Sequence Similarity Searching

Genome-wide identification and analysis of GH1-containing H1 histones among poplar species

Histone H1s are basic nuclear proteins, which played key role in the binding of DNA and nucleosome, eventually the stability of eukaryotic chromatin. In most species, H1s possess an evolutionarily conserved nucleosome-DNA binding globular domain (GH1), which is conserved between species, especially in mammals. However, there is limited information on the phylogeny, structure and function of H1s in poplar. In the present research, 21 GH1-containing proteins found in Populus trichocarpa were classified into three subgroups (H1s, Myb (SANK) GH1 and AT-hook GH1) based on their domains. The Populus H1 proteins contained lysine-rich N-, C-terminal tails and a conserved GH1 domain, particularly the characteristic amino acids in the helix and strand structures of the five H1 subtypes. The phylogenetic and structure diversity analysis of GH1 proteins across different Populus species and model plants revealed three conserved subgroups with characteristic amino acids. The variation in the number of members across the five subtypes was consistent with the evolutionary relationships among Populus species. The conserved characteristic amino acids among same Populus subtype can be served as markers for subtype identification. Furthermore, the abundance analysis of H1s in Populus indicated their unique functions in young tissues and stages, which may be related to DNA methylation. The consistent expression pattern of H1 across Populus species was in accordance with collinearity pairs. Present analyses provided valuable information on the diversity and evolution of H1s in Populus, advocating further research of H1s in plants.

Functional characterization of Camptotheca acuminata 7-deoxyloganetic acid synthases and 7-deoxyloganetic acid glucosyltransferases involved in camptothecin biosynthesis

Camptothecin (CAM), a well-known plant-derived antitumor compound, is a structurally complex pentacyclic pyrroloquinoline monoterpene indole alkaloid (MIA) found in various plant species. As a specific MIA, CAM had been thought to share a common upstream biosynthetic pathway with other MIAs such as the antitumor vinblastine and vincristine from Catharanthus roseus. Nevertheless the key enzymes responsible for the consecutive three-step oxidation of the -CH3 of nepetalactol to form the -COOH of 7-deoxyloganetic acid and the subsequent glycosylation of 7-deoxyloganetic acid to yield 7-deoxyloganic acid have yet to be functionally characterized. Here we established an in vivo tandem catalysis assay for the enzymatic catalytic activity characterization of 7-deoxyloganetic acid synthase (7DLS) and 7-deoxyloganetic acid glucosyltransferase (7DLGT), two crucial catalytic enzymes in MIAs biosynthesis, thereby avoiding the difficulty in the detection of the unstable biosynthetic intermediates. The enzyme activity assay platform was conducted through the co-expression of functionally characterized Cr7DLS and Cr7DLGT in Saccharomyces cerevisiae WAT11, substrate feeding, and enzymatic product verification. Two cytochrome P450 enzymes (CYPs) from Camptotheca acuminata, the prestigious resource for CAM, CaCYP76A75 and CaCYP76A76, were identified and functionally characterized to be responsible for the consecutive three-step oxidation of nepetalactol to yield 7-deoxyloganetic acid through reciprocal replacement of Cr7DLS in the in vivo tandem enzyme activity assay platform. Two uridine 5'-diphosphate glycosyltransferases (UGTs), CaUGT709C10 and CaUGT709C11, were functionally characterized to be capable of glycosylating 7-deoxyloganetic acid to yield 7-deoxyloganic acid. This study provides two CYPs as 7DLSs and two UGTs as 7DLGTs, offering potential applications in MIAs biosynthesis.

DescribePROT Database of Residue-Level Protein Structure and Function Annotations

DescribePROT is a freely available online database of structural and functional descriptors of proteins at the amino acid level. It provides access to 13 diverse descriptors that include sequence conservation, putative secondary structure, solvent accessibility, intrinsic disorder, and signal peptides, and putative annotations of residues that interact with proteins, peptides and nucleic acids. These data can be used to elucidate protein functions, to support efforts to develop therapeutics, and to develop and evaluate future predictors of protein structure and function. DescribePROT includes 7.8 billion predictions for 1.4 million proteins from 83 complete proteomes of popular model organisms. This information can be downloaded at multiple levels of scope (entire database, specific organisms, and individual proteins) and can be interacted with using a graphical interface that simultaneously displays data on multiple descriptors. We describe the contents of this resource, provide directions on how to use its interface, and offer instructions on how to obtain and interact with the underlying data. Moreover, we briefly discuss plans for a future expansion of this database. DescribePROT is available at http://biomine.cs.vcu.edu/servers/DESCRIBEPROT/ .

Accurate and Fast Prediction of Intrinsic Disorder Using flDPnn

Intrinsically disordered proteins (IDPs) that include one or more intrinsically disordered regions (IDRs) are abundant across all domains of life and viruses and play numerous functional roles in various cellular processes. Due to a relatively low throughput and high cost of experimental techniques for identifying IDRs, there is a growing need for fast and accurate computational algorithms that accurately predict IDRs/IDPs from protein sequences. We describe one of the leading disorder predictors, flDPnn. Results from a recent community-organized Critical Assessment of Intrinsic Disorder (CAID) experiment show that flDPnn provides fast and state-of-the-art predictions of disorder, which are supplemented with the predictions of several major disorder functions. This chapter provides a practical guide to flDPnn, which includes a brief explanation of its predictive model, descriptions of its web server and standalone versions, and a case study that showcases how to read and understand flDPnn's predictions.

Impact of Multi-Factor Features on Protein Secondary Structure Prediction

Protein secondary structure prediction (PSSP) plays a crucial role in resolving protein functions and properties. Significant progress has been made in this field in recent years, and the use of a variety of protein-related features, including amino acid sequences, position-specific score matrices (PSSM), amino acid properties, and secondary structure trend factors, to improve prediction accuracy is an important technical route for it. However, a comprehensive evaluation of the impact of these factor features in secondary structure prediction is lacking in the current work. This study quantitatively analyzes the impact of several major factors on secondary structure prediction models using a more explanatory four-class machine learning approach. The applicability of each factor in the different types of methods, the extent to which the different methods work on each factor, and the evaluation of the effect of multi-factor combinations are explored in detail. Through experiments and analyses, it was found that PSSM performs best in methods with strong high-dimensional features and complex feature extraction capabilities, while amino acid sequences, although performing poorly overall, perform relatively well in methods with strong linear processing capabilities. Also, the combination of amino acid properties and trend factors significantly improved the prediction performance. This study provides empirical evidence for future researchers to optimize multi-factor feature combinations and apply them to protein secondary structure prediction models, which is beneficial in further optimizing the use of these factors to enhance the performance of protein secondary structure prediction models.

Bacteremia due to Lachnoanaerobaculum umeaense in a patient with acute myeloid leukemia during chemotherapy: A case report, and a review of the literature

The present case reports a bacteremia due to Lachnoanaerobaculum umeaense (a Gram-positive, filamentous, rod-shaped, anaerobic, spore-forming bacillus present in the human oral microbiota) in a patient treated for acute myeloid leukemia. After failed identification by MALDI-TOF, identification was done by sequencing of 16s rRNA. The patient was successfully treated with Amoxicillin-clavulanic acid and ciprofloxacin for seven days. Comparison of V1-V3 regions of the bacterial 16S rRNA gene gene with published sequences failed to classify the strain as pathogenic or non-pathogenic based on this phylogenetic classification alone. Although Lachnoanaerobaculum gingivalis are known to be associated with bacteremia in patients with acute myeloid leukemia, this clinical case of infection by L. umeaense argues for further studies that will lead to more efficient classification of the infection by these microorganisms.

A Comparative Experimental and Computational Study on the Nature of the Pangolin-CoV and COVID-19 Omicron

The relationship between pangolin-CoV and SARS-CoV-2 has been a subject of debate. Further evidence of a special relationship between the two viruses can be found by the fact that all known COVID-19 viruses have an abnormally hard outer shell (low M disorder, i.e., low content of intrinsically disordered residues in the membrane (M) protein) that so far has been found in CoVs associated with burrowing animals, such as rabbits and pangolins, in which transmission involves virus remaining in buried feces for a long time. While a hard outer shell is necessary for viral survival, a harder inner shell could also help. For this reason, the N disorder range of pangolin-CoVs, not bat-CoVs, more closely matches that of SARS-CoV-2, especially when Omicron is included. The low N disorder (i.e., low content of intrinsically disordered residues in the nucleocapsid (N) protein), first observed in pangolin-CoV-2017 and later in Omicron, is associated with attenuation according to the Shell-Disorder Model. Our experimental study revealed that pangolin-CoV-2017 and SARS-CoV-2 Omicron (XBB.1.16 subvariant) show similar attenuations with respect to viral growth and plaque formation. Subtle differences have been observed that are consistent with disorder-centric computational analysis.

SpSIZ1 from hyperaccumulator Sedum plumbizincicola orchestrates SpABI5 to fine-tune cadmium tolerance

Sedum plumbizincicola is a renowned hyperaccumulator of cadmium (Cd), possesses significant potential for eco-friendly phytoremediation of soil contaminated with Cd. Nevertheless, comprehension of the mechanisms underpinning its Cd stress response remains constrained, primarily due to the absence of a comprehensive genome sequence and an established genetic transformation system. In this study, we successfully identified a novel protein that specifically responds to Cd stress through early comparative iTRAQ proteome and transcriptome analyses under Cd stress conditions. To further investigate its structure, we employed AlphaFold, a powerful tool for protein structure prediction, and found that this newly identified protein shares a similar structure with Arabidopsis AtSIZ1. Therefore, we named it Sedum plumbizincicola SIZ1 (SpSIZ1). Our study revealed that SpSIZ1 plays a crucial role in positively regulating Cd tolerance through its coordination with SpABI5. Overexpression of SpSIZ1 significantly enhanced plant resistance to Cd stress and reduced Cd accumulation. Expression pattern analysis revealed higher levels of SpSIZ1 expression in roots compared to stems and leaves, with up-regulation under Cd stress induction. Importantly, overexpressing SpSIZ1 resulted in lower Cd translocation factors (Tfs) but maintained relatively constant Cd levels in roots under Cd stress, leading to enhanced Cd stress resistance in plants. Protein interaction analysis revealed that SpSIZ1 interacts with SpABI5, and the expression of genes responsive to abscisic acid (ABA) through SpABI5-dependent signaling was significantly up-regulated in SpSIZ1-overexpressing plants with Cd stress treatment. Collectively, our results illustrate that SpSIZ1 interacts with SpABI5, enhancing the expression of ABA downstream stress-related genes through SpABI5, thereby increasing Cd tolerance in plants.

A novel approach to exploring the dark genome and its application to mapping of the vertebrate virus fossil record

BACKGROUND

Genomic regions that remain poorly understood, often referred to as the dark genome, contain a variety of functionally relevant and biologically informative features. These include endogenous viral elements (EVEs)-virus-derived sequences that can dramatically impact host biology and serve as a virus fossil record. In this study, we introduce a database-integrated genome screening (DIGS) approach to investigate the dark genome in silico, focusing on EVEs found within vertebrate genomes.

RESULTS

Using DIGS on 874 vertebrate genomes, we uncover approximately 1.1 million EVE sequences, with over 99% originating from endogenous retroviruses or transposable elements that contain EVE DNA. We show that the remaining 6038 sequences represent over a thousand distinct horizontal gene transfer events across 10 virus families, including some that have not previously been reported as EVEs. We explore the genomic and phylogenetic characteristics of non-retroviral EVEs and determine their rates of acquisition during vertebrate evolution. Our study uncovers novel virus diversity, broadens knowledge of virus distribution among vertebrate hosts, and provides new insights into the ecology and evolution of vertebrate viruses.

CONCLUSIONS

We comprehensively catalog and analyze EVEs within 874 vertebrate genomes, shedding light on the distribution, diversity, and long-term evolution of viruses and reveal their extensive impact on vertebrate genome evolution. Our results demonstrate the power of linking a relational database management system to a similarity search-based screening pipeline for in silico exploration of the dark genome.

Oligomerization mediated by the D2 domain of DTX3L is critical for DTX3L-PARP9 reading function of mono-ADP-ribosylated androgen receptor

Deltex proteins are a family of E3 ubiquitin ligases that encode C-terminal RING and DTC domains that mediate interactions with E2 ubiquitin-conjugating enzymes and recognize ubiquitination substrates. DTX3L is unique among the Deltex proteins based on its N-terminal domain architecture. The N-terminal D1 and D2 domains of DTX3L mediate homo-oligomerization, and the D3 domain interacts with PARP9, a protein that contains tandem macrodomains with ADP-ribose reader function. While DTX3L and PARP9 are known to heterodimerize, and assemble into a high molecular weight oligomeric complex, the nature of the oligomeric structure, including whether this contributes to the ADP-ribose reader function is unknown. Here, we report a crystal structure of the DTX3L N-terminal D2 domain and show that it forms a tetramer with, conveniently, D2 symmetry. We identified two interfaces in the structure: a major, conserved interface with a surface of 973 Å2 and a smaller one of 415 Å2. Using native mass spectrometry, we observed molecular species that correspond to monomers, dimers and tetramers of the D2 domain. Reconstitution of DTX3L knockout cells with a D1-D2 deletion mutant showed the domain is dispensable for DTX3L-PARP9 heterodimer formation, but necessary to assemble an oligomeric complex with efficient reader function for ADP-ribosylated androgen receptor. Our results suggest that homo-oligomerization of DTX3L is important for the DTX3L-PARP9 complex to read mono-ADP-ribosylation on a ligand-regulated transcription factor.

Homozygous CNP Mutation and Neurodegeneration in Weimaraners: Myelin Abnormalities and Accumulation of Lipofuscin-like Inclusions

A progressive neurological disorder was observed in a male neutered Weimaraner. Clinical signs included fecal incontinence, lethargy, moderate paraparesis, proprioceptive pelvic limb ataxia, falling, cognitive decline, incoordination, decreased interest in food, changes in posture, and episodes of trance-like behavior. Neurologic signs were first observed at approximately 4 years, 10 months of age and progressed slowly. Magnetic resonance imaging showed generalized brain atrophy with areas of white matter pathology. Humane euthanasia was elected at 6 years, 7 months of age due to increasing severity of the neurological signs. Autofluorescent intracellular granules were observed in the cerebral and cerebellar cortexes, optic nerve, and cardiac muscle of the affected dog. These abnormal inclusions in the cerebral cortex and cardiac muscle immunolabeled with antibodies to mitochondrial ATP synthase subunit c protein, like that observed in the neuronal ceroid lipofuscinosis group of lysosomal storage diseases. Immunolabeling also demonstrated pronounced neuroinflammation in brain tissues. The ultrastructural appearances of the disease-related inclusion bodies in the brain and optic nerve were quite variable. The ultrastructure and locations of many of the inclusions in the nervous tissues suggested that they were derived, at least in part, from the myelin surrounding axons. The storage bodies in the cardiac muscle were located in mitochondria-rich regions and consisted of parallel arrays of membrane-like components interspersed with electron-dense flocculent material. The disease was characterized by pronounced abnormalities in the myelin of the brain and optic nerve consisting of distinctive areas of ballooning between the layers of myelin. The whole genome sequence generated from the affected dog contained a homozygous G-to-A missense mutation in CNP, which encodes proteins with CNPase enzyme activity and a structural role in myelin. The mutation predicts a Thr42Met amino acid sequence substitution. Genotyping of archived Weimaraner DNA samples identified an additional G > A variant homozygote with a clinical history and brain lesions similar to those of the proband. Of 304 Weimaraners and over 4000 other dogs of various breeds, the proband and the other Weimaraner that exhibited similar signs were the only two that were homozygous for the CNP missense variant. CNPase immunolabeling was widespread in brain tissues from normal dogs but was undetectable in the same tissues from the proband. Based on the clinical history, fluorescence and electron-microscopy, immunohistochemistry, and molecular genetic findings, the late-onset Weimaraner disorder likely results from the missense mutation that results in CNPase deficiency, leading to myelin abnormalities, accumulation of lysosomal storage bodies, and brain atrophy. Similar disorders have been associated with different CNP variants in Dalmatians and in human subjects.

DescribePROT in 2023: more, higher-quality and experimental annotations and improved data download options

The DescribePROT database of amino acid-level descriptors of protein structures and functions was substantially expanded since its release in 2020. This expansion includes substantial increase in the size, scope, and quality of the underlying data, the addition of experimental structural information, the inclusion of new data download options, and an upgraded graphical interface. DescribePROT currently covers 19 structural and functional descriptors for proteins in 273 reference proteomes generated by 11 accurate and complementary predictive tools. Users can search our resource in multiple ways, interact with the data using the graphical interface, and download data at various scales including individual proteins, entire proteomes, and whole database. The annotations in DescribePROT are useful for a broad spectrum of studies that include investigations of protein structure and function, development and validation of predictive tools, and to support efforts in understanding molecular underpinnings of diseases and development of therapeutics. DescribePROT can be freely accessed at http://biomine.cs.vcu.edu/servers/DESCRIBEPROT/.

Differential expression of brummer and levels of TAG in different developmental stages Aedes aegypti (Diptera: Culicidae), including fasted adults

Lipid storage in the form of triacylglycerol (TAG) is essential for insect life, as it enables flight, development, and reproduction. The activity of the lipase brummer (bmm) has been shown to be essential to insects' homeostasis. The objective of this study was to evaluate how bmm expression occurs in Aedes aegypti larvae and adults, and to observe TAG levels during fasting in adult females. The bmm sequence was identified in A. aegypti and exhibited a patatin-like phospholipase domain reinforced by the presence of a catalytic dyad with serine and aspartate residues, revealing a high degree of similarity with other organisms. Bmm expression was differentiated in the larvae and adult fat body (FB) following TAG reserve dynamics. Bmm was expressed three times in larval stages L3, L4, and pupae compared with L1 and L2, which could indicate its role in the maturation of these insects. In the postemergence (PE) and post-blood meal (PBM) FB of adult insects, bmm expression varied over several days. PE adults showed a pronounced bmm increase from the third day onward compared with those not subjected to fasting. This was accompanied by a decrease in TAG from the third day onward, suggesting the participation of bmm. Six hours after blood feeding, TAG levels increased in mosquitos reared in the absence of sucrose, suggesting lipid accumulation to guarantee reproduction. Bmm responded positively to fasting, followed by TAG mobilization in adult FB. During the previtellogenic period, bmm levels responded to low TAG levels, unlike the PBM period.

WIV, a protein domain found in a wide number of arthropod viruses, which probably facilitates infection

The most powerful approach to detect distant homologues of a protein is based on structure prediction and comparison. Yet this approach is still inapplicable to many viral proteins. Therefore, we applied a powerful sequence-based procedure to identify distant homologues of viral proteins. It relies on three principles: (1) traces of sequence similarity can persist beyond the significance cutoff of homology detection programmes; (2) candidate homologues can be identified among proteins with weak sequence similarity to the query by using 'contextual' information, e.g. taxonomy or type of host infected; (3) these candidate homologues can be validated using highly sensitive profile-profile comparison. As a test case, this approach was applied to a protein without known homologues, encoded by ORF4 of Lake Sinai viruses (which infect bees). We discovered that the ORF4 protein contains a domain that has homologues in proteins from >20 taxa of viruses infecting arthropods. We called this domain 'widespread, intriguing, versatile' (WIV), because it is found in proteins with a wide variety of functions and within varied domain contexts. For example, WIV is found in the NSs protein of tospoviruses, a global threat to food security, which infect plants as well as their arthropod vectors; in the RNA2 ORF1-encoded protein of chronic bee paralysis virus, a widespread virus of bees; and in various proteins of cypoviruses, which infect the silkworm Bombyx mori. Structural modelling with AlphaFold indicated that the WIV domain has a previously unknown fold, and bibliographical evidence suggests that it facilitates infection of arthropods.

Identification and production of novel potential pathogen-specific biomarkers for diagnosis of histoplasmosis

IMPORTANCE

Histoplasmosis is considered one of the most important mycoses due to the increasing number of individuals susceptible to develop severe clinical forms, particularly those with HIV/AIDS or receiving immunosuppressive biological therapies, the high mortality rates reported when antifungal treatment is not initiated in a timely manner, and the limitations of conventional diagnostic methods. In this context, there is a clear need to improve the capacity of diagnostic tools to specifically detect the fungal pathogen, regardless of the patient's clinical condition or the presence of other co-infections. The proposed novel pathogen-specific biomarkers have the potential to be used in immunodiagnostic platforms and antifungal treatment monitoring in histoplasmosis. In addition, the bioinformatics strategy used in this study could be applied to identify potential diagnostic biomarkers in other models of fungal infection of public health importance.

Oligomerisation mediated by the D2 domain of DTX3L is critical for DTX3L-PARP9 reading function of mono-ADP-ribosylated androgen receptor

Deltex proteins are a family of E3 ubiquitin ligases that encode C-terminal RING and DTC domains that mediate interactions with E2 ubiquitin-conjugating enzymes and recognise ubiquitination substrates. DTX3L is unique among the Deltex proteins based on its N-terminal domain architecture. The N-terminal D1 and D2 domains of DTX3L mediate homo-oligomerisation, and the D3 domain interacts with PARP9, a protein that contains tandem macrodomains with ADP-ribose reader function. While DTX3L and PARP9 are known to heterodimerize, they assemble into a high molecular weight oligomeric complex, but the nature of the oligomeric structure, including whether this contributes to the ADP-ribose reader function is unknown. Here, we report a crystal structure of the DTX3L N-terminal D2 domain and show that it forms a tetramer with, conveniently, D2 symmetry. We identified two interfaces in the structure: a major, conserved interface with a surface of 973 Å2 and a smaller one of 415 Å2. Using native mass spectrometry, we observed molecular species that correspond to monomers, dimers and tetramers of the D2 domain. Reconstitution of DTX3L knockout cells with a D1-D2 deletion mutant showed the domain is dispensable for DTX3L-PARP9 heterodimer formation, but necessary to assemble an oligomeric complex with efficient reader function for ADP-ribosylated androgen receptor. Our results suggest that homo-oligomerisation of DTX3L is important for mono-ADP-ribosylation reading by the DTX3L-PARP9 complex and to a ligand-regulated transcription factor.

de B. Pereira H, M. da Rocha Filho T, A. S. Machado B, A. Moret M. New distance measure for comparing protein using cellular automata image

One of the first steps in protein sequence analysis is comparing sequences to look for similarities. We propose an information theoretical distance to compare cellular automata representing protein sequences, and determine similarities. Our approach relies in a stationary Hamming distance for the evolution of the automata according to a properly chosen rule, and to build a pairwise similarity matrix and determine common ancestors among different species in a simpler and less computationally demanding computer codes when compared to other methods.

Genomic Analysis and Taxonomic Characterization of Seven Bacteriophage Genomes Metagenomic-Assembled from the Dishui Lake

Viruses in aquatic ecosystems exhibit remarkable abundance and diversity. However, scattered studies have been conducted to mine uncultured viruses and identify them taxonomically in lake water. Here, whole genomes (29-173 kbp) of seven uncultured dsDNA bacteriophages were discovered in Dishui Lake, the largest artificial lake in Shanghai. We analyzed their genomic signatures and found a series of viral auxiliary metabolic genes closely associated with protein synthesis and host metabolism. Dishui Lake phages shared more genes with uncultivated environmental viruses than with reference viruses based on the gene-sharing network classification. Phylogeny of proteomes and comparative genomics delineated three new genera within two known viral families of Kyanoviridae and Autographiviridae, and four new families in Caudoviricetes for these seven novel phages. Their potential hosts appeared to be from the dominant bacterial phyla in Dishui Lake. Altogether, our study provides initial insights into the composition and diversity of bacteriophage communities in Dishui Lake, contributing valuable knowledge to the ongoing research on the roles played by viruses in freshwater ecosystems.

From genotype to phenotype: computational approaches for inferring microbial traits relevant to the food industry

When selecting microbial strains for the production of fermented foods, various microbial phenotypes need to be taken into account to achieve target product characteristics, such as biosafety, flavor, texture, and health-promoting effects. Through continuous advances in sequencing technologies, microbial whole-genome sequences of increasing quality can now be obtained both cheaper and faster, which increases the relevance of genome-based characterization of microbial phenotypes. Prediction of microbial phenotypes from genome sequences makes it possible to quickly screen large strain collections in silico to identify candidates with desirable traits. Several microbial phenotypes relevant to the production of fermented foods can be predicted using knowledge-based approaches, leveraging our existing understanding of the genetic and molecular mechanisms underlying those phenotypes. In the absence of this knowledge, data-driven approaches can be applied to estimate genotype-phenotype relationships based on large experimental datasets. Here, we review computational methods that implement knowledge- and data-driven approaches for phenotype prediction, as well as methods that combine elements from both approaches. Furthermore, we provide examples of how these methods have been applied in industrial biotechnology, with special focus on the fermented food industry.

Four distinct isolates of a novel polymycovirus identified in Setosphaeria turcica

Isolation and analysis of double-stranded RNA (dsRNA) from the phytopathogenic fungus Setosphaeria turcica f. sp. zeae revealed the presence of a new double-stranded RNA (dsRNA) virus, tentatively named "Setosphaeria turcica polymycovirus 2" (StPmV2). The genome of StPmV2 consists of five segments (dsRNA1-5), ranging in size from 965 bp to 2462 bp. Each dsRNA contains one open reading frame (ORF) flanked by 5' and 3' untranslated regions (UTRs) with conserved terminal sequences. The putative protein encoded by dsRNA1 shows 64.52% amino acid sequence identity to the RNA-dependent RNA polymerase (RdRp) of the most closely related virus, Cladosporium cladosporioides virus 1, which belongs to the family Polymycoviridae. dsRNAs 2-4 encode the putative coat protein, methyltransferase (MTR), and proline-alanine-serine-rich protein (PASrp), respectively, and dsRNA5 encodes a protein of unknown function. Phylogenetic analysis based on the RdRp protein indicated that StPmV2 clustered with members of the family Polymycoviridae and is therefore a new mycovirus belonging to the genus Polymycovirus in the family Polymycoviridae. In addition, three other distinct isolates of StPmV2 were identified: one isolated from S. turcica f. sp. zeae and two from S. turcica f. sp. sorghi. To our knowledge, this is the first report of a polymycovirus infecting both S. turcica f. sp. zeae and S. turcica f. sp. sorghi.

Chlamydia psittaci inclusion membrane protein CPSIT_0842 induces macrophage apoptosis through MAPK/ERK-mediated autophagy

Chlamydia psittaci is a multi-host zoonotic pathogen, which mainly infects poultry and inflicts an appreciable economic burden on the livestock farming industry. C. psittaci inclusion membrane proteins are uniquely positioned at the host-pathogen interface and are important virulence proteins. We have previously confirmed that Incs regulate host cell survival to help Chlamydia sp. evade host-cell-mediated defense mechanisms. However, the role of the Inc, CPSIT_0842, in the regulation of cell death following the establishment of persistent C. psittaci infection remains unknown. This study explored the effect of CPSIT_0842 on the crosstalk between the autophagic and apoptotic pathways in macrophages. Results showed that CPSIT_0842 initiated autophagy and blocked autophagic flux in human macrophages, as indicated by autophagy-related protein LC3-II, Beclin-1, and p62 upregulation, autophagosome accumulation, and lysosomal protein LAMP1 diminution. We also showed that the disruption of autophagic flux had a regulatory effect on CPSIT_0842-induced apoptosis. Moreover, the suppression of autophagy initiation by 3-methyladenine attenuated CPSIT_0842-induced apoptosis. By contrast, the induction of autophagic flux by rapamycin did not significantly affect CPSIT_0842-induced apoptosis. Taken together, these findings demonstrate that CPSIT_0842 induced macrophage apoptosis by initiating incomplete autophagy through the MAPK/ERK/mTOR signaling pathway, which may be instrumental to the ability of C. psittaci to evade the host innate immune response and establish persistent infection. The improved understanding of the autophagic and cell death pathways triggered upon bacterial inclusion will likely help in the development of novel treatment strategies for chlamydia infection.

First Bacteremia Due to Corynebacterium gottingense in an Immunocompromised Child: A Case Report, 16S rDNA-Based Phylogenetic Analyses and Review of the Literature

Corynebacterium gottingense is a Gram-positive bacillus that has not been reported as pathogenic in pediatric patients. Herein, a case of catheter-associated bloodstream infection by C. gottingense in a 13-year-old immunocompromised child with febrile neutropenia induced for osteosarcoma is reported. The species was identified by Sanger sequencing of the 16s rRNA sequence of the bacterial strain and was compared phylogenetically with published sequences. As suggested in the literature, the presented strain was multi-susceptible, particularly to amoxicillin. The patient was treated with piperacillin/tazobactam for seven days in the context of a urinary co-infection, resulting in resolution of fever within 48 h and then relaunched with oral amoxicillin for 3 days (for a total of 10 days of antibiotic therapy). Phylogenetic analyses based on 16S rDNA demonstrated the complexity of the genus Corynebacterium spp. but failed to demonstrate a direct benefit in predicting clinical outcome based on this single information.

CLIP: accurate prediction of disordered linear interacting peptides from protein sequences using co-evolutionary information

One of key features of intrinsically disordered regions (IDRs) is facilitation of protein-protein and protein-nucleic acids interactions. These disordered binding regions include molecular recognition features (MoRFs), short linear motifs (SLiMs) and longer binding domains. Vast majority of current predictors of disordered binding regions target MoRFs, with a handful of methods that predict SLiMs and disordered protein-binding domains. A new and broader class of disordered binding regions, linear interacting peptides (LIPs), was introduced recently and applied in the MobiDB resource. LIPs are segments in protein sequences that undergo disorder-to-order transition upon binding to a protein or a nucleic acid, and they cover MoRFs, SLiMs and disordered protein-binding domains. Although current predictors of MoRFs and disordered protein-binding regions could be used to identify some LIPs, there are no dedicated sequence-based predictors of LIPs. To this end, we introduce CLIP, a new predictor of LIPs that utilizes robust logistic regression model to combine three complementary types of inputs: co-evolutionary information derived from multiple sequence alignments, physicochemical profiles and disorder predictions. Ablation analysis suggests that the co-evolutionary information is particularly useful for this prediction and that combining the three inputs provides substantial improvements when compared to using these inputs individually. Comparative empirical assessments using low-similarity test datasets reveal that CLIP secures area under receiver operating characteristic curve (AUC) of 0.8 and substantially improves over the results produced by the closest current tools that predict MoRFs and disordered protein-binding regions. The webserver of CLIP is freely available at http://biomine.cs.vcu.edu/servers/CLIP/ and the standalone code can be downloaded from http://yanglab.qd.sdu.edu.cn/download/CLIP/.

Genome-Based Multi-Antigenic Epitopes Vaccine Construct Designing against Staphylococcus hominis Using Reverse Vaccinology and Biophysical Approaches

Staphylococcus hominis is a Gram-positive bacterium from the staphylococcus genus; it is also a member of coagulase-negative staphylococci because of its opportunistic nature and ability to cause life-threatening bloodstream infections in immunocompromised patients. Gram-positive and opportunistic bacteria have become a major concern for the medical community. It has also drawn the attention of scientists due to the evaluation of immune evasion tactics and the development of multidrug-resistant strains. This prompted the need to explore novel therapeutic approaches as an alternative to antibiotics. The current study aimed to develop a broad-spectrum, multi-epitope vaccine to control bacterial infections and reduce the burden on healthcare systems. A computational framework was designed to filter the immunogenic potent vaccine candidate. This framework consists of pan-genomics, subtractive proteomics, and immunoinformatics approaches to prioritize vaccine candidates. A total of 12,285 core proteins were obtained using a pan-genome analysis of all strains. The screening of the core proteins resulted in the selection of only two proteins for the next epitope prediction phase. Eleven B-cell derived T-cell epitopes were selected that met the criteria of different immunoinformatics approaches such as allergenicity, antigenicity, immunogenicity, and toxicity. A vaccine construct was formulated using EAAAK and GPGPG linkers and a cholera toxin B subunit. This formulated vaccine construct was further used for downward analysis. The vaccine was loop refined and improved for structure stability through disulfide engineering. For an efficient expression, the codons were optimized as per the usage pattern of the E coli (K12) expression system. The top three refined docked complexes of the vaccine that docked with the MHC-I, MHC-II, and TLR-4 receptors were selected, which proved the best binding potential of the vaccine with immune receptors; this was followed by molecular dynamic simulations. The results indicate the best intermolecular bonding between immune receptors and vaccine epitopes and that they are exposed to the host's immune system. Finally, the binding energies were calculated to confirm the binding stability of the docked complexes. This work aimed to provide a manageable list of immunogenic and antigenic epitopes that could be used as potent vaccine candidates for experimental in vivo and in vitro studies.

Evaluation of Intracellular Gene Transfers from Plastome to Nuclear Genome across Progressively Improved Assemblies for Arabidopsis thaliana and Oryza sativa

DNA originating from organellar genomes are regularly discovered in nuclear sequences during genome assembly. Nevertheless, such insertions are sometimes omitted during the process of nuclear genome assembly because the inserted DNA is assigned to organellar genomes, leading to a systematic underestimation of their frequency. With the rapid development of high-throughput sequencing technology, more inserted fragments from organelle genomes can now be detected. Therefore, it is necessary to be aware of the insertion events from organellar genomes during nuclear genome assembly to properly attribute the impact and rate of such insertions in the evolution of nuclear genomes. Here, we investigated the impact of intracellular gene transfer (IGT) from the plastome to the nuclear genome using genome assemblies that were refined through time with technological improvements from two model species, Arabidopsis thaliana and Oryza sativa. We found that IGT from the plastome to the nuclear genome is a dynamic and ongoing process in both A. thaliana and O. sativa, and mostly occurred recently, as the majority of transferred sequences showed over 95% sequence similarity with plastome sequences of origin. Differences in the plastome-to-nuclear genome IGT between A. thaliana and O. sativa varied among the different assembly versions and were associated with the quality of the nuclear genome assembly. IGTs from the plastome to nuclear genome occurred more frequently in intergenic regions, which were often associated with transposable elements (TEs). This study provides new insights into intracellular genome evolution and nuclear genome assembly by characterizing and comparing IGT from the plastome into the nuclear genome for two model plant species.

Genomic Analysis of the Serratia marcescens Bacteriophage BUCT660

Here, we report the complete genome sequence of bacteriophage BUCT660, which comprises a linear double-stranded DNA (dsDNA) genome of 272,720 bp and a G+C content of 47%. BUCT660 contains 316 open reading frames and 2 tRNA-encoding genes. The results of transmission electron microscopy (TEM) indicate that BUCT660 is a member of the family Caudooviricetes.

Mycovirus Hunting Revealed the Presence of Diverse Viruses in a Single Isolate of the Phytopathogenic Fungus Diplodia seriata From Pakistan

Diplodia seriata in the family Botryosphaeriaceae is a cosmopolitan phytopathogenic fungus and is responsible for causing cankers, fruit rot and leaf spots on economically important plants. In this study, we characterized the virome of a single Pakistani strain (L3) of D. seriata. Several viral-like contig sequences were obtained via a previously conducted next-generation sequencing analysis. Multiple infection of the L3 strain by eight RNA mycoviruses was confirmed through RT-PCR using total RNA samples extracted from this strain; the entire genomes were determined via Sanger sequencing of RT-PCR and RACE clones. A BLAST search and phylogenetic analyses indicated that these eight mycoviruses belong to seven different viral families. Four identified mycoviruses belong to double-stranded RNA viral families, including Polymycoviridae, Chrysoviridae, Totiviridae and Partitiviridae, and the remaining four identified mycoviruses belong to single-stranded RNA viral families, i.e., Botourmiaviridae, and two previously proposed families "Ambiguiviridae" and "Splipalmiviridae". Of the eight, five mycoviruses appear to represent new virus species. A morphological comparison of L3 and partially cured strain L3ht1 suggested that one or more of the three viruses belonging to Polymycoviridae, "Splipalmiviridae" and "Ambiguiviridae" are involved in the irregular colony phenotype of L3. To our knowledge, this is the first report of diverse virome characterization from D. seriata.

An endolysin Salmcide-p1 from bacteriophage fmb-p1 against gram-negative bacteria

AIMS

A novel endolysin Salmcide-p1 was developed as a promising candidate of new preservative and a supplement to effective enzyme preparations against gram-negative bacterial contaminations.

METHODS AND RESULTS

Salmcide-p1 was identified by complementing the genomic sequence of a virulent Salmonella phage fmb-p1. Salmcide-p1 of 112 μg ml^-1 could quickly kill Salmonella incubated with 100 mmol l^-1 EDTA, with no haemolytic activity. Meanwhile, Salmcide-p1 had a high activity of lysing Salmonella cell wall peptidoglycan. At different temperatures (4-75°C), pH (4-11) and NaCl concentration (10-200 mmol l^-1 ), the relative activity of Salmcide-p1 was above 60%. At 4°C, the combination of Salmcide-p1 and EDTA-2Na could inhibit the number of Salmonella Typhimurium CMCC 50115 in skim milk to less than 4 log CFU ml^-1 by 3 days, and the number of Shigella flexneri CMCC 51571 was lower than 4 log CFU ml^-1 by 9 days.

CONCLUSIONS

Salmcide-p1 had a wide bactericidal activity against gram-negative bacteria and showed a broader anti-Salmonella spectrum than the phage fmb-p1. The combination strategy of Salmcide-p1 and EDTA-2Na could significantly inhibit the growth of gram-negative bacteria inoculated in skim milk.

SIGNIFICANCE AND IMPACT OF THE STUDY

Bacteriophage endolysin as an antibacterial agent is considered to be a new strategy against bacterial contamination.

Discriminatory Gleason grade group signatures of prostate cancer: An application of machine learning methods

One of the most precise methods to detect prostate cancer is by evaluation of a stained biopsy by a pathologist under a microscope. Regions of the tissue are assessed and graded according to the observed histological pattern. However, this is not only laborious, but also relies on the experience of the pathologist and tends to suffer from the lack of reproducibility of biopsy outcomes across pathologists. As a result, computational approaches are being sought and machine learning has been gaining momentum in the prediction of the Gleason grade group. To date, machine learning literature has addressed this problem by using features from magnetic resonance imaging images, whole slide images, tissue microarrays, gene expression data, and clinical features. However, there is a gap with regards to predicting the Gleason grade group using DNA sequences as the only input source to the machine learning models. In this work, using whole genome sequence data from South African prostate cancer patients, an application of machine learning and biological experiments were combined to understand the challenges that are associated with the prediction of the Gleason grade group. A series of machine learning binary classifiers (XGBoost, LSTM, GRU, LR, RF) were created only relying on DNA sequences input features. All the models were not able to adequately discriminate between the DNA sequences of the studied Gleason grade groups (Gleason grade group 1 and 5). However, the models were further evaluated in the prediction of tumor DNA sequences from matched-normal DNA sequences, given DNA sequences as the only input source. In this new problem, the models performed acceptably better than before with the XGBoost model achieving the highest accuracy of 74 ± 01, F1 score of 79 ± 01, recall of 99 ± 0.0, and precision of 66 ± 0.1.

In silico Methods for Identification of Potential Therapeutic Targets

At the initial stage of drug discovery, identifying novel targets with maximal efficacy and minimal side effects can improve the success rate and portfolio value of drug discovery projects while simultaneously reducing cycle time and cost. However, harnessing the full potential of big data to narrow the range of plausible targets through existing computational methods remains a key issue in this field. This paper reviews two categories of in silico methods—comparative genomics and network-based methods—for finding potential therapeutic targets among cellular functions based on understanding their related biological processes. In addition to describing the principles, databases, software, and applications, we discuss some recent studies and prospects of the methods. While comparative genomics is mostly applied to infectious diseases, network-based methods can be applied to infectious and non-infectious diseases. Nonetheless, the methods often complement each other in their advantages and disadvantages. The information reported here guides toward improving the application of big data-driven computational methods for therapeutic target discovery.

Graphical abstract

Complementarity of the residue-level protein function and structure predictions in human proteins

Sequence-based predictors of the residue-level protein function and structure cover a broad spectrum of characteristics including intrinsic disorder, secondary structure, solvent accessibility and binding to nucleic acids. They were catalogued and evaluated in numerous surveys and assessments. However, methods focusing on a given characteristic are studied separately from predictors of other characteristics, while they are typically used on the same proteins. We fill this void by studying complementarity of a representative collection of methods that target different predictions using a large, taxonomically consistent, and low similarity dataset of human proteins. First, we bridge the gap between the communities that develop structure-trained vs. disorder-trained predictors of binding residues. Motivated by a recent study of the protein-binding residue predictions, we empirically find that combining the structure-trained and disorder-trained predictors of the DNA-binding and RNA-binding residues leads to substantial improvements in predictive quality. Second, we investigate whether diverse predictors generate results that accurately reproduce relations between secondary structure, solvent accessibility, interaction sites, and intrinsic disorder that are present in the experimental data. Our empirical analysis concludes that predictions accurately reflect all combinations of these relations. Altogether, this study provides unique insights that support combining results produced by diverse residue-level predictors of protein function and structure.

Genomic analysis of Acinetobacter phage BUCT629, a newly isolated member of the genus Obolenskvirus

A new virulent Acinetobacter phage, BUCT629 (GenBank no. MZ712044.1), was isolated from hospital sewage. Next-generation sequencing (NGS) results demonstrated that the double-stranded linear DNA genome of phage BUCT629 is 46,325 bp in length with a G+C content of 38%. The BLASTn analysis showed that the genome sequence shared similarity with Acinetobacter phage vB_AbaM_IME285, with 65% query coverage and 98.23% identity, suggesting that phage BUCT629 is a novel phage. The phage genome contains 89 putative protein-coding genes, and no rRNA or tRNA genes were identified. The results of this study may be helpful for discovering new antibacterial agents and for understanding the evolution and genetic diversity of Acinetobacter phages.

Assembled and annotated 26.5 Gbp coast redwood genome: a resource for estimating evolutionary adaptive potential and investigating hexaploid origin

Sequencing, assembly, and annotation of the 26.5 Gbp hexaploid genome of coast redwood (Sequoia sempervirens) was completed leading toward discovery of genes related to climate adaptation and investigation of the origin of the hexaploid genome. Deep-coverage short-read Illumina sequencing data from haploid tissue from a single seed were combined with long-read Oxford Nanopore Technologies sequencing data from diploid needle tissue to create an initial assembly, which was then scaffolded using proximity ligation data to produce a highly contiguous final assembly, SESE 2.1, with a scaffold N50 size of 44.9 Mbp. The assembly included several scaffolds that span entire chromosome arms, confirmed by the presence of telomere and centromere sequences on the ends of the scaffolds. The structural annotation produced 118,906 genes with 113 containing introns that exceed 500 Kbp in length and one reaching 2 Mb. Nearly 19 Gbp of the genome represented repetitive content with the vast majority characterized as long terminal repeats, with a 2.9:1 ratio of Copia to Gypsy elements that may aid in gene expression control. Comparison of coast redwood to other conifers revealed species-specific expansions for a plethora of abiotic and biotic stress response genes, including those involved in fungal disease resistance, detoxification, and physical injury/structural remodeling and others supporting flavonoid biosynthesis. Analysis of multiple genes that exist in triplicate in coast redwood but only once in its diploid relative, giant sequoia, supports a previous hypothesis that the hexaploidy is the result of autopolyploidy rather than any hybridizations with separate but closely related conifer species.

Surveying over 100 predictors of intrinsic disorder in proteins

INTRODUCTION

Intrinsic disorder prediction field develops, assesses, and deploys computational predictors of disorder in protein sequences and constructs and disseminates databases of these predictions. Over 40 years of research resulted in the release of numerous resources.

AREAS COVERED

We identify and briefly summarize the most comprehensive to date collection of over 100 disorder predictors. We focus on their predictive models, availability and predictive performance. We categorize and study them from a historical point of view to highlight informative trends.

EXPERT OPINION

We find a consistent trend of improvements in predictive quality as newer and more advanced predictors are developed. The original focus on machine learning methods has shifted to meta-predictors in early 2010s, followed by a recent transition to deep learning. The use of deep learners will continue in foreseeable future given recent and convincing success of these methods. Moreover, a broad range of resources that facilitate convenient collection of accurate disorder predictions is available to users. They include web servers and standalone programs for disorder prediction, servers that combine prediction of disorder and disorder functions, and large databases of pre-computed predictions. We also point to the need to address the shortage of accurate methods that predict disordered binding regions.

DeepDISOBind: accurate prediction of RNA-, DNA- and protein-binding intrinsically disordered residues with deep multi-task learning

Proteins with intrinsically disordered regions (IDRs) are common among eukaryotes. Many IDRs interact with nucleic acids and proteins. Annotation of these interactions is supported by computational predictors, but to date, only one tool that predicts interactions with nucleic acids was released, and recent assessments demonstrate that current predictors offer modest levels of accuracy. We have developed DeepDISOBind, an innovative deep multi-task architecture that accurately predicts deoxyribonucleic acid (DNA)-, ribonucleic acid (RNA)- and protein-binding IDRs from protein sequences. DeepDISOBind relies on an information-rich sequence profile that is processed by an innovative multi-task deep neural network, where subsequent layers are gradually specialized to predict interactions with specific partner types. The common input layer links to a layer that differentiates protein- and nucleic acid-binding, which further links to layers that discriminate between DNA and RNA interactions. Empirical tests show that this multi-task design provides statistically significant gains in predictive quality across the three partner types when compared to a single-task design and a representative selection of the existing methods that cover both disorder- and structure-trained tools. Analysis of the predictions on the human proteome reveals that DeepDISOBind predictions can be encoded into protein-level propensities that accurately predict DNA- and RNA-binding proteins and protein hubs. DeepDISOBind is available at https://www.csuligroup.com/DeepDISOBind/.

Legionella effector LegA15/AnkH contains an unrecognized cysteine protease-like domain and displays structural similarity to LegA3/AnkD, but differs in host cell localization

Legionella pneumophila is a human pathogen that causes Legionnaires' disease, a severe form of pneumonia. It can be found in various aquatic environments ranging from cooling towers to ponds. In addition to causing disease in humans, it can also infect free-living amoebae commonly found in various aquatic environments. Once inside a human lung macrophage, it creates a niche called the Legionella-containing vacuole where it can evade phagolysosomal degradation and replicate. During infection, normal cellular functions are hijacked by proteins that are secreted by the pathogen, called bacterial effectors. Here, the structural characterization of the effector LegA15/AnkD is reported. The protein contains an ankyrin-repeat domain followed by a cysteine protease-like (CPL) domain with a putative catalytic triad consisting of His268-Asn290-Cys361. The CPL domain shows similarity to the CE clan in the MEROPS database, which contains ubiquitin-like hydrolases. The C-terminal segment of LegA15, including the CPL domain, shows structural similarity to another effector, LegA3/AnkH, while they share only 12% sequence identity. When expressed in mammalian cells, LegA15 is localized within the cytoplasm, in contrast to LegA3, which localizes to the nucleus.

DNAgenie: accurate prediction of DNA-type-specific binding residues in protein sequences

Efforts to elucidate protein-DNA interactions at the molecular level rely in part on accurate predictions of DNA-binding residues in protein sequences. While there are over a dozen computational predictors of the DNA-binding residues, they are DNA-type agnostic and significantly cross-predict residues that interact with other ligands as DNA binding. We leverage a custom-designed machine learning architecture to introduce DNAgenie, first-of-its-kind predictor of residues that interact with A-DNA, B-DNA and single-stranded DNA. DNAgenie uses a comprehensive physiochemical profile extracted from an input protein sequence and implements a two-step refinement process to provide accurate predictions and to minimize the cross-predictions. Comparative tests on an independent test dataset demonstrate that DNAgenie outperforms the current methods that we adapt to predict residue-level interactions with the three DNA types. Further analysis finds that the use of the second (refinement) step leads to a substantial reduction in the cross predictions. Empirical tests show that DNAgenie's outputs that are converted to coarse-grained protein-level predictions compare favorably against recent tools that predict which DNA-binding proteins interact with double-stranded versus single-stranded DNAs. Moreover, predictions from the sequences of the whole human proteome reveal that the results produced by DNAgenie substantially overlap with the known DNA-binding proteins while also including promising leads for several hundred previously unknown putative DNA binders. These results suggest that DNAgenie is a valuable tool for the sequence-based characterization of protein functions. The DNAgenie's webserver is available at http://biomine.cs.vcu.edu/servers/DNAgenie/.

Accurate Sequence-Based Prediction of Deleterious nsSNPs with Multiple Sequence Profiles and Putative Binding Residues

Non-synonymous single nucleotide polymorphisms (nsSNPs) may result in pathogenic changes that are associated with human diseases. Accurate prediction of these deleterious nsSNPs is in high demand. The existing predictors of deleterious nsSNPs secure modest levels of predictive performance, leaving room for improvements. We propose a new sequence-based predictor, DMBS, which addresses the need to improve the predictive quality. The design of DMBS relies on the observation that the deleterious mutations are likely to occur at the highly conserved and functionally important positions in the protein sequence. Correspondingly, we introduce two innovative components. First, we improve the estimates of the conservation computed from the multiple sequence profiles based on two complementary databases and two complementary alignment algorithms. Second, we utilize putative annotations of functional/binding residues produced by two state-of-the-art sequence-based methods. These inputs are processed by a random forests model that provides favorable predictive performance when empirically compared against five other machine-learning algorithms. Empirical results on four benchmark datasets reveal that DMBS achieves AUC > 0.94, outperforming current methods, including protein structure-based approaches. In particular, DMBS secures AUC = 0.97 for the SNPdbe and ExoVar datasets, compared to AUC = 0.70 and 0.88, respectively, that were obtained by the best available methods. Further tests on the independent HumVar dataset shows that our method significantly outperforms the state-of-the-art method SNPdryad. We conclude that DMBS provides accurate predictions that can effectively guide wet-lab experiments in a high-throughput manner.

The robustness of the chaos game representation to mutations and its application in free-alignment methods

Numerical representation of biological sequences plays an important role in bioinformatics and has many practical applications. One of the most popular approaches is the chaos game representation. In this paper, the authors propose a novel look into chaos game construction - an analytical description of this procedure. This type enables to build more general number sequences using different weight functions. The authors suggest three conditions that these functions should hold. Additionally, they present some criteria to compare them and check whether they provide a unique representation. One of the most important advantages of our approach is the possibility to construct such a description that is less sensitive to mutations and as a result, give more reliable values for free-alignment phylogenetic trees constructions. Finally, the authors applied the DFT method using four types of functions and compared the obtained results using the BLAST tool.

DescribePROT: database of amino acid-level protein structure and function predictions

We present DescribePROT, the database of predicted amino acid-level descriptors of structure and function of proteins. DescribePROT delivers a comprehensive collection of 13 complementary descriptors predicted using 10 popular and accurate algorithms for 83 complete proteomes that cover key model organisms. The current version includes 7.8 billion predictions for close to 600 million amino acids in 1.4 million proteins. The descriptors encompass sequence conservation, position specific scoring matrix, secondary structure, solvent accessibility, intrinsic disorder, disordered linkers, signal peptides, MoRFs and interactions with proteins, DNA and RNAs. Users can search DescribePROT by the amino acid sequence and the UniProt accession number and entry name. The pre-computed results are made available instantaneously. The predictions can be accesses via an interactive graphical interface that allows simultaneous analysis of multiple descriptors and can be also downloaded in structured formats at the protein, proteome and whole database scale. The putative annotations included by DescriPROT are useful for a broad range of studies, including: investigations of protein function, applied projects focusing on therapeutics and diseases, and in the development of predictors for other protein sequence descriptors. Future releases will expand the coverage of DescribePROT. DescribePROT can be accessed at http://biomine.cs.vcu.edu/servers/DESCRIBEPROT/.

PROBselect: accurate prediction of protein-binding residues from proteins sequences via dynamic predictor selection

Motivation

Knowledge of protein-binding residues (PBRs) improves our understanding of protein−protein interactions, contributes to the prediction of protein functions and facilitates protein−protein docking calculations. While many sequence-based predictors of PBRs were published, they offer modest levels of predictive performance and most of them cross-predict residues that interact with other partners. One unexplored option to improve the predictive quality is to design consensus predictors that combine results produced by multiple methods.

Results

We empirically investigate predictive performance of a representative set of nine predictors of PBRs. We report substantial differences in predictive quality when these methods are used to predict individual proteins, which contrast with the dataset-level benchmarks that are currently used to assess and compare these methods. Our analysis provides new insights for the cross-prediction concern, dissects complementarity between predictors and demonstrates that predictive performance of the top methods depends on unique characteristics of the input protein sequence. Using these insights, we developed PROBselect, first-of-its-kind consensus predictor of PBRs. Our design is based on the dynamic predictor selection at the protein level, where the selection relies on regression-based models that accurately estimate predictive performance of selected predictors directly from the sequence. Empirical assessment using a low-similarity test dataset shows that PROBselect provides significantly improved predictive quality when compared with the current predictors and conventional consensuses that combine residue-level predictions. Moreover, PROBselect informs the users about the expected predictive quality for the prediction generated from a given input protein.

Availability and implementation

PROBselect is available at http://bioinformatics.csu.edu.cn/PROBselect/home/index.

Supplementary information

Supplementary data are available at Bioinformatics online.

Comparative Assessment of Intrinsic Disorder Predictions with a Focus on Protein and Nucleic Acid-Binding Proteins

With over 60 disorder predictors, users need help navigating the predictor selection task. We review 28 surveys of disorder predictors, showing that only 11 include assessment of predictive performance. We identify and address a few drawbacks of these past surveys. To this end, we release a novel benchmark dataset with reduced similarity to the training sets of the considered predictors. We use this dataset to perform a first-of-its-kind comparative analysis that targets two large functional families of disordered proteins that interact with proteins and with nucleic acids. We show that limiting sequence similarity between the benchmark and the training datasets has a substantial impact on predictive performance. We also demonstrate that predictive quality is sensitive to the use of the well-annotated order and inclusion of the fully structured proteins in the benchmark datasets, both of which should be considered in future assessments. We identify three predictors that provide favorable results using the new benchmark set. While we find that VSL2B offers the most accurate and robust results overall, ESpritz-DisProt and SPOT-Disorder perform particularly well for disordered proteins. Moreover, we find that predictions for the disordered protein-binding proteins suffer low predictive quality compared to generic disordered proteins and the disordered nucleic acids-binding proteins. This can be explained by the high disorder content of the disordered protein-binding proteins, which makes it difficult for the current methods to accurately identify ordered regions in these proteins. This finding motivates the development of a new generation of methods that would target these difficult-to-predict disordered proteins. We also discuss resources that support users in collecting and identifying high-quality disorder predictions.

Practical Considerations for Atomistic Structure Modeling with Cryo-EM Maps

We describe common approaches to atomistic structure modeling with single particle analysis derived cryo-EM maps. Several strategies for atomistic model building and atomistic model fitting methods are discussed, including selection criteria and implementation procedures. In covering basic concepts and caveats, this short perspective aims to help facilitate active discussion between scientists at different levels with diverse backgrounds.

Thompson loop: opportunities for antitubercular drug design by targeting the weak spot in demethylmenaquinone methyltransferase protein

Graphical superimposed snapshots of the Thompson novel loop (yellow) of menG protein: apo (A) and bound (B) systems. The loop switches between open and closed conformations; critical for therapeutic activity.

Molecular docking, pharmacophore based virtual screening and molecular dynamics studies towards the identification of potential leads for the management of H. pylori

The enzyme pantothenate synthetase panC is one of the potential new antimicrobial drug targets, but it is poorly characterized in H. pylori. H. pylori infection can cause gastric cancer and the management of H. pylori infection is crucial in various gastric ulcers and gastric cancer. The current study describes the use of innovative drug discovery and design approaches like comparative metabolic pathway analysis (Metacyc), exploration of database of essential genes (DEG), homology modelling, pharmacophore based virtual screening, ADMET studies and molecular dynamics simulations in identifying potential lead compounds for the H. pylori specific panC. The top ranked virtual hits STOCK1N-60270, STOCK1N-63040, STOCK1N-44424 and STOCK1N-63231 can act as templates for synthesis of new H. pylori inhibitors and they hold a promise in the management of gastric cancers caused by H. pylori.