Searching for hypothetical proteins: theory and practice based upon original data and literature

An immuno-informatics approach for annotation of hypothetical proteins and multi-epitope vaccine designed against the Mpox virus

A worrying new outbreak of Monkeypox (Mpox) in humans is caused by the Mpox virus (MpoxV). The pathogen has roughly 28 hypothetical proteins of unknown structure, function, and pathogenicity. Using reliable bioinformatics tools, we attempted to analyze the MpoxV genome, identify the role of hypothetical proteins (HPs), and design a potential candidate vaccine. Out of 28, we identified seven hypothetical proteins using multi-server validation with high confidence for the occurrence of conserved domains. Their physical, chemical, and functional characterizations, including molecular weight, theoretical isoelectric point, 3D structures, GRAVY value, subcellular localization, functional motifs, antigenicity, and virulence factors, were performed. We predicted possible cytotoxic T cell (CTL), helper T cell (HTL) and linear and conformational B cell epitopes, which were combined in a 219 amino acid multiepitope vaccine with human β defensin as a linker. This multi-epitopic vaccine was structurally modelled and docked with toll-like receptor-3 (TLR-3). The dynamical stability of the vaccine-TLR-3 docked complexes exhibited stable interactions based on RMSD and RMSF tests. Additionally, the modelled vaccine was cloned in-silico in an E. coli host to check the appropriate expression of the final vaccine built. Our results might conform to an immunogenic and safe vaccine, which would require further experimental validation.Communicated by Ramaswamy H. Sarma.

In-silico approach to characterize the structure and function of a hypothetical protein of Monkeypox virus exploring Chordopox-A20R domain-containing protein activity

Background: Monkeypox has emerged as a noteworthy worldwide issue due to its daily escalating case count. This illness presents diverse symptoms, including skin manifestations, which have the potential to spread through contact. The transmission of this infectious agent is intricate and readily transfers between individuals.Methods: The hypothetical protein MPXV-SI-2022V502225_00135 strain of monkeypox underwent structural and functional analysis using NCBI-CD Search, Pfam, and InterProScan. Quality assessment utilized PROCHECK, QMEAN, Verify3D, and ERRAT, followed by protein-ligand docking, visualization, and a 100-nanosecond simulation on Schrodinger Maestro.Results: Different physicochemical properties were estimated, indicating a stable molecular weight (49147.14) and theoretical pI (5.62) with functional annotation tools predicting the target protein to contain the domain of Chordopox_A20R domain. In secondary structure analysis, the helix coil was found to be predominant. The three-dimensional (3D) structure of the protein was obtained using a template protein (PDB ID: 6zyc.1), which became more stable after YASARA energy minimization and was validated by quality assessment tools like PROCHECK, QMEAN, Verify3D, and ERRAT. Protein-ligand docking was conducted using PyRx 9.0 software to examine the binding and interactions between a ligand and a hypothetical protein, focusing on various amino acids. The model structure, active site, and binding site were visualized using the CASTp server, FTsite, and PyMOL. A 100 nanosecond simulation was performed with ligand CID_16124688 to evaluate the efficiency of this protein.Conclusion: The analysis revealed significant binding interactions and enhanced stability, aiding in drug or vaccine design for effective antiviral treatment and patient management.

Biochemical and structural characterization reveals Rv3400 codes for β-phosphoglucomutase in Mycobacterium tuberculosis

Mycobacterium tuberculosis (Mtb) adapt to various host environments and utilize a variety of sugars and lipids as carbon sources. Among these sugars, maltose and trehalose, also play crucial role in bacterial physiology and virulence. However, some key enzymes involved in trehalose and maltose metabolism in Mtb are not yet known. Here we structurally and functionally characterized a conserved hypothetical gene Rv3400. We determined the crystal structure of Rv3400 at 1.7 Å resolution. The crystal structure revealed that Rv3400 adopts Rossmann fold and shares high structural similarity with haloacid dehalogenase family of proteins. Our comparative structural analysis suggested that Rv3400 could perform either phosphatase or pyrophosphatase or β-phosphoglucomutase (β-PGM) activity. Using biochemical studies, we further confirmed that Rv3400 performs β-PGM activity and hence, Rv3400 encodes for β-PGM in Mtb. Our data also confirm that Mtb β-PGM is a metal dependent enzyme having broad specificity for divalent metal ions. β-PGM converts β-D-glucose-1-phosphate to β-D-glucose-6-phosphate which is required for the generation of ATP and NADPH through glycolysis and pentose phosphate pathway, respectively. Using site directed mutagenesis followed by biochemical studies, we show that two Asp residues in the highly conserved DxD motif, D29 and D31, are crucial for enzyme activity. While D29A, D31A, D29E, D31E and D29N mutants lost complete activity, D31N mutant retained about 30% activity. This study further helps in understanding the role of β-PGM in the physiology of Mtb.

In silico analysis of a novel hypothetical protein (YP_498675.1) from Staphylococcus aureus unravels the protein of tryptophan synthase beta superfamily (Try-synth-beta_ II)

BACKGROUND

Staphylococcus aureus is a gram-positive spherical bacteria and the most common cause of nosocomial infections in the world. Given its clinical significance, the genome sequence of S. aureus has been elucidated to enhance our comprehension of its lifestyle and pathogenicity. The research aimed to summarize a potential hypothetical protein that may play an important role in S. aureus virulence and pathogenicity, covering its anticipated structure, probable biological functions, and importance in this context.

RESULTS

A hypothetical protein, YP_498675.1 with 281 amino acid residues of S. aureus, was chosen for analysis and modeling by several bioinformatics tools and databases in this work. According to primary and secondary structure analyses, YP_498675.1 is a stable hydrophilic protein with a significant proportion of α-helices. Subcellular localization predictions by CELLO, PSORTb, and SOSUI server indicate that it is a cytoplasmic protein. NCBI-CDD, Pfam, and InterProScan functional genomics research revealed that the hypothetical protein may include the pyridoxal phosphate (PLP)-dependent 2, 3-diaminopropionate biosynthesis protein SbnA domain. In the homology modeling method, the HHpred server was employed to create its 3D structure using the template structure of a Staphyloferrin B precursor biosynthetic enzyme SbnA bound to PLP (PDB ID: 5D84_A), an X-ray diffraction model having 100% sequence identity with the hypothetical protein. After energy minimization, several quality assessments and validation factors determined that the generated protein model was reliable and of reasonable quality.

CONCLUSION

The present study has characterized and functionally annotated the hypothetical protein YP_498675.1 of S. aureus. Further experimental validation would aid in determining the actual function of YP_498675.1 as well as confirm the protein's value as a therapeutic target.

In Silico Functional Characterization of a Hypothetical Protein From Pasteurella Multocida Reveals a Novel S-Adenosylmethionine-Dependent Methyltransferase Activity

Genomes may now be sequenced in a matter of weeks, leading to an influx of "hypothetical" proteins (HP) whose activities remain a mystery in GenBank. The information included inside these genes has quickly grown in prominence. Thus, we selected to look closely at the structure and function of an HP (AFF25514.1; 246 residues) from Pasteurella multocida (PM) subsp. multocida str. HN06. Possible insights into bacterial adaptation to new environments and metabolic changes might be gained by studying the functions of this protein. The PM HN06 2293 gene encodes an alkaline cytoplasmic protein with a molecular weight of 28352.60 Da, an isoelectric point (pI) of 9.18, and an overall average hydropathicity of around -0.565. One of its functional domains, tRNA (adenine (37)-N6)-methyltransferase TrmO, is a S-adenosylmethionine (SAM)-dependent methyltransferase (MTase), suggesting that it belongs to the Class VIII SAM-dependent MTase family. The tertiary structures represented by HHpred and I-TASSER models were found to be flawless. We predicted the model's active site using the Computed Atlas of Surface Topography of Proteins (CASTp) and FTSite servers, and then displayed it in 3 dimensional (3D) using PyMOL and BIOVIA Discovery Studio. Based on molecular docking (MD) results, we know that HP interacts with SAM and S-adenosylhomocysteine (SAH), 2 crucial metabolites in the tRNA methylation process, with binding affinities of 7.4 and 7.5 kcal/mol, respectively. Molecular dynamic simulations (MDS) of the docked complex, which included only modest structural adjustments, corroborated the strong binding affinity of SAM and SAH to the HP. Evidence for HP's possible role as an SAM-dependent MTase was therefore given by the findings of Multiple sequence alignment (MSA), MD, and molecular dynamic modeling. These in silico data suggest that the investigated HP might be used as a useful adjunct in the investigation of Pasteurella infections and the development of drugs to treat zoonotic pasteurellosis.

Inhibitor Assessment against the LpxC Enzyme of Antibiotic-resistant Acinetobacter baumannii Using Virtual Screening, Dynamics Simulation, and in vitro Assays

BACKGROUND

Bacterial resistance is currently a significant global public health problem. Acinetobacter baumannii has been ranked in the list of the World Health Organization as the most critical and priority pathogen for which new antibiotics are urgently needed. In this context, computational methods play a central role in the modern drug discovery process. The purpose of the current study was to identify new potential therapeutic molecules to neutralize MDR A. baumannii bacteria.

METHODS

A total of 3686 proteins retrieved from the A. baumannii proteome were subjected to subtractive proteomic analysis to narrow down the spectrum of drug targets. The SWISS-MODEL server was used to perform a 3D homology model of the selected target protein. The SAVES server was used to evaluate the overall quality of the model. A dataset of 74500 analogues retrieved from the PubChem database was docked with LpxC using the AutoDock software.

RESULTS

In this study, we predicted a putative new inhibitor for the Lpxc enzyme of A. baumannii. The LpxC enzyme was selected as the most appropriate drug target for A. baumannii. According to the virtual screening results, N-[(2S)-3-amino-1-(hydroxyamino)-1-oxopropan-2-yl]-4-(4-bromophenyl) benzamide (CS250) could be a promising drug candidate targeting the LpxC enzyme. This molecule shows polar interactions with six amino acids and non-polar interactions with eight other residues. In vitro experimental validation was performed through the inhibition assay.

CONCLUSION

To the best of our knowledge, this is the first study that suggests CS250 as a promising inhibitory molecule that can be exploited to target this gram-negative pathogen.

Elucidating Cellular Metabolism and Protein Difference Data from DIGE Proteomics Experiments Using Enzyme Assays

Assays for measuring enzyme activity can be useful tools for proteomics applications. Enzyme testing can be performed to validate an experimental system prior to a difference gel electrophoresis (DIGE) proteomic experiment and can also be utilized as an integral part of multifaceted experiment in conjunction with DIGE. Data from enzyme tests can be used to corroborate results of DIGE proteomic experiments where an enzyme or enzymes are demonstrated by DIGE to be differentially expressed. Enzyme testing can also be utilized to support data from DIGE experiments that demonstrate metabolic changes in a biological system. The different types of enzyme assays that can be performed in conjunction with DIGE experiments are reviewed alongside a discussion of experimental approaches for designing enzyme assays.

AnnotaPipeline: An integrated tool to annotate eukaryotic proteins using multi-omics data

Assignment of gene function has been a crucial, laborious, and time-consuming step in genomics. Due to a variety of sequencing platforms that generates increasing amounts of data, manual annotation is no longer feasible. Thus, the need for an integrated, automated pipeline allowing the use of experimental data towards validation of in silico prediction of gene function is of utmost relevance. Here, we present a computational workflow named AnnotaPipeline that integrates distinct software and data types on a proteogenomic approach to annotate and validate predicted features in genomic sequences. Based on FASTA (i) nucleotide or (ii) protein sequences or (iii) structural annotation files (GFF3), users can input FASTQ RNA-seq data, MS/MS data from mzXML or similar formats, as the pipeline uses both transcriptomic and proteomic information to corroborate annotations and validate gene prediction, providing transcription and expression evidence for functional annotation. Reannotation of the available Arabidopsis thaliana, Caenorhabditis elegans, Candida albicans, Trypanosoma cruzi, and Trypanosoma rangeli genomes was performed using the AnnotaPipeline, resulting in a higher proportion of annotated proteins and a reduced proportion of hypothetical proteins when compared to the annotations publicly available for these organisms. AnnotaPipeline is a Unix-based pipeline developed using Python and is available at: https://github.com/bioinformatics-ufsc/AnnotaPipeline.

Functional Annotation of Hypothetical Proteins From the Enterobacter cloacae B13 Strain and Its Association With Pathogenicity

Enterobacter cloacae B13 strain is a rod-shaped gram-negative bacterium that belongs to the Enterobacteriaceae family. It can cause respiratory and urinary tract infections, and is responsible for several outbreaks in hospitals. E. cloacae has become an important pathogen and an emerging global threat because of its opportunistic and multidrug resistant ability. However, little knowledge is present about a large portion of its proteins and functions. Therefore, functional annotation of the hypothetical proteins (HPs) can provide an improved understanding of this organism and its virulence activity. The workflow in the study included several bioinformatic tools which were utilized to characterize functions, family and domains, subcellular localization, physiochemical properties, and protein-protein interactions. The E. cloacae B13 strain has overall 604 HPs, among which 78 were functionally annotated with high confidence. Several proteins were identified as enzymes, regulatory, binding, and transmembrane proteins with essential functions. Furthermore, 23 HPs were predicted to be virulent factors. These virulent proteins are linked to pathogenesis with their contribution to biofilm formation, quorum sensing, 2-component signal transduction or secretion. Better knowledge about the HPs’ characteristics and functions will provide a greater overview of the proteome. Moreover, it will help against E. cloacae in neonatal intensive care unit (NICU) outbreaks and nosocomial infections.

PANDA2: protein function prediction using graph neural networks

High-throughput sequencing technologies have generated massive protein sequences, but the annotations of protein sequences highly rely on the low-throughput and expensive biological experiments. Therefore, accurate and fast computational alternatives are needed to infer functional knowledge from protein sequences. The gene ontology (GO) directed acyclic graph (DAG) contains the hierarchical relationships between GO terms but is hard to be integrated into machine learning algorithms for functional predictions. We developed a deep learning system named PANDA2 to predict protein functions, which used the cutting-edge graph neural network to model the topology of the GO DAG and integrated the features generated by transformer protein language models. Compared with the top 10 methods in CAFA3, PANDA2 ranked first in cellular component ontology (CCO), tied first in biological process ontology (BPO) but had a higher coverage rate, and second in molecular function ontology (MFO). Compared with other recently-developed cutting-edge predictors DeepGOPlus, GOLabeler, and DeepText2GO, and benchmarked on another independent dataset, PANDA2 ranked first in CCO, first in BPO, and second in MFO. PANDA2 can be freely accessed from http://dna.cs.miami.edu/PANDA2/.

Identification and Functional Characterization of Toxoneuron nigriceps Ovarian Proteins Involved in the Early Suppression of Host Immune Response

The endophagous parasitoid Toxoneuron nigriceps (Viereck) (Hymenoptera, Braconidae) of the larval stages of the tobacco budworm Heliothis virescens (Fabricius) (Lepidoptera, Noctuidae) injects the egg, the venom, the calyx fluid, which includes a Polydnavirus (T. nigriceps BracoVirus: TnBV) and the Ovarian Proteins (OPs) into the host body during oviposition. The host metabolism and immune system are disrupted prematurely shortly after parasitization by the combined action of the TnBV, venom, and OPs. OPs are involved in the early suppression of host immune response, before TnBV infects and expresses its genes in the host tissues. In this work, we evaluated the effect of HPLC fractions deriving from in toto OPs. Two fractions caused a reduction in hemocyte viability and were subsequently tested to detect changes in hemocyte morphology and functionality. The two fractions provoked severe oxidative stress and actin cytoskeleton disruption, which might explain the high rate of hemocyte mortality, loss of hemocyte functioning, and hence the host’s reduced hemocyte encapsulation ability. Moreover, through a transcriptome and proteomic approach we identify the proteins of the two fractions: eight proteins were identified that might be involved in the observed host hemocyte changes. Our findings will contribute to a better understanding of the secreted ovarian components and their role in parasitoid wasp strategy for evading host immune responses.

Comparative proteomic analysis to annotate the structural and functional association of the hypothetical proteins of S. maltophilia k279a and predict potential T and B cell targets for vaccination

Stenotrophomonas maltophilia is a multidrug-resistant bacterium with no precise clinical treatment. This bacterium can be a vital cause for death and different organ failures in immune-compromised, immune-competent, and long-time hospitalized patients. Extensive quorum sensing capability has become a challenge to develop new drugs against this pathogen. Moreover, the organism possesses about 789 proteins which function, structure, and pathogenesis remain obscured. In this piece of work, we tried to enlighten the aforementioned sectors using highly reliable bioinformatics tools validated by the scientific community. At first, the whole proteome sequence of the organism was retrieved and stored. Then we separated the hypothetical proteins and searched for the conserved domain with a high confidence level and multi-server validation, which resulted in 24 such proteins. Furthermore, all of their physical and chemical characterizations were performed, such as theoretical isoelectric point, molecular weight, GRAVY value, and many more. Besides, the subcellular localization, protein-protein interactions, functional motifs, 3D structures, antigenicity, and virulence factors were also evaluated. As an extension of this work, 'RTFAMSSER' and 'PAAPQPSAS' were predicted as potential T and B cell epitopes, respectively. We hope our findings will help in better understating the pathogenesis and smoothen the way to the cure.

In Silico Characterization of a Hypothetical Protein from Shigella dysenteriae ATCC 12039 Reveals a Pathogenesis-Related Protein of the Type-VI Secretion System

Shigellosis caused by Shigella dysenteriae is a major public health concern worldwide, particularly in developing countries. The bacterial genome is known, but there are many hypothetical proteins whose functions are yet to be discovered. A hypothetical protein (accession no. WP_128879999.1, 161 residues) of S. dysenteriae ATCC 12039 strain was selected in this study for comprehensive structural and functional analysis. Subcellular localization and different physicochemical properties of this hypothetical protein were estimated indicating it as a stable, soluble, and extracellular protein. Functional annotation tools, such as NCBI-CD Search, Pfam, and InterProScan, predicted our target protein to be an amidase effector protein 4 (Tae4) of type-VI secretion system (T6SS). Multiple sequence alignment of the homologous sequences coincided with previous findings. Random coil was found to be predominant in secondary structure. Three-dimensional (3D) structure of the protein was obtained using homology modeling method by SWISS-MODEL server using a template protein (PDB ID: 4J30) of 80.12% sequence identity. The 3D structure became more stable after YASARA energy minimization and was validated by several quality assessment tools like PROCHECK, QMEAN, Verify3D, and ERRAT. Superimposition of the target with the template protein by UCSF Chimera generated RMSD value of 0.115 Å, suggesting a reliable 3D structure. The active site of the modeled structure was predicted and visualized by CASTp server and PyMOL. Interestingly, similar binding affinity and key interacting residues were found for the target protein and a Salmonella enterica Tae4 protein with the ligand L-Ala D-Glu-mDAP by molecular docking analysis. Protein-protein docking was also performed between the target protein and hemolysin coregulated protein 1 of T6SS. Finally, the protein was found to be a unique protein of S. dysenteriae nonhomologous to human by comparative genomics approach indicating a potential therapeutic target. Most pathogens harboring T6SS in their system pose a significant threat to the human health. Many T6SSs and their effectors are associated with interbacterial competition, pathogenesis, and virulency; however, relationships between these effectors and pathogenicity of S. dysenteriae are yet to be determined. The study findings provide a lucrative platform for future antibacterial treatment.

An integrated transcriptomic and proteomic approach to identify the main Torymus sinensis venom components

During oviposition, ectoparasitoid wasps not only inject their eggs but also a complex mixture of proteins and peptides (venom) in order to regulate the host physiology to benefit their progeny. Although several endoparasitoid venom proteins have been identified, little is known about the components of ectoparasitoid venom. To characterize the protein composition of Torymus sinensis Kamijo (Hymenoptera: Torymidae) venom, we used an integrated transcriptomic and proteomic approach and identified 143 venom proteins. Moreover, focusing on venom gland transcriptome, we selected additional 52 transcripts encoding putative venom proteins. As in other parasitoid venoms, hydrolases, including proteases, phosphatases, esterases, and nucleases, constitute the most abundant families in T. sinensis venom, followed by protease inhibitors. These proteins are potentially involved in the complex parasitic syndrome, with different effects on the immune system, physiological processes and development of the host, and contribute to provide nutrients to the parasitoid progeny. Although additional in vivo studies are needed, initial findings offer important information about venom factors and their putative host effects, which are essential to ensure the success of parasitism.

Protein Composition and Biomedical Potential of the Skin Secretion of Hylarana erythraea (Schlegel, 1837) (Anura: Ranidae) from Langkawi Archipelago, Kedah, Peninsular Malaysia

The skin secretion of amphibians is known for its high content of bioactive compounds. These bioactive compounds are essential for the advancement of biomedical industries. Four wild green paddy frogs, Hylarana erythraea, were collected from various habitat types within the Langkawi Archipelago. These frogs' skin secretions were collected, extracted, and analysed for their protein compounds together with biomedical potentials using liquid chromatography-mass spectrometry (LC-MS). The total protein concentration of H. erythraea skin secretions was determined as 0.269 mg/mL. Based on the UniProt (Anura) database, we identified 29 proteins. These proteins were categorised as antimicrobial (AMP) (38%), followed by hormone (17%), enzyme (17%), unreviewed proteins (17%), structural proteins (7%), and regulatory proteins (4%). The AMPs identified were from the family of esculentin-1, esculentin-2, brevinin-1, and frenatin-4, while the hormones belonged to the cholecystokinin group. The enzymes detected were adenylate cyclase 9, the suppressor of tumorigenicity 14 protein homolog, and the HGF activator. The structural proteins belonged to toe pad keratin 2 and Krt5.7 proteins, while the single regulatory protein is CCR4-NOT transcription complex subunit 6-like. These proteins have a wide range of biomedical importance, such as wound healings, facilitate digestions, anti-tumours, and anti-cancer effect.

Proteome dynamics and transcriptome profiling in sorghum [Sorghum bicolor (L.) Moench] under salt stress

Sorghum is a C₄ cereal grain crop which is well adapted to harsh environment. It is a potential model for gaining better understanding of the molecular mechanism due to its wider adaptability to abiotic stresses. In this study, protein extraction was standardized using different methods to study the electrophoretic pattern of sorghum leaves under different salinity levels. The extraction of soluble protein with lysis buffer, followed by its clean-up was found to be the most effective method. The different profiles of salt-responsive proteins were analyzed in G-46 and CSV 44F sorghum genotypes based on their tolerance behavior towards salinity. The kafirin level also changed depending upon the concentration and exposure time to salts suggesting the stored proteins as energy source under stress conditions. The relative expression of salt-responsive genes was studied using Reverse Transcriptase-Polymerase Chain Reaction (RT-PCR) which might be used as a molecular screening tool for identification of salt-tolerant genotypes in affected areas. The validated responses were examined in terms of metabolic changes and the expression of stress-induced proteins-viz. heat shock proteins (hsp) via immunoblotting assay. The results showed that the two sorghum genotypes adopted distinct approaches in response to salinity, with G-46 performing better in terms of leaf function. Also, we have standardized different protein extraction methods followed by their clean-up for electrophoretic profiling.

In silico functional prediction of hypothetical proteins from the core genome of Corynebacterium pseudotuberculosis biovar ovis

Corynebacterium pseudotuberculosis is a pathogen of veterinary relevance diseases, being divided into two biovars: equi and ovis; causing ulcerative lymphangitis and caseous lymphadenitis, respectively. The isolation and sequencing of C. pseudotuberculosis biovar ovis strains in the Northern and Northeastern regions of Brazil exhibited the emergence of this pathogen, which causes economic losses to small ruminant producers, and condemnation of carcasses and skins of animals. Through the pan-genomic approach, it is possible to determine and analyze genes that are shared by all strains of a species—the core genome. However, many of these genes do not have any predicted function, being characterized as hypothetical proteins (HP). In this study, we considered 32 C. pseudotuberculosis biovar ovis genomes for the pan-genomic analysis, where were identified 172 HP present in a core genome composed by 1255 genes. We are able to functionally annotate 80 sequences previously characterized as HP through the identification of structural features as conserved domains and families. Furthermore, we analyzed the physicochemical properties, subcellular localization and molecular function. Additionally, through RNA-seq data, we investigated the differential gene expression of the annotated HP. Genes inserted in pathogenicity islands had their virulence potential evaluated. Also, we have analyzed the existence of functional associations for their products based on protein–protein interaction networks, and perform the structural prediction of three targets. Due to the integration of different strategies, this study can underlie deeper in vitro researches in the characterization of these HP and the search for new solutions for combat this pathogen.

A deep learning ensemble for function prediction of hypothetical proteins from pathogenic bacterial species

Protein function prediction is a crucial task in the post-genomics era due to their diverse irreplaceable roles in a biological system. Traditional methods involved cost-intensive and time-consuming molecular biology techniques but they proved to be ineffective after the outburst of sequencing data through the advent of cost-effective and advanced sequencing techniques. To manage the pace of annotation with that of data generation, there is a shift to computational approaches which are based on homology, sequence and structure-based features, protein-protein interaction networks, phylogenetic profiles, and physicochemical properties, etc. A combination of these features has proven to be promising for protein function prediction in terms of improving prediction accuracy. In the present work, we have employed a combination of features based on sequence, physicochemical property, subsequence and annotation features with a total of 9890 features extracted and/or calculated for 171,212 reviewed prokaryotic proteins of 9 bacterial phyla from UniProtKB, to train a supervised deep learning ensemble model with the aim to categorize a bacterial hypothetical/unreviewed protein's function into 1739 GO terms as functional classes. The proposed system being fully dedicated to bacterial organisms is a novel attempt amongst various existing machine learning based protein function prediction systems based on mixed organisms. Experimental results demonstrate the success of the proposed deep learning ensemble model based on deep neural network method with F1 measure of 0.7912 on the prepared Test dataset 1 of reviewed proteins.

Functional Genomics of the Retina to Elucidate its Construction and Deconstruction

The retina is the light sensitive part of the eye and nervous tissue that have been used extensively to characterize the function of the central nervous system. The retina has a central position both in fundamental biology and in the physiopathology of neurodegenerative diseases. We address the contribution of functional genomics to the understanding of retinal biology by reviewing key events in their historical perspective as an introduction to major findings that were obtained through the study of the retina using genomics, transcriptomics and proteomics. We illustrate our purpose by showing that most of the genes of interest for retinal development and those involved in inherited retinal degenerations have a restricted expression to the retina and most particularly to photoreceptors cells. We show that the exponential growth of data generated by functional genomics is a future challenge not only in terms of storage but also in terms of accessibility to the scientific community of retinal biologists in the future. Finally, we emphasize on novel perspectives that emerge from the development of redox-proteomics, the new frontier in retinal biology.

Landscape of ROD9 Island: Functional annotations and biological network of hypothetical proteins in Salmonella enterica

Salmonella, an Enterobacteria is a therapeutically important pathogen for the host. The advancement of genome sequencing of S. enterica serovar Enteritidis have identified a distinct ROD9 pathogenic island, imparting virulence. The occurrence of 17 ROD9 hypothetical proteins, necessitates subsequent bioinformatics approach for structural and functional aspects of protein-protein relations or networks in different pathogenic phenotypes express. A collective analysis using predictive bioinformatics tools that includes NCBI-BLASTp and BLAST2GO annotated the motif patterns and functional significance. The VFDB identified 10 virulence proteins at both genomic and metagenomic level. Phylogenetic analysis revealed a divergent and convergent relationship between 17 ROD9 and 41 SP-1 proteins. Here, combining a comprehensive approach from sequence based, motif recognitions, domain identification, virulence ability to structural modelling provides a precise function to ROD9 proteins biological network, for which no experimental information is available.

Structure to function analysis with antigenic characterization of a hypothetical protein,HPAG1_0576 from Helicobacter pylori HPAG1

Helicobacter pylori, a unique gastric pathogen causing chronic inflammation in the gastric mucosa with a possibility to develop gastric cancer has one-third of its proteins still uncharacterized. In this study, a hypothetical protein (HP) namely HPAG1_0576 from H. pylori HPAG1 was chosen for detailed computational analysis of its structural, functional and epitopic properties. The primary, secondary and 3D structure/model of the selected HP was constructed. Then refinement and structure validation were done, which indicated a good quality of the newly constructed model. ProFunc and STRING suggested that HPAG1_0576 shares 98% identity with a carcinogenic factor, TNF-α inducing protein (Tip-α ) of H. pylori. IEDB immunoinformatics tool predicted VLMLQACTCPNTSQRNS from position 19-35 as most potential B-cell linear epitope and SFLKSKQL from position 5-12 as most potent conformational epitope. Alternatively, FALVRARGF and FLCGLGVLM were predicted as most immunogenic CD8+ and CD4+ T-cell epitopes respectively. At the same time findings of IFN epitope tool suggests that, HPAG1_0576 had a great potential to evoke interferon-gamma (IFN-γ) mediated immune response. However, this experiment is a primary approach for in silico vaccine designing from a HP, findings of this study will provide significant insights in further investigations and will assist in identifying new drug targets/vaccine candidates.

Re-annotation for hypothetical protein CA803_03125 of Methicillin-Resistant Staphylococcus aureus strain SO-1977 isolated from Sudan

This study aims to describe the global detection and functional inference of hypothetical protein CA803_03125 from Staphylococcus aureus SO-1977. Computational methods were utilized to study this protein based on sequence similarity and presence of known protein domains. The BLASTp result revealed a significant similarity between the hypothetical protein (CA803_03125) and ADP-ribose hydrolase protein from four S. aureus strains (MW2, MRSA252, COL, and N315). Evolutionary tree diagram revealed a close relationship between the hypothetical protein and proteins of MW2 and COL strains. The physicochemical characterization revealed that all proteins were found to be stable, soluble, hydrophilic and acidic in their nature. The Macro domain was found to exist within all proteins. Moreover, the proteins were of pronounced similarity in terms of primary, secondary and tertiary organization. The protein CA803_03125 (SO-1977) is already known and well characterized as ADP-ribose hydrolase; therefore, we would recommend that its NCBI data has to be updated to be submitted under the name of ADP-ribose hydrolase.

An Effective Protocol for Proteome Analysis of Medaka (Oryzias latipes) after Acute Exposure to Ionizing Radiation

All terrestrial organisms are subject to evolutionary pressures associated with natural sources of ionizing radiation (IR). The legacy of human-induced IR associated with energy, weapons production, medicine, and research has changed the distribution and magnitude of these evolutionary pressures. To date, no study has systematically examined the effects of environmentally relevant doses of radiation exposure across an organismal proteome. This void in knowledge has been due, in part, to technological deficiencies that have hampered quantifiable environmentally relevant IR doses and sensitive detection of proteomic responses. Here, we describe a protocol that addresses both needs, combining quantifiable IR delivery with a reliable method to yield proteomic comparisons of control and irradiated Medaka fish. Exposures were conducted at the Savannah River Ecology Laboratory (SREL, in Aiken, SC), where fish were subsequently dissected into three tissue sets (carcasses, organs and intestines) and frozen until analysis. Tissue proteins were extracted, resolved by Sodium Dodecyl Sulfate-Polyacrylamide Gel Electrophoresis (SDS-PAGE), and each sample lane was divided into ten equal portions. Following in-gel tryptic digestion, peptides released from each gel portion were identified and quantified by Liquid Chromatography-Mass Spectrometry (LC-MS/MS) to obtain the most complete, comparative study to date of proteomic responses to environmentally relevant doses of IR. This method provides a simple approach for use in ongoing epidemiologic studies of chronic exposure to environmentally relevant levels of IR and should also serve well in physiological, developmental, and toxicological studies.

Protein Subcellular Localization Prediction based on PSI-BLAST Profile and Principal Component Analysis

Background:

Prediction of protein subcellular location is a meaningful task which attracts much attention in recent years. Particularly, the number of new protein sequences yielded by the highthroughput sequencing technology in the post genomic era has increased explosively.

Objective:

Protein subcellular localization prediction based solely on sequence data remains to be a challenging problem of computational biology.

Methods:

In this paper, three sets of evolutionary features are derived from the position-specific scoring matrix, which has shown great potential in other bioinformatics problems. A fusion model is built up by the optimal parameters combination. Finally, principal component analysis and support vector machine classifier is applied to predict protein subcellular localization on NNPSL dataset and Cell- PLoc 2.0 dataset.

Results:

Our experimental results show that the proposed method remarkably improved the prediction accuracy, and the features derived from PSI-BLAST profile only are appropriate for protein subcellular localization prediction.

Structural and Functional Annotation of Conserved Virulent Hypothetical Proteins in Chlamydia Trachomatis: An In-Silico Approach

Background:

Though after a start of genome sequencing most of the protein sequences are deposited in databases, some proteins remain to be unannotated and functionally uncharacterized. Chlamydia trachomatis L2C is a gram-negative pathogen bacterium involved in causing severe disorders like lymphogranuloma venereum, nongonococcal urethritis, and cervicitis. <P> Objectives: Analyzing and annotating the hypothetical proteins can help to understand its pathogenicity and therapeutic hotspots. Its genome encodes a total of 221 hypothetical proteins and out of these, 14 hypothetical proteins are declared as virulent by virulence prediction server (VirulentPred). <P> Methods: In this study, the functional and structural analysis was carried out by conserve domain finding servers, protein function annotators and physiochemical properties predictors. Proteinprotein interactions studies revealed the involvement of these virulent HPs in a number of pathways, which would be of interest for drug designers. <P> Results: Classifier tool was used to classify the virulent hypothetical proteins into enzymes, membrane protein, transporter and regulatory protein groups. <P> Conclusion: Our study would help to understand the mechanisms of pathogenesis and new potential therapeutic targets for a couple of diseases caused by C. trachomatis.

Spindlin docking protein (SPIN.DOC) interaction with SPIN1 (a histone code reader) regulates Wnt signaling

Indepth studies of protein-protein interactions are essential for discovering the molecular mechanisms and the biological context of protein functions. Even though previous study on the purification of SPIN1 interacting protein complex has shown Spindlin docking protein (SPIN.DOC) as the most abundant interacting protein partner; the study on the molecular function of SPIN.DOC is limited. Since the role of SPIN1 has been previously documented as a histone code reader and transcriptional coactivator of Wnt signaling, SPIN.DOC may probably involve in epigenetic regulation and Wnt signaling. This study aims to purify SPIN.DOC interacting protein complex and characterize the molecular function of SPIN.DOC. The finding of this study revealed that the suppression of SPIN.DOC expression in HEK293 cells by shRNA, slightly destabilized SPIN1 without any change in its chromatin localization. However, knockdown of SPIN1 decreased the expression and chromatin localization of SPIN.DOC. Nevertheless, overexpression of SPIN.DOC increased the expression and chromatin localization of SPIN1 but no change in the SPIN.DOC protein expression and chromatin localization when SPIN1 is overexpressed. TOPflash reporter assays revealed that SPIN.DOC regulates gene expression in Wnt signaling pathway and act as transcriptional repressor. Further, we show that C-terminal deleted mutant of SPIN.DOC is unable to interact with SPIN1. Unlike the wild type SPIN.DOC which acts as transcriptional repressor, overexpression of C-terminal deletion mutant activates Wnt signaling suggesting that SPIN.DOC-SPIN1 complex may act as transcriptional repressor. Overall, our data revealed new molecular functions of SPIN.DOC.

Functional Prediction of Hypothetical Proteins from Shigella flexneri and Validation of the Predicted Models by Using ROC Curve Analysis

Shigella spp. constitutes some of the key pathogens responsible for the global burden of diarrhoeal disease. With over 164 million reported cases per annum, shigellosis accounts for 1.1 million deaths each year. Majority of these cases occur among the children of the developing nations and the emergence of multi-drug resistance Shigella strains in clinical isolates demands the development of better/new drugs against this pathogen. The genome of Shigella flexneri was extensively analyzed and found 4,362 proteins among which the functions of 674 proteins, termed as hypothetical proteins (HPs) had not been previously elucidated. Amino acid sequences of all these 674 HPs were studied and the functions of a total of 39 HPs have been assigned with high level of confidence. Here we have utilized a combination of the latest versions of databases to assign the precise function of HPs for which no experimental information is available. These HPs were found to belong to various classes of proteins such as enzymes, binding proteins, signal transducers, lipoprotein, transporters, virulence and other proteins. Evaluation of the performance of the various computational tools conducted using receiver operating characteristic curve analysis and a resoundingly high average accuracy of 93.6% were obtained. Our comprehensive analysis will help to gain greater understanding for the development of many novel potential therapeutic interventions to defeat Shigella infection.

In silico Leishmania proteome mining applied to identify drug target potential to be used to treat against visceral and tegumentary leishmaniasis

New therapeutic strategies against leishmaniasis are desirable, since the treatment against disease presents problems, such as the toxicity, high cost and/or parasite resistance. As consequence, new antileishmanial compounds are necessary to be identified, as presenting high activity against Leishmania, but low toxicity in mammalian hosts. In the present study, a Leishmania proteome mining strategy was developed, in order to select new drug targets with low homology to human proteins, but that are considered relevant for the parasite' survival. Results showed a hypothetical protein, which was functionally annotated as a glucosidase-like protein, as presenting such characteristics. This protein was associated with the metabolic network of the N-Glycan biosynthesis pathway in Leishmania, and two specific inhibitors - acarbose and miglitol - were predicted to be potential targets against it. In this context, miglitol [1-(2-Hydroxyethyl)-2-(hydroxymethyl)piperidine-3,4,5-triol] was tested against stationary promastigotes and axenic amastigotes of the Leishmania amazonensis and L. infantum species, and results showed high values of antileishmanial inhibition against both parasite species. Miglitol showed also efficacy in the treatment of Leishmania-infected macrophages; thus denoting its potential use as an antileishmanial candidate. In conclusion, this work presents a new drug target identified by a proteome mining strategy associated with bioinformatics tools, and suggested its use as a possible candidate to be applied in the treatment against disease.

Analysis of cyclin E co-expression genes reveals nuclear transcription factor Y subunit alpha is an oncogene in gastric cancer

Objective

To explore genes potentially co-expressed with cyclin E in gastric cancer and discover possible targets for gastric cancer treatment.

Methods

The Cancer Genome Atlas (TCGA) stomach adenocarcinoma sequencing data were used to predict genes co-expressed with cyclin E. Co-expression genes predicted by cBioPortal online analysis with Pearson correlation coefficient ≥0.4 were analyzed by gene ontology (GO) enrichment annotation using the PANTHER online platform (Ver. 7). Interactions between proteins encoded by these genes were analyzed using the STRING online platform (Ver. 10.5) and Cytoscape software (Ver. 3.5.1). Genes displaying a high degree of connection were analyzed by transcription factor enrichment prediction using FunRich software (Ver. 3). The significant transcription factor and cyclin E expression levels and their impact on gastric cancer progression were analyzed by Western blotting and Kaplan–Meier survival curve analysis.

Results

After filtering the co-expression gene prediction results, 78 predicted genes that included 73 protein coding genes and 5 non-coding genes with Pearson correlation coefficient ≥0.4 were selected. The expressions of the genes were considered to be correlated with cyclin E expression. Among the 78 genes co-expressed with cyclin E, 19 genes at the central of the regulatory network associated with cyclin E were discovered. Nuclear transcription factor Y subunit alpha (NF-YA) was identified as a significant transcription factor associated with cyclin E co-expressing genes. Analysis of specimen donors’ clinical records revealed that high expression of NF-YA tended to be associated with increased cyclin E expression. The expression of both was associated with progression of gastric cancer. Western blotting results showed that compared with normal tissues, NF-YA and cyclin E were highly expressed in tumor tissues (P < 0.001). Survival curve analysis clearly demonstrated relatively poor overall survival of gastric cancer patients with high cyclin E or high NF-YA expression level, compared to patients with low cyclin E or NF-YA expression (P < 0.05).

Conclusions

NF-YA may promote gastric cancer progression by increasing the transcription of cyclin E and other cell cycle regulatory genes. NF-YA might be a potential therapeutically useful prognostic factor for gastric cancer.

PMLPR: A novel method for predicting subcellular localization based on recommender systems

The importance of protein subcellular localization problem is due to the importance of protein's functions in different cell parts. Moreover, prediction of subcellular locations helps to identify the potential molecular targets for drugs and has an important role in genome annotation. Most of the existing prediction methods assign only one location for each protein. But, since some proteins move between different subcellular locations, they can have multiple locations. In recent years, some multiple location predictors have been introduced. However, their performances are not accurate enough and there is much room for improvement. In this paper, we introduced a method, PMLPR, to predict locations for a protein. PMLPR predicts a list of locations for each protein based on recommender systems and it can properly overcome the multiple location prediction problem. For evaluating the performance of PMLPR, we considered six datasets RAT, FLY, HUMAN, Du et al., DBMLoc and Höglund. The performance of this algorithm is compared with six state-of-the-art algorithms, YLoc, WOLF-PSORT, prediction channel, MDLoc, Du et al. and MultiLoc2-HighRes. The results indicate that our proposed method is significantly superior on RAT and Fly proteins, and decent on HUMAN proteins. Moreover, on the datasets introduced by Du et al., DBMLoc and Höglund, PMLPR has comparable results. For the case study, we applied the algorithms on 8 proteins which are important in cancer research. The results of comparison with other methods indicate the efficiency of PMLPR.

In silico and in vitro analysis of boAP3d1 protein interaction with bovine leukaemia virus gp51

The envelope glycoprotein 51 (gp51) is essential for bovine leukaemia virus (BLV) entry to bovine B-lymphocytes. Although the bovine adaptor protein 3 complex subunit delta-1 (boAP3D1) has been proposed as the potential receptor, the specific ligand-receptor interaction has not yet been completely defined and boAP3D1 receptor and gp51 3D structures have not been determined. This study was thus aimed at a functional annotation of boAP3D1 cellular adaptor protein and BLV gp51 and, proposing a reliable model for gp51-AP3D1 interaction using bioinformatics tools. The boAP3D1 receptor interaction patterns were calculated based on models of boAP3D1 receptor and gp51 complexes’ 3D structures, which were constructed using homology techniques and data-driven docking strategy. The results showed that the participation of 6 key amino acids (aa) on gp51 (Asn170, Trp127, His115, Ala97, Ser98 and Glu128) and 4 aa on AP3D1 (Lys925, Asp807, Asp695 and Arg800) was highly probable in the interaction between gp51 and BLVR domains. Three gp51 recombinant peptides were expressed and purified to validate these results: the complete domain (rgp51), the N-terminal portion (rNgp51) and the C-terminal fragment (rCgp51); and binding assays to Madin-Darby bovine kidney (MDBK) cells were then carried out with each recombinant. It was found that rNgp51 preferentially bound to MDBK cells, suggesting this domain’s functional role during invasion. The rNgp51-MDBK cell interaction was sensitive to trypsin (98% reduction) and chymotrypsin treatment (80% reduction). These results highlighted that the N-terminal portion of gp51 interacted in vitro with the AP3D1 receptor and provides a plausible in silico interaction model.

FAM188B enhances cell survival via interaction with USP7

We have previously reported that FAM188B showed significant differential exon usage in cancers (NCBI GEO GSE30727), but the expression and function of FAM188B is not well characterized. In the present study, we explored the functions of FAM188B by a knockdown strategy, using siRNAs specific for FAM188B in colon cancer cell lines. FAM188B is a novel gene that encodes a protein that is evolutionarily conserved among mammals. Its mRNA has been found to be highly expressed in most solid tumors, including colorectal cancer. FAM188B knockdown induced cell growth inhibition due to an increase in apoptosis in colon cancer cell lines. Interestingly, siFAM188B treatment induced the upregulation and activation of p53, and consequently increased p53-regulated pro-apoptotic proteins, PUMA and BAX. Proteomic analysis of FAM188B immunocomplexes revealed p53 and USP7 as putative FAM188B-interacting proteins. Deletion of the putative USP7-binding motif in FAM188B reduced complex formation of FAM188B with USP7. It is noteworthy that FAM188B knockdown resulted in a decrease in overall ubiquitination in the p53 immunocomplexes, as well as p53 ubiquitination, because USP7 is involved in p53 deubiquitination. FAM188B knockdown inhibited both colony formation and anchorage-independent growth in vitro. In addition, FAM188B knockdown by siRNA reduced tumor growth in xenografted mice, with an increase in p53 proteins. Taken together, our data suggest that FAM188B is a putative oncogene that functions via interaction with USP7. Therefore, control of FAM188B could be a possible target to inhibit tumor growth.

A Computational Approach Using Bioinformatics to Screening Drug Targets for Leishmania infantum Species

Background

The development of new therapeutic strategies to treat patients for leishmaniasis has become a priority. The antileishmanial activity of the strychnobiflavone flavonoid was recently demonstrated against Leishmania amazonensis and Leishmania infantum amastigotes and promastigotes. The biological effect of this molecule was identified due to its capacity to interfere in the parasite mitochondrial membrane; however, the underlying molecular mechanism remains unclear.

Methods and Results

In this study, a computational approach using bioinformatics was performed to screen biological targets of strychnobiflavone in L. infantum. Computational programs, such as the target fishing approach and molecular docking assays, were used. Results showed that the putative pathway targeted by strychnobiflavone in L. infantum is the methylglyoxal degradation superpathway, and one hydrolase-like protein was predicted to be the molecular target of this flavonoid in the parasites.

Conclusion

In this context, this study provides the basis for understanding the mechanism of action of strychnobiflavone in L. infantum and presents a strategy based on bioinformatics programs to screen targets of other molecules with biological action against distinct pathogens.

Elucidating Cellular Metabolism and Protein Difference Data from DIGE Proteomics Experiments Using Enzyme Assays

Assays for measuring enzyme activity can be useful tools for proteomics applications. Enzyme testing can be performed to validate an experimental system prior to a Difference Gel Electrophoresis (DIGE) proteomic experiment and can also be utilized as an integral part of multifaceted experiment in conjunction with DIGE. Data from enzyme tests can be used to corroborate results of DIGE proteomic experiments where an enzyme or enzymes are demonstrated by DIGE to be differentially expressed. Enzyme testing can also be utilized to support data from DIGE experiments that demonstrate metabolic changes in a biological system. The different types of enzyme assays that can be performed in conjunction with DIGE experiments are reviewed alongside a discussion of experimental approaches for designing enzyme assays.

Proteome characterization of copper stress responses in the roots of sorghum

Copper (Cu) is a important micronutrient for plants, but it is extremely toxic to plants at high concentration and can inactivate and disturb protein structures. To explore the Cu stress-induced tolerance mechanism, the present study was conducted on the roots of sorghum seedlings exposed to 50 and 100 µM CuSO₄ for 5 days. Accumulation of Cu increased in roots when the seedlings were treated with the highest concentration of Cu²⁺ ions (100 μM). Elevated Cu concentration provoked notable reduction of Fe, Zn, Ca, and Mn uptake in the roots of sorghum seedlings. In the proteome analysis, high-throughput two-dimensional polyacrylamide gel electrophoresis combined with MALDI-TOF-TOF MS was performed to explore the molecular responses of Cu-induced sorghum seedling roots. In two-dimensional silver-stained gels, 422 protein spots were identified in the 2-D gel whereas twenty-one protein spots (≥1.5-fold) were used to analyze mass spectrometry from Cu-induced sorghum roots. Among the 21 differentially expressed proteins, 10 proteins were increased, while 11 proteins were decreased due to the intake of Cu ions by roots of sorghum. Abundance of most of the identified proteins from the roots that function in stress response and metabolism was remarkably enhanced, while proteins involved in transcription and regulation were severely reduced. Taken together, these results imply insights into a potential molecular mechanism towards Cu stress in C₄ plant, sorghum.

In silico search of inhibitors of Streptococcus mutans for the control of dental plaque

Biofilm is an extremely complex microbial community arranged in a matrix of polysaccharides and attached to a substrate. Its development is crucial in the pathophysiology of oral infections like dental caries, as well as in periodontal, pulp, and periapical diseases. Streptococcus mutans is one of the most effective microorganisms in lactic acid production of the dental biofilm. Identifying essential Streptococcus mutans proteins using bioinformatics methods helps to search for alternative therapies. To this end, the bacterial genomes of several Streptococcus mutans strains and representative strains of other cariogenic and non-cariogenic bacteria were analysed by identifying pathogenicity islands and alignments with other bacteria, and by detecting the exclusive genes of cariogenic species in comparison to the non-pathogenic ones. This study used tools for orthology prediction such as BLAST and OrthoMCL, as well as the server IslandViewer for the detection of pathogenicity islands. In addition, the potential interactome of Streptococcus mutans was rebuilt by comparing it to interologues of other species phylogenetically close to or associated with cariogenicity. This protocol yielded a final list of 20 proteins related to potentially virulent factors that can be used as therapeutic targets in future analyses. The EIIA and EIIC enzymatic subunits of the phosphotransferase system (PTS) were prioritized, as well as the pyruvate kinase enzyme, which are directly involved in the metabolism of carbohydrates and in obtaining the necessary energy for the microorganism's survival. These results will guide a subsequent experimental trial to develop new, safe, and effective molecules in the treatment of dental caries.

Evaluation of a hypothetical protein for serodiagnosis and as a potential marker for post-treatment serological evaluation of tegumentary leishmaniasis patients

The serodiagnosis for tegumentary leishmaniasis (TL) presents problems related to the sensitivity and/or specificity of the tests. In the present study, an enzyme-linked immunosorbent assay (ELISA) technique was used to evaluate the performance from a Leishmania braziliensis hypothetical protein, LbHyM, in an attempt to compare its serological reactivity with a soluble Leishmania antigenic preparation (SLA) for the serodiagnosis of cutaneous (CL) and mucosal (ML) leishmaniasis. LbHyM was predicted to be a kinesin-like protein by bioinformatics tools. Serum samples were collected from both CL and ML patients, as well as from those with Chagas disease and from healthy subjects living in endemic or non-endemic areas of TL. Also, sera were collected from patients before and after the treatments, seeking to evaluate their serological follow-up in relation to the anti-protein and anti-parasite antibody levels. When an ELISA-rLbHyM assay was performed, it proved to be significantly more sensitive than ELISA-L. braziliensis SLA in detecting both CL and ML patients. Also, when using sera from Chagas disease patients, the ELISA-rLbHyM proved to be more specific than ELISA-SLA. The anti-protein and anti-parasite antibody levels were also evaluated 6 months after the treatments, and treated patients showed significantly lower levels of specific-rLbHyM antibodies, when compared to the anti-parasite antibody levels. In conclusion, the ELISA-rLbHyM assay can be considered a confirmatory serological technique for the serodiagnosis of L. braziliensis infection and can also be used in the serological follow-up of treated patients, aiming to correlate the low anti-protein antibody levels with the improvement of the healthy state of the patients.

Leaf proteome characterization in the context of physiological and morphological changes in response to copper stress in sorghum

Copper (Cu) is an essential micronutrient required for normal growth and development of plants; however, at elevated concentrations in soil, copper is also generally considered to be one of the most toxic metals to plant cells due to its inhibitory effects against many physiological and biochemical processes. In spite of its potential physiological and economical significance, molecular mechanisms under Cu stress has so far been grossly overlooked in sorghum. To explore the molecular alterations that occur in response to copper stress, the present study was performed in ten-day-old Cu-exposed leaves of sorghum seedlings. The growth characteristics were markedly inhibited, and ionic alterations were prominently observed in the leaves when the seedlings were exposed to different concentrations (0, 100, and 150 µM) of CuSO4. Using two-dimensional gels with silver staining, 643 differentially expressed protein spots (≥1.5-fold) were identified as either significantly increased or reduced in abundance. Of these spots, a total of 24 protein spots (≥1.5-fold) from Cu-exposed sorghum leaves were successfully analyzed by MALDI-TOF-TOF mass spectrometry. Of the 24 differentially expressed proteins from Cu-exposed sorghum leaves, 13 proteins were up-regulated, and 11 proteins were down-regulated. The abundance of most identified protein species, which function in carbohydrate metabolism, stress defense and protein translation, was significantly enhanced, while that of another protein species involved in energy metabolism, photosynthesis and growth and development were severely reduced. The resulting differences in protein expression patterns together with related morpho-physiological processes suggested that these results could help to elucidate plant adaptation to Cu stress and provide insights into the molecular mechanisms of Cu responses in C4 plants.

Global Proteomic Analysis Reveals an Exclusive Role of Thylakoid Membranes in Bioenergetics of a Model Cyanobacterium

Cyanobacteria are photosynthetic microbes with highly differentiated membrane systems. These organisms contain an outer membrane, plasma membrane, and an internal system of thylakoid membranes where the photosynthetic and respiratory machinery are found. This existence of compartmentalization and differentiation of membrane systems poses a number of challenges for cyanobacterial cells in terms of organization and distribution of proteins to the correct membrane system. Proteomics studies have long sought to identify the components of the different membrane systems in cyanobacteria, and to date about 450 different proteins have been attributed to either the plasma membrane or thylakoid membrane. Given the complexity of these membranes, many more proteins remain to be identified, and a comprehensive catalogue of plasma membrane and thylakoid membrane proteins is needed. Here we describe the identification of 635 differentially localized proteins in Synechocystis sp. PCC 6803 by quantitative iTRAQ isobaric labeling; of these, 459 proteins were localized to the plasma membrane and 176 were localized to the thylakoid membrane. Surprisingly, we found over 2.5 times the number of unique proteins identified in the plasma membrane compared with the thylakoid membrane. This suggests that the protein composition of the thylakoid membrane is more homogeneous than the plasma membrane, consistent with the role of the plasma membrane in diverse cellular processes including protein trafficking and nutrient import, compared with a more specialized role for the thylakoid membrane in cellular energetics. Thus, our data clearly define the two membrane systems with distinct functions. Overall, the protein compositions of the Synechocystis 6803 plasma membrane and thylakoid membrane are quite similar to that of the plasma membrane of Escherichia coli and thylakoid membrane of Arabidopsis chloroplasts, respectively. Synechocystis 6803 can therefore be described as a Gram-negative bacterium with an additional internal membrane system that fulfills the energetic requirements of the cell.

Sparse regressions for predicting and interpreting subcellular localization of multi-label proteins

Background

Predicting protein subcellular localization is indispensable for inferring protein functions. Recent studies have been focusing on predicting not only single-location proteins, but also multi-location proteins. Almost all of the high performing predictors proposed recently use gene ontology (GO) terms to construct feature vectors for classification. Despite their high performance, their prediction decisions are difficult to interpret because of the large number of GO terms involved.

Results

This paper proposes using sparse regressions to exploit GO information for both predicting and interpreting subcellular localization of single- and multi-location proteins. Specifically, we compared two multi-label sparse regression algorithms, namely multi-label LASSO (mLASSO) and multi-label elastic net (mEN), for large-scale predictions of protein subcellular localization. Both algorithms can yield sparse and interpretable solutions. By using the one-vs-rest strategy, mLASSO and mEN identified 87 and 429 out of more than 8,000 GO terms, respectively, which play essential roles in determining subcellular localization. More interestingly, many of the GO terms selected by mEN are from the biological process and molecular function categories, suggesting that the GO terms of these categories also play vital roles in the prediction. With these essential GO terms, not only where a protein locates can be decided, but also why it resides there can be revealed.

Conclusions

Experimental results show that the output of both mEN and mLASSO are interpretable and they perform significantly better than existing state-of-the-art predictors. Moreover, mEN selects more features and performs better than mLASSO on a stringent human benchmark dataset. For readers’ convenience, an online server called SpaPredictor for both mLASSO and mEN is available at http://bioinfo.eie.polyu.edu.hk/SpaPredictorServer/.

Classification and Functional Analyses of Putative Conserved Proteins from Chlamydophila pneumoniae CWL029

Chlamydophila pneumoniae, a Gram-negative bacterium belongs to the family Chlamydiaceae, is known to cause community-acquired pneumonia and bronchitis. There is a need for genomic analyses of C. pneumoniae as its chronic infections result in reactive airway disease, lung cancer and asthma. Recent advancement in the sequencing techniques led to the generation of large genomic data. In order to utilize these data, sequence-based function predictions were used for annotating the uncharacterized genes. The genome of C. pneumoniae encodes 1052 proteins, which include a group of 366 functionally uncharacterized proteins, known as "hypothetical proteins" (HPs). Functions of these HPs were predicted by utilizing an integrated approach that combines varieties of bioinformatics tools. The functions of 142 proteins were successfully predicted and categorized into different classes of enzymes, transport proteins, binding proteins and virulence factors. Among these functionally annotated HPs, we were able to identify 12 virulent HPs. Furthermore, the HP with the highest virulence score was subjected to molecular dynamics (MD) simulations to better understand their dynamical behavior in explicit water conditions. These analyses could be utilized for an in-depth understanding of virulence mechanism. The functional knowledge of these proteins could be useful in drug design and discovery process of infections caused by C. pneumoniae.

Structure-based functional annotation of hypothetical proteins from Candida dubliniensis: a quest for potential drug targets

Candida dubliniensis is an emerging pathogenic yeast in humans and infections are usually restricted to mucosal parts of the body. However, its presence in specimens of immunocompromised individuals, especially in HIV-positive patients, is of major medical concern. There is a large fraction of genomes of C. dubliniensis in the database which are uncharacterized for their biochemical, biophysical, and/or cellular functions, and are identified as hypothetical proteins (HPs). Function annotation of Candida genome is, therefore, essentially required to facilitate the understanding of mechanisms of pathogenesis and biochemical pathways important for selecting novel therapeutic target. Here, we carried out an extensive analysis to explain the functional properties of genome, using available protein structure and function analysis tools. We successfully modeled the structures of eight HPs for which a template with moderate sequence similarity was available in the protein data bank. All modeled structures were analyzed and we found that these proteins may act as transporter, kinase, transferase, ketosteroid, isomerase, hydrolase, oxidoreductase, and binding targets for DNA and RNA. Since these unique HPs of Candida showed no homologs in humans, these proteins are expected to be a potential target for future antifungal therapy.

Genome Wide Re-Annotation of Caldicellulosiruptor saccharolyticus with New Insights into Genes Involved in Biomass Degradation and Hydrogen Production

Caldicellulosiruptor saccharolyticus has proven itself to be an excellent candidate for biological hydrogen (H₂) production, but still it has major drawbacks like sensitivity to high osmotic pressure and low volumetric H₂ productivity, which should be considered before it can be used industrially. A whole genome re-annotation work has been carried out as an attempt to update the incomplete genome information that causes gap in the knowledge especially in the area of metabolic engineering, to improve the H₂ producing capabilities of C. saccharolyticus. Whole genome re-annotation was performed through manual means for 2,682 Coding Sequences (CDSs). Bioinformatics tools based on sequence similarity, motif search, phylogenetic analysis and fold recognition were employed for re-annotation. Our methodology could successfully add functions for 409 hypothetical proteins (HPs), 46 proteins previously annotated as putative and assigned more accurate functions for the known protein sequences. Homology based gene annotation has been used as a standard method for assigning function to novel proteins, but over the past few years many non-homology based methods such as genomic context approaches for protein function prediction have been developed. Using non-homology based functional prediction methods, we were able to assign cellular processes or physical complexes for 249 hypothetical sequences. Our re-annotation pipeline highlights the addition of 231 new CDSs generated from MicroScope Platform, to the original genome with functional prediction for 49 of them. The re-annotation of HPs and new CDSs is stored in the relational database that is available on the MicroScope web-based platform. In parallel, a comparative genome analyses were performed among the members of genus Caldicellulosiruptor to understand the function and evolutionary processes. Further, with results from integrated re-annotation studies (homology and genomic context approach), we strongly suggest that Csac_0437 and Csac_0424 encode for glycoside hydrolases (GH) and are proposed to be involved in the decomposition of recalcitrant plant polysaccharides. Similarly, HPs: Csac_0732, Csac_1862, Csac_1294 and Csac_0668 are suggested to play a significant role in biohydrogen production. Function prediction of these HPs by using our integrated approach will considerably enhance the interpretation of large-scale experiments targeting this industrially important organism.