Dynamic human liver proteome atlas reveals functional insights into disease pathways

Deeper understanding of liver pathophysiology would benefit from a comprehensive quantitative proteome resource at cell type resolution to predict outcome and design therapy. Here, we quantify more than 150,000 sequence‐unique peptides aggregated into 10,000 proteins across total liver, the major liver cell types, time course of primary cell cultures, and liver disease states. Bioinformatic analysis reveals that half of hepatocyte protein mass is comprised of enzymes and 23% of mitochondrial proteins, twice the proportion of other liver cell types. Using primary cell cultures, we capture dynamic proteome remodeling from tissue states to cell line states, providing useful information for biological or pharmaceutical research. Our extensive data serve as spectral library to characterize a human cohort of non‐alcoholic steatohepatitis and cirrhosis. Dramatic proteome changes in liver tissue include signatures of hepatic stellate cell activation resembling liver cirrhosis and providing functional insights. We built a web‐based dashboard application for the interactive exploration of our resource (www.liverproteome.org).     

SARS‐CoV‐2 structural coverage map reveals viral protein assembly,mimicry, and hijacking mechanisms

We modeled 3D structures of all SARS‐CoV‐2 proteins, generating 2,060 models that span 69% of the viral proteome and provide details not available elsewhere. We found that ˜6% of the proteome mimicked human proteins, while ˜7% was implicated in hijacking mechanisms that reverse post‐translational modifications, block host translation, and disable host defenses; a further ˜29% self‐assembled into heteromeric states that provided insight into how the viral replication and translation complex forms. To make these 3D models more accessible, we devised a structural coverage map, a novel visualization method to show what is—and is not—known about the 3D structure of the viral proteome. We integrated the coverage map into an accompanying online resource (https://aquaria.ws/covid) that can be used to find and explore models corresponding to the 79 structural states identified in this work. The resulting Aquaria‐COVID resource helps scientists use emerging structural data to understand the mechanisms underlying coronavirus infection and draws attention to the 31% of the viral proteome that remains structurally unknown or dark.     

TRIP Database 2.0: A Manually Curated Information Hub for Accessing TRP Channel Interaction Network

Transient receptor potential (TRP) channels are a family of Ca²⁺-permeable cation channels that play a crucial role in biological and disease processes. To advance TRP channel research, we previously created the TRIP (TRansient receptor potential channel-Interacting Protein) Database, a manually curated database that compiles scattered information on TRP channel protein-protein interactions (PPIs). However, the database needs to be improved for information accessibility and data utilization. Here, we present the TRIP Database 2.0 (http://www.trpchannel.org) in which many helpful, user-friendly web interfaces have been developed to facilitate knowledge acquisition and inspire new approaches to studying TRP channel functions: 1) the PPI information found in the supplementary data of referred articles was curated; 2) the PPI summary matrix enables users to intuitively grasp overall PPI information; 3) the search capability has been expanded to retrieve information from ‘PubMed’ and ‘PIE the search’ (a specialized search engine for PPI-related articles); and 4) the PPI data are available as sif files for network visualization and analysis using ‘Cytoscape’. Therefore, our TRIP Database 2.0 is an information hub that works toward advancing data-driven TRP channel research.     

In silico prediction of HIV-1-host molecular interactions and their directionality

Human immunodeficiency virus type 1 (HIV-1) continues to be a major cause of disease and premature death. As with all viruses, HIV-1 exploits a host cell to replicate. Improving our understanding of the molecular interactions between virus and human host proteins is crucial for a mechanistic understanding of virus biology, infection and host antiviral activities. This knowledge will potentially permit the identification of host molecules for targeting by drugs with antiviral properties. Here, we propose a data-driven approach for the analysis and prediction of the HIV-1 interacting proteins (VIPs) with a focus on the directionality of the interaction: host-dependency versus antiviral factors. Using support vector machine learning models and features encompassing genetic, proteomic and network properties, our results reveal some significant differences between the VIPs and non-HIV-1 interacting human proteins (non-VIPs). As assessed by comparison with the HIV-1 infection pathway data in the Reactome database (sensitivity > 90%, threshold = 0.5), we demonstrate these models have good generalization properties. We find that the ‘direction’ of the HIV-1-host molecular interactions is also predictable due to different characteristics of ‘forward’/pro-viral versus ‘backward’/pro-host proteins. Additionally, we infer the previously unknown direction of the interactions between HIV-1 and 1351 human host proteins. A web server for performing predictions is available at http://hivpre.cvr.gla.ac.uk/.     

PINAT1.0: Protein interaction network analysis tool

Cellular processes are regulated by interaction of various proteins i.e. multiprotein complexes and absences of these interactions are often the cause of disorder or disease. Such type of protein interactions are of great interest for drug designing. In host­parasite diseases like Tuberculosis, non-homologous proteins as drug target are first preference. Most potent drug target can be identifying among large number of non-homologous protein through protein interaction network analysis. Drug target should be those non-homologous protein which is associated with maximum number of functional proteins i.e. has highest number of interactants, so that maximum harm can be caused to pathogen only. In present work, Protein Interaction Network Analysis Tool (PINAT) has been developed to identification of potential protein interaction for drug target identification. PINAT is standalone, GUI application software made for protein-protein interaction (PPI) analysis and network building by using co­evolutionary profile. PINAT is very useful for large data PPI study with easiest handling among available softwares. PINAT provides excellent facilities for the assembly of data for network building with visual presentation of the results and interaction score. The software is written in JAVA and provides reliability through transparency with user.

Availability

PINAT is available at www.manit.ac.in/pinat     

A Randomized Controlled Trial of the Efficacy and Safety of CCX282-B,an Orally-Administered Blocker of Chemokine Receptor CCR9, for Patients with Crohn’s Disease

CCX282-B, also called vercirnon, is a specific, orally-administered chemokine receptor CCR9 antagonist that regulates migration and activation of inflammatory cells in the intestine. This randomized, placebo-controlled trial was conducted to evaluate the safety and efficacy of CCX282-B in 436 patients with Crohn’s disease. Crohn’s Disease Activity Index (CDAI) scores were 250–450 and C-reactive protein >7.5 mg/L at study entry. In addition to stable concomitant Crohn’s medication (85% of subjects), subjects received placebo or CCX282-B (250 mg once daily, 250 mg twice daily, or 500 mg once daily) for 12 weeks. They then received 250 mg CCX282-B twice daily, open-label, through week 16. Subjects who had a clinical response (a ≥70 point drop in CDAI) at week 16 were randomly assigned to groups given placebo or CCX282-B (250 mg, twice daily) for 36 weeks. Primary endpoints were clinical response at Week 8 and sustained clinical response at Week 52. During the 12-week Induction period, the clinical response was highest in the group given 500 mg CCX282-B once daily. Response rates at week 8 were 49% in the placebo group, 52% in the group given CCX282-B 250 mg once daily (odds ratio [OR] = 1.12; p = .667 vs placebo), 48% in the group given CCX282-B 250 mg twice daily (OR = 0.95; p = .833), and 60% in the group given CCX282-B 500 mg once daily (OR = 1.53; p = .111). At week 12, response rates were 47%, 56% (OR = 1.44; p = .168), 49% (OR = 1.07; p = .792), and 61% (OR = 1.74; p = .039), respectively. At the end of the Maintenance period (week 52), 47% of subjects on CCX282-B were in remission, compared to 31% on placebo (OR = 2.01; p = .012); 46% showed sustained clinical responses, compared to 42% on placebo (OR = 1.14; p = .629). CCX282-B was well tolerated. Encouraging results from this clinical trial led to initiation of Phase 3 clinical trials in Crohn’s disease.

Trial Registration

ClinicalTrials.gov {"type":"clinical-trial","attrs":{"text":"NCT00306215","term_id":"NCT00306215"}}NCT00306215.     

A Comprehensive Molecular Interaction Map for Rheumatoid Arthritis

Background

Computational biology contributes to a variety of areas related to life sciences and, due to the growing impact of translational medicine - the scientific approach to medicine in tight relation with basic science -, it is becoming an important player in clinical-related areas. In this study, we use computation methods in order to improve our understanding of the complex interactions that occur between molecules related to Rheumatoid Arthritis (RA).

Methodology

Due to the complexity of the disease and the numerous molecular players involved, we devised a method to construct a systemic network of interactions of the processes ongoing in patients affected by RA. The network is based on high-throughput data, refined semi-automatically with carefully curated literature-based information. This global network has then been topologically analysed, as a whole and tissue-specifically, in order to translate the experimental molecular connections into topological motifs meaningful in the identification of tissue-specific markers and targets in the diagnosis, and possibly in the therapy, of RA.

Significance

We find that some nodes in the network that prove to be topologically important, in particular AKT2, IL6, MAPK1 and TP53, are also known to be associated with drugs used for the treatment of RA. Importantly, based on topological consideration, we are also able to suggest CRKL as a novel potentially relevant molecule for the diagnosis or treatment of RA. This type of finding proves the potential of in silico analyses able to produce highly refined hypotheses, based on vast experimental data, to be tested further and more efficiently. As research on RA is ongoing, the present map is in fieri, despite being -at the moment- a reflection of the state of the art. For this reason we make the network freely available in the standardised and easily exportable .xml CellDesigner format at ‘www.picb.ac.cn/ClinicalGenomicNTW/temp.html’ and ‘www.celldesigner.org’.     

Village-Randomized Clinical Trial of Home Distribution of Zinc for Treatment of Childhood Diarrhea in Rural Western Kenya

Background

Zinc treatment shortens diarrhea episodes and can prevent future episodes. In rural Africa, most children with diarrhea are not brought to health facilities. In a village-randomized trial in rural Kenya, we assessed if zinc treatment might have a community-level preventive effect on diarrhea incidence if available at home versus only at health facilities.

Methods

We randomized 16 Kenyan villages (1,903 eligible children) to receive a 10-day course of zinc and two oral rehydration solution (ORS) sachets every two months at home and 17 villages (2,241 eligible children) to receive ORS at home, but zinc at the health–facility only. Children’s caretakers were educated in zinc/ORS use by village workers, both unblinded to intervention arm. We evaluated whether incidence of diarrhea and acute lower respiratory illness (ALRI) reported at biweekly home visits and presenting to clinic were lower in zinc villages, using poisson regression adjusting for baseline disease rates, distance to clinic, and children’s age.

Results

There were no differences between village groups in diarrhea incidence either reported at the home or presenting to clinic. In zinc villages (1,440 children analyzed), 61.2% of diarrheal episodes were treated with zinc, compared to 5.4% in comparison villages (1,584 children analyzed, p<0.0001). There were no differences in ORS use between zinc (59.6%) and comparison villages (58.8%). Among children with fever or cough without diarrhea, zinc use was low (<0.5%). There was a lower incidence of reported ALRI in zinc villages (adjusted RR 0.68, 95% CI 0.46–0.99), but not presenting at clinic.

Conclusions

In this study, home zinc use to treat diarrhea did not decrease disease rates in the community. However, with proper training, availability of zinc at home could lead to more episodes of pediatric diarrhea being treated with zinc in parts of rural Africa where healthcare utilization is low.

Trial Registration

ClinicalTrials.gov {"type":"clinical-trial","attrs":{"text":"NCT00530829","term_id":"NCT00530829"}}NCT00530829     

Circumpolar dataset of sequenced specimens of Promachocrinus kerguelensis (Echinodermata,Crinoidea)

This circumpolar dataset of the comatulid (Echinodermata: Crinoidea) Promachocrinus kerguelensis (Carpenter, 1888) from the Southern Ocean, documents biodiversity associated with the specimens sequenced in Hemery et al. (2012). The aim of Hemery et al. (2012) paper was to use phylogeographic and phylogenetic tools to assess the genetic diversity, demographic history and evolutionary relationships of this very common and abundant comatulid, in the context of the glacial history of the Antarctic and Sub-Antarctic shelves (Thatje et al. 2005, 2008). Over one thousand three hundred specimens (1307) used in this study were collected during seventeen cruises from 1996 to 2010, in eight regions of the Southern Ocean: Kerguelen Plateau, Davis Sea, Dumont d’Urville Sea, Ross Sea, Amundsen Sea, West Antarctic Peninsula, East Weddell Sea and Scotia Arc including the tip of the Antarctic Peninsula and the Bransfield Strait. We give here the metadata of this dataset, which lists sampling sources (cruise ID, ship name, sampling date, sampling gear), sampling sites (station, geographic coordinates, depth) and genetic data (phylogroup, haplotype, sequence ID) for each of the 1307 specimens. The identification of the specimens was controlled by an expert taxonomist specialist of crinoids (Marc Eléaume, Muséum national d’Histoire naturelle, Paris) and all the COI sequences were matched against those available on the Barcode of Life Data System (BOLD: http://www.boldsystems.org/index.php/IDS_OpenIdEngine). This dataset can be used by studies dealing with, among other interests, Antarctic and/or crinoid diversity (species richness, distribution patterns), biogeography or habitat / ecological niche modeling. This dataset is accessible through the GBIF network at http://ipt.biodiversity.aq/resource.do?r=proke.     

in silico identification of protein-protein interactions in Silkworm,Bombyx mori

The Domesticated silkworm, Bombyx mori, an economically important insect has been used as a lepidopteran molecular model next only to Drosophila. Compared to the genomic information in silkworm, the protein-protein interaction data are limited. Therefore experimentally identified PPI maps from five model organisms such as E.coli, C.elegans, D.melanogaster, H. sapiens, S. cerevisiae were used to infer the PPI network of silkworm using the well-recognized Interlog based method. Among the 14623 silkworm proteins, 7736 protein-protein interaction pairs were predicted which include 2700 unique proteins of the silkworms. Using the iPfam interaction domains and the gene expression data, these predictions were validated. In that 625 PPI pairs of predicted network were associated with the iPfam domain-domain interactions and the random network has average of 9. In the gene expression method, the average PCC value of the predicted network and random network was 0.29 and 0.23100±0.00042 respectively. It reveals that the predicted PPI networks of silkworm are highly significant and reliable. This is the first PPI network for the silkworm which will provide a framework for deciphering the cellular processes governing key metabolic pathways in the silkworm, Bombyx mori and available at SilkPPI (http://210.212.197.30/SilkPPI/).     

CyanoPhyChe: A Database for Physico-Chemical Properties,Structure and Biochemical Pathway Information of Cyanobacterial Proteins

CyanoPhyChe is a user friendly database that one can browse through for physico-chemical properties, structure and biochemical pathway information of cyanobacterial proteins. We downloaded all the protein sequences from the cyanobacterial genome database for calculating the physico-chemical properties, such as molecular weight, net charge of protein, isoelectric point, molar extinction coefficient, canonical variable for solubility, grand average hydropathy, aliphatic index, and number of charged residues. Based on the physico-chemical properties, we provide the polarity, structural stability and probability of a protein entering in to an inclusion body (PEPIB). We used the data generated on physico-chemical properties, structure and biochemical pathway information of all cyanobacterial proteins to construct CyanoPhyChe. The data can be used for optimizing methods of expression and characterization of cyanobacterial proteins. Moreover, the ‘Search’ and data export options provided will be useful for proteome analysis. Secondary structure was predicted for all the cyanobacterial proteins using PSIPRED tool and the data generated is made accessible to researchers working on cyanobacteria. In addition, external links are provided to biological databases such as PDB and KEGG for molecular structure and biochemical pathway information, respectively. External links are also provided to different cyanobacterial databases. CyanoPhyChe can be accessed from the following URL: http://bif.uohyd.ac.in/cpc.     

The Functional Human C-Terminome

All translated proteins end with a carboxylic acid commonly called the C-terminus. Many short functional sequences (minimotifs) are located on or immediately proximal to the C-terminus. However, information about the function of protein C-termini has not been consolidated into a single source. Here, we built a new “C-terminome” database and web system focused on human proteins. Approximately 3,600 C-termini in the human proteome have a minimotif with an established molecular function. To help evaluate the function of the remaining C-termini in the human proteome, we inferred minimotifs identified by experimentation in rodent cells, predicted minimotifs based upon consensus sequence matches, and predicted novel highly repetitive sequences in C-termini. Predictions can be ranked by enrichment scores or Gene Evolutionary Rate Profiling (GERP) scores, a measurement of evolutionary constraint. By searching for new anchored sequences on the last 10 amino acids of proteins in the human proteome with lengths between 3–10 residues and up to 5 degenerate positions in the consensus sequences, we have identified new consensus sequences that predict instances in the majority of human genes. All of this information is consolidated into a database that can be accessed through a C-terminome web system with search and browse functions for minimotifs and human proteins. A known consensus sequence-based predicted function is assigned to nearly half the proteins in the human proteome. Weblink: http://cterminome.bio-toolkit.com.     

Effects of Resistant Starch on Symptoms,Fecal Markers,and Gut Microbiota in Parkinson’s Disease — The RESISTA-PD Trial

The composition of the gut microbiota is linked to multiple diseases, including Parkinson’s disease (PD). Abundance of bacteria producing short-chain fatty acids (SCFAs) and fecal SCFA concentrations are reduced in PD. SCFAs exert various beneficial functions in humans. In the interventional, monocentric, open-label clinical trial “Effects of Resistant Starch on Bowel Habits, Short Chain Fatty Acids and Gut Microbiota in Parkinson’sDisease” (RESISTA-PD; ID: {"type":"clinical-trial","attrs":{"text":"NCT02784145","term_id":"NCT02784145"}}NCT02784145), we aimed at altering fecal SCFAs by an 8-week prebiotic intervention with resistant starch (RS). We enrolled 87 subjects in three study-arms: 32 PD patients received RS (PD + RS), 30 control subjects received RS, and 25 PD patients received solely dietary instructions. We performed paired-end 100 bp length metagenomic sequencing of fecal samples using the BGISEQ platform at an average of 9.9 GB. RS was well-tolerated. In the PD + RS group, fecal butyrate concentrations increased significantly, and fecal calprotectin concentrations dropped significantly after 8 weeks of RS intervention. Clinically, we observed a reduction in non-motor symptom load in the PD + RS group. The reference-based analysis of metagenomes highlighted stable alpha-diversity and beta-diversity across the three groups, including bacteria producing SCFAs. Reference-free analysis suggested punctual, yet pronounced differences in the metagenomic signature in the PD + RS group. RESISTA-PD highlights that a prebiotic treatment with RS is safe and well-tolerated in PD. The stable alpha-diversity and beta-diversity alongside altered fecal butyrate and calprotectin concentrations call for long-term studies, also investigating whether RS is able to modify the clinical course of PD.     

White adipose tissue reference network: a knowledge resource for exploring health-relevant relations

Optimal health is maintained by interaction of multiple intrinsic and environmental factors at different levels of complexity—from molecular, to physiological, to social. Understanding and quantification of these interactions will aid design of successful health interventions. We introduce the reference network concept as a platform for multi-level exploration of biological relations relevant for metabolic health, by integration and mining of biological interactions derived from public resources and context-specific experimental data. A White Adipose Tissue Health Reference Network (WATRefNet) was constructed as a resource for discovery and prioritization of mechanism-based biomarkers for white adipose tissue (WAT) health status and the effect of food and drug compounds on WAT health status. The WATRefNet (6,797 nodes and 32,171 edges) is based on (1) experimental data obtained from 10 studies addressing different adiposity states, (2) seven public knowledge bases of molecular interactions, (3) expert’s definitions of five physiologically relevant processes key to WAT health, namely WAT expandability, Oxidative capacity, Metabolic state, Oxidative stress and Tissue inflammation, and (4) a collection of relevant biomarkers of these processes identified by BIOCLAIMS (http://bioclaims.uib.es). The WATRefNet comprehends multiple layers of biological complexity as it contains various types of nodes and edges that represent different biological levels and interactions. We have validated the reference network by showing overrepresentation with anti-obesity drug targets, pathology-associated genes and differentially expressed genes from an external disease model dataset. The resulting network has been used to extract subnetworks specific to the above-mentioned expert-defined physiological processes. Each of these process-specific signatures represents a mechanistically supported composite biomarker for assessing and quantifying the effect of interventions on a physiological aspect that determines WAT health status. Following this principle, five anti-diabetic drug interventions and one diet intervention were scored for the match of their expression signature to the five biomarker signatures derived from the WATRefNet. This confirmed previous observations of successful intervention by dietary lifestyle and revealed WAT-specific effects of drug interventions. The WATRefNet represents a sustainable knowledge resource for extraction of relevant relationships such as mechanisms of action, nutrient intervention targets and biomarkers and for assessment of health effects for support of health claims made on food products.

Electronic supplementary material

The online version of this article (doi:10.1007/s12263-014-0439-x) contains supplementary material, which is available to authorized users.     

Practice facilitation for improving cardiovascular care: secondary evaluation of a stepped wedge cluster randomized controlled trial using population-based administrative data

BackgroundPractice facilitation (PF), a multifaceted approach in which facilitators (external health care professionals) help family physicians to improve their adoption of best practices, has been highly successful. Improved Delivery of Cardiovascular Care (IDOCC) was an innovative PF trial designed to improve evidence-based care for people who have, or are at risk of, cardiovascular disease (CVD). The intervention was found to be ineffective as assessed by a patient-level composite score based on chart reviews from a subsample of patients (N = 5292). Here, we used population-based administrative data to examine IDOCC’s effect on CVD-related hospitalizations.MethodsIDOCC used a pragmatic, stepped wedge cluster randomized controlled design involving primary care providers recruited across Eastern Ontario, Canada. IDOCC’s effect on CVD-related hospitalizations was assessed in the 2 years of active intervention and post-intervention years. Marginal and mixed-effects regression analyses were used to account for the study design and to control for patient, physician, and practice characteristics. Secondary and subgroup analyses investigated robustness.ResultsOur sample included 262,996 patient/year observations representing 54,085 unique patients who had, or were at risk of, CVD, from 70 practices. There was a strong decreasing secular trend in CVD-related hospitalizations but no statistically significant effect of IDOCC. Relative to patients in the control condition, patients in the intervention condition were estimated to have 4 % lower odds of CVD-related hospitalizations (adjOR = 0.96, 99 % CI 0.83 to 1.11). The nonsignificant result persisted across robustness analyses.ConclusionsClinical outcomes from administrative databases were examined to form a more complete picture of the (in)effectiveness of a large-scale quality improvement intervention. IDOCC did not have a significant effect on CVD hospitalizations, suggesting that the results from the primary composite adherence score analysis were neither due to choice of outcome nor relatively short follow-up period.

Trial registration

ClinicalTrials.gov {"type":"clinical-trial","attrs":{"text":"NCT00574808","term_id":"NCT00574808"}}NCT00574808, registered on 14 December 2007.

Electronic supplementary material

The online version of this article (doi:10.1186/s13063-016-1547-2) contains supplementary material, which is available to authorized users.     

MitProNet: A Knowledgebase and Analysis Platform of Proteome,Interactome and Diseases for Mammalian Mitochondria

Mitochondrion plays a central role in diverse biological processes in most eukaryotes, and its dysfunctions are critically involved in a large number of diseases and the aging process. A systematic identification of mitochondrial proteomes and characterization of functional linkages among mitochondrial proteins are fundamental in understanding the mechanisms underlying biological functions and human diseases associated with mitochondria. Here we present a database MitProNet which provides a comprehensive knowledgebase for mitochondrial proteome, interactome and human diseases. First an inventory of mammalian mitochondrial proteins was compiled by widely collecting proteomic datasets, and the proteins were classified by machine learning to achieve a high-confidence list of mitochondrial proteins. The current version of MitProNet covers 1124 high-confidence proteins, and the remainders were further classified as middle- or low-confidence. An organelle-specific network of functional linkages among mitochondrial proteins was then generated by integrating genomic features encoded by a wide range of datasets including genomic context, gene expression profiles, protein-protein interactions, functional similarity and metabolic pathways. The functional-linkage network should be a valuable resource for the study of biological functions of mitochondrial proteins and human mitochondrial diseases. Furthermore, we utilized the network to predict candidate genes for mitochondrial diseases using prioritization algorithms. All proteins, functional linkages and disease candidate genes in MitProNet were annotated according to the information collected from their original sources including GO, GEO, OMIM, KEGG, MIPS, HPRD and so on. MitProNet features a user-friendly graphic visualization interface to present functional analysis of linkage networks. As an up-to-date database and analysis platform, MitProNet should be particularly helpful in comprehensive studies of complicated biological mechanisms underlying mitochondrial functions and human mitochondrial diseases. MitProNet is freely accessible at http://bio.scu.edu.cn:8085/MitProNet.     

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.