Profile Hidden Markov Models Research Articles

CW-PRED: Prediction of C-terminal surface anchoring sorting signals in bacteria and Archaea.

Sorting signals are crucial for the anchoring of proteins to the cell surface in archaea and bacteria. These proteins often feature distinct motifs at their C-terminus, cleaved by sortase or sortase-like enzymes. Gram-positive bacteria exhibit the LPXTGX consensus motif, cleaved by sortases, while Gram-negative bacteria employ exosortases recognizing motifs like PEP. Archaea utilize exosortase homologs known as archaeosortases for signal anchoring. Traditionally identification of such C-terminal sorting signals was performed with profile Hidden Markov Models (pHMMs). The Cell-Wall PREDiction (CW-PRED) method introduced for the first time a custom-made class HMM for proteins in Gram-positive bacteria that contain a cell wall sorting signal which begins with an LPXTG motif, followed by a hydrophobic domain and a tail of positively charged residues. Here we present a new and updated version of CW-PRED for predicting C-terminal sorting signals in Archaea, Gram-positive, and Gram-negative bacteria. We used a large training set and several model enhancements that improve motif identification in order to achieve better discrimination between C-terminal signals and other proteins. Cross-validation demonstrates CW-PRED's superiority in sensitivity and specificity compared to other methods. Application of the method in reference proteomes reveals a large number of potential surface proteins not previously identified. The method is available for academic use at http://195.251.108.230/apps.compgen.org/CW-PRED/ and as standalone software.

Sequence and structure analyses of lytic polysaccharide monooxygenases mined from metagenomic DNA of humus samples around white-rot fungi in Cuc Phuong tropical forest, Vietnam

Background White-rot fungi and bacteria communities are unique ecosystems with different types of symbiotic interactions occurring during wood decomposition, such as cooperation, mutualism, nutritional competition, and antagonism. The role of chitin-active lytic polysaccharide monooxygenases (LPMOs) in these symbiotic interactions is the subject of this study. Method In this study, bioinformatics tools were used to analyze the sequence and structure of putative LPMOs mined by hidden Markov model (HMM) profiles from the bacterial metagenomic DNA database of collected humus samples around white-rot fungi in Cuc Phuong primary forest, Vietnam. Two genes encoding putative LPMOs were expressed in E. coli and purified for enzyme activity assay. Result Thirty-one full-length proteins annotated as putative LPMOs according to HMM profiles were confirmed by amino acid sequence comparison. The comparison results showed that although the amino acid sequences of the proteins were very different, they shared nine conserved amino acids, including two histidine and one phenylalanine that characterize the H1-Hx-Yz motif of the active site of bacterial LPMOs. Structural analysis of these proteins revealed that they are multidomain proteins with different functions. Prediction of the catalytic domain 3-D structure of these putative LPMOs using Alphafold2 showed that their spatial structures were very similar in shape, although their protein sequences were very different. The results of testing the activity of proteins GL0247266 and GL0183513 show that they are chitin-active LPMOs. Prediction of the 3-D structures of these two LPMOs using Alphafold2 showed that GL0247266 had five functional domains, while GL0183513 had four functional domains, two of which that were similar to the GbpA_2 and GbpA_3 domains of protein GbpA of Vibrio cholerae bacteria. The GbpA_2 - GbpA_3 complex was also detected in 11 other proteins. Based on the structural characteristics of functional domains, it is possible to hypothesize the role of chitin-active GbpA-like LPMOs in the relationship between fungal and bacterial communities coexisting on decomposing trees in primary forests.

Enhancing Botnet Detection in Network Security Using Profile Hidden Markov Models

A botnet is a network of compromised computer systems, or bots, remotely controlled by an attacker through bot controllers. This covert network poses a threat through large-scale cyber attacks, including phishing, distributed denial of service (DDoS), data theft, and server crashes. Botnets often camouflage their activity by utilizing common internet protocols, such as HTTP and IRC, making their detection challenging. This paper addresses this threat by proposing a method to identify botnets based on distinctive communication patterns between command and control servers and bots. Recognizable traits in botnet behavior, such as coordinated attacks, heartbeat signals, and periodic command distribution, are analyzed. Probabilistic models, specifically Hidden Markov Models (HMMs) and Profile Hidden Markov Models (PHMMs), are employed to learn and identify these activity patterns in network traffic data. This work utilizes publicly available datasets containing a combination of botnet, normal, and background traffic to train and test these models. The comparative analysis reveals that both HMMs and PHMMs are effective in detecting botnets, with PHMMs exhibiting superior accuracy in botnet detection compared to HMMs.

NeoRdRp2 with improved seed data, annotations, and scoring

RNA-dependent RNA polymerase (RdRp) is a marker gene for RNA viruses; thus, it is widely used to identify RNA viruses from metatranscriptome data. However, because of the high diversity of RdRp domains, it remains difficult to identify RNA viruses using RdRp sequences. To overcome this problem, we created a NeoRdRp database containing 1,182 hidden Markov model (HMM) profiles utilizing 12,502 RdRp domain sequences. Since the development of this database, more RNA viruses have been discovered, mainly through metatranscriptome sequencing analyses. To identify RNA viruses comprehensively and specifically, we updated the NeoRdRp by incorporating recently reported RNA viruses. To this end, 557,197 RdRp-containing sequences were used as seed RdRp datasets. These sequences were processed through deduplication, clustering, alignment, and splitting, thereby generating 19,394 HMM profiles. We validated the updated NeoRdRp database, using the UniProtKB dataset and found that the recall and specificity rates were improved to 99.4% and 81.6%, from 97.2% and 76.8% in the previous version, respectively. Comparisons of eight different RdRp search tools showed that NeoRdRp2 exhibited balanced RdRp and nonspecific detection power. Expansion of the annotated RdRp datasets is expected to further accelerate the discovery of novel RNA viruses from various transcriptome datasets. The HMM profiles of NeoRdRp2 and their annotations are available at https://github.com/shoichisakaguchi/NeoRdRp.

Annotating microbial functions with ProkFunFind.

Analyzing microbial genomes has become an essential part of microbiology research, giving valuable insights into the functions and evolution of microbial species. Identifying genes of interest and assigning putative annotations to those genes is a central task in genome analysis, and a plethora of tools and approaches have been developed for this task. The ProkFunFind tool was developed to bridge the gap between these various annotation approaches, providing a flexible and customizable search approach to annotate microbial functions. ProkFunFind is designed around hierarchical definitions of biological functions, where individual genes can be identified using heterogeneous search terms consisting of sequences, profile hidden Markov models, protein domains, and orthology groups. This flexible and customizable search approach allows for searches to be tailored to specific biological functions, and the search results are output in multiple formats to facilitate downstream analyses. The utility of the ProkFunFind search tool was demonstrated through its application in searching for bacterial flagella, which are complex organelles composed of multiple genes. Overall, ProkFunFind provides an accessible and flexible way to integrate multiple types of annotation and sequence data while annotating biological functions in microbial genomes.IMPORTANCEGenome sequencing and analysis are increasingly important parts of microbiology, providing a way to predict metabolic functions, identify virulence factors, and understand the evolution of microbes. The expanded use of genome sequencing has also brought an abundance of search and annotation methods, but integrating the information from these different methods can be challenging and is often done through ad hoc approaches. To bridge the gap between different types of annotations, we developed ProkFunFind, a flexible and customizable search tool incorporating multiple search approaches and annotation types to annotate microbial functions. We demonstrated the utility of ProkFunFind by searching for gene clusters encoding flagellar genes using a combination of different annotation types and searches. Overall, ProkFunFind provides a reproducible and flexible way to identify gene clusters of interest, facilitating the meaningful analysis of new and existing microbial genomes.

ApHMM: Accelerating Profile Hidden Markov Models for Fast and Energy-efficient Genome Analysis

Profile hidden Markov models (pHMMs) are widely employed in various bioinformatics applications to identify similarities between biological sequences, such as DNA or protein sequences. In pHMMs, sequences are represented as graph structures, where states and edges capture modifications (i.e., insertions, deletions, and substitutions) by assigning probabilities to them. These probabilities are subsequently used to compute the similarity score between a sequence and a pHMM graph. The Baum-Welch algorithm, a prevalent and highly accurate method, utilizes these probabilities to optimize and compute similarity scores. Accurate computation of these probabilities is essential for the correct identification of sequence similarities. However, the Baum-Welch algorithm is computationally intensive, and existing solutions offer either software-only or hardware-only approaches with fixed pHMM designs. When we analyze state-of-the-art works, we identify an urgent need for a flexible, high-performance, and energy-efficient hardware-software co-design to address the major inefficiencies in the Baum-Welch algorithm for pHMMs. We introduce ApHMM , the first flexible acceleration framework designed to significantly reduce both computational and energy overheads associated with the Baum-Welch algorithm for pHMMs. ApHMM employs hardware-software co-design to tackle the major inefficiencies in the Baum-Welch algorithm by (1) designing flexible hardware to accommodate various pHMM designs, (2) exploiting predictable data dependency patterns through on-chip memory with memoization techniques, (3) rapidly filtering out unnecessary computations using a hardware-based filter, and (4) minimizing redundant computations. ApHMM achieves substantial speedups of 15.55×–260.03×, 1.83×–5.34×, and 27.97× when compared to CPU, GPU, and FPGA implementations of the Baum-Welch algorithm, respectively. ApHMM outperforms state-of-the-art CPU implementations in three key bioinformatics applications: (1) error correction, (2) protein family search, and (3) multiple sequence alignment, by 1.29×–59.94×, 1.03×–1.75×, and 1.03×–1.95×, respectively, while improving their energy efficiency by 64.24×–115.46×, 1.75×, and 1.96×.

Sensitive and error-tolerant annotation of protein-coding DNA with BATH.

We present BATH, a tool for highly sensitive annotation of protein-coding DNA based on direct alignment of that DNA to a database of protein sequences or profile hidden Markov models (pHMMs). BATH is built on top of the HMMER3 code base, and simplifies the annotation workflow for pHMM-based translated sequence annotation by providing a straightforward input interface and easy-to-interpret output. BATH also introduces novel frameshift-aware algorithms to detect frameshift-inducing nucleotide insertions and deletions (indels). BATH matches the accuracy of HMMER3 for annotation of sequences containing no errors, and produces superior accuracy to all tested tools for annotation of sequences containing nucleotide indels. These results suggest that BATH should be used when high annotation sensitivity is required, particularly when frameshift errors are expected to interrupt protein-coding regions, as is true with long-read sequencing data and in the context of pseudogenes. The software is available at https://github.com/TravisWheelerLab/BATH.

Evolution of protein domain repertoires of CALHM6.

Calcium (Ca2+) homeostasis is essential in conducting various cellular processes including nerve transmission, muscular movement, and immune response. Changes in Ca2+concentration in the cytoplasm are significant in bringing about various immune responses such as pathogen clearance and apoptosis. Various key players are involved in calcium homeostasis such as calcium binders, pumps, and channels. Sequence-based evolutionary information has recently been exploited to predict the biophysical behaviors of proteins, giving critical clues about their functionality. Ion channels are reportedly the first channels developed during evolution. Calcium homeostasis modulator protein 6 (CALHM6) is one such channel. Comprised of a single domain called Ca_hom_mod, CALHM6 is a stable protein interacting with various other proteins in calcium regulation. No previous attempt has been made to trace the exact evolutionary events in the domain of CALHM6, leaving plenty of room for exploring its evolution across a wide range of organisms. The current study aims to answer the questions by employing a computational-based strategy that used profile Hidden Markov Models (HMMs) to scan for the CALHM6 domain, integrated the data with a time-calibrated phylogenetic tree using BEAST and Mesquite, and visualized through iTOL. Around 4,000 domains were identified, and 14,000 domain gain, loss, and duplication events were observed at the end which also included various protein domains other than CALHM6. The data were analyzed concerning CALHM6 evolution as well as the domain gain, loss, and duplication of its interacting partners: Calpain, Vinculin, protein S100-A7, Thioredoxin, Peroxiredoxin, and Calmodulin-like protein 5. Duplication events of CALHM6 near higher eukaryotes showed its increasing complexity in structure and function. This in-silico phylogenetic approach applied to trace the evolution of CALHM6 was an effective approach to get a better understanding of the protein CALHM6.

In silico analysis of Ffp1, an ancestral Porphyromonas spp. fimbrillin, shows differences with Fim and Mfa.

Background. Scant information is available regarding fimbrillins within the genus Porphyromonas, with the notable exception of those belonging to Porphyromonas gingivalis, which have been extensively researched for several years. Besides fim and mfa, a third P. gingivalis adhesin called filament-forming protein 1 (Ffp1) has recently been described and seems to be pivotal for outer membrane vesicle (OMV) production. Objective. We aimed to investigate the distribution and diversity of type V fimbrillin, particularly Ffp1, in the genus Porphyromonas. Methods. A bioinformatics phylogenomic analysis was conducted using all accessible Porphyromonas genomes to generate a domain search for fimbriae, using hidden Markov model profiles. Results. Ffp1 was identified as the sole fimbrillin present in all analysed genomes. After manual verification (i.e. biocuration) of both structural and functional annotations and 3D modelling, this protein was determined to be a type V fimbrillin, with a closer structural resemblance to a Bacteroides ovatus fimbrillin than to FimA or Mfa1 from P. gingivalis. Conclusion. It appears that Ffp1 is an ancestral fimbria, transmitted through vertical inheritance and present across all Porphyromonas species. Additional investigations are necessary to elucidate the biogenesis of Ffp1 fimbriae and their potential role in OMV production and niche adaptation.

Phylogenetic and Structure-Function Analyses of ENA ATPases: A Case Study of the ENA1 Protein from the Fungus Neurospora crassa.

ENA transporters are a group of P-type ATPases that are characterized by actively moving Na+ or K+ out of the cell against their concentration gradient. The existence of these transporters was initially attributed to some fungi, although more recently they have also been identified in mosses, liverworts, and some protozoa. Given the current increase in the number of organisms whose genomes are completely sequenced, we set out to expand our knowledge about the existence of ENA in organisms belonging to other phylogenetic groups. For that, a hidden Markov model profile was constructed to identify homologous sequences to ENA proteins in protein databases. This analysis allowed us to identify the existence of ENA-type ATPases in the most primitive groups of fungi, as well as in other eukaryotic organisms not described so far. In addition, this study has allowed the identification of a possible new group of P-ATPases, initially proposed as ENA but which maintain phylogenetic distances with these proteins. Finally, this work has also addressed this study of the structure of ENA proteins, which remained unknown due to the lack of crystallographic data. For this purpose, a 3D structure prediction of the NcENA1 protein of the fungus Neurospora crassa was performed using AlphaFold2 software v2.3.1. From this structure, the electrostatic potential of the protein was analyzed. With all these data, the protein regions and the amino acids involved in the transport of Na+ or K+ ions across the membrane were proposed for the first time. Targeted mutagenesis of some of these residues has confirmed their relevant participation in the transport function of ENA proteins.

EasyCGTree: a pipeline for prokaryotic phylogenomic analysis based on core gene sets

BackgroundGenome-scale phylogenetic analysis based on core gene sets is routinely used in microbiological research. However, the techniques are still not approachable for individuals with little bioinformatics experience. Here, we present EasyCGTree, a user-friendly and cross-platform pipeline to reconstruct genome-scale maximum-likehood (ML) phylogenetic tree using supermatrix (SM) and supertree (ST) approaches.ResultsEasyCGTree was implemented in Perl programming languages and was built using a collection of published reputable programs. All the programs were precompiled as standalone executable files and contained in the EasyCGTree package. It can run after installing Perl language environment. Several profile hidden Markov models (HMMs) of core gene sets were prepared in advance to construct a profile HMM database (PHD) that was enclosed in the package and available for homolog searching. Customized gene sets can also be used to build profile HMM and added to the PHD via EasyCGTree. Taking 43 genomes of the genus Paracoccus as the testing data set, consensus (a variant of the typical SM), SM, and ST trees were inferred via EasyCGTree successfully, and the SM trees were compared with those inferred via the pipelines UBCG and bcgTree, using the metrics of cophenetic correlation coefficients (CCC) and Robinson–Foulds distance (topological distance). The results suggested that EasyCGTree can infer SM trees with nearly identical topology (distance < 0.1) and accuracy (CCC > 0.99) to those of trees inferred with the two pipelines.ConclusionsEasyCGTree is an all-in-one automatic pipeline from input data to phylogenomic tree with guaranteed accuracy, and is much easier to install and use than the reference pipelines. In addition, ST is implemented in EasyCGTree conveniently and can be used to explore prokaryotic evolutionary signals from a different perspective. The EasyCGTree version 4 is freely available for Linux and Windows users at Github (https://github.com/zdf1987/EasyCGTree4).

VIRify: An integrated detection, annotation and taxonomic classification pipeline using virus-specific protein profile hidden Markov models.

The study of viral communities has revealed the enormous diversity and impact these biological entities have on various ecosystems. These observations have sparked widespread interest in developing computational strategies that support the comprehensive characterisation of viral communities based on sequencing data. Here we introduce VIRify, a new computational pipeline designed to provide a user-friendly and accurate functional and taxonomic characterisation of viral communities. VIRify identifies viral contigs and prophages from metagenomic assemblies and annotates them using a collection of viral profile hidden Markov models (HMMs). These include our manually-curated profile HMMs, which serve as specific taxonomic markers for a wide range of prokaryotic and eukaryotic viral taxa and are thus used to reliably classify viral contigs. We tested VIRify on assemblies from two microbial mock communities, a large metagenomics study, and a collection of publicly available viral genomic sequences from the human gut. The results showed that VIRify could identify sequences from both prokaryotic and eukaryotic viruses, and provided taxonomic classifications from the genus to the family rank with an average accuracy of 86.6%. In addition, VIRify allowed the detection and taxonomic classification of a range of prokaryotic and eukaryotic viruses present in 243 marine metagenomic assemblies. Finally, the use of VIRify led to a large expansion in the number of taxonomically classified human gut viral sequences and the improvement of outdated and shallow taxonomic classifications. Overall, we demonstrate that VIRify is a novel and powerful resource that offers an enhanced capability to detect a broad range of viral contigs and taxonomically classify them.

DIPS-Plus: The enhanced database of interacting protein structures for interface prediction

In this work, we expand on a dataset recently introduced for protein interface prediction (PIP), the Database of Interacting Protein Structures (DIPS), to present DIPS-Plus, an enhanced, feature-rich dataset of 42,112 complexes for machine learning of protein interfaces. While the original DIPS dataset contains only the Cartesian coordinates for atoms contained in the protein complex along with their types, DIPS-Plus contains multiple residue-level features including surface proximities, half-sphere amino acid compositions, and new profile hidden Markov model (HMM)-based sequence features for each amino acid, providing researchers a curated feature bank for training protein interface prediction methods. We demonstrate through rigorous benchmarks that training an existing state-of-the-art (SOTA) model for PIP on DIPS-Plus yields new SOTA results, surpassing the performance of some of the latest models trained on residue-level and atom-level encodings of protein complexes to date.

Efficient Fingerprinting Attack on Web Applications: An Adaptive Symbolization Approach

Website fingerprinting is valuable for many security solutions as it provides insights into applications that are active on the network. Unfortunately, the existing techniques primarily focus on fingerprinting individual webpages instead of webpage transitions. However, it is a common scenario for users to follow hyperlinks to carry out their actions. In this paper, an adaptive symbolization method based on packet distribution information is proposed to represent network traffic. The Profile Hidden Markov Model (PHMM exploits positional information contained in network traffic sequences and is sensitive to webpage transitional information) is used to construct users’ action patterns. We also construct user role models to represent different kinds of users and apply them to our web application identification framework to uncover more information. The experimental results demonstrate that compared to the equal interval and K-means symbolization algorithms, the adaptive symbolization method retains the maximum amount of information and is less time-consuming. The PHMM-based user action identification method has higher accuracy than the existing traditional classifiers do.

Genome-wide analyses of LATERAL ORGAN BOUNDARIES in cassava reveal the role of LBD47 in defence against bacterial blight.

The Arabidopsis thaliana ASYMMETRIC LEAVES2 (AS2) gene is responsible for the development of flat, symmetric, and extended leaf laminae and their veins. The AS2 gene belongs to the plant-specific AS2-LIKE/LATERAL ORGAN BOUNDARIES (LOB)-domain (ASL/LBD), which consists of 42 proteins in Arabidopsis with a conserved amino-terminal domain known as the AS2/LOB domain, and a variable carboxyl-terminal region. AS2/LOB domain consists of an amino-terminal (N-terminal) that contains a cysteine repeat (the C-motif), a conserved glycine residue, and a leucine-zipper-like. AS2/LOB domain has been characterised in plants such as A. thaliana, Zea mays, and Oryza sativum. Nevertheless, it remains uncharacterised in cassava (Manihot esculenta). Characterisation and identification of cassava ASL/LBD genes using the computational algorithms, hidden Markov model profiles (PF03195), determined 55 ASL/LBD genes (MeASLBD1 to MeASLBD55). The gene structure and motif composition were conserved in MeASLBDs, while the expression profiles of these genes were highly diverse, implying that they are associated with diverse functions. Weighted gene co-expression network analysis (WGCNA) of target genes and promoter analysis suggest that these MeASLBDs may be involved in hormone and stress responses. Furthermore, the analysis of cis-regulatory elements in promoter regions suggested that MeASLBDs may be involved in the plant phytohormone signal response. The transcriptome data of cassava under biotic and abiotic stresses revealed that MeASLBD46 and MeASLBD47 greatly respond to disease and drought. The MeASLBD47 gene was selected for functional analysis. The result indicated that MeASLBD47 significantly mitigated the virulence of cassava bacterial blight (XamCHN11) through Real-Time Quantitative Reverse Transcription PCR (qRT-PCR) and Virus-induced gene silencing (VIGS). These findings provided a comprehensive analysis of ASL/LBD genes and laid the groundwork for future research to understand ASL/LBD genes.

Rational Design of Profile HMMs for Sensitive and Specific Sequence Detection with Case Studies Applied to Viruses, Bacteriophages, and Casposons.

Profile hidden Markov models (HMMs) are a powerful way of modeling biological sequence diversity and constitute a very sensitive approach to detecting divergent sequences. Here, we report the development of protocols for the rational design of profile HMMs. These methods were implemented on TABAJARA, a program that can be used to either detect all biological sequences of a group or discriminate specific groups of sequences. By calculating position-specific information scores along a multiple sequence alignment, TABAJARA automatically identifies the most informative sequence motifs and uses them to construct profile HMMs. As a proof-of-principle, we applied TABAJARA to generate profile HMMs for the detection and classification of two viral groups presenting different evolutionary rates: bacteriophages of the Microviridae family and viruses of the Flavivirus genus. We obtained conserved models for the generic detection of any Microviridae or Flavivirus sequence, and profile HMMs that can specifically discriminate Microviridae subfamilies or Flavivirus species. In another application, we constructed Cas1 endonuclease-derived profile HMMs that can discriminate CRISPRs and casposons, two evolutionarily related transposable elements. We believe that the protocols described here, and implemented on TABAJARA, constitute a generic toolbox for generating profile HMMs for the highly sensitive and specific detection of sequence classes.

Genome-wide identification and expression analysis of the HD2 protein family and its response to drought and salt stress in Gossypium species.

Histone deacetylase 2 (HD2) proteins play an important role in the regulation of gene expression. This helps with the growth and development of plants and also plays a crucial role in responses to biotic and abiotic stress es. HD2s comprise a C2H2-type Zn2+ finger at their C-terminal and an HD2 label, deacetylation and phosphorylation sites, and NLS motifs at their N-terminal. In this study, a total of 27 HD2 members were identified, using Hidden Markov model profiles, in two diploid cotton genomes (Gossypium raimondii and Gossypium arboretum) and two tetraploid cotton genomes (Gossypium hirsutum and Gossypium barbadense). These cotton HD2 members were classified into 10 major phylogenetic groups (I-X), of which group III was found to be the largest with 13 cotton HD2 members. An evolutionary investigation showed that the expansion of HD2 members primarily occurred as a result of segmental duplication in paralogous gene pairs. Further qRT-PCR validation of nine putative genes using RNA-Seq data suggested that GhHDT3D.2 exhibits significantly higher levels of expression at 12h, 24h, 48h, and 72h of exposure to both drought and salt stress conditions compared to a control measure at 0h. Furthermore, gene ontology, pathways, and co-expression network study of GhHDT3D.2 gene affirmed their significance in drought and salt stress responses.

Enolase in Meyerozyma guilliermondii strain SO: Sequential and structural insights of MgEno4581 as a putative virulence factor and host-fungal interactions through comprehensive in silico approaches

Meyerozyma guilliermondii is a rare opportunistic fungal pathogen that causes deadly invasive candidiasis in human. M. guilliermondii strain SO is a local yeast isolate that possesses huge industrial interests but also pathogenic towards zebrafish embryos. Enolases that bind to human extracellular matrix (ECM) proteins are among the fungal virulence factors. To understand its pathogenicity mechanism down to molecular level, especially in the rare M. guilliermondii, this study aimed to identify and characterize the potentially virulence-associated enolase in M. guilliermondii strain SO using bioinformatics approaches. Profile Hidden-Markov model was implemented to identify enolase-related sequences in the fungal proteome. Sequence analysis deciphered only one (MgEno4581) out of nine sequences exhibited potent virulence traits observed similarly in the pathogenic Candida albicans. MgEno4581 structure that was predicted via SWISS-MODEL using C. albicans enolase (CaEno1; PDB ID: 7vrd) as the homology modeling template portrayed a highly identical motif with CaEno1 that facilitates ECM proteins binding. Amino acid substitutions (D234K, K235A, Y238H, K239D, G243K, V248C and Y254F) in ECM-binding motif of Saccharomyces cerevisiae enolase (ScEno) compared to MgEno4581 and CaEno1 caused changes in motif's surface charges. Protein-protein docking indicated F253 in ScEno only interacted hydrophobically with human plasminogen (HPG). Hydrogen linkages were observed for both MgEno4581 and CaEno1, suggesting a stronger interaction with HPG in the hydrophilic host microenvironments. Thus, our in silico characterizations on MgEno4581 provided new perspectives on its potential roles in candidiasis (fungal-host interactions) caused by M. guilliermondii, especially M. guilliermondii strain SO on zebrafish embryos that mimic the immunocompromised individuals as previously evident.

A Guide to Gene-Centric Analysis Using TreeSAPP.

Gene-centric analysis is commonly used to chart the structure, function, and activity of microbial communities in natural and engineered environments. A common approach is to create custom ad hoc reference marker gene sets, but these come with the typical disadvantages of inaccuracy and limited utility beyond assigning query sequences taxonomic labels. The Tree-based Sensitive and Accurate Phylogenetic Profiler (TreeSAPP) software package standardizes analysis of phylogenetic and functional marker genes and improves predictive performance using a classification algorithm that leverages information-rich reference packages consisting of a multiple sequence alignment, a profile hidden Markov model, taxonomic lineage information, and a phylogenetic tree. Here, we provide a set of protocols that link the various analysis modules in TreeSAPP into a coherent process that both informs and directs the user experience. This workflow, initiated from a collection of candidate reference sequences, progresses through construction and refinement of a reference package to marker identification and normalized relative abundance calculations for homologous sequences in metagenomic and metatranscriptomic datasets. The alpha subunit of methyl-coenzyme M reductase (McrA) involved in biological methane cycling is presented as a use case given its dual role as a phylogenetic and functional marker gene driving an ecologically relevant process. These protocols fill several gaps in prior TreeSAPP documentation and provide best practices for reference package construction and refinement, including manual curation steps from trusted sources in support of reproducible gene-centric analysis. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Creating reference packages Support Protocol 1: Installing TreeSAPP Support Protocol 2: Annotating traits within a phylogenetic context Basic Protocol 2: Updating reference packages Basic Protocol 3: Calculating relative abundance of genes in metagenomic and metatranscriptomic datasets.

CusProSe: a customizable protein annotation software with an application to the prediction of fungal secondary metabolism genes

We report here a new application, CustomProteinSearch (CusProSe), whose purpose is to help users to search for proteins of interest based on their domain composition. The application is customizable. It consists of two independent tools, IterHMMBuild and ProSeCDA. IterHMMBuild allows the iterative construction of Hidden Markov Model (HMM) profiles for conserved domains of selected protein sequences, while ProSeCDA scans a proteome of interest against an HMM profile database, and annotates identified proteins using user-defined rules. CusProSe was successfully used to identify, in fungal genomes, genes encoding key enzyme families involved in secondary metabolism, such as polyketide synthases (PKS), non-ribosomal peptide synthetases (NRPS), hybrid PKS-NRPS and dimethylallyl tryptophan synthases (DMATS), as well as to characterize distinct terpene synthases (TS) sub-families. The highly configurable characteristics of this application makes it a generic tool, which allows the user to refine the function of predicted proteins, to extend detection to new enzymes families, and may also be applied to biological systems other than fungi and to other proteins than those involved in secondary metabolism.