Enhancing protein-vitamin binding residues prediction by multiple heterogeneous subspace SVMs ensemble

Background

Vitamins are typical ligands that play critical roles in various metabolic processes. The accurate identification of the vitamin-binding residues solely based on a protein sequence is of significant importance for the functional annotation of proteins, especially in the post-genomic era, when large volumes of protein sequences are accumulating quickly without being functionally annotated.

Results

In this paper, a new predictor called TargetVita is designed and implemented for predicting protein-vitamin binding residues using protein sequences. In TargetVita, features derived from the position-specific scoring matrix (PSSM), predicted protein secondary structure, and vitamin binding propensity are combined to form the original feature space; then, several feature subspaces are selected by performing different feature selection methods. Finally, based on the selected feature subspaces, heterogeneous SVMs are trained and then ensembled for performing prediction.

Conclusions

The experimental results obtained with four separate vitamin-binding benchmark datasets demonstrate that the proposed TargetVita is superior to the state-of-the-art vitamin-specific predictor, and an average improvement of 10% in terms of the Matthews correlation coefficient (MCC) was achieved over independent validation tests. The TargetVita web server and the datasets used are freely available for academic use at http://csbio.njust.edu.cn/bioinf/TargetVita or http://www.csbio.sjtu.edu.cn/bioinf/TargetVita.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-297) contains supplementary material, which is available to authorized users.     

EPMLR: sequence-based linear B-cell epitope prediction method using multiple linear regression

Background

B-cell epitopes have been studied extensively due to their immunological applications, such as peptide-based vaccine development, antibody production, and disease diagnosis and therapy. Despite several decades of research, the accurate prediction of linear B-cell epitopes has remained a challenging task.

Results

In this work, based on the antigen’s primary sequence information, a novel linear B-cell epitope prediction model was developed using the multiple linear regression (MLR). A 10-fold cross-validation test on a large non-redundant dataset was performed to evaluate the performance of our model. To alleviate the problem caused by the noise of negative dataset, 300 experiments utilizing 300 sub-datasets were performed. We achieved overall sensitivity of 81.8%, precision of 64.1% and area under the receiver operating characteristic curve (AUC) of 0.728.

Conclusions

We have presented a reliable method for the identification of linear B cell epitope using antigen’s primary sequence information. Moreover, a web server EPMLR has been developed for linear B-cell epitope prediction: http://www.bioinfo.tsinghua.edu.cn/epitope/EPMLR/.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0414-y) contains supplementary material, which is available to authorized users.     

miRSystem: An Integrated System for Characterizing Enriched Functions and Pathways of MicroRNA Targets

Background

Many prediction tools for microRNA (miRNA) targets have been developed, but inconsistent predictions were observed across multiple algorithms, which can make further analysis difficult. Moreover, the nomenclature of human miRNAs changes rapidly. To address these issues, we developed a web-based system, miRSystem, for converting queried miRNAs to the latest annotation and predicting the function of miRNA by integrating miRNA target gene prediction and function/pathway analyses.

Results

First, queried miRNA IDs were converted to the latest annotated version to prevent potential conflicts resulting from multiple aliases. Next, by combining seven algorithms and two validated databases, potential gene targets of miRNAs and their functions were predicted based on the consistency across independent algorithms and observed/expected ratios. Lastly, five pathway databases were included to characterize the enriched pathways of target genes through bootstrap approaches. Based on the enriched pathways of target genes, the functions of queried miRNAs could be predicted.

Conclusions

MiRSystem is a user-friendly tool for predicting the target genes and their associated pathways for many miRNAs simultaneously. The web server and the documentation are freely available at http://mirsystem.cgm.ntu.edu.tw/.     

A Novel B-Cell Epitope Identified within Mycobacterium tuberculosis CFP10/ESAT-6 Protein

Background

The 10-kDa culture filtrate protein (CFP10) and 6-kDa early-secreted target antigen (ESAT-6) play important roles in mycobacterial virulence and pathogenesis through a 1∶1 complex formation (CFP10/ESAT-6 protein, CE protein), which have been used in discriminating TB patients from BCG-vaccinated individuals. The B-cell epitopes of CFP10 and ESAT-6 separately have been analyzed before, however, the epitopes of the CE protein are unclear and the precise epitope in the positions 40 to 62 of ESAT-6 is still unknown.

Methods

In the present study, we searched for the B-cell epitopes of CE protein by using phage-display library biopanning with the anti-CE polyclonal antibodies. The epitopes were identified by sequence alignment, binding affinity and specificity detection, generation of polyclonal mouse sera and detection of TB patient sera.

Results

One linear B-cell epitope (KWDAT) consistent with the 162^nd–166^th sequence of CE and the 57^th–61^st sequence of ESAT-6 protein was selected and identified. Significantly higher titers of E5 peptide-binding antibodies were found in the sera of TB patients compared with those of healthy individuals.

Conclusion

There was a B-cell epitope for CE and ESAT-6 protein in the position 40 to 62 of ESAT-6. E5 peptide may be useful in the serodiagnosis of tuberculosis, which need to be further confirmed by more sera samples.     

In silico analysis and modeling of ACP-MIP–PilQ chimeric antigen from Neisseria meningitidis serogroup B

Background:

Neisseria meningitidis, a life-threatening human pathogen with the potential to cause large epidemics, can be isolated from the nasopharynx of 5–15% of adults. The aim of the current study was to evaluate biophysical and biochemical properties and immunological aspects of chimeric acyl-carrier protein-macrophage infectivity potentiator protein-type IV pilus biogenesis protein antigen (ACP-MIP-PilQ) from N. meningitidis serogroup B strain.

Methods:

Biochemical properties and multiple alignments were predicted by appropriate web servers. Secondary molecular structures were predicted based on Chou and Fasman, Garnier-Osguthorpe-Robson, and Neural Network methods. Tertiary modeling elucidated conformational properties of the chimeric protein. Proteasome cleavage and transporter associated with antigen processing (TAP) binding sites, and T- and B-cell antigenic epitopes, were predicted using bioinformatic web servers.

Results:

Based on our in silico and immunoinformatics analyses, the ACP-MIP-PilQ protein (AMP) can induce high-level cross-strain bactericidal activity. In addition, several immune proteasomal cleavage sites were detected. The 22 epitopes associated with MHC class I and class II (DR) alleles were confirmed in the AMP. Thirty linear B-cell epitopes as antigenic regions were predicted from the full-length protein.

Conclusion:

All predicted properties of the AMP indicate it could be a good candidate for further immunological in vitro and in vivo studies.Key Words: Chimeric protein, In silico, Neisseria meningitides, serogroup B, Vaccine     

Re-Inspection of Small RNA Sequence Datasets Reveals Several Novel Human miRNA Genes

Background

miRNAs are key players in gene expression regulation. To fully understand the complex nature of cellular differentiation or initiation and progression of disease, it is important to assess the expression patterns of as many miRNAs as possible. Thereby, identifying novel miRNAs is an essential prerequisite to make possible a comprehensive and coherent understanding of cellular biology.

Methodology/Principal Findings

Based on two extensive, but previously published, small RNA sequence datasets from human embryonic stem cells and human embroid bodies, respectively [1], we identified 112 novel miRNA-like structures and were able to validate miRNA processing in 12 out of 17 investigated cases. Several miRNA candidates were furthermore substantiated by including additional available small RNA datasets, thereby demonstrating the power of combining datasets to identify miRNAs that otherwise may be assigned as experimental noise.

Conclusions/Significance

Our analysis highlights that existing datasets are not yet exhaustedly studied and continuous re-analysis of the available data is important to uncover all features of small RNA sequencing.     

A meta-learning approach for B-cell conformational epitope prediction

Background

One of the major challenges in the field of vaccine design is identifying B-cell epitopes in continuously evolving viruses. Various tools have been developed to predict linear or conformational epitopes, each relying on different physicochemical properties and adopting distinct search strategies. We propose a meta-learning approach for epitope prediction based on stacked and cascade generalizations. Through meta learning, we expect a meta learner to be able integrate multiple prediction models, and outperform the single best-performing model. The objective of this study is twofold: (1) to analyze the complementary predictive strengths in different prediction tools, and (2) to introduce a generic computational model to exploit the synergy among various prediction tools. Our primary goal is not to develop any particular classifier for B-cell epitope prediction, but to advocate the feasibility of meta learning to epitope prediction. With the flexibility of meta learning, the researcher can construct various meta classification hierarchies that are applicable to epitope prediction in different protein domains.

Results

We developed the hierarchical meta-learning architectures based on stacked and cascade generalizations. The bottom level of the hierarchy consisted of four conformational and four linear epitope prediction tools that served as the base learners. To perform consistent and unbiased comparisons, we tested the meta-learning method on an independent set of antigen proteins that were not used previously to train the base epitope prediction tools. In addition, we conducted correlation and ablation studies of the base learners in the meta-learning model. Low correlation among the predictions of the base learners suggested that the eight base learners had complementary predictive capabilities. The ablation analysis indicated that the eight base learners differentially interacted and contributed to the final meta model. The results of the independent test demonstrated that the meta-learning approach markedly outperformed the single best-performing epitope predictor.

Conclusions

Computational B-cell epitope prediction tools exhibit several differences that affect their performances when predicting epitopic regions in protein antigens. The proposed meta-learning approach for epitope prediction combines multiple prediction tools by integrating their complementary predictive strengths. Our experimental results demonstrate the superior performance of the combined approach in comparison with single epitope predictors.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0378-y) contains supplementary material, which is available to authorized users.     

Probing metagenomics by rapid cluster analysis of very large datasets

Background

The scale and diversity of metagenomic sequencing projects challenge both our technical and conceptual approaches in gene and genome annotations. The recent Sorcerer II Global Ocean Sampling (GOS) expedition yielded millions of predicted protein sequences, which significantly altered the landscape of known protein space by more than doubling its size and adding thousands of new families (Yooseph et al., 2007 PLoS Biol 5, e16). Such datasets, not only by their sheer size, but also by many other features, defy conventional analysis and annotation methods.

Methodology/Principal Findings

In this study, we describe an approach for rapid analysis of the sequence diversity and the internal structure of such very large datasets by advanced clustering strategies using the newly modified CD-HIT algorithm. We performed a hierarchical clustering analysis on the 17.4 million Open Reading Frames (ORFs) identified from the GOS study and found over 33 thousand large predicted protein clusters comprising nearly 6 million sequences. Twenty percent of these clusters did not match known protein families by sequence similarity search and might represent novel protein families. Distributions of the large clusters were illustrated on organism composition, functional class, and sample locations.

Conclusion/Significance

Our clustering took about two orders of magnitude less computational effort than the similar protein family analysis of original GOS study. This approach will help to analyze other large metagenomic datasets in the future. A Web server with our clustering results and annotations of predicted protein clusters is available online at http://tools.camera.calit2.net/gos under the CAMERA project.     

jMOTU and Taxonerator: turning DNA Barcode sequences into annotated operational taxonomic units

Background

DNA barcoding and other DNA sequence-based techniques for investigating and estimating biodiversity require explicit methods for associating individual sequences with taxa, as it is at the taxon level that biodiversity is assessed. For many projects, the bioinformatic analyses required pose problems for laboratories whose prime expertise is not in bioinformatics. User-friendly tools are required for both clustering sequences into molecular operational taxonomic units (MOTU) and for associating these MOTU with known organismal taxonomies.

Results

Here we present jMOTU, a Java program for the analysis of DNA barcode datasets that uses an explicit, determinate algorithm to define MOTU. We demonstrate its usefulness for both individual specimen-based Sanger sequencing surveys and bulk-environment metagenetic surveys using long-read next-generation sequencing data. jMOTU is driven through a graphical user interface, and can analyse tens of thousands of sequences in a short time on a desktop computer. A companion program, Taxonerator, that adds traditional taxonomic annotation to MOTU, is also presented. Clustering and taxonomic annotation data are stored in a relational database, and are thus amenable to subsequent data mining and web presentation.

Conclusions

jMOTU efficiently and robustly identifies the molecular taxa present in survey datasets, and Taxonerator decorates the MOTU with putative identifications. jMOTU and Taxonerator are freely available from http://www.nematodes.org/.     

Comparative structural analysis of lipid binding START domains

Background

Steroidogenic acute regulatory (StAR) protein related lipid transfer (START) domains are small globular modules that form a cavity where lipids and lipid hormones bind. These domains can transport ligands to facilitate lipid exchange between biological membranes, and they have been postulated to modulate the activity of other domains of the protein in response to ligand binding. More than a dozen human genes encode START domains, and several of them are implicated in a disease.

Principal Findings

We report crystal structures of the human STARD1, STARD5, STARD13 and STARD14 lipid transfer domains. These represent four of the six functional classes of START domains.

Significance

Sequence alignments based on these and previously reported crystal structures define the structural determinants of human START domains, both those related to structural framework and those involved in ligand specificity.

Enhanced version

This article can also be viewed as an enhanced version in which the text of the article is integrated with interactive 3D representations and animated transitions. Please note that a web plugin is required to access this enhanced functionality. Instructions for the installation and use of the web plugin are available in Text S1.     

Analyzing Multi-locus Plant Barcoding Datasets with a Composition Vector Method Based on Adjustable Weighted Distance

Background

The composition vector (CV) method has been proved to be a reliable and fast alignment-free method to analyze large COI barcoding data. In this study, we modify this method for analyzing multi-gene datasets for plant DNA barcoding. The modified method includes an adjustable-weighted algorithm for the vector distance according to the ratio in sequence length of the candidate genes for each pair of taxa.

Methodology/Principal Findings

Three datasets, matK+rbcL dataset with 2,083 sequences, matK+rbcL dataset with 397 sequences and matK+rbcL+trnH-psbA dataset with 397 sequences, were tested. We showed that the success rates of grouping sequences at the genus/species level based on this modified CV approach are always higher than those based on the traditional K2P/NJ method. For the matK+rbcL datasets, the modified CV approach outperformed the K2P-NJ approach by 7.9% in both the 2,083-sequence and 397-sequence datasets, and for the matK+rbcL+trnH-psbA dataset, the CV approach outperformed the traditional approach by 16.7%.

Conclusions

We conclude that the modified CV approach is an efficient method for analyzing large multi-gene datasets for plant DNA barcoding. Source code, implemented in C++ and supported on MS Windows, is freely available for download at http://math.xtu.edu.cn/myphp/math/research/source/Barcode_source_codes.zip.     

Internet-based CBT for depression with and without telephone tracking in a national helpline: randomised controlled trial

Background

Telephone helplines are frequently and repeatedly used by individuals with chronic mental health problems and web interventions may be an effective tool for reducing depression in this population.

Aim

To evaluate the effectiveness of a 6 week, web-based cognitive behaviour therapy (CBT) intervention with and without proactive weekly telephone tracking in the reduction of depression in callers to a helpline service.

Method

155 callers to a national helpline service with moderate to high psychological distress were recruited and randomised to receive either Internet CBT plus weekly telephone follow-up; Internet CBT only; weekly telephone follow-up only; or treatment as usual.

Results

Depression was lower in participants in the web intervention conditions both with and without telephone tracking compared to the treatment as usual condition both at post intervention and at 6 month follow-up. Telephone tracking provided by a lay telephone counsellor did not confer any additional advantage in terms of symptom reduction or adherence.

Conclusions

A web-based CBT program is effective both with and without telephone tracking for reducing depression in callers to a national helpline.

Trial Registration

Controlled-Trials.com ISRCTN93903959     

MendeLIMS: a web-based laboratory information management system for clinical genome sequencing

Background

Large clinical genomics studies using next generation DNA sequencing require the ability to select and track samples from a large population of patients through many experimental steps. With the number of clinical genome sequencing studies increasing, it is critical to maintain adequate laboratory information management systems to manage the thousands of patient samples that are subject to this type of genetic analysis.

Results

To meet the needs of clinical population studies using genome sequencing, we developed a web-based laboratory information management system (LIMS) with a flexible configuration that is adaptable to continuously evolving experimental protocols of next generation DNA sequencing technologies. Our system is referred to as MendeLIMS, is easily implemented with open source tools and is also highly configurable and extensible. MendeLIMS has been invaluable in the management of our clinical genome sequencing studies.

Conclusions

We maintain a publicly available demonstration version of the application for evaluation purposes at http://mendelims.stanford.edu. MendeLIMS is programmed in Ruby on Rails (RoR) and accesses data stored in SQL-compliant relational databases. Software is freely available for non-commercial use at http://dna-discovery.stanford.edu/software/mendelims/.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-290) contains supplementary material, which is available to authorized users.     

Synthesized Multiple Antigenic Polypeptide Vaccine Based on B-Cell Epitopes of Human Heparanase Could Elicit a Potent Antimetastatic Effect on Human Hepatocellular Carcinoma In Vivo

Aims

The aim of this study was to investigate the antimetastatic effect of multiple antigenic polypeptide (MAP) vaccine based on B-cell epitopes of heparanase (HPSE) on human hepatocellular carcinoma (HCC) in vivo.

Methods

The antiserum against B-cell epitopes of HPSE was isolated, purified and characterized after immunizing white-hair-black-eye (WHBY) rabbit with freshly synthesized MAP vaccine. Tumor-bearing murine models of orthotopic implants using HCC-97H cell line were built in BALB/c nude mice. Anti-MAP polyclonal antibodies induced by MAP vaccine were administrated to the models. The impact on metastasis was assessed, the expressions of VEGF/bFGF in hepatoma tissues and in murine sera were evaluated, and the micro-vessel density (MVD) was counted as well. In addition, the possible impairments of the HPSE MAP vaccine on certain HPSE positive normal organs and blood cells were investigated.

Results

The antiserum was harvested, purified and identified. The antibodies induced by MAP vaccine could specifically react with the dominant epitopes of both precursor protein and large subunit monomer of HPSE, markedly decrease HPSE activity, suppress the expressions of both VEGF and bFGF, and reduce the MVD. Pulmonary metastasis was also attenuated significantly by the anti-MAP polyclonal antibodies. In addition, no obvious impairment could be observed in certain HPSE positive organs and cells.

Conclusion

MAP vaccine based on B-cell epitopes of HPSE is capable of alleviating HCC metastasis in vivo, mainly through inhibiting the HPSE activity and tumor associated angiogenesis, by virtue of the specific anti-MAP polyclonal antibodies. Furthermore, these HPSE-specific antibodies do not cause obvious abnormalities on certain HPSE positive blood cells and organs. Our study provides theoretical evidences for the clinical use of the synthesized MAP vaccine based on B-cell epitopes of HPSE in preventing HCC metastasis.     

RIEMS: a software pipeline for sensitive and comprehensive taxonomic classification of reads from metagenomics datasets

Background

Fuelled by the advent and subsequent development of next generation sequencing technologies, metagenomics became a powerful tool for the analysis of microbial communities both scientifically and diagnostically. The biggest challenge is the extraction of relevant information from the huge sequence datasets generated for metagenomics studies. Although a plethora of tools are available, data analysis is still a bottleneck.

Results

To overcome the bottleneck of data analysis, we developed an automated computational workflow called RIEMS – Reliable Information Extraction from Metagenomic Sequence datasets. RIEMS assigns every individual read sequence within a dataset taxonomically by cascading different sequence analyses with decreasing stringency of the assignments using various software applications. After completion of the analyses, the results are summarised in a clearly structured result protocol organised taxonomically. The high accuracy and performance of RIEMS analyses were proven in comparison with other tools for metagenomics data analysis using simulated sequencing read datasets.

Conclusions

RIEMS has the potential to fill the gap that still exists with regard to data analysis for metagenomics studies. The usefulness and power of RIEMS for the analysis of genuine sequencing datasets was demonstrated with an early version of RIEMS in 2011 when it was used to detect the orthobunyavirus sequences leading to the discovery of Schmallenberg virus.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0503-6) contains supplementary material, which is available to authorized users.     

UniDrug-target: a computational tool to identify unique drug targets in pathogenic bacteria

Background

Targeting conserved proteins of bacteria through antibacterial medications has resulted in both the development of resistant strains and changes to human health by destroying beneficial microbes which eventually become breeding grounds for the evolution of resistances. Despite the availability of more than 800 genomes sequences, 430 pathways, 4743 enzymes, 9257 metabolic reactions and protein (three-dimensional) 3D structures in bacteria, no pathogen-specific computational drug target identification tool has been developed.

Methods

A web server, UniDrug-Target, which combines bacterial biological information and computational methods to stringently identify pathogen-specific proteins as drug targets, has been designed. Besides predicting pathogen-specific proteins essentiality, chokepoint property, etc., three new algorithms were developed and implemented by using protein sequences, domains, structures, and metabolic reactions for construction of partial metabolic networks (PMNs), determination of conservation in critical residues, and variation analysis of residues forming similar cavities in proteins sequences. First, PMNs are constructed to determine the extent of disturbances in metabolite production by targeting a protein as drug target. Conservation of pathogen-specific protein''s critical residues involved in cavity formation and biological function determined at domain-level with low-matching sequences. Last, variation analysis of residues forming similar cavities in proteins sequences from pathogenic versus non-pathogenic bacteria and humans is performed.

Results

The server is capable of predicting drug targets for any sequenced pathogenic bacteria having fasta sequences and annotated information. The utility of UniDrug-Target server was demonstrated for Mycobacterium tuberculosis (H37Rv). The UniDrug-Target identified 265 mycobacteria pathogen-specific proteins, including 17 essential proteins which can be potential drug targets.

Conclusions/Significance

UniDrug-Target is expected to accelerate pathogen-specific drug targets identification which will increase their success and durability as drugs developed against them have less chance to develop resistances and adverse impact on environment. The server is freely available at http://117.211.115.67/UDT/main.html. The standalone application (source codes) is available at http://www.bioinformatics.org/ftp/pub/bioinfojuit/UDT.rar.