of StAR-related lipid transfer) domain. Using profile hidden Markov models (profile HMMs) and phylogenetic analysis we found that this domain is conserved among eukaryotes and generally associates with various lipid-binding domains. Proteins containing both a GRAM and a VASt domain include notably the yeast Ysp2 cell death regulator and numerous uncharacterized proteins. Using structure-based phylogeny, we found that the VASt domain is structurally related to Bet v1-like domains.ConclusionWe identified a novel protein domain ubiquitous in Eukaryotic genomes and belonging to the Bet v1-like superfamily. Our findings open perspectives for the functional analysis of VASt-containing proteins and the characterization of novel mechanisms regulating PCD.

Redefining the PF06864 Pfam family based on Burkholderia pseudomallei PilO2(Bp) S-SAD crystal structure.

Type IV pili are surface-exposed filaments and bacterial virulence factors, represented by the Tfpa and Tfpb types, which assemble via specific machineries. The Tfpb group is further divided into seven variants, linked to heterogeneity in the assembly machineries. Here we focus on PilO2Bp, a protein component of the Tfpb R64 thin pilus variant assembly machinery from the pathogen Burkholderia pseudomallei. PilO2Bp belongs to the PF06864 Pfam family, for which an improved definition is presented based on newly derived Hidden Markov Model (HMM) profiles. The 3D structure of the N-terminal domain of PilO2Bp (N-PilO2Bp), here reported, is the first structural representative of the PF06864 family. N-PilO2Bp presents an actin-like ATPase fold that is shown to be present in BfpC, a different variant assembly protein; the new HMM profiles classify BfpC as a PF06864 member. Our results provide structural insight into the PF06864 family and on the Type IV pili assembly machinery.

Genome-wide identification, domain architectures and phylogenetic analysis provide new insights into the early evolution of shikimate pathway in prokaryotes

Despite intense scrutiny from researchers in the fields of biochemistry and metabolism, our understanding of the evolutionary history of the key anabolic shikimate pathway remains limited. To shed light on the early evolutionary events leading to the assembly of the pathway, we investigated the distributions, domain architectures and phylogenies of component enzymes using a bioinformatic procedure based on Hidden Markov Model profiles. The aro genes for the canonical shikimate pathway had most wider distribution in prokaryotes; and the variant pathway coordinated by 2-amino-3,7-dideoxy-D-threo-hept-6-ulosonic acid (ADH) synthase and type II 3-dehydroquinate (DHQ) synthase could be identified in most of archaeal species. In addition, the ancient bidirectional horizontal gene transfer events had happened between two prokaryotic domains: Bacteria and Archaea. Besides 3-deoxy-D-arabino-heptulosonate 7-phosphate (DAHP) synthase, the phylogenetically distinct subfamilies of 5-enolpyruvylshikimate 3-phosphate (EPSP) synthase and chorismate synthase had ever emerged in the evolutionary history of shikimate pathway. These findings provide new insight into the early evolution of the shikimate pathway and advance our understanding of the evolution of metabolic pathways.

Diversity of Conotoxin Gene Superfamilies in the Venomous Snail, Conus victoriae

Animal venoms represent a vast library of bioactive peptides and proteins with proven potential, not only as research tools but also as drug leads and therapeutics. This is illustrated clearly by marine cone snails (genus Conus), whose venoms consist of mixtures of hundreds of peptides (conotoxins) with a diverse array of molecular targets, including voltage- and ligand-gated ion channels, G-protein coupled receptors and neurotransmitter transporters. Several conotoxins have found applications as research tools, with some being used or developed as therapeutics. The primary objective of this study was the large-scale discovery of conotoxin sequences from the venom gland of an Australian cone snail species, Conus victoriae. Using cDNA library normalization, high-throughput 454 sequencing, de novo transcriptome assembly and annotation with BLASTX and profile hidden Markov models, we discovered over 100 unique conotoxin sequences from 20 gene superfamilies, the highest diversity of conotoxins so far reported in a single study. Many of the sequences identified are new members of known conotoxin superfamilies, some help to redefine these superfamilies and others represent altogether new classes of conotoxins. In addition, we have demonstrated an efficient combination of methods to mine an animal venom gland and generate a library of sequences encoding bioactive peptides.

LABEL: fast and accurate lineage assignment with assessment of H5N1 and H9N2 influenza A hemagglutinins.

The evolutionary classification of influenza genes into lineages is a first step in understanding their molecular epidemiology and can inform the subsequent implementation of control measures. We introduce a novel approach called Lineage Assignment By Extended Learning (LABEL) to rapidly determine cladistic information for any number of genes without the need for time-consuming sequence alignment, phylogenetic tree construction, or manual annotation. Instead, LABEL relies on hidden Markov model profiles and support vector machine training to hierarchically classify gene sequences by their similarity to pre-defined lineages. We assessed LABEL by analyzing the annotated hemagglutinin genes of highly pathogenic (H5N1) and low pathogenicity (H9N2) avian influenza A viruses. Using the WHO/FAO/OIE H5N1 evolution working group nomenclature, the LABEL pipeline quickly and accurately identified the H5 lineages of uncharacterized sequences. Moreover, we developed an updated clade nomenclature for the H9 hemagglutinin gene and show a similarly fast and reliable phylogenetic assessment with LABEL. While this study was focused on hemagglutinin sequences, LABEL could be applied to the analysis of any gene and shows great potential to guide molecular epidemiology activities, accelerate database annotation, and provide a data sorting tool for other large-scale bioinformatic studies.

Skylign: a tool for creating informative, interactive logos representing sequence alignments and profile hidden Markov models

BackgroundLogos are commonly used in molecular biology to provide a compact graphical representation of the conservation pattern of a set of sequences. They render the information contained in sequence alignments or profile hidden Markov models by drawing a stack of letters for each position, where the height of the stack corresponds to the conservation at that position, and the height of each letter within a stack depends on the frequency of that letter at that position.ResultsWe present a new tool and web server, called Skylign, which provides a unified framework for creating logos for both sequence alignments and profile hidden Markov models. In addition to static image files, Skylign creates a novel interactive logo plot for inclusion in web pages. These interactive logos enable scrolling, zooming, and inspection of underlying values. Skylign can avoid sampling bias in sequence alignments by down-weighting redundant sequences and by combining observed counts with informed priors. It also simplifies the representation of gap parameters, and can optionally scale letter heights based on alternate calculations of the conservation of a position.ConclusionSkylign is available as a website, a scriptable web service with a RESTful interface, and as a software package for download. Skylign’s interactive logos are easily incorporated into a web page with just a few lines of HTML markup. Skylign may be found at http://skylign.org.

Towards a Comprehensive Search of Putative Chitinases Sequences in Environmental Metagenomic Databases

Chitinases catalyze the hydrolysis of chitin, a linear homopolymer of β-(1,4)-linked N-acetylglucosamine. The broad range of applications of chitinolytic enzymes makes their identification and study very promising. Metagenomic approaches offer access to functional genes in uncultured representatives of the microbiota and hold great potential in the discovery of novel enzymes, but tools to extensively explore these data are still scarce. In this study, we develop a chitinase mining pipeline to facilitate the comprehensive search of these enzymes in environmental metagenomic databases and also to explore phylogenetic relationships among the retrieved sequences. In order to perform the analyses, UniprotKB fungal and bacterial chitinases sequences belonging to the glycoside hydrolases (GH) family-18, 19 and 20 were used to generate 15 reference datasets, which were then used to generate high quality seed alignments with the MAFFT program. Profile Hidden Markov Models (pHMMs) were built from each seed alignment using the hmmbuild program of HMMER v3.0 package. The best-hit sequences returned by hmmsearch against two environmental metagenomic databases (Community Cyberinfrastructure for Advanced Microbial Ecology Research and Analysis—CAMERA and Integrated Microbial Genomes—IMG/M) were retrieved and further analyzed. The NJ trees generated for each chitinase dataset showed some variability in the catalytic domain region of the metagenomic sequences and revealed common sequence patterns among all the trees. The scanning of the retrieved metagenomic sequences for chitinase conserved domains/signatures using both the InterPro and the RPS-BLAST tools confirmed the efficacy and sensitivity of our pHMM-based approach in detecting putative chitinases sequences. These analyses provide insight into the potential reservoir of novel molecules in metagenomic databases while supporting the chitinase mining pipeline developed in this work. By using our chitinase mining pipeline, a larger number of previously unannotated metagenomic chitinase sequences can be classified, enabling further studies on these enzymes.

Generalized Baum-Welch Algorithm Based on the Similarity between Sequences

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.

501Genome-wide Analysis of WRKY Transcription Factors in <I>Solanum tuberosum</I>

The WRKY transcription factor family is widely involved in regulating plant development and defense response against various biotic and abiotic stresses.The Hidden Markov Model Profile(HMM) of WRKY domain was used to identify WRKY transcription factors in potato genome with HMMER 3.0.All together 81 potato WRKY transcription factors were identified and then classified into three main groups,with 5 subgroups in Group Ⅱ,namely Ⅱ-a,Ⅱ-b,Ⅱ-c,Ⅱ-d,and Ⅱ-e.The constructed phylogenetic tree of WRKY genes among potato,Arabidopsis and rice demonstrated that the distinct gene expansion events occurred in the genome of potato and rice,respectively,while the gene expansion of Group Ⅱ-e was unique in potato genome.The potato WRKY transcription factors showed similar motif compositions within each group,but different ones among different groups,though several conserved motifs were shared by all.Furthermore,28 potato WRKY genes showed distinct expression patterns in response to stresses of wounding,drought,salt,and virus invasion.The expressions of several potato WRKY genes were significantly up/down-regulated,implying that these members might participate in regulating the defense response against various biotic and abiotic stresses.

ToPS: A Framework to Manipulate Probabilistic Models of Sequence Data

Discrete Markovian models can be used to characterize patterns in sequences of values and have many applications in biological sequence analysis, including gene prediction, CpG island detection, alignment, and protein profiling. We present ToPS, a computational framework that can be used to implement different applications in bioinformatics analysis by combining eight kinds of models: (i) independent and identically distributed process; (ii) variable-length Markov chain; (iii) inhomogeneous Markov chain; (iv) hidden Markov model; (v) profile hidden Markov model; (vi) pair hidden Markov model; (vii) generalized hidden Markov model; and (viii) similarity based sequence weighting. The framework includes functionality for training, simulation and decoding of the models. Additionally, it provides two methods to help parameter setting: Akaike and Bayesian information criteria (AIC and BIC). The models can be used stand-alone, combined in Bayesian classifiers, or included in more complex, multi-model, probabilistic architectures using GHMMs. In particular the framework provides a novel, flexible, implementation of decoding in GHMMs that detects when the architecture can be traversed efficiently.

Predicting Interacting Residues Using Long-Distance Information and Novel Decoding in Hidden Markov Models

Identification of interacting residues involved in protein-protein and protein-ligand interaction is critical for the prediction and understanding of the interaction and has practical impact on mutagenesis and drug design. In this work, we introduce a new decoding algorithm, ETB-Viterbi, with an early traceback mechanism, and apply it to interaction profile hidden Markov models (ipHMMs) to enable optimized incorporation of long-distance correlations between interacting residues, leading to improved prediction accuracy. The method was applied and tested to a set of domain-domain interaction families from the 3DID database, and showed statistically significant improvement in accuracy measured by F-score. To gauge and assess the method's effectiveness and robustness in capturing the correlation signals, sets of simulated data based on the 3DID dataset with controllable correlation between interacting residues were also used, as well as reversed sequence orientation. It was demonstrated that the prediction consistently improves as the correlations increase and is not significantly affected by sequence orientation.