Profile Hidden Markov Models Research Articles

TIAMMAt: Leveraging Biodiversity to Revise Protein Domain Models, Evidence from Innate Immunity.

Sequence annotation is fundamental for studying the evolution of protein families, particularly when working with nonmodel species. Given the rapid, ever-increasing number of species receiving high-quality genome sequencing, accurate domain modeling that is representative of species diversity is crucial for understanding protein family sequence evolution and their inferred function(s). Here, we describe a bioinformatic tool called Taxon-Informed Adjustment of Markov Model Attributes (TIAMMAt) which revises domain profile hidden Markov models (HMMs) by incorporating homologous domain sequences from underrepresented and nonmodel species. Using innate immunity pathways as a case study, we show that revising profile HMM parameters to directly account for variation in homologs among underrepresented species provides valuable insight into the evolution of protein families. Following adjustment by TIAMMAt, domain profile HMMs exhibit changes in their per-site amino acid state emission probabilities and insertion/deletion probabilities while maintaining the overall structure of the consensus sequence. Our results show that domain revision can heavily impact evolutionary interpretations for some families (i.e., NLR’s NACHT domain), whereas impact on other domains (e.g., rel homology domain and interferon regulatory factor domains) is minimal due to high levels of sequence conservation across the sampled phylogenetic depth (i.e., Metazoa). Importantly, TIAMMAt revises target domain models to reflect homologous sequence variation using the taxonomic distribution under consideration by the user. TIAMMAt’s flexibility to revise any subset of the Pfam database using a user-defined taxonomic pool will make it a valuable tool for future protein evolution studies, particularly when incorporating (or focusing) on nonmodel species.

Accurate Identification of Antioxidant Proteins Based on a Combination of Machine Learning Techniques and Hidden Markov Model Profiles.

Antioxidant proteins (AOPs) play important roles in the management and prevention of several human diseases due to their ability to neutralize excess free radicals. However, the identification of AOPs by using wet-lab experimental techniques is often time-consuming and expensive. In this study, we proposed an accurate computational model, called AOP-HMM, to predict AOPs by extracting discriminatory evolutionary features from hidden Markov model (HMM) profiles. First, auto cross-covariance (ACC) variables were applied to transform the HMM profiles into fixed-length feature vectors. Then, we performed the analysis of variance (ANOVA) method to reduce the dimensionality of the raw feature space. Finally, a support vector machine (SVM) classifier was adopted to conduct the prediction of AOPs. To comprehensively evaluate the performance of the proposed AOP-HMM model, the 10-fold cross-validation (CV), the jackknife CV, and the independent test were carried out on two widely used benchmark datasets. The experimental results demonstrated that AOP-HMM outperformed most of the existing methods and could be used to quickly annotate AOPs and guide the experimental process.

Efam: an expanded, metaproteome-supported HMM profile database of viral protein families.

MotivationViruses infect, reprogram and kill microbes, leading to profound ecosystem consequences, from elemental cycling in oceans and soils to microbiome-modulated diseases in plants and animals. Although metagenomic datasets are increasingly available, identifying viruses in them is challenging due to poor representation and annotation of viral sequences in databases.ResultsHere, we establish efam, an expanded collection of Hidden Markov Model (HMM) profiles that represent viral protein families conservatively identified from the Global Ocean Virome 2.0 dataset. This resulted in 240 311 HMM profiles, each with at least 2 protein sequences, making efam >7-fold larger than the next largest, pan-ecosystem viral HMM profile database. Adjusting the criteria for viral contig confidence from ‘conservative’ to ‘eXtremely Conservative’ resulted in 37 841 HMM profiles in our efam-XC database. To assess the value of this resource, we integrated efam-XC into VirSorter viral discovery software to discover viruses from less-studied, ecologically distinct oxygen minimum zone (OMZ) marine habitats. This expanded database led to an increase in viruses recovered from every tested OMZ virome by ∼24% on average (up to ∼42%) and especially improved the recovery of often-missed shorter contigs (<5 kb). Additionally, to help elucidate lesser-known viral protein functions, we annotated the profiles using multiple databases from the DRAM pipeline and virion-associated metaproteomic data, which doubled the number of annotations obtainable by standard, single-database annotation approaches. Together, these marine resources (efam and efam-XC) are provided as searchable, compressed HMM databases that will be updated bi-annually to help maximize viral sequence discovery and study from any ecosystem.Availability and implementationThe resources are available on the iVirus platform at (doi.org/10.25739/9vze-4143).Supplementary information Supplementary data are available at Bioinformatics online.

PPalign: optimal alignment of Potts models representing proteins with direct coupling information

BackgroundTo assign structural and functional annotations to the ever increasing amount of sequenced proteins, the main approach relies on sequence-based homology search methods, e.g. BLAST or the current state-of-the-art methods based on profile Hidden Markov Models, which rely on significant alignments of query sequences to annotated proteins or protein families. While powerful, these approaches do not take coevolution between residues into account. Taking advantage of recent advances in the field of contact prediction, we propose here to represent proteins by Potts models, which model direct couplings between positions in addition to positional composition, and to compare proteins by aligning these models. Due to non-local dependencies, the problem of aligning Potts models is hard and remains the main computational bottleneck for their use.MethodsWe introduce here an Integer Linear Programming formulation of the problem and PPalign, a program based on this formulation, to compute the optimal pairwise alignment of Potts models representing proteins in tractable time. The approach is assessed with respect to a non-redundant set of reference pairwise sequence alignments from SISYPHUS benchmark which have lowest sequence identity (between 3% and 20%) and enable to build reliable Potts models for each sequence to be aligned. This experimentation confirms that Potts models can be aligned in reasonable time (1'37'' in average on these alignments). The contribution of couplings is evaluated in comparison with HHalign and independent-site PPalign. Although Potts models were not fully optimized for alignment purposes and simple gap scores were used, PPalign yields a better mean F_1 score and finds significantly better alignments than HHalign and PPalign without couplings in some cases.ConclusionsThese results show that pairwise couplings from protein Potts models can be used to improve the alignment of remotely related protein sequences in tractable time. Our experimentation suggests yet that new research on the inference of Potts models is now needed to make them more comparable and suitable for homology search. We think that PPalign’s guaranteed optimality will be a powerful asset to perform unbiased investigations in this direction.

DigIS: towards detecting distant and putative novel insertion sequence elements in prokaryotic genomes

BackgroundThe insertion sequence elements (IS elements) represent the smallest and the most abundant mobile elements in prokaryotic genomes. It has been shown that they play a significant role in genome organization and evolution. To better understand their function in the host genome, it is desirable to have an effective detection and annotation tool. This need becomes even more crucial when considering rapid-growing genomic and metagenomic data. The existing tools for IS elements detection and annotation are usually based on comparing sequence similarity with a database of known IS families. Thus, they have limited ability to discover distant and putative novel IS elements.ResultsIn this paper, we present digIS, a software tool based on profile hidden Markov models assembled from catalytic domains of transposases. It shows a very good performance in detecting known IS elements when tested on datasets with manually curated annotation. The main contribution of digIS is in its ability to detect distant and putative novel IS elements while maintaining a moderate level of false positives. In this category it outperforms existing tools, especially when tested on large datasets of archaeal and bacterial genomes.ConclusionWe provide digIS, a software tool using a novel approach based on manually curated profile hidden Markov models, which is able to detect distant and putative novel IS elements. Although digIS can find known IS elements as well, we expect it to be used primarily by scientists interested in finding novel IS elements. The tool is available at https://github.com/janka2012/digIS.

Mix and match: Patchwork domain evolution of the land plant-specific Ca2+-permeable mechanosensitive channel MCA.

Multidomain proteins can have a complex evolutionary history that may involve de novo domain evolution, recruitment and / or recombination of existing domains and domain losses. Here, the domain evolution of the plant-specific Ca2+-permeable mechanosensitive channel protein, MID1-COMPLEMENTING ACTIVITY (MCA), was investigated. MCA, a multidomain protein, possesses a Ca2+-influx-MCAfunc domain and a PLAC8 domain. Profile Hidden Markov Models (HMMs) of domains were assessed in 25 viridiplantae proteomes. While PLAC8 was detected in plants, animals, and fungi, MCAfunc was found in streptophytes but not in chlorophytes. Full MCA proteins were only found in embryophytes. We identified the MCAfunc domain in all streptophytes including charophytes where it appeared in E3 ubiquitin ligase-like proteins. Our Maximum Likelihood (ML) analyses suggested that the MCAfunc domain evolved early in the history of streptophytes. The PLAC8 domain showed similarity to Plant Cadmium Resistance (PCR) genes, and the coupling of MCAfunc and PLAC8 seemed to represent a single evolutionary event. This combination is unique in MCA, and does not exist in other plant mechanosensitive channels. Within angiosperms, gene duplications increased the number of MCAs. Considering their role in mechanosensing in roots, MCA might be instrumental for the rise of land plants. This study provides a textbook example of de novo domain emergence, recombination, duplication, and losses, leading to the convergence of function of proteins in plants.

Shared ancestry of core-histone subunits and non-histone plant proteins containing the Histone Fold Motif (HFM).

The three helical Histone Fold Motif (HFM) of core histone proteins provides an evolutionarily favored site for the protein-DNA interface. Despite significant variation in sequence, the HFM retains a distinctive structural fold that has diversified into several non-histone protein families. In this work, we explore the ancestry of non-histone HFM containing families in the plant kingdom. A sequence search algorithm was developed using iterative profile Hidden Markov Models to identify remote homologs of core-histone proteins. The resulting hits were functionally annotated, classified into families, and subjected to comprehensive phylogenetic analyses via Maximum likelihood and Bayesian methods. We have identified 4390 HFM containing proteins in the plant kingdom that are not histones, mostly existing as diverse transcription factor families, distributed widely within and across taxonomic groups. Patterns of homology suggest that core histone subunit H2A has evolved into newer families like NF-YC and DRAP1, whereas the H2B subunit of core histones shares a common ancestry with NF-YB and DR1 class of TFs. Core histone subunits H3 and H4 were found to have evolved into DPE and TAF proteins, respectively. Taken together these results provide insights into diversification events during the evolution of the HFM, including sub-functionalization and neo-functionalization of the HFM.

English

Pseudomonas aeruginosa has developed antibiotic resistance, a major health concern worldwide, through different mechanisms including the formation of biofilms. Thus far, typing has been primarily assay based. However, the many sequences available from the US National Center for Biotechnology Information (NCBI) and the International Consortium of Pseudomonas Database (IPCD) offer alternative ways of characterizing the biofilm formation genes which would reveal novel therapeutic targets for intervention. The current study employed profile hidden Markov models (pHMMs) in the characterization of biofilm formation genes and identification of possible variations based on the different ecological niches occupied by strains of P. aeruginosa. Statistical analyses were performed in R (v. 3.1.3) to determine the significance of these variations. Validated pHMMs identified a total of 197 hits for the 13 different ecological niches, with the human metagenomes recording 144 hits (73%) while the non-human metagenomes recorded 53 hits (27%). Human metagenomes had a significantly higher density of hits, with the abscess metagenomes indicating the highest density of hits. The overall result indicated a significant variation in density of hits between the different sites within the human metagenomes. This study successfully highlighted the significant value of already sequenced metagenomes in the identification of potential targets for novel therapeutic compounds. The profile hidden Markov model successfully identified 197 unique biofilm gene clusters emphasizing its importance in analyzing different sequenced pathogenic strains. The study recommends that experimental assays could be carried out to shed further light on the different biofilm formation gene clusters. &nbsp; Key words: Profile hidden Markov model, metagenomics, biofilm formation, antibiotic resistance.

V-pipe: a computational pipeline for assessing viral genetic diversity from high-throughput data.

MotivationHigh-throughput sequencing technologies are used increasingly not only in viral genomics research but also in clinical surveillance and diagnostics. These technologies facilitate the assessment of the genetic diversity in intra-host virus populations, which affects transmission, virulence and pathogenesis of viral infections. However, there are two major challenges in analysing viral diversity. First, amplification and sequencing errors confound the identification of true biological variants, and second, the large data volumes represent computational limitations.ResultsTo support viral high-throughput sequencing studies, we developed V-pipe, a bioinformatics pipeline combining various state-of-the-art statistical models and computational tools for automated end-to-end analyses of raw sequencing reads. V-pipe supports quality control, read mapping and alignment, low-frequency mutation calling, and inference of viral haplotypes. For generating high-quality read alignments, we developed a novel method, called ngshmmalign, based on profile hidden Markov models and tailored to small and highly diverse viral genomes. V-pipe also includes benchmarking functionality providing a standardized environment for comparative evaluations of different pipeline configurations. We demonstrate this capability by assessing the impact of three different read aligners (Bowtie 2, BWA MEM, ngshmmalign) and two different variant callers (LoFreq, ShoRAH) on the performance of calling single-nucleotide variants in intra-host virus populations. V-pipe supports various pipeline configurations and is implemented in a modular fashion to facilitate adaptations to the continuously changing technology landscape.Availabilityand implementationV-pipe is freely available at https://github.com/cbg-ethz/V-pipe.Supplementary information Supplementary data are available at Bioinformatics online.

Profile Hidden Markov Models Are Not Identifiable.

Profile Hidden Markov Models (HMMs) are graphical models that can be used to produce finite length sequences from a distribution. In fact, although they were only introduced for bioinformatics 25 years ago (by Haussler et al., Hawaii International Conference on Systems Science, 1993), they are arguably the most commonly used statistical model in bioinformatics, with multiple applications, including protein structure and function prediction, classifications of novel proteins into existing protein families and superfamilies, metagenomics, and multiple sequence alignment. The standard use of profile HMMs in bioinformatics has two steps: first a profile HMM is built for a collection of molecular sequences (which may not be in a multiple sequence alignment), and then the profile HMM is used in some subsequent analysis of new molecular sequences. The construction of the profile thus is itself a statistical estimation problem, since any given set of sequences might potentially fit more than one model well. Hence, a basic question about profile HMMs is whether they are statistically identifiable, which means that no two profile HMMs can produce the same distribution on finite length sequences. Indeed, statistical identifiability is a fundamental aspect of any statistical model, and yet it is not known whether profile HMMs are statistically identifiable. In this paper, we report on preliminary results towards characterizing the statistical identifiability of profile HMMs in one of the standard forms used in bioinformatics.

A New Visual Cryptography Method Based on the Profile Hidden Markov Model

Digital image capturing technologies and the internet are widely used today. These technologies make it very easy and fast to capture and share personal images in daily life. This caus ...

DeepNOG: fast and accurate protein orthologous group assignment.

MotivationProtein orthologous group databases are powerful tools for evolutionary analysis, functional annotation or metabolic pathway modeling across lineages. Sequences are typically assigned to orthologous groups with alignment-based methods, such as profile hidden Markov models, which have become a computational bottleneck.ResultsWe present DeepNOG, an extremely fast and accurate, alignment-free orthology assignment method based on deep convolutional networks. We compare DeepNOG against state-of-the-art alignment-based (HMMER, DIAMOND) and alignment-free methods (DeepFam) on two orthology databases (COG, eggNOG 5). DeepNOG can be scaled to large orthology databases like eggNOG, for which it outperforms DeepFam in terms of precision and recall by large margins. While alignment-based methods still provide the most accurate assignments among the investigated methods, computing time of DeepNOG is an order of magnitude lower on CPUs. Optional GPU usage further increases throughput massively. A command-line tool enables rapid adoption by users.Availabilityand implementationSource code and packages are freely available at https://github.com/univieCUBE/deepnog. Install the platform-independent Python program with $pip install deepnog.Supplementary information Supplementary data are available at Bioinformatics online.

Conserved Peptides Recognition by Ensemble of Neural Networks for Mining Protein Data – LPMO Case Study

Mining protein data is a recent promising area of modern bioinformatics. In this work, we suggested a novel approach for mining protein data – conserved peptides recognition by ensemble of neural networks (CPRENN). This approach was applied for mining lytic polysaccharide monooxygenases (LPMOs) in 19 ascomycete, 18 basidiomycete, and 18 bacterial proteomes. LPMOs are recently discovered enzymes and their mining is of high relevance for biotechnology of lignocellulosic materials. CPRENN was compared with two conventional bioinformatic methods for mining protein data – profile hidden Markov models (HMMs) search (HMMER program) and peptide pattern recognition (PPR program combined with Hotpep application). The maximum number of hypothetical LPMO amino acid sequences was discovered by HMMER. Profile HMMs search proved to be more sensitive method for mining LPMOs than conserved peptides recognition. Totally, CPRENN found 76 %, 67 %, and 65 % of hypothetical ascomycete, basidiomycete, and bacterial LPMOs discovered by HMMER, respectively. For AA9, AA10, and AA11 families which contain the major part of all LPMOs in the carbohydrate-active enzymes database (CAZy), CPRENN and PPR + Hotpep found 69–98 % and 62–95 % of amino acid sequences discovered by HMMER, respectively. In contrast with PPR + Hotpep, CPRENN possessed perfect precision and provided more complete mining of basidiomycete and bacterial LPMOs.

Genome-wide identification of wheat (Triticum aestivum L.) expansin genes and functional characterization of TaEXPB1A

Expansins are plant cell wall-loosening proteins that involve in response to environmental stimuli and various development processes. In this study, 1858 expansin members were identified in 40 plant species by using Hidden Markov Model profiles and searching against to rice and Arabidopsis expansins. They were classified into four groups: EXPA, EXPB, EXLA, and EXLB. The distribution of expansin genes in different plant species indicated that EXPA genes emerged first, and genes in EXPB, EXLA, and EXLB subgroups might be resulted from gene duplication. In wheat, there are 104 expansins, including 88 TaEXPAs, 8 TaEXPBs, and 8 TaEXLAs. Phylogenetic analysis showed that wheat, rice, and Arabidopsis expansins shared high bootstrap values, and the phylogenetic relationship of wheat expansins were supported by conserved motifs and gene structures. Evolutionary analysis of Triticum urartu, Triticum turgidum, and wheat expansin genes revealed that gene duplication, gene loss, and chromosomal rearrangement events occurred during the evolution process of wheat expansins. Three kinds of cis-elements, including development-related, hormones-related, and abiotic stress-related were identified in the promoters of wheat expansin genes. Meanwhile, the expression of wheat expansin genes is extensive and affected by abiotic stresses. Additionally, by overexpressing in Arabidopsis, the functions of TaEXPB1A in root and seed development were characterized. In combination with previous reports, these results indicated the extensive functions of wheat expansins. Shortly, this study identified wheat expansins more accurately, and extended the knowledge of expansin genes.

Single-stranded and double-stranded DNA-binding protein prediction using HMM profiles

BackgroundDNA-binding proteins perform important roles in cellular processes and are involved in many biological activities. These proteins include crucial protein-DNA binding domains and can interact with single-stranded or double-stranded DNA, and accordingly classified as single-stranded DNA-binding proteins (SSBs) or double-stranded DNA-binding proteins (DSBs). Computational prediction of SSBs and DSBs helps in annotating protein functions and understanding of protein-binding domains. ResultsPerformance is reported using the DNA-binding protein dataset that was recently introduced by Wang et al., [1]. The proposed method achieved a sensitivity of 0.600, specificity of 0.792, AUC of 0.758, MCC of 0.369, accuracy of 0.744, and F-measure of 0.536, on the independent test set. ConclusionThe proposed method with the hidden Markov model (HMM) profiles for feature extraction, outperformed the benchmark method in the literature and achieved an overall improvement of approximately 3%. The source code and supplementary information of the proposed method is available at https://github.com/roneshsharma/Predict-DNA-binding-proteins/wiki.

RVDB-prot, a reference viral protein database and its HMM profiles.

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.

De novo biosynthesis of simple aromatic compounds by an arthropod (Archegozetes longisetosus).

The ability to synthesize simple aromatic compounds is well known from bacteria, fungi and plants, which all share an exclusive biosynthetic route—the shikimic acid pathway. Some of these organisms further evolved the polyketide pathway to form core benzenoids via a head-to-tail condensation of polyketide precursors. Arthropods supposedly lack the ability to synthesize aromatics and instead rely on aromatic amino acids acquired from food, or from symbiotic microorganisms. The few studies purportedly showing de novo biosynthesis via the polyketide synthase (PKS) pathway failed to exclude endosymbiotic bacteria, so their results are inconclusive. We investigated the biosynthesis of aromatic compounds in defence secretions of the oribatid mite Archegozetes longisetosus. Exposing the mites to a diet containing high concentrations of antibiotics removed potential microbial partners but did not affect the production of defensive benzenoids. To gain insights into benzenoid biosynthesis, we fed mites with stable-isotope labelled precursors and monitored incorporation with mass spectrometry. Glucose, malonic acid and acetate, but not phenylalanine, were incorporated into the benzenoids, further evidencing autogenous biosynthesis. Whole-transcriptome sequencing with hidden Markov model profile search of protein domain families and subsequent phylogenetic analysis revealed a putative PKS domain similar to an actinobacterial PKS, possibly indicating a horizontal gene transfer.

Domain-centric database to uncover structure of minimally characterized viral genomes

Protein domain-based approaches to analyzing sequence data are valuable tools for examining and exploring genomic architecture across genomes of different organisms. Here, we present a complete dataset of domains from the publicly available sequence data of 9,051 reference viral genomes. The data provided contain information such as sequence position and neighboring domains from 30,947 pHMM-identified domains from each reference viral genome. Domains were identified from viral whole-genome sequence using automated profile Hidden Markov Models (pHMM). This study also describes the framework for constructing “domain neighborhoods”, as well as the dataset representing it. These data can be used to examine shared and differing domain architectures across viral genomes, to elucidate potential functional properties of genes, and potentially to classify viruses.

PL-search: a profile-link-based search method for protein remote homology detection

Protein remote homology detection is a fundamental and important task for protein structure and function analysis. Several search methods have been proposed to improve the detection performance of the remote homologues and the accuracy of ranking lists. The position-specific scoring matrix (PSSM) profile and hidden Markov model (HMM) profile can contribute to improving the performance of the state-of-the-art search methods. In this paper, we improved the profile-link (PL) information for constructing PSSM or HMM profiles, and proposed a PL-based search method (PL-search). In PL-search, more robust PLs are constructed through the double-link and iterative extending strategies, and an accurate similarity score of sequence pairs is calculated from the two-level Jaccard distance for remote homologues. We tested our method on two widely used benchmark datasets. Our results show that whether HHblits, JackHMMER or position-specific iterated-BLAST is used, PL-search obviously improves the search performance in terms of ranking quality as well as the number of detected remote homologues. For ease of use of PL-search, both its stand-alone tool and the web server are constructed, which can be accessed at http://bliulab.net/PL-search/.

HMMPred: Accurate Prediction of DNA-Binding Proteins Based on HMM Profiles and XGBoost Feature Selection.

Prediction of DNA-binding proteins (DBPs) has become a popular research topic in protein science due to its crucial role in all aspects of biological activities. Even though considerable efforts have been devoted to developing powerful computational methods to solve this problem, it is still a challenging task in the field of bioinformatics. A hidden Markov model (HMM) profile has been proved to provide important clues for improving the prediction performance of DBPs. In this paper, we propose a method, called HMMPred, which extracts the features of amino acid composition and auto- and cross-covariance transformation from the HMM profiles, to help train a machine learning model for identification of DBPs. Then, a feature selection technique is performed based on the extreme gradient boosting (XGBoost) algorithm. Finally, the selected optimal features are fed into a support vector machine (SVM) classifier to predict DBPs. The experimental results tested on two benchmark datasets show that the proposed method is superior to most of the existing methods and could serve as an alternative tool to identify DBPs.