Protein Sequence Similarity Networks Research Articles

Sequence similarity network and protein structure prediction offer insights into the evolution of microbial pathways for ferrous iron oxidation.

Dissimilatory ferrous iron [Fe(II)] oxidation is a well-established microbial energy generation strategy. This study aims to comprehensively investigate the distribution and evolution of recognized Fe(II) oxidation pathways through comparative analysis. Interestingly, we have discovered a wide range of taxonomic groups that harbor homologs to known Fe(II) oxidation proteins. The presence of these homologs among phylogenetically distant lineages and their frequent association with mobile genetic elements strongly suggest horizontal gene transfer events involving Fe(II) oxidation proteins, such as the rus operon of Acidithiobacillus and Cyc572 from Leptospirillum lineages belonging to classes Gammaproteobacteria and Betaproteobacteria often present at the hub positions of the protein sequence similarity networks from which homologs of other taxa are derived. In addition, RoseTTAFold predictions have provided valuable insights into the structural characteristics of previously unknown Fe(II) oxidation components. Despite having limited sequence identity, a significant number of acknowledged proteins involved in different Fe(II) oxidation pathways exhibit close structural similarities, including Cyc2 and Cyc572. Collectively, this study significantly enhances our understanding of the distribution and evolution of microbial ferrous iron oxidation pathways. IMPORTANCE Microbial Fe(II) oxidation is a crucial process that harnesses and converts the energy available in Fe, contributing significantly to global element cycling. However, there are still many aspects of this process that remain unexplored. In this study, we utilized a combination of comparative genomics, sequence similarity network analysis, and artificial intelligence-driven structure modeling methods to address the lack of structural information on Fe(II) oxidation proteins and offer a comprehensive perspective on the evolution of Fe(II) oxidation pathways. Our findings suggest that several microbial Fe(II) oxidation pathways currently known may have originated within classes Gammaproteobacteria and Betaproteobacteria.

HNetGO: protein function prediction via heterogeneous network transformer.

Protein function annotation is one of the most important research topics for revealing the essence of life at molecular level in the post-genome era. Current research shows that integrating multisource data can effectively improve the performance of protein function prediction models. However, the heavy reliance on complex feature engineering and model integration methods limits the development of existing methods. Besides, models based on deep learning only use labeled data in a certain dataset to extract sequence features, thus ignoring a large amount of existing unlabeled sequence data. Here, we propose an end-to-end protein function annotation model named HNetGO, which innovatively uses heterogeneous network to integrate protein sequence similarity and protein-protein interaction network information and combines the pretraining model to extract the semantic features of the protein sequence. In addition, we design an attention-based graph neural network model, which can effectively extract node-level features from heterogeneous networks and predict protein function by measuring the similarity between protein nodes and gene ontology term nodes. Comparative experiments on the human dataset show that HNetGO achieves state-of-the-art performance on cellular component and molecular function branches.

A broad inhibitor of acyl-acyl carrier protein synthetases

The acyl-acyl carrier protein synthetase enzyme enables some bacteria to scavenge free fatty acids from the environment for direct use in lipids. This fatty acid recycling pathway can help pathogens circumvent fatty acid synthase (FAS) inhibition with established antibiotics and those in clinical development. AasS enzymes are surprisingly hard to identify as they show high sequence similarity to other adenylate forming enzymes, and only a handful have been correctly annotated to date. Four recently discovered AasS enzymes from Gram negative bacteria, Chlamydia trachomatis, Neisseria gonorrhoeae, and Alistipes finegoldii, form distinct clusters in protein sequence similarity networks and have varying substrate preferences. We previously synthesized C10-AMS, an inhibitor of AasS that mimics the acyl-AMP reaction intermediate. Here we tested its ability to be broadly applicable to enzymes in this class, and found it inhibits all four newly annotated AasS enzymes. C10-AMS therefore provides a tool to study the role of AasS in fatty acid recycling in pathogenic bacteria as well as offers a platform for antibiotic development.

Building the Chordata Olfactory Receptor Database using more than 400,000 receptors annotated by Genome2OR.

Olfactory receptors are poorly annotated for most genome-sequenced chordates. To address this deficiency, we developed a nhmmer-based olfactory receptor annotation tool Genome2OR ( https://github.com/ToHanwei/Genome2OR.git ), and used it to process 1,695 sequenced chordate genomes in the NCBI Assembly database as of January, 2021. In total, 765,248 olfactory receptor genes were annotated, with 404,426 functional genes and 360,822 pseudogenes, which represents a four-fold increase in the number of annotated olfactory receptors. Based on the annotation data, we built a database called Chordata Olfactory Receptor Database (CORD, https://cord.ihuman.shanghaitech.edu.cn ) for archiving, analysing and disseminating the data. Beyond the primary data, we offer derivative information, including pictures of species, cross references to public databases, structural models, sequence similarity networks and sequence profiles in the CORD. Furthermore, we did brief analyses on these receptors, including building a huge protein sequence similarity network covering all receptors in the database, and clustering them into 20 communities, classifying the 20 communities into three categories based on their presences/absences in ray-finned fish and/or lobe-finned fish. We infer that olfactory receptors should have unique activation and desensitization mechanisms by analysing their sequences and structural models. We believe the CORD can benefit the researchers and the general public who are interested in olfaction.

Metagenomic Screening for Lipolytic Genes Reveals an Ecology-Clustered Distribution Pattern.

Lipolytic enzymes are one of the most important enzyme types for application in various industrial processes. Despite the continuously increasing demand, only a small portion of the so far encountered lipolytic enzymes exhibit adequate stability and activities for biotechnological applications. To explore novel and/or extremophilic lipolytic enzymes, microbial consortia in two composts at thermophilic stage were analyzed using function-driven and sequence-based metagenomic approaches. Analysis of community composition by amplicon-based 16S rRNA genes and transcripts, and direct metagenome sequencing revealed that the communities of the compost samples were dominated by members of the phyla Actinobacteria, Proteobacteria, Firmicutes, Bacteroidetes, and Chloroflexi. Function-driven screening of the metagenomic libraries constructed from the two samples yielded 115 unique lipolytic enzymes. The family assignment of these enzymes was conducted by analyzing the phylogenetic relationship and generation of a protein sequence similarity network according to an integrated classification system. The sequence-based screening was performed by using a newly developed database, containing a set of profile Hidden Markov models, highly sensitive and specific for detection of lipolytic enzymes. By comparing the lipolytic enzymes identified through both approaches, we demonstrated that the activity-directed complements sequence-based detection, and vice versa. The sequence-based comparative analysis of lipolytic genes regarding diversity, function and taxonomic origin derived from 175 metagenomes indicated significant differences between habitats. Analysis of the prevalent and distinct microbial groups providing the lipolytic genes revealed characteristic patterns and groups driven by ecological factors. The here presented data suggests that the diversity and distribution of lipolytic genes in metagenomes of various habitats are largely constrained by ecological factors.

PSIII-12 Identification and Classification of Novel Animal Associated Viruses from the Human Skin Virome of Pet Owners and Agricultural Workers

Abstract Viruses are the most abundant biological entity on earth. Despite this, viral diversity in current reference databases is poorly described. With limited number of studies investigating environmental viromes, our understanding of the virosphere composition and diversity is limited. Improvement in metagenomic sequencing methodologies has allowed for identification of novel genomic sequences, especially for new and emerging viral taxa, and improvement in existing ambiguous viral phylogenetic groupings. Here we describe a viral discovery-based study that evaluated viral metagenomes collected from the hands and scalps of 42 human subjects (n = 642) for identification of environmentally contracted DNA viruses. Due to direct interaction between humans and animals either through owning pets or through vocation, it is hypothesized that surveillance of the human skin virome will contain animal associated viruses contracted through environmental contact that can then potentially become zoonotic pathogens. To investigate this, viral genome assemblies from the metagenomes were bioinformatically predicted using model and reference-based tools. A gene and protein sequence variation and binning approach was then employed to resolve viral dark matter. Evaluation of animal associated viruses resulted in the identification of a feline papillomavirus from a subject who reportedly owned a cat. Phylogenetic analysis suggests this isolate is related to feline papillomavirus type 2 sharing approximately 75% sequence synteny thus making it a novel papillomavirus isolate. Additionally, a new grouping of porcine associated Cressdnaviricota viruses were identified through protein sequence similarity network clustering of conserved Rep genes and all other NCBI reported Rep gene sequences. This grouping contained both novel viral genomes collected from the human skin virome dataset and unclassified Cressdnaviruses all collected from porcine samples obtained from NCBI. Further phylogenetic analysis showed similarity to this novel cluster to that of porcine circovirus (PCV). This discovery and phylogenetic based investigation of novel viral genome assemblies demonstrates that animal associated viruses can be identified from the skin of humans and thus studying human viromes can provide information into the spread and emergence of novel viruses.

Predicting Drug-Target Interaction Via Self-Supervised Learning.

Recent advances in graph representation learning provide new opportunities for computational drug-target interaction (DTI) prediction. However, it still suffers from deficiencies of dependence on manual labels and vulnerability to attacks. Inspired by the success of self-supervised learning (SSL) algorithms, which can leverage input data itself as supervision,we propose SupDTI, a SSL-enhanced drug-target interaction prediction framework based on a heterogeneous network (i.e., drug-protein, drug-drug, and protein-protein interaction network; drug-disease, drug-side-effect, and protein-disease association network; drug-structure and protein-sequence similarity network). Specifically, SupDTI is an end-to-end learning framework consisting of five components. First, localized and globalized graph convolutions are designed to capture the nodes' information from both local and global perspectives, respectively. Then, we develop a variational autoencoder to constrain the nodes' representation to have desired statistical characteristics. Finally, a unified self-supervised learning strategy is leveraged to enhance the nodes' representation, namely, a contrastive learning module is employed to enable the nodes' representation to fit the graph-level representation, followed by a generative learning module which further maximizes the node-level agreement across the global and local views by learning the probabilistic connectivity distribution of the original heterogeneous network. Experimental results show that our model can achieve better prediction performance than state-of-the-art methods.

Complex evolutionary history of felid anelloviruses

Anellovirus infections are highly prevalent in mammals, however, prior to this study only a handful of anellovirus genomes had been identified in members of the Felidae family. Here we characterise anelloviruses in pumas (Puma concolor), bobcats (Lynx rufus), Canada lynx (Lynx canadensis), caracals (Caracal caracal) and domestic cats (Felis catus). The complete anellovirus genomes (n = 220) recovered from 149 individuals were diverse. ORF1 protein sequence similarity network analysis coupled with phylogenetic analysis, revealed two distinct clusters that are populated by felid-derived anellovirus sequences, a pattern mirroring that observed for the porcine anelloviruses. Of the two-felid dominant anellovirus groups, one includes sequences from bobcats, pumas, domestic cats and an ocelot, and the other includes sequences from caracals, Canada lynx, domestic cats and pumas. Coinfections of diverse anelloviruses appear to be common among the felids. Evidence of recombination, both within and between felid-specific anellovirus groups, supports a long coevolution history between host and virus.

Visualizing the superfamily of metallo-β-lactamases through sequence similarity network neighborhood connectivity analysis

Protein sequence similarity networks (SSNs) constitute a convenient approach to analyze large polypeptide sequence datasets, and have been successfully applied to study a number of protein families over the past decade. SSN analysis is herein combined with traditional cladistic and phenetic phylogenetic analysis (respectively based on multiple sequence alignments and all-against-all three-dimensional protein structure comparisons) in order to assist the ancestral reconstruction and integrative revision of the superfamily of metallo-β-lactamases (MBLs). It is shown that only 198 out of 15,292 representative nodes contain at least one experimentally obtained protein structure in the Protein Data Bank or a manually annotated SwissProt entry, that is to say, only 1.3 % of the superfamily has been functionally and/or structurally characterized. Besides, neighborhood connectivity coloring, which measures local network interconnectivity, is introduced for detection of protein families within SSN clusters. This approach provides a clear picture of how many families remain unexplored in the superfamily, while most MBL research is heavily biased towards a few families. Further research is suggested in order to determine the SSN topological properties, which will be instrumental for the improvement of automated sequence annotation methods.

Utilizing Whole Fusobacterium Genomes To Identify, Correct, and Characterize Potential Virulence Protein Families.

Fusobacterium spp. are Gram-negative, anaerobic, opportunistic pathogens involved in multiple diseases, including a link between the oral pathogen Fusobacterium nucleatum and the progression and severity of colorectal cancer. The identification and characterization of virulence factors in the genus Fusobacterium has been greatly hindered by a lack of properly assembled and annotated genomes. Using newly completed genomes from nine strains and seven species of Fusobacterium, we report the identification and corrected annotation of verified and potential virulence factors from the type 5 secreted autotransporter, FadA, and MORN2 protein families, with a focus on the genetically tractable strain F. nucleatum subsp. nucleatum ATCC 23726 and type strain F. nucleatum subsp. nucleatum ATCC 25586. Within the autotransporters, we used sequence similarity networks to identify protein subsets and show a clear differentiation between the prediction of outer membrane adhesins, serine proteases, and proteins with unknown function. These data have identified unique subsets of type 5a autotransporters, which are key proteins associated with virulence in F. nucleatum However, we coupled our bioinformatic data with bacterial binding assays to show that a predicted weakly invasive strain of F. necrophorum that lacks a Fap2 autotransporter adhesin strongly binds human colonocytes. These analyses confirm a gap in our understanding of how autotransporters, MORN2 domain proteins, and FadA adhesins contribute to host interactions and invasion. In summary, we identify candidate virulence genes in Fusobacterium, and caution that experimental validation of host-microbe interactions should complement bioinformatic predictions to increase our understanding of virulence protein contributions in Fusobacterium infections and disease.IMPORTANCEFusobacterium spp. are emerging pathogens that contribute to mammalian and human diseases, including colorectal cancer. Despite a validated connection with disease, few proteins have been characterized that define a direct molecular mechanism for Fusobacterium pathogenesis. We report a comprehensive examination of virulence-associated protein families in multiple Fusobacterium species and show that complete genomes facilitate the correction and identification of multiple, large type 5a secreted autotransporter genes in previously misannotated or fragmented genomes. In addition, we use protein sequence similarity networks and human cell interaction experiments to show that previously predicted noninvasive strains can indeed bind to and potentially invade human cells and that this could be due to the expansion of specific virulence proteins that drive Fusobacterium infections and disease.

Noninvasive Brain Stimulation Enhances Memory Acquisition and Is Associated with Synaptoneurosome Modification in the Rat Hippocampus.

Transcranial direct-current stimulation (tDCS) is a non-invasive brain stimulation approach previously shown to enhance memory acquisition, but more studies are needed to elucidate the underlying mechanisms. Here, we examined the effects of anodal tDCS (0.25 mA for 30 min) on the memory performance of male Sprague Dawley rats in the passive avoidance test (PAT) and the associated modifications to the hippocampal proteomes. Results indicate anodal tDCS applied before the acquisition period significantly enhanced memory performance in the PAT. Following PAT, synaptoneurosomes were biochemically purified from the hippocampi of tDCS-treated or sham-treated rats and individual protein abundances were determined by bottom-up liquid chromatography mass spectrometry analysis. Proteomic analysis identified 184 differentially expressed hippocampal proteins when comparing the sham to the tDCS before memory acquisition treatment group. Ingenuity pathway analysis (IPA) showed anodal tDCS before memory acquisition significantly enhanced pathways associated with memory, cognition, learning, transmission, neuritogenesis, and long-term potentiation (LTP). IPA identified significant upstream regulators including bdnf, shank3, and gsk3b. Protein-protein interaction (PPI) and protein sequence similarity (PSS) networks show that glutamate receptor pathways, ion channel activity, memory, learning, cognition, and long-term memory were significantly associated with anodal tDCS. Centrality measures from both networks identified key proteins including dlg, shank, grin, and gria that were significantly modified by tDCS applied before the acquisition period. Together, our results provide descriptive molecular evidence that anodal tDCS enhances memory performance in the PAT by modifying hippocampal synaptic plasticity related proteins.

A subfamily roadmap of the evolutionarily diverse glycoside hydrolase family 16 (GH16)

Glycoside hydrolase family (GH) 16 comprises a large and taxonomically diverse family of glycosidases and transglycosidases that adopt a common β-jelly-roll fold and are active on a range of terrestrial and marine polysaccharides. Presently, broadly insightful sequence–function correlations in GH16 are hindered by a lack of a systematic subfamily structure. To fill this gap, we have used a highly scalable protein sequence similarity network analysis to delineate nearly 23,000 GH16 sequences into 23 robust subfamilies, which are strongly supported by hidden Markov model and maximum likelihood molecular phylogenetic analyses. Subsequent evaluation of over 40 experimental three-dimensional structures has highlighted key tertiary structural differences, predominantly manifested in active-site loops, that dictate substrate specificity across the GH16 evolutionary landscape. As for other large GH families (i.e. GH5, GH13, and GH43), this new subfamily classification provides a roadmap for functional glycogenomics that will guide future bioinformatics and experimental structure–function analyses. The GH16 subfamily classification is publicly available in the CAZy database. The sequence similarity network workflow used here, SSNpipe, is freely available from GitHub.

In silico characterization of hypothetical proteins from Orientia tsutsugamushi str. Karp uncovers virulence genes

Scrub typhus also known as bush typhus is a disease with symptoms similar to Chikungunya infection. It is caused by a gram-negative bacterium Orientia tsutsugamushi which resides in its vertebrate host, Mites. The genome of Orientia tsutsugamushi str. Karp encodes for 1,563 proteins, of which 344 are characterized as hypothetical ones. In the present study, we tried to identify the probable functions of these 344 hypothetical proteins (HPs). All the characterized hypothetical proteins (HPs) belong to the various protein classes like enzymes, transporters, binding proteins, metabolic process and catalytic activity and kinase activity. These hypothetical proteins (HPs) were further analyzed for virulence factors with 62 proteins identified as the most virulent proteins among these hypothetical proteins (HPs). In addition, we studied the protein sequence similarity network for visualizing functional trends across protein superfamilies from the context of sequence similarity and it shows great potential for generating testable hypotheses about protein structure-function relationships. Furthermore, we calculated toplogical properties of the network and found them to obey network power law distributions showing a fractal nature. We also identifed two highly interconnected modules in the main network which contained five hub proteins (KJV55465, KJV56211, KJV57212, KJV57203 and KJV57216) having 1.0 clustering coefficient. The structural modeling (2D and 3D structure) of these five hub proteins was carried out and the catalytic site essential for its functioning was analyzed. The outcome of the present study may facilitate a better understanding of the mechanism of virulence, pathogenesis, adaptability to host and up-to-date annotations will make unknown genes easy to identify and target for experimentation. The information on the functional attributes and virulence characteristic of these hypothetical proteins (HPs) are envisaged to facilitate effective development of novel antibacterial drug targets of Orientia tsutsugamushi.

Origins and Evolution of the α-L-Fucosidases: From Bacteria to Metazoans.

α-L-fucosidases (EC 3.2.1.51, FUC), belonging to the glycoside hydrolase family 29 (GH29), play important roles in several biological processes and are markers used for detecting hepatocellular carcinoma. In this study, a protein sequence similarity network (SSN) was generated and a subsequent evolutionary analysis was performed to understand the enzymes comprehensively. The SSN indicated that the proteins in the FUC family are mainly present in bacteria, fungi, metazoans, plants, as well as in archaea, but less abundantly. The sequences in bacteria were found to be more diverse than those in other taxonomic groups. The SSN and a phylogenetic tree both supported that the proteins in the FUC family can be classified into 3 subfamilies. FUCs in each subfamily are under the pressure of negative selection. The enzymes from metazoans, fungi, and plants separated into the three subfamilies and shared high similarity with the bacterial homologs. The multiple sequence alignment results indicated that the amino acid residues for binding α-L-fucosidase and catalysis are highly conserved in the 3 subfamilies; however, the evolutionary patterns were different, based on the coevolution analysis in the subfamily of metazoans and bacteria. Finally, gene duplication plays an important role for α-L-fucosidase evolution, not only in metazoans, but also in bacteria and fungi.

A comprehensive in silico analysis of sortase superfamily.

Sortases are cysteine transpeptidases that assemble surface proteins and pili in their cell envelope. Encoded by all Gram-positive bacteria, few Gram-negative bacteria and archaea, sortases are currently divided into six classes (A-F). Due to the steep increase in bacterial genome data in recent years, the number of sortase homologues have also escalated rapidly. In this study, we used protein sequence similarity networks to explore the taxonomic diversity of sortases and also to evaluate the current classification of these enzymes. The resultant data suggest that sortase classes A, B, and D predominate in Firmicutes and classes E and F are enriched in Actinobacteria, whereas class C is distributed in both Firmicutes and Actinobacteria except Streptomyces family. Sortases were also observed in various Gram-negatives and euryarchaeota, which should be recognized as novel classes of sortases. Motif analysis around the catalytic cysteine was also performed and suggested that the residue at 2nd position from cysteine may help distinguish various sortase classes. Moreover, the sequence analysis indicated that the catalytic arginine is highly conserved in almost all classes except sortase F in which arginine is replaced by asparagine in Actinobacteria. Additionally, class A sortases showed higher structural variation as compared to other sortases, whereas inter-class comparisons suggested structures of class C and D2 exhibited best similarities. A better understanding of the residues highlighted in this study should be helpful in elucidating their roles in substrate binding and the sortase function, and successively could help in the development of strong sortase inhibitors.

New insight into the classification and evolution of glucose transporters in the Metazoa.

Because glucose is an essential energy source for living organisms, glucose transporters (GLUTs) are present in all species worldwide. Encoded by the solute carrier family 2 gene family, the GLUT proteins generally have 12 transmembrane helices (TMHs). In total, 14 GLUT proteins have been identified in humans (hGLUTs), and they are divided into 3 classes on the basis of their transport characteristics and sequence similarities. Herein, we report the use of protein sequence similarity networks (SSNs) to visualize the sequence trends of 4101 GLUT proteins across the Metazoa. The SSNs separated the metazoan proteins into 3 new classes that were different from the traditional classification system. In the new system, 9 of the 14 hGLUTs (hGLUT1-5, 7, 9, 11, and 14) were grouped into class I, 3 (hGLUT10, 12, and 13) were grouped into class II, and 2 (hGLUT6 and 8) were grouped into class III, as also supported by the phylogenetic tree. Multiple sequence alignments further showed that the conserved residues in each class were different. Furthermore, the hGLUTs in each class showed unique evolutionary characteristics, with similar nonsynonymous-to-synonymous divergence ratios and similar regions under conservative selection pressure. Of note, GLUTs with 3, 6, 18, 24, and 36 TMHs were identified among the metazoan genomes, and 1 Chinese hamster protein with 6 TMHs showed GLUT activity. In summary, this large-scale sequence analysis provided new insights into the classification and evolution of GLUTs and further showed that gene duplication and fusion could have been important drivers during the evolution of these transporter molecules.-Jia, B., Yuan, D. P., Lan, W. J., Xuan, Y. H., Jeon, C. O. New insight into the classification and evolution of glucose transporters in the Metazoa.

Biochemical Characterization and Phylogenetic Analysis of the Virulence Factor Lysine Decarboxylase From Vibrio vulnificus.

Cadaverine is produced in organisms from the amino acid lysine in a decarboxylation reaction catalyzed by lysine decarboxylase (EC 4.1.1.18). The inducible lysine decarboxylase CadA plays a vital role in acid stress response for enteric bacteria. Vibrio vulnificus is an extremely virulent human pathogen causing gastroenteritis when the acid conditions that prevent survival of V. vulnificus in the stomach or small intestine are overcome. A gene encoding CadA was identified from V. vulnificus. Subsequent analyses showed that CadA from V. vulnificus (VvCadA) is a decamer with a 82-kDa subunit. Homogenous VvCadA was purified from Escherichia coli and used for lysine decarboxylation with an optimal pH of 6.0 and optimal temperature of 37°C. The apparent Vmax and Km for lysine were 9.45 ± 0.24 μM/min and 0.45 ± 0.05 mM, respectively. Mutation analysis suggested that the amino-acid-binding pyridoxal phosphate, the cofactor of the enzyme, plays a vital role in the reaction. Mutation of the negatively charged residues interacting with lysine also affected the activity of the enzyme to some extent. Quantitative RT-PCR showed that expression of VvcadA was up-regulated under low pH, low salinity, and oxidative stresses. Furthermore, the concentration of cadaverine released to the cell exterior also increased under these stresses. Protein sequence similarity network (SSN) analysis indicated that lysine decarboxylases with ornithine decarboxylases and arginine decarboxylases shared a common ancestor, and that lysine decarboxylases are more conserved during evolution. Our data provide evidence for the biochemical characteristics and important roles of VvCadA under stress conditions.

Network Analysis of RAD51 Proteins in Metazoa and the Evolutionary Relationships With Their Archaeal Homologs.

The RAD51 (DNA repair protein RAD51) recombinases are essential for homologous recombination, DNA repair, and genome stability. Overexpression of RAD51 proteins has been observed in many cancer cells, such as thyroid carcinoma, breast cancer, pancreatic cancer, and others. In Metazoa, there are multiple members of RAD51 (RAD51, RAD51B, RAD51C, RAD51D, DMC1) (DNA meiotic recombinase 1), XRCC2 (X-ray repair cross-complementing 2), and XRCC3. In this study, we used a protein sequence similarity network (SSN) to analyze the evolutionary relationship within this protein family. The SSN based on the RAD51 proteins from Metazoa indicated that there are several proteins that have yet to be functionally defined. The SSN based on the distribution of the proteins supports the hypothesis that horizontal gene transfer plays an important role in the evolution of RAD51 proteins. Multiple sequence alignments with structural information revealed that the amino acid residues for ATP and Mg2+ are highly conserved. The seven RAD51 proteins in humans are under different selective pressure: RAD51 and DMC1 are under stringent negative selection, while other proteins are subject to relatively relaxed negative selection. Furthermore, the expression levels of the seven genes in different tissues showed that the genes in the same cluster in the phylogenetic tree showed similar expression profiles. Finally, the SSN based on the RAD51 proteins from both eukaryotes and prokaryotes suggested that the eukaryotic RAD51 recombinases share a common ancestor with the archaeal homologs, but XRCC2 may have a different origin. These findings expand the understanding of the evolution and diversity of RAD51 recombinases in Metazoa.

Integrative View of the Diversity and Evolution of SWEET and SemiSWEET Sugar Transporters

Sugars Will Eventually be Exported Transporter (SWEET) and SemiSWEET are recently characterized families of sugar transporters in eukaryotes and prokaryotes, respectively. SemiSWEETs contain 3 transmembrane helices (TMHs), while SWEETs contain 7. Here, we performed sequence-based comprehensive analyses for SWEETs and SemiSWEETs across the biosphere. In total, 3,249 proteins were identified and ≈60% proteins were found in green plants and Oomycota, which include a number of important plant pathogens. Protein sequence similarity networks indicate that proteins from different organisms are significantly clustered. Of note, SemiSWEETs with 3 or 4 TMHs that may fuse to SWEET were identified in plant genomes. 7-TMH SWEETs were found in bacteria, implying that SemiSWEET can be fused directly in prokaryote. 15-TMH extraSWEET and 25-TMH superSWEET were also observed in wild rice and oomycetes, respectively. The transporters can be classified into 4, 2, 2, and 2 clades in plants, Metazoa, unicellular eukaryotes, and prokaryotes, respectively. The consensus and coevolution of amino acids in SWEETs were identified by multiple sequence alignments. The functions of the highly conserved residues were analyzed by molecular dynamics analysis. The 19 most highly conserved residues in the SWEETs were further confirmed by point mutagenesis using SWEET1 from Arabidopsis thaliana. The results proved that the conserved residues located in the extrafacial gate (Y57, G58, G131, and P191), the substrate binding pocket (N73, N192, and W176), and the intrafacial gate (P43, Y83, F87, P145, M161, P162, and Q202) play important roles for substrate recognition and transport processes. Taken together, our analyses provide a foundation for understanding the diversity, classification, and evolution of SWEETs and SemiSWEETs using large-scale sequence analysis and further show that gene duplication and gene fusion are important factors driving the evolution of SWEETs.

Large-scale examination of functional and sequence diversity of 2-oxoglutarate/Fe(II)-dependent oxygenases in Metazoa

BackgroundThe 2-oxoglutarate/Fe(II)-dependent oxygenase (2OG oxygenase) superfamily in Metazoa is responsible for protein modification, nucleic acid repair and/or modification, and fatty acid metabolism. MethodsPhylogenetic analysis, protein sequence similarity network (SSN) and other bioinformatics tools were used to analyze the evolutionary relationship and make functional inferences of Metazoa 2OG oxygenases. ResultsSixty-four 2OG oxygenases have been previously found in Homo sapiens; they catalyze two reactions: hydroxylation and demethylation. Phylogenetic analyses indicated that enzymes with similar domain architecture are always clustered together, and the redox function can be performed by the 2OG oxygenase domain or Jumonji C (JmjC) domain, where the JmjC domain is always fused to other functional domains. We used the SSN to make functional inferences and to conduct distribution analysis of Metazoa 2OG oxygenases. >11,000 putative 2OG oxygenases across Metazoa could be assigned potential functions based on the SSN. The multiple sequence alignments showed that the residues binding iron are most highly conserved in both the 2OG oxygenase domain and JmjC domain. In contrast, the residues binding oxoglutarate are quite different in the two domains: the 2OG oxygenase domain tends to have an Arg/Lys at the C terminus, whereas the JmjC domain, an Asn/Lys residue in the middle region. ConclusionsThe results indicated that gene duplication and vertical gene transfer have played important roles in 2OG oxygenase evolution in Metazoa and clarified the difference between the 2OG oxygenase domain and JmjC domain. General significanceThese findings expand the understanding of the diversity, evolution, and functions of 2OG oxygenases.