The SUPERFAMILY 2.0 database: a significant proteome update and a new webserver

Proteome-wide analysis reveals G protein-coupled receptor-like proteins in rice (Oryza sativa)

G protein-coupled receptors (GPCRs) constitute the largest family of transmembrane proteins in metazoans that mediate the regulation of various physiological responses to discrete ligands through heterotrimeric G protein subunits. The existence of GPCRs in plant is contentious, but their comparable crucial role in various signaling pathways necessitates the identification of novel remote GPCR-like proteins that essentially interact with the plant G protein α subunit and facilitate the transduction of various stimuli. In this study, we identified three putative GPCR-like proteins (OsGPCRLPs) (LOC_Os06g09930.1, LOC_Os04g36630.1, and LOC_Os01g54784.1) in the rice proteome using a stringent bioinformatics workflow. The identified OsGPCRLPs exhibited a canonical GPCR 'type I' 7TM topology, patterns, and biologically significant sites for membrane anchorage and desensitization. Cluster-based interactome mapping revealed that the identified proteins interact with the G protein α subunit which is a characteristic feature of GPCRs. Computational results showing the interaction of identified GPCR-like proteins with G protein α subunit and its further validation by the membrane yeast-two-hybrid assay strongly suggest the presence of GPCR-like 7TM proteins in the rice proteome. The absence of a regulator of G protein signaling (RGS) box in the C- terminal domain, and the presence of signature motifs of canonical GPCR in the identified OsGPCRLPs strongly suggest that the rice proteome contains GPCR-like proteins that might be involved in signal transduction.

Leucine-rich repeat receptor kinase BM41 regulates cuticular wax deposition in sorghum

Cuticular wax (CW) is the first defensive barrier of plants that forms a waterproof barrier, protects the plant from desiccation, and defends against insects, pathogens, and UV radiation. Sorghum, an important grass crop with high heat and drought tolerance, exhibits a much higher wax load than other grasses and the model plant Arabidopsis. In this study, we explored the regulation of sorghum CW biosynthesis using a bloomless mutant. The CW on leaf sheaths of the bloomless 41 (bm41) mutant showed significantly reduced very long-chain fatty acids (VLCFAs), triterpenoids, alcohols, and other wax components, with an overall 86% decrease in total wax content compared with the wild type. Notably, the 28-carbon and 30-carbon VLCFAs were decreased in the mutants. Using bulk segregant analysis, we identified the causal gene of the bloomless phenotype as a leucine-rich repeat transmembrane protein kinase. Transcriptome analysis of the wild-type and bm41 mutant leaf sheaths revealed BM41 as a positive regulator of lipid biosynthesis and steroid metabolism. BM41 may regulate CW biosynthesis by regulating the expression of the gene encoding 3-ketoacyl-CoA synthase 6. Identification of BM41 as a new regulator of CW biosynthesis provides fundamental knowledge for improving grass crops' heat and drought tolerance by increasing CW.

Are rare heterozygous SYNJ1 variants associated with Parkinson's disease?

Previous studies have established that rare biallelic SYNJ1 mutations cause autosomal recessive parkinsonism and Parkinson's disease (PD). We analyzed 8165 PD cases, 818 early-onset-PD (EOPD, < 50 years) and 70,363 controls. Burden meta-analysis revealed an association between rare nonsynonymous variants and variants with high Combined Annotation-Dependent Depletion score (> 20) in the Sac1 SYNJ1 domain and PD (Pfdr = 0.040). A meta-analysis of EOPD patients demonstrated an association between all rare heterozygous SYNJ1 variants and PD (Pfdr = 0.029).

Computational identification and characterization of chitinase 1 and chitinase 2 from neotropical isolates of Beauveria bassiana

Background

The fungus Beauveria bassiana is widely used for agronomical applications, mainly in biological control. B. bassiana uses chitinase enzymes to degrade chitin, a major chemical component found in insect exoskeletons and fungal cell walls. However, until recently, genomic information on neotropical isolates, as well as their metabolic and biotechnological potential, has been limited.

Methods

Eight complete B. bassiana genomes of Neotropical origin and three references were studied to identify chitinase genes and its corresponding proteins, which were curated and characterized using manual curation and computational tools. We conducted a computational study to highlight functional differences and similarities for chitinase proteins in these Neotropical isolates.

Results

Eleven chitinase 1 genes were identified, categorized as chitinase 1.1 and chitinase 1.2. Five chitinase 2 genes were identified but presented a higher sequence conservation across all sequences. Interestingly, physicochemical parameters were more similar between chitinase 1.1 and chitinase 2 than between chitinase 1.1 and 1.2.

Conclusion

Chitinases 1 and 2 demonstrated variations, especially within chitinase 1, which presented a potential paralog. These differences were observed in their physical parameters. Additionally, CHIT2 completely lacks a signal peptide. This implies that CHIT1 might be associated with infection processes, while CHIT2 could be involved in morphogenesis and cellular growth. Therefore, our work highlights the importance of computational studies on local isolates, providing valuable resources for further experimental validation. Intrinsic changes within local species can significantly impact our understanding of complex pathogen-host interactions and offer practical applications, such as biological control.

Unveiling lignocellulolytic potential: a genomic exploration of bacterial lineages within the termite gut

BACKGROUND

The microbial landscape within termite guts varies across termite families. The gut microbiota of lower termites (LT) is dominated by cellulolytic flagellates that sequester wood particles in their digestive vacuoles, whereas in the flagellate-free higher termites (HT), cellulolytic activity has been attributed to fiber-associated bacteria. However, little is known about the role of individual lineages in fiber digestion, particularly in LT.

RESULTS

We investigated the lignocellulolytic potential of 2223 metagenome-assembled genomes (MAGs) recovered from the gut metagenomes of 51 termite species. In the flagellate-dependent LT, cellulolytic enzymes are restricted to MAGs of Bacteroidota (Dysgonomonadaceae, Tannerellaceae, Bacteroidaceae, Azobacteroidaceae) and Spirochaetota (Breznakiellaceae) and reflect a specialization on cellodextrins, whereas their hemicellulolytic arsenal features activities on xylans and diverse heteropolymers. By contrast, the MAGs derived from flagellate-free HT possess a comprehensive arsenal of exo- and endoglucanases that resembles that of termite gut flagellates, underlining that Fibrobacterota and Spirochaetota occupy the cellulolytic niche that became vacant after the loss of the flagellates. Furthermore, we detected directly or indirectly oxygen-dependent enzymes that oxidize cellulose or modify lignin in MAGs of Pseudomonadota (Burkholderiales, Pseudomonadales) and Actinomycetota (Actinomycetales, Mycobacteriales), representing lineages located at the hindgut wall.

CONCLUSIONS

The results of this study refine our concept of symbiotic digestion of lignocellulose in termite guts, emphasizing the differential roles of specific bacterial lineages in both flagellate-dependent and flagellate-independent breakdown of cellulose and hemicelluloses, as well as a so far unappreciated role of oxygen in the depolymerization of plant fiber and lignin in the microoxic periphery during gut passage in HT. Video Abstract.

LysR-type transcriptional regulators: state of the art

The LysR-type transcriptional regulators (LTTRs) are DNA-binding proteins present in bacteria, archaea, and in algae. Knowledge about their distribution, abundance, evolution, structural organization, transcriptional regulation, fundamental roles in free life, pathogenesis, and bacteria-plant interaction has been generated. This review focuses on these aspects and provides a current picture of LTTR biology.

Systematic identification and functional analysis of root meristem growth factors (RGFs) reveals role of PgRGF1 in modulation of root development and ginsenoside production in Panax ginseng

Panax ginseng C.A. Mey., known for its medicinal and dietary supplement properties, primarily contains pharmacologically active ginsenosides. However, the regulatory mechanisms linking ginseng root development with ginsenoside biosynthesis are still unclear. Root meristem growth factors (RGFs) are crucial for regulating plant root growth. In our study, we identified five ginseng RGF peptide sequences from the ginseng genome and transcriptome libraries. We treated Arabidopsis and ginseng adventitious roots with exogenous Panax ginseng RGFs (PgRGFs) to assess their activities. Our results demonstrate that PgRGF1 influences gravitropic responses and reduces lateral root formation in Arabidopsis. PgRGF1 has been found to restrict the number and length of ginseng adventitious root branches in ginseng. Given the medicinal properties of ginseng, We determined the ginsenoside content and performed transcriptomic analysis of PgRGF1-treated ginseng adventitious roots. Specifically, the total ginsenoside content in ginseng adventitious roots decreased by 19.98 % and 63.71 % following treatments with 1 μM and 10 μM PgRGF1, respectively, compared to the control. The results revealed that PgRGF1 affects the accumulation of ginsenosides by regulating the expression of genes associated with auxin transportation and ginsenoside biosynthesis. These findings suggest that PgRGF1, as a peptide hormone regulator in ginseng, can modulate adventitious root growth and ginsenoside accumulation.

Multisubstrate specificity shaped the complex evolution of the aminotransferase family across the tree of life

Aminotransferases (ATs) are an ancient enzyme family that play central roles in core nitrogen metabolism, essential to all organisms. However, many of the AT enzyme functions remain poorly defined, limiting our fundamental understanding of the nitrogen metabolic networks that exist in different organisms. Here, we traced the deep evolutionary history of the AT family by analyzing AT enzymes from 90 species spanning the tree of life (ToL). We found that each organism has maintained a relatively small and constant number of ATs. Mapping the distribution of ATs across the ToL uncovered that many essential AT reactions are carried out by taxon-specific AT enzymes due to wide-spread nonorthologous gene displacements. This complex evolutionary history explains the difficulty of homology-based AT functional prediction. Biochemical characterization of diverse aromatic ATs further revealed their broad substrate specificity, unlike other core metabolic enzymes that evolved to catalyze specific reactions today. Interestingly, however, we found that these AT enzymes that diverged over billion years share common signatures of multisubstrate specificity by employing different nonconserved active site residues. These findings illustrate that AT family enzymes had leveraged their inherent substrate promiscuity to maintain a small yet distinct set of multifunctional AT enzymes in different taxa. This evolutionary history of versatile ATs likely contributed to the establishment of robust and diverse nitrogen metabolic networks that exist throughout the ToL. The study provides a critical foundation to systematically determine diverse AT functions and underlying nitrogen metabolic networks across the ToL.

Excavation of Biomarker Candidates for the Diagnosis of Talaromyces marneffei Infection via Genome-Wide Prediction and Functional Annotation of Secreted Proteins

Talaromyces marneffei is the third most common infectious pathogen in AIDS patients and leads to the highest death rate in Guangxi, China. The lack of reliable biomarkers is one of the major obstacles in current clinical diagnosis, which largely contributes to this high mortality. Here, we present a study that aimed at identifying diagnostic biomarker candidates through genome-wide prediction and functional annotation of Talaromyces marneffei secreted proteins. A total of 584 secreted proteins then emerged, including 382 classical and 202 nonclassical ones. Among them, there were 87 newly obtained functional annotations in this study. The annotated proteins were further evaluated by combining RNA profiling and a homology comparison. Three proteins were ultimately highlighted as biomarker candidates with robust expression and remarkable specificity. The predicted phosphoinositide phospholipase C and the galactomannoprotein were suggested to play an interactive immune game through metabolism of arachidonic acid. Therefore, they hold promise in developing new tools for clinical diagnosis of Talaromyces marneffei and also possibly serve as molecular targets for future therapy.

Are rare heterozygous SYNJ1 variants associated with Parkinson's disease?

Previous studies have suggested that rare biallelic SYNJ1 mutations may cause autosomal recessive parkinsonism and Parkinson's disease (PD). Our study explored the impact of rare SYNJ1 variants in non-familial settings, including 8,165 PD cases, 818 early-onset PD (EOPD, <50 years) and 70,363 controls. Burden meta-analysis using optimized sequence Kernel association test (SKAT-O) revealed an association between rare nonsynonymous variants in the Sac1 SYNJ1 domain and PD (Pfdr=0.040). Additionally, a meta-analysis focusing on patients with EOPD demonstrated an association between all rare SYNJ1 variants and PD (Pfdr=0.029). Rare SYNJ1 variants may be associated with sporadic PD, and more specifically with EOPD.

A Novel Thermo-Alkaline Stable GDSL/SGNH Esterase with Broad Substrate Specificity from a Deep-Sea Pseudomonas sp

Marine environments harbor a plethora of microorganisms that represent a valuable source of new biomolecules of biotechnological interest. In particular, enzymes from marine bacteria exhibit unique properties due to their high catalytic activity under various stressful and fluctuating conditions, such as temperature, pH, and salinity, fluctuations which are common during several industrial processes. In this study, we report a new esterase (EstGoM) from a marine Pseudomonas sp. isolated at a depth of 1000 m in the Gulf of Mexico. Bioinformatic analyses revealed that EstGoM is an autotransporter esterase (type Va) and belongs to the lipolytic family II, forming a new subgroup. The purified recombinant EstGoM, with a molecular mass of 67.4 kDa, showed the highest hydrolytic activity with p-nitrophenyl octanoate (p-NP C8), although it was also active against p-NP C4, C5, C10, and C12. The optimum pH and temperature for EstGoM were 9 and 60 °C, respectively, but it retained more than 50% of its activity over the pH range of 7-11 and temperature range of 10-75 °C. In addition, EstGoM was tolerant of up to 1 M NaCl and resistant to the presence of several metal ions, detergents, and chemical reagents, such as EDTA and β-mercaptoethanol. The enzymatic properties of EstGoM make it a potential candidate for several industrial applications.

Genetic structure and diversity of the rfb locus of pathogenic species of the genus Leptospira

Leptospirosis is caused by pathogenic strains of the genus Leptospira and is considered the most widespread zoonotic bacterial disease. The genus is characterized by the large number of serology variants, which challenges developing effective serotyping methods and vaccines with a broad spectrum. Because knowledge on the genetic basis of the serological diversity among leptospires is still limited, we aimed to explore the genetic structure and patterns of the rfb locus, which is involved in the biosynthesis of lipopolysaccharides, the major surface antigen that defines the serovar in leptospires. Here, we used genomic data of 722 pathogenic samples and compared the gene composition of their rfb locus by hierarchical clustering. Clustering analysis showed that the rfb locus gene composition is species-independent and strongly associated with the serological classification. The samples were grouped into four well-defined classes, which cluster together samples either belonging to the same serogroup or from different serogroups but sharing serological affinity. Our findings can assist in the development of new strategies based on molecular methods, which can lead to better tools for serological identification in this zoonosis.

Bioinformatics approach for structure modeling, vaccine design, and molecular docking of Brucella candidate proteins BvrR, OMP25, and OMP31

Brucellosis is a zoonotic disease with significant economic and healthcare costs. Despite the eradication efforts, the disease persists. Vaccines prevent disease in animals while antibiotics cure humans with limitations. This study aims to design vaccines and drugs for brucellosis in animals and humans, using protein modeling, epitope prediction, and molecular docking of the target proteins (BvrR, OMP25, and OMP31). Tertiary structure models of three target proteins were constructed and assessed using RMSD, TM-score, C-score, Z-score, and ERRAT. The best models selected from AlphaFold and I-TASSER due to their superior performance according to CASP 12 - CASP 15 were chosen for further analysis. The motif analysis of best models using MotifFinder revealed two, five, and five protein binding motifs, however, the Motif Scan identified seven, six, and eight Post-Translational Modification sites (PTMs) in the BvrR, OMP25, and OMP31 proteins, respectively. Dominant B cell epitopes were predicted at (44-63, 85-93, 126-137, 193-205, and 208-237), (26-46, 52-71, 98-114, 142-155, and 183-200), and (29-45, 58-82, 119-142, 177-198, and 222-251) for the three target proteins. Additionally, cytotoxic T lymphocyte epitopes were detected at (173-181, 189-197, and 202-210), (61-69, 91-99, 159-167, and 181-189), and (3-11, 24-32, 167-175, and 216-224), while T helper lymphocyte epitopes were displayed at (39-53, 57-65, 150-158, 163-171), (79-87, 95-108, 115-123, 128-142, and 189-197), and (39-47, 109-123, 216-224, and 245-253), for the respective target protein. Furthermore, structure-based virtual screening of the ZINC and DrugBank databases using the docking MOE program was followed by ADMET analysis. The best five compounds of the ZINC database revealed docking scores ranged from (- 16.8744 to - 15.1922), (- 16.0424 to - 14.1645), and (- 14.7566 to - 13.3222) for the BvrR, OMP25, and OMP31, respectively. These compounds had good ADMET parameters and no cytotoxicity, while DrugBank compounds didn't meet Lipinski's rule criteria. Therefore, the five selected compounds from the ZINC20 databases may fulfill the pharmacokinetics and could be considered lead molecules for potentially inhibiting Brucella's proteins.

Utilizing profile hidden Markov model databases for discovering viruses from metagenomic data: a comprehensive review

Profile hidden Markov models (pHMMs) are able to achieve high sensitivity in remote homology search, making them popular choices for detecting novel or highly diverged viruses in metagenomic data. However, many existing pHMM databases have different design focuses, making it difficult for users to decide the proper one to use. In this review, we provide a thorough evaluation and comparison for multiple commonly used profile HMM databases for viral sequence discovery in metagenomic data. We characterized the databases by comparing their sizes, their taxonomic coverage, and the properties of their models using quantitative metrics. Subsequently, we assessed their performance in virus identification across multiple application scenarios, utilizing both simulated and real metagenomic data. We aim to offer researchers a thorough and critical assessment of the strengths and limitations of different databases. Furthermore, based on the experimental results obtained from the simulated and real metagenomic data, we provided practical suggestions for users to optimize their use of pHMM databases, thus enhancing the quality and reliability of their findings in the field of viral metagenomics.

A comprehensive review and comparison of existing computational methods for protein function prediction

Protein function prediction is critical for understanding the cellular physiological and biochemical processes, and it opens up new possibilities for advancements in fields such as disease research and drug discovery. During the past decades, with the exponential growth of protein sequence data, many computational methods for predicting protein function have been proposed. Therefore, a systematic review and comparison of these methods are necessary. In this study, we divide these methods into four different categories, including sequence-based methods, 3D structure-based methods, PPI network-based methods and hybrid information-based methods. Furthermore, their advantages and disadvantages are discussed, and then their performance is comprehensively evaluated and compared. Finally, we discuss the challenges and opportunities present in this field.

Systematic Investigation of the Trafficking of Glycoproteins on the Cell Surface

Glycoproteins located on the cell surface play a pivotal role in nearly every extracellular activity. N-glycosylation is one of the most common and important protein modifications in eukaryotic cells, and it often regulates protein folding and trafficking. Glycosylation of cell-surface proteins undergoes meticulous regulation by various enzymes in the endoplasmic reticulum (ER) and the Golgi, ensuring their proper folding and trafficking to the cell surface. However, the impacts of protein N-glycosylation, N-glycan maturity, and protein folding status on the trafficking of cell-surface glycoproteins remain to be explored. In this work, we comprehensively and site-specifically studied the trafficking of cell-surface glycoproteins in human cells. Integrating metabolic labeling, bioorthogonal chemistry, and multiplexed proteomics, we investigated 706 N-glycosylation sites on 396 cell-surface glycoproteins in monocytes, either by inhibiting protein N-glycosylation, disturbing N-glycan maturation, or perturbing protein folding in the ER. The current results reveal their distinct impacts on the trafficking of surface glycoproteins. The inhibition of protein N-glycosylation dramatically suppresses the trafficking of many cell-surface glycoproteins. The N-glycan immaturity has more substantial effects on proteins with high N-glycosylation site densities, while the perturbation of protein folding in the ER exerts a more pronounced impact on surface glycoproteins with larger sizes. Furthermore, for N-glycosylated proteins, their trafficking to the cell surface is related to the secondary structures and adjacent amino acid residues of glycosylation sites. Systematic analysis of surface glycoprotein trafficking advances our understanding of the mechanisms underlying protein secretion and surface presentation.

Annelid adult cell type diversity and their pluripotent cellular origins

Many annelids can regenerate missing body parts or reproduce asexually, generating all cell types in adult stages. However, the putative adult stem cell populations involved in these processes, and the diversity of cell types generated by them, are still unknown. To address this, we recover 75,218 single cell transcriptomes of the highly regenerative and asexually-reproducing annelid Pristina leidyi. Our results uncover a rich cell type diversity including annelid specific types as well as novel types. Moreover, we characterise transcription factors and gene networks that are expressed specifically in these populations. Finally, we uncover a broadly abundant cluster of putative stem cells with a pluripotent signature. This population expresses well-known stem cell markers such as vasa, piwi and nanos homologues, but also shows heterogeneous expression of differentiated cell markers and their transcription factors. We find conserved expression of pluripotency regulators, including multiple chromatin remodelling and epigenetic factors, in piwi+ cells. Finally, lineage reconstruction analyses reveal computational differentiation trajectories from piwi+ cells to diverse adult types. Our data reveal the cell type diversity of adult annelids by single cell transcriptomics and suggest that a piwi+ cell population with a pluripotent stem cell signature is associated with adult cell type differentiation.

Bioinformatics approach for prediction and analysis of the Non-Structural Protein 4B (NSP4B) of the Zika virus

BACKGROUND

The Nonstructural Protein (NSP) 4B of Zika virus of 251 amino acids from (ZIKV/Human/POLG_ZIKVF) with accession number (A0A024B7W1), Induces the production of Endoplasmic Reticulum ER-derived membrane vesicles, which are the sites of viral replication. To understand the physical basis of how proteins fold in nature and to solve the challenge of protein structure prediction, Ab-initio and comparative modeling are crucial tools.

RESULTS

The systematic in silico technique, ThreaDom, had only predicted one domain (4 - 190) of NSP4B. I-TASSER, and Alphafold were ranked as the best servers for full-length 3-D protein structure predictions of NSP4B, where the predicted models were evaluated quantitatively using benchmarked metrics including C-score (-3.43), TM-score (0.77949), RMSD (2.73), and Z-score (1.561). The functional and protein binding motifs were realized using motif databases, secondary and surface accessibility predictions combined with Post-Translational Modification Sites (PTMs) prediction. Two highly conserved protein-binding motifs (Flavi NS4B and Bacillus papRprotein), together with three (PTMs) (Casein Kinase II, Myristyl site, and ASN-Glycosylation site) were predicted utilizing the Motif scan and Scanprosite servers. These patterns and PTMs were associated with NSP4B's role in triggering the development of the viral replication complex and its participation in the localization of NS3 and NS5 on the membrane. Only one hit from Structural Classification of Protein (SCOP) matched the protein sequence at positions 10 to 397 and was categorized six-hairpin glycosidases superfamily according to CATH (Class, Architecture, Topology, and Homology). Integrating this NSP4B information with the templates' SCOP and CATH annotations achieves it easier to attribute structure-function/evolution links to both previously known and recently discovered protein structures.

FunPredCATH: An ensemble method for predicting protein function using CATH

MOTIVATION

The growth of unannotated proteins in UniProt increases at a very high rate every year due to more efficient sequencing methods. However, the experimental annotation of proteins is a lengthy and expensive process. Using computational techniques to narrow the search can speed up the process by providing highly specific Gene Ontology (GO) terms.

METHODOLOGY

We propose an ensemble approach that combines three generic base predictors that predict Gene Ontology (BP, CC and MF) terms from sequences across different species. We train our models on UniProtGOA annotation data and use the CATH domain resources to identify the protein families. We then calculate a score based on the prevalence of individual GO terms in the functional families that is then used as an indicator of confidence when assigning the GO term to an uncharacterised protein.

METHODS

In the ensemble, we use a statistics-based method that scores the occurrence of GO terms in a CATH FunFam against a background set of proteins annotated by the same GO term. We also developed a set-based method that uses Set Intersection and Set Union to score the occurrence of GO terms within the same CATH FunFam. Finally, we also use FunFams-Plus, a predictor method developed by the Orengo Group at UCL to predict GO terms for uncharacterised proteins in the CAFA3 challenge.

EVALUATION

We evaluated the methods against the CAFA3 benchmark and DomFun. We used the Precision, Recall and Fmax metrics and the benchmark datasets that are used in CAFA3 to evaluate our models and compare them to the CAFA3 results. Our results show that FunPredCATH compares well with top CAFA methods in the different ontologies and benchmarks.

CONTRIBUTIONS

FunPredCATH compares well with other prediction methods on CAFA3, and the ensemble approach outperforms the base methods. We show that non-IEA models obtain higher Fmax scores than the IEA counterparts, while the models including IEA annotations have higher coverage at the expense of a lower Fmax score.

Carbohydrate-active enzyme annotation in microbiomes using dbCAN

CAZymes or carbohydrate-active enzymes are critically important for human gut health, lignocellulose degradation, global carbon recycling, soil health, and plant disease. We developed dbCAN as a web server in 2012 and actively maintain it for automated CAZyme annotation. Considering data privacy and scalability, we provide run_dbcan as a standalone software package since 2018 to allow users perform more secure and scalable CAZyme annotation on their local servers. Here, we offer a comprehensive computational protocol on automated CAZyme annotation of microbiome sequencing data, covering everything from short read pre-processing to data visualization of CAZyme and glycan substrate occurrence and abundance in multiple samples. Using a real-world metagenomic sequencing dataset, this protocol describes commands for dataset and software preparation, metagenome assembly, gene prediction, CAZyme prediction, CAZyme gene cluster (CGC) prediction, glycan substrate prediction, and data visualization. The expected results include publication-quality plots for the abundance of CAZymes, CGCs, and substrates from multiple CAZyme annotation routes (individual sample assembly, co-assembly, and assembly-free). For the individual sample assembly route, this protocol takes ∼33h on a Linux computer with 40 CPUs, while other routes will be faster. This protocol does not require programming experience from users, but it does assume a familiarity with the Linux command-line interface and the ability to run Python scripts in the terminal. The target audience includes the tens of thousands of microbiome researchers who routinely use our web server. This protocol will encourage them to perform more secure, rapid, and scalable CAZyme annotation on their local computer servers.

Interpreting Gene Ontology Annotations Derived from Sequence Homology Methods

The Gene Ontology (GO) project describes the functions of the gene products of organisms from all kingdoms of life in a standardized way, enabling powerful analyses of experiments involving genome-wide analysis. The scientific literature is used to convert experimental results into GO annotations that systematically classify gene products' functions. However, to address the fact that only a minor fraction of all genes has been characterized experimentally, multiple predictive methods to assign GO annotations have been developed since the inception of GO. Sequence homologies between novel genes and genes with known functions help to approximate the roles of these non-characterized genes. Here we describe the main sequence homology methods to produce annotations: pairwise comparison (BLAST), protein profile models (InterPro), and phylogenetic-based annotation (PAINT). Some of these methods can be implemented with genome analysis pipelines (BLAST and InterPro2GO), while PAINT is curated by the GO consortium.

Comparative genomics sheds light on transcription factor-mediated regulation in the extreme acidophilic Acidithiobacillia representatives

Extreme acidophiles thrive in acidic environments, confront a multitude of challenges, and demonstrate remarkable adaptability in their metabolism to cope with the ever-changing environmental fluctuations, which encompass variations in temperature, pH levels, and the availability of electron acceptors and donors. The survival and proliferation of members within the Acidithiobacillia class rely on the deployment of transcriptional regulatory systems linked to essential physiological traits. The study of these transcriptional regulatory systems provides valuable insights into critical processes, such as energy metabolism and nutrient assimilation, and how they integrate into major genetic-metabolic circuits. In this study, we examined the transcriptional regulatory repertoires and potential interactions of forty-three Acidithiobacillia complete and draft genomes, encompassing nine species. To investigate the function and diversity of Transcription Factors (TFs) and their DNA Binding Sites (DBSs), we conducted a genome-wide comparative analysis, which allowed us to identify these regulatory elements in representatives of Acidithiobacillia. We classified TFs into gene families and compared their occurrence among all representatives, revealing conservation patterns across the class. The results identified conserved regulators for several pathways, including iron and sulfur oxidation, the main pathways for energy acquisition, providing new evidence for viable regulatory interactions and branch-specific conservation in Acidithiobacillia. The identification of TFs and DBSs not only corroborates existing experimental information for selected species, but also introduces novel candidates for experimental validation. Moreover, these promising candidates have the potential for further extension to new representatives within the class.

LPI-IBWA: Predicting lncRNA-protein interactions based on an improved Bi-Random walk algorithm

Many studies have shown that long-chain noncoding RNAs (lncRNAs) are involved in a variety of biological processes such as post-transcriptional gene regulation, splicing, and translation by combining with corresponding proteins. Predicting lncRNA-protein interactions is an effective approach to infer the functions of lncRNAs. The paper proposes a new computational model named LPI-IBWA. At first, LPI-IBWA uses similarity kernel fusion (SKF) to integrate various types of biological information to construct lncRNA and protein similarity networks. Then, a bounded matrix completion model and a weighted k-nearest known neighbors algorithm are utilized to update the initial sparse lncRNA-protein interaction matrix. Based on the updated lncRNA-protein interaction matrix, the lncRNA similarity network and the protein similarity network are integrated into a heterogeneous network. Finally, an improved Bi-Random walk algorithm is used to predict novel latent lncRNA-protein interactions. 5-fold cross-validation experiments on a benchmark dataset showed that the AUC and AUPR of LPI-IBWA reach 0.920 and 0.736, respectively, which are higher than those of other state-of-the-art methods. Furthermore, the experimental results of case studies on a novel dataset also illustrated that LPI-IBWA could efficiently predict potential lncRNA-protein interactions.

Origins and Functional Significance of Eukaryotic Protein Folds

Folds are the architecture and topology of a protein domain. Categories of folds are very few compared to the astronomical number of sequences. Eukaryotes have more protein folds than Archaea and Bacteria. These folds are of two types: shared with Archaea and/or Bacteria on one hand and specific to eukaryotic clades on the other hand. The first kind of folds is inherited from the first endosymbiosis and confirms the mixed origin of eukaryotes. In a dataset of 1073 folds whose presence or absence has been evidenced among 210 species equally distributed in the three super-kingdoms, we have identified 28 eukaryotic folds unambiguously inherited from Bacteria and 40 eukaryotic folds unambiguously inherited from Archaea. Compared to previous studies, the repartition of informational function is higher than expected for folds originated from Bacteria and as high as expected for folds inherited from Archaea. The second type of folds is specifically eukaryotic and associated with an increase of new folds within eukaryotes distributed in particular clades. Reconstructed ancestral states coupled with dating of each node on the tree of life provided fold appearance rates. The rate is on average twice higher within Eukaryota than within Bacteria or Archaea. The highest rates are found in the origins of eukaryotes, holozoans, metazoans, metazoans stricto sensu, and vertebrates: the roots of these clades correspond to bursts of fold evolution. We could correlate the functions of some of the fold synapomorphies within eukaryotes with significant evolutionary events. Among them, we find evidence for the rise of multicellularity, adaptive immune system, or virus folds which could be linked to an ecological shift made by tetrapods.

Enrichment of native plastic-associated biofilm communities to enhance polyester degrading activity

Plastic pollution is an increasing worldwide problem urgently requiring a solution. While recycling rates are increasing globally, only 9% of all plastic waste has been recycled, and with the cost and limited downstream uses of recycled plastic, an alternative is needed. Here, we found that expanded polystyrene (EPS) promoted high levels of bacterial biofilm formation and sought out environmental EPS waste to characterize these native communities. We demonstrated that the EPS attached communities had limited plastic degrading activity. We then performed a long-term enrichment experiment where we placed a robust selection pressure on these communities by limiting carbon availability such that the waste plastic was the only carbon source. Seven of the resulting enriched bacterial communities had increased plastic degrading activity compared to the starting bacterial communities. Pseudomonas stutzeri was predominantly identified in six of the seven enriched communities as the strongest polyester degrader. Sequencing of one isolate of P. stutzeri revealed two putative polyesterases and one putative MHETase. This indicates that waste plastic-associated biofilms are a source for bacteria that have plastic-degrading potential, and that this potential can be unlocked through selective pressure and further in vitro enrichment experiments, resulting in biodegradative communities that are better than nature.

Machine Learning-Guided Protein Engineering

Recent progress in engineering highly promising biocatalysts has increasingly involved machine learning methods. These methods leverage existing experimental and simulation data to aid in the discovery and annotation of promising enzymes, as well as in suggesting beneficial mutations for improving known targets. The field of machine learning for protein engineering is gathering steam, driven by recent success stories and notable progress in other areas. It already encompasses ambitious tasks such as understanding and predicting protein structure and function, catalytic efficiency, enantioselectivity, protein dynamics, stability, solubility, aggregation, and more. Nonetheless, the field is still evolving, with many challenges to overcome and questions to address. In this Perspective, we provide an overview of ongoing trends in this domain, highlight recent case studies, and examine the current limitations of machine learning-based methods. We emphasize the crucial importance of thorough experimental validation of emerging models before their use for rational protein design. We present our opinions on the fundamental problems and outline the potential directions for future research.

LncRNA-protein interaction prediction with reweighted feature selection

LncRNA-protein interactions are ubiquitous in organisms and play a crucial role in a variety of biological processes and complex diseases. Many computational methods have been reported for lncRNA-protein interaction prediction. However, the experimental techniques to detect lncRNA-protein interactions are laborious and time-consuming. Therefore, to address this challenge, this paper proposes a reweighting boosting feature selection (RBFS) method model to select key features. Specially, a reweighted apporach can adjust the contribution of each observational samples to learning model fitting; let higher weights are given more influence samples than those with lower weights. Feature selection with boosting can efficiently rank to iterate over important features to obtain the optimal feature subset. Besides, in the experiments, the RBFS method is applied to the prediction of lncRNA-protein interactions. The experimental results demonstrate that our method achieves higher accuracy and less redundancy with fewer features.

Evolutionary conservation of an ancient retroviral gagpol gene in Artiodactyla

The genomes of mammals contain fingerprints of past infections by ancient retroviruses that invaded the germline of their ancestors. Most of these endogenous retroviruses (ERVs) contain only remnants of the original retrovirus; however, on rare occasions, ERV genes can be co-opted for a beneficial host function. While most studies of co-opted ERVs have focused on envelope genes, including the syncytins that function in placentation, there are examples of co-opted gag genes including one we recently discovered in simian primates. Here, we searched for other intact gag genes in non-primate mammalian lineages. We began by examining the genomes of extant camel species, which represent a basal lineage in the order Artiodactyla. This identified a gagpol gene with a large open reading frame (ORF) (>3,500 bp) in the same orthologous location in Artiodactyla species but that is absent in other mammals. Thus, this ERV was fixed in the common ancestor of all Artiodactyla at least 64 million years ago. The amino acid sequence of this gene, termed ARTgagpol, contains recognizable matrix, capsid, nucleocapsid, and reverse transcriptase domains in ruminants, with an RNase H domain in camels and pigs. Phylogenetic analysis and structural prediction of its reverse transcriptase and RNase H domains groups ARTgagpol with gammaretroviruses. Transcriptomic analysis shows ARTgagpol expression in multiple tissues suggestive of a co-opted host function. These findings identify the oldest and largest ERV-derived gagpol gene with an intact ORF in mammals, an intriguing milestone in the co-evolution of mammals and retroviruses. IMPORTANCE Retroviruses are unique among viruses that infect animals as they integrate their reverse-transcribed double-stranded DNA into host chromosomes. When this happens in a germline cell, such as sperm, egg, or their precursors, the integrated retroviral copies can be passed on to the next generation as endogenous retroviruses (ERVs). On rare occasions, the genes of these ERVs can be domesticated by the host. In this study we used computational similarity searches to identify an ancient ERV with an intact viral gagpol gene in the genomes of camels that is also found in the same genomic location in other even-toed ungulates suggesting that it is at least 64 million years old. Broad tissue expression and predicted preservation of the reverse transcriptase fold of this protein suggest that it may be domesticated for a host function. This is the oldest known intact gagpol gene of an ancient retrovirus in mammals.

Uncultured prokaryotic genomes in the spotlight: An examination of publicly available data from metagenomics and single-cell genomics

Owing to the ineffectiveness of traditional culture techniques for the vast majority of microbial species, culture-independent analyses utilizing next-generation sequencing and bioinformatics have become essential for gaining insight into microbial ecology and function. This mini-review focuses on two essential methods for obtaining genetic information from uncultured prokaryotes, metagenomics and single-cell genomics. We analyzed the registration status of uncultured prokaryotic genome data from major public databases and assessed the advantages and limitations of both the methods. Metagenomics generates a significant quantity of sequence data and multiple prokaryotic genomes using straightforward experimental procedures. However, in ecosystems with high microbial diversity, such as soil, most genes are presented as brief, disconnected contigs, and lack association of highly conserved genes and mobile genetic elements with individual species genomes. Although technically more challenging, single-cell genomics offers valuable insights into complex ecosystems by providing strain-resolved genomes, addressing issues in metagenomics. Recent technological advancements, such as long-read sequencing, machine learning algorithms, and in silico protein structure prediction, in combination with vast genomic data, have the potential to overcome the current technical challenges and facilitate a deeper understanding of uncultured microbial ecosystems and microbial dark matter genes and proteins. In light of this, it is imperative that continued innovation in both methods and technologies take place to create high-quality reference genome databases that will support future microbial research and industrial applications.

Characterisation and comparative genomics of three new Varanus-associated Borrelia spp. from Indonesia and Australia

BACKGROUND

Borrelia are important disease-causing tick- and louse-borne spirochaetes than can infect a wide variety of vertebrates, including humans and reptiles. Reptile-associated (REP) Borrelia, once considered a peculiarity, are now recognised as a distinct and important evolutionary lineage, and are increasingly being discovered worldwide in association with novel hosts. Numerous novel Borrelia spp. associated with monitor lizards (Varanus spp.) have been recently identified throughout the Indo-Pacific region; however, there is a lack of genomic data on these Borrelia.

METHODS

We used metagenomic techniques to sequence almost complete genomes of novel Borrelia spp. from Varanus varius and Varanus giganteus from Australia, and used long- and short-read technologies to sequence the complete genomes of two strains of a novel Borrelia sp. previously isolated from ticks infesting Varanus salvator from Indonesia. We investigated intra- and interspecies genomic diversity, including plasmid diversity and relatedness, among Varanus-associated Borrelia and other available REP Borrelia and, based on 712 whole genome orthologues, produced the most complete phylogenetic analysis, to the best of our knowledge, of REP Borrelia to date.

RESULTS

The genomic architecture of Varanus-associated Borrelia spp. is similar to that of Borrelia spp. that cause relapsing fever (RF), and includes a highly conserved megaplasmid and numerous smaller linear and circular plasmids that lack structural consistency between species. Analysis of PF32 and PF57/62 plasmid partitioning genes indicated that REP Borrelia plasmids fall into at least six distinct plasmid families, some of which are related to previously defined Borrelia plasmid families, whereas the others appear to be unique. REP Borrelia contain immunogenic variable major proteins that are homologous to those found in Borrelia spp. that cause RF, although they are limited in copy number and variability and have low sequence identities to RF variable major proteins. Phylogenetic analyses based on single marker genes and 712 single copy orthologs also definitively demonstrated the monophyly of REP Borrelia as a unique lineage.

CONCLUSIONS

In this work we present four new genomes from three novel Borrelia, and thus double the number of REP Borrelia genomes publicly available. The genomic characterisation of these Borrelia clearly demonstrates their distinctiveness as species, and we propose the names Borrelia salvatorii, 'Candidatus Borrelia undatumii', and 'Candidatus Borrelia rubricentralis' for them.

Systematic analysis of the impact of phosphorylation and O-GlcNAcylation on protein subcellular localization

The subcellular localization of proteins is critical for their functions in eukaryotic cells and is tightly correlated with protein modifications. Here, we comprehensively investigate the nuclear-cytoplasmic distributions of the phosphorylated, O-GlcNAcylated, and non-modified forms of proteins to dissect the correlation between protein distribution and modifications. Phosphorylated and O-GlcNAcylated proteins have overall higher nuclear distributions than non-modified ones. Different distributions among the phosphorylated, O-GlcNAcylated, and non-modified forms of proteins are associated with protein size, structure, and function, as well as local environment and adjacent residues around modification sites. Moreover, we perform site-mutagenesis experiments using phosphomimetic and phospho-null mutants of two proteins to validate the proteomic results. Additionally, the effects of the OGT/OGA inhibition on glycoprotein distribution are systematically investigated, and the distribution changes of glycoproteins are related to their abundance changes under the inhibitions. Systematic investigation of the relationship between protein modification and localization advances our understanding of protein functions.

Lampshade web spider Ectatosticta davidi chromosome-level genome assembly provides evidence for its phylogenetic position

The spider of Ectatosticta davidi, belonging to the lamp-shade web spider family, Hypochilidae, which is closely related to Hypochilidae and Filistatidae and recovered as sister of the rest Araneomorphs spiders. Here we show the final assembled genome of E. davidi with 2.16 Gb in 15 chromosomes. Then we confirm the evolutionary position of Hypochilidae. Moreover, we find that the GMC gene family exhibit high conservation throughout the evolution of true spiders. We also find that the MaSp genes of E. davidi may represent an early stage of MaSp and MiSp genes in other true spiders, while CrSp shares a common origin with AgSp and PySp but differ from MaSp. Altogether, this study contributes to addressing the limited availability of genomic sequences from Hypochilidae spiders, and provides a valuable resource for investigating the genomic evolution of spiders.

The genome of the glasshouse plant noble rhubarb (Rheum nobile) provides a window into alpine adaptation

Glasshouse plants are species that trap warmth via specialized morphology and physiology, mimicking a human glasshouse. In the Himalayan alpine region, the highly specialized glasshouse morphology has independently evolved in distinct lineages to adapt to intensive UV radiation and low temperature. Here we demonstrate that the glasshouse structure - specialized cauline leaves - is highly effective in absorbing UV light but transmitting visible and infrared light, creating an optimal microclimate for the development of reproductive organs. We reveal that this glasshouse syndrome has evolved at least three times independently in the rhubarb genus Rheum. We report the genome sequence of the flagship glasshouse plant Rheum nobile and identify key genetic network modules in association with the morphological transition to specialized glasshouse leaves, including active secondary cell wall biogenesis, upregulated cuticular cutin biosynthesis, and suppression of photosynthesis and terpenoid biosynthesis. The distinct cell wall organization and cuticle development might be important for the specialized optical property of glasshouse leaves. We also find that the expansion of LTRs has likely played an important role in noble rhubarb adaptation to high elevation environments. Our study will enable additional comparative analyses to identify the genetic basis underlying the convergent occurrence of glasshouse syndrome.

Four PQQ-Dependent Alcohol Dehydrogenases Responsible for the Oxidative Detoxification of Deoxynivalenol in a Novel Bacterium Ketogulonicigenium vulgare D3_3 Originated from the Feces of Tenebrio molitor Larvae

Deoxynivalenol (DON) is frequently detected in cereals and cereal-based products and has a negative impact on human and animal health. In this study, an unprecedented DON-degrading bacterial isolate D3_3 was isolated from a sample of Tenebrio molitor larva feces. A 16S rRNA-based phylogenetic analysis and genome-based average nucleotide identity comparison clearly revealed that strain D3_3 belonged to the species Ketogulonicigenium vulgare. This isolate D3_3 could efficiently degrade 50 mg/L of DON under a broad range of conditions, such as pHs of 7.0-9.0 and temperatures of 18-30 °C, as well as during aerobic or anaerobic cultivation. 3-keto-DON was identified as the sole and finished DON metabolite using mass spectrometry. In vitro toxicity tests revealed that 3-keto-DON had lower cytotoxicity to human gastric epithelial cells and higher phytotoxicity to Lemna minor than its parent mycotoxin DON. Additionally, four genes encoding pyrroloquinoline quinone (PQQ)-dependent alcohol dehydrogenases in the genome of isolate D3_3 were identified as being responsible for the DON oxidation reaction. Overall, as a highly potent DON-degrading microbe, a member of the genus Ketogulonicigenium is reported for the first time in this study. The discovery of this DON-degrading isolate D3_3 and its four dehydrogenases will allow microbial strains and enzyme resources to become available for the future development of DON-detoxifying agents for food and animal feed.

Annelid adult cell type diversity and their pluripotent cellular origins

Annelids are a broadly distributed, highly diverse, economically and environmentally important group of animals. Most species can regenerate missing body parts, and many are able to reproduce asexually. Therefore, many annelids can generate all adult cell types in adult stages. However, the putative adult stem cell populations involved in these processes, as well as the diversity of adult cell types generated by them, are still unknown. Here, we recover 75,218 single cell transcriptomes of Pristina leidyi, a highly regenerative and asexually-reproducing freshwater annelid. We characterise all major annelid adult cell types, and validate many of our observations by HCR in situ hybridisation. Our results uncover complex patterns of regionally expressed genes in the annelid gut, as well as neuronal, muscle and epidermal specific genes. We also characterise annelid-specific cell types such as the chaetal sacs and globin+ cells, and novel cell types of enigmatic affinity, including a vigilin+ cell type, a lumbrokinase+ cell type, and a diverse set of metabolic cells. Moreover, we characterise transcription factors and gene networks that are expressed specifically in these populations. Finally, we uncover a broadly abundant cluster of putative stem cells with a pluripotent signature. This population expresses well-known stem cell markers such as vasa, piwi and nanos homologues, but also shows heterogeneous expression of differentiated cell markers and their transcription factors. In these piwi+ cells, we also find conserved expression of pluripotency regulators, including multiple chromatin remodelling and epigenetic factors. Finally, lineage reconstruction analyses reveal the existence of differentiation trajectories from piwi+ cells to diverse adult types. Our data reveal the cell type diversity of adult annelids for the first time and serve as a resource for studying annelid cell types and their evolution. On the other hand, our characterisation of a piwi+ cell population with a pluripotent stem cell signature will serve as a platform for the study of annelid stem cells and their role in regeneration.

Combining Selective Enrichment and a Boosting Approach to Globally and Site-Specifically Characterize Protein Co-translational O-GlcNAcylation

Protein O-GlcNAcylation plays extremely important roles in mammalian cells, regulating signal transduction and gene expression. This modification can happen during protein translation, and systematic and site-specific analysis of protein co-translational O-GlcNAcylation can advance our understanding of this important modification. However, it is extraordinarily challenging because normally O-GlcNAcylated proteins are very low abundant and the abundances of co-translational ones are even much lower. Here, we developed a method integrating selective enrichment, a boosting approach, and multiplexed proteomics to globally and site-specifically characterize protein co-translational O-GlcNAcylation. The boosting approach using the TMT labeling dramatically enhances the detection of co-translational glycopeptides with low abundance when enriched O-GlcNAcylated peptides from cells with a much longer labeling time was used as a boosting sample. More than 180 co-translational O-GlcNAcylated proteins were site-specifically identified. Further analyses revealed that among co-translational glycoproteins, those related to DNA binding and transcription are highly overrepresented using the total identified O-GlcNAcylated proteins in the same cells as the background. Compared with the glycosylation sites on all glycoproteins, co-translational sites have different local structures and adjacent amino acid residues. Overall, an integrative method was developed to identify protein co-translational O-GlcNAcylation, which is very useful to advance our understanding of this important modification.

ProteInfer, deep neural networks for protein functional inference

Predicting the function of a protein from its amino acid sequence is a long-standing challenge in bioinformatics. Traditional approaches use sequence alignment to compare a query sequence either to thousands of models of protein families or to large databases of individual protein sequences. Here we introduce ProteInfer, which instead employs deep convolutional neural networks to directly predict a variety of protein functions - Enzyme Commission (EC) numbers and Gene Ontology (GO) terms - directly from an unaligned amino acid sequence. This approach provides precise predictions which complement alignment-based methods, and the computational efficiency of a single neural network permits novel and lightweight software interfaces, which we demonstrate with an in-browser graphical interface for protein function prediction in which all computation is performed on the user's personal computer with no data uploaded to remote servers. Moreover, these models place full-length amino acid sequences into a generalised functional space, facilitating downstream analysis and interpretation. To read the interactive version of this paper, please visit https://google-research.github.io/proteinfer/.

Virulence network of interacting domains of influenza a and mouse proteins

There exist several databases that provide virus-host protein interactions. While most provide curated records of interacting virus-host protein pairs, information on the strain-specific virulence factors or protein domains involved, is lacking. Some databases offer incomplete coverage of influenza strains because of the need to sift through vast amounts of literature (including those of major viruses including HIV and Dengue, besides others). None have offered complete, strain specific protein-protein interaction records for the influenza A group of viruses. In this paper, we present a comprehensive network of predicted domain-domain interaction(s) (DDI) between influenza A virus (IAV) and mouse host proteins, that will allow the systematic study of disease factors by taking the virulence information (lethal dose) into account. From a previously published dataset of lethal dose studies of IAV infection in mice, we constructed an interacting domain network of mouse and viral protein domains as nodes with weighted edges. The edges were scored with the Domain Interaction Statistical Potential (DISPOT) to indicate putative DDI. The virulence network can be easily navigated via a web browser, with the associated virulence information (LD50 values) prominently displayed. The network will aid influenza A disease modeling by providing strain-specific virulence levels with interacting protein domains. It can possibly contribute to computational methods for uncovering influenza infection mechanisms mediated through protein domain interactions between viral and host proteins. It is available at https://iav-ppi.onrender.com/home.

InterPro in 2022

The InterPro database (https://www.ebi.ac.uk/interpro/) provides an integrative classification of protein sequences into families, and identifies functionally important domains and conserved sites. Here, we report recent developments with InterPro (version 90.0) and its associated software, including updates to data content and to the website. These developments extend and enrich the information provided by InterPro, and provide a more user friendly access to the data. Additionally, we have worked on adding Pfam website features to the InterPro website, as the Pfam website will be retired in late 2022. We also show that InterPro's sequence coverage has kept pace with the growth of UniProtKB. Moreover, we report the development of a card game as a method of engaging the non-scientific community. Finally, we discuss the benefits and challenges brought by the use of artificial intelligence for protein structure prediction.

Theoretical 3D Modeling of NLRP3 Inflammasome Complex

The NOD-like receptor pyrin domain containing 3 (NLRP3) is a multidomain protein that plays a key role in innate immune response. Structures of NLRP3 in different conformational states and bound to cognate partners are available. In this chapter we present an approach to model the oligomeric structure of NLRP3 by homology modeling using multiple templates, symmetry, and refinement. The overall process presented here represents advanced exercise in structural modeling that provides unique insights into the biological role and activation of NLRP3 oligomer. Finally, the same approach can be easily adapted to the rest of the members of the NLRP family.

Homology Modeling in the Twilight Zone: Improved Accuracy by Sequence Space Analysis

The analysis of the relationship between sequence and structure similarities during the evolution of a protein family has revealed a limit of sequence divergence for which structural conservation can be confidently assumed and homology modeling is reliable. Below this limit, the twilight zone corresponds to sequence divergence for which homology modeling becomes increasingly difficult and requires specific methods. Either with conventional threading methods or with recent deep learning methods, such as AlphaFold, the challenge relies on the identification of a template that shares not only a common ancestor (homology) but also a conserved structure with the query. As both homology and structural conservation are transitive properties, mining of sequence databases followed by multidimensional scaling (MDS) of the query sequence space can reveal intermediary sequences to infer homology and structural conservation between the query and the template. Here, as a case study, we studied the plethodontid receptivity factor isoform 1 (PRF1) from Plethodon jordani, a member of a pheromone protein family present only in lungless salamanders and weakly related to cytokines of the IL6 family. A variety of conventional threading methods led to the cytokine CNTF as a template. Sequence mining, followed by phylogenetic and MDS analysis, provided missing links between PRF1 and CNTF and allowed reliable homology modeling. In addition, we compared automated models obtained from web servers to a customized model to show how modeling can be improved by expert information.

Chromosome-level genome and the identification of sex chromosomes in Uloborus diversus

The orb web is a remarkable example of animal architecture that is observed in families of spiders that diverged over 200 million years ago. While several genomes exist for araneid orb-weavers, none exist for other orb-weaving families, hampering efforts to investigate the genetic basis of this complex behavior. Here we present a chromosome-level genome assembly for the cribellate orb-weaving spider Uloborus diversus. The assembly reinforces evidence of an ancient arachnid genome duplication and identifies complete open reading frames for every class of spidroin gene, which encode the proteins that are the key structural components of spider silks. We identified the 2 X chromosomes for U. diversus and identify candidate sex-determining loci. This chromosome-level assembly will be a valuable resource for evolutionary research into the origins of orb-weaving, spidroin evolution, chromosomal rearrangement, and chromosomal sex determination in spiders.

The IMG/M data management and analysis system v.7: content updates and new features

The Integrated Microbial Genomes & Microbiomes system (IMG/M: https://img.jgi.doe.gov/m/) at the Department of Energy (DOE) Joint Genome Institute (JGI) continues to provide support for users to perform comparative analysis of isolate and single cell genomes, metagenomes, and metatranscriptomes. In addition to datasets produced by the JGI, IMG v.7 also includes datasets imported from public sources such as NCBI Genbank, SRA, and the DOE National Microbiome Data Collaborative (NMDC), or submitted by external users. In the past couple years, we have continued our effort to help the user community by improving the annotation pipeline, upgrading the contents with new reference database versions, and adding new analysis functionalities such as advanced scaffold search, Average Nucleotide Identity (ANI) for high-quality metagenome bins, new cassette search, improved gene neighborhood display, and improvements to metatranscriptome data display and analysis. We also extended the collaboration and integration efforts with other DOE-funded projects such as NMDC and DOE Biology Knowledgebase (KBase).

Finding lncRNA-Protein Interactions Based on Deep Learning With Dual-Net Neural Architecture

The identification of lncRNA-protein interactions (LPIs) is important to understand the biological functions and molecular mechanisms of lncRNAs. However, most computational models are evaluated on a unique dataset, thereby resulting in prediction bias. Furthermore, previous models have not uncovered potential proteins (or lncRNAs) interacting with a new lncRNA (or protein). Finally, the performance of these models can be improved. In this study, we develop a Deep Learning framework with Dual-net Neural architecture to find potential LPIs (LPI-DLDN). First, five LPI datasets are collected. Second, the features of lncRNAs and proteins are extracted by Pyfeat and BioTriangle, respectively. Third, these features are concatenated as a vector after dimension reduction. Finally, a deep learning model with dual-net neural architecture is designed to classify lncRNA-protein pairs. LPI-DLDN is compared with six state-of-the-art LPI prediction methods (LPI-XGBoost, LPI-HeteSim, LPI-NRLMF, PLIPCOM, LPI-CNNCP, and Capsule-LPI) under four cross validations. The results demonstrate the powerful LPI classification performance of LPI-DLDN. Case study analyses show that there may be interactions between RP11-439E19.10 and Q15717, and between RP11-196G18.22 and Q9NUL5. The novelty of LPI-DLDN remains, integrating various biological features, designing a novel deep learning-based LPI identification framework, and selecting the optimal LPI feature subset based on feature importance ranking.

Chromosome-level reference genome for European flat oyster (Ostrea edulis L.)

The European flat oyster (Ostrea edulis L.) is a bivalve naturally distributed across Europe, which was an integral part of human diets for centuries, until anthropogenic activities and disease outbreaks severely reduced wild populations. Despite a growing interest in genetic applications to support population management and aquaculture, a reference genome for this species is lacking to date. Here, we report a chromosome-level assembly and annotation for the European Flat oyster genome, generated using Oxford Nanopore, Illumina, Dovetail OmniC™ proximity ligation and RNA sequencing. A contig assembly (N50: 2.38 Mb) was scaffolded into the expected karyotype of 10 pseudochromosomes. The final assembly is 935.13 Mb, with a scaffold-N50 of 95.56 Mb, with a predicted repeat landscape dominated by unclassified elements specific to O. edulis. The assembly was verified for accuracy and completeness using multiple approaches, including a novel linkage map built with ddRAD-Seq technology, comprising 4016 SNPs from four full-sib families (eight parents and 163 F1 offspring). Annotation of the genome integrating multitissue transcriptome data, comparative protein evidence and ab-initio gene prediction identified 35,699 protein-coding genes. Chromosome-level synteny was demonstrated against multiple high-quality bivalve genome assemblies, including an O. edulis genome generated independently for a French O. edulis individual. Comparative genomics was used to characterize gene family expansions during Ostrea evolution that potentially facilitated adaptation. This new reference genome for European flat oyster will enable high-resolution genomics in support of conservation and aquaculture initiatives, and improves our understanding of bivalve genome evolution.

DomainMapper: Accurate domain structure annotation including those with non-contiguous topologies

Automated domain annotation is an important tool for structural informatics. These pipelines typically involve searching query sequences against hidden Markov model (HMM) profiles, yielding matches to profiles for various domains. However, domain annotation can be ambiguous or inaccurate when proteins contain domains with non-contiguous residue ranges, and especially when insertional domains are hosted within them. Here, we present DomainMapper, an algorithm that accurately assigns a unique domain structure annotation to a query sequence, including those with complex topologies. We validate our domain assignments using the AlphaFold database and confirm that non-contiguity is pervasive (10.74% of all domains in yeast and 4.52% in human). Using this resource, we find that certain folds have strong propensities to be non-contiguous or insertional across the Tree of Life. DomainMapper is freely available and can be ran as a single command-line function.

Phylogenomics of a Saccharomyces cerevisiae cocoa strain reveals adaptation to a West African fermented food population

Various yeast strains have been proposed as candidate starter cultures for cocoa fermentation, especially strains of Saccharomyces cerevisiae. In the current study, the genome of the cocoa strain S. cerevisiae IMDO 050523 was unraveled based on a combination of long- and short-read sequencing. It consisted of 16 nuclear chromosomes and a mitochondrial chromosome, which were organized in 20 contigs, with only two small gaps. A phylogenomic analysis of this genome together with another 105 S cerevisiae genomes, among which 20 from cocoa strains showed a geographical distribution of the latter, including S. cerevisiae IMDO 050523. Its genome clustered together with that of a West African fermented food population, indicating a wider adaptation to West African food niches than cocoa. Furthermore, S. cerevisiae IMDO 050523 contained genetic signatures involved in sucrose hydrolysis, pectin degradation, osmotolerance, and conserved amino acid changes in key ester-producing enzymes that could point toward specific niche adaptations.