In-silico prediction of blood-secretory human proteins using a ranking algorithm

<p>Abstract</p> <p>Background</p> <p>Computational identification of blood-secretory proteins, especially proteins with differentially expressed genes in diseased tissues, can provide highly useful information in linking transcriptomic data to proteomic studies for targeted disease biomarker discovery in serum.</p> <p>Results</p> <p>A new algorithm for prediction of blood-secretory proteins is presented using an information-retrieval technique, called <it>manifold ranking</it>. On a dataset containing 305 known blood-secretory human proteins and a large number of other proteins that are either not blood-secretory or unknown, the new method performs better than the previous published method, measured in terms of the area under the recall-precision curve (AUC). A key advantage of the presented method is that it does not explicitly require a negative training set, which could often be noisy or difficult to derive for most biological problems, hence making our method more applicable than classification-based data mining methods in general biological studies.</p> <p>Conclusion</p> <p>We believe that our program will prove to be very useful to biomedical researchers who are interested in finding serum markers, especially when they have candidate proteins derived through transcriptomic or proteomic analyses of diseased tissues. A computer program is developed for prediction of blood-secretory proteins based on manifold ranking, which is accessible at our website <url>http://csbl.bmb.uga.edu/publications/materials/qiliu/blood_secretory_protein.html</url>.</p

\u3ci\u3eIn-silico\u3c/i\u3e prediction of blood-secretory human proteins using a ranking algorithm

Background: Computational identification of blood-secretory proteins, especially proteins with differentially expressed genes in diseased tissues, can provide highly useful information in linking transcriptomic data to proteomic studies for targeted disease biomarker discovery in serum. Results: A new algorithm for prediction of blood-secretory proteins is presented using an information-retrieval technique, called manifold ranking. On a dataset containing 305 known blood-secretory human proteins and a large number of other proteins that are either not blood-secretory or unknown, the new method performs better than the previous published method, measured in terms of the area under the recall-precision curve (AUC). A key advantage of the presented method is that it does not explicitly require a negative training set, which could often be noisy or difficult to derive for most biological problems, hence making our method more applicable than classification-based data mining methods in general biological studies. Conclusion: We believe that our program will prove to be very useful to biomedical researchers who are interested in finding serum markers, especially when they have candidate proteins derived through transcriptomic or proteomic analyses of diseased tissues. A computer program is developed for prediction of blood-secretory proteins based on manifold ranking, which is accessible at our website http://csbl.bmb.uga.edu/publications/materials/qiliu/ blood_secretory_protein.html

Computational Prediction of Human Salivary Proteins from Blood Circulation and Application to Diagnostic Biomarker Identification

Proteins can move from blood circulation into salivary glands through active transportation, passive diffusion or ultrafiltration, some of which are then released into saliva and hence can potentially serve as biomarkers for diseases if accurately identified. We present a novel computational method for predicting salivary proteins that come from circulation. The basis for the prediction is a set of physiochemical and sequence features we found to be discerning between human proteins known to be movable from circulation to saliva and proteins deemed to be not in saliva. A classifier was trained based on these features using a support-vector machine to predict protein secretion into saliva. The classifier achieved 88.56% average recall and 90.76% average precision in 10-fold cross-validation on the training data, indicating that the selected features are informative. Considering the possibility that our negative training data may not be highly reliable (i.e., proteins predicted to be not in saliva), we have also trained a ranking method, aiming to rank the known salivary proteins from circulation as the highest among the proteins in the general background, based on the same features. This prediction capability can be used to predict potential biomarker proteins for specific human diseases when coupled with the information of differentially expressed proteins in diseased versus healthy control tissues and a prediction capability for blood-secretory proteins. Using such integrated information, we predicted 31 candidate biomarker proteins in saliva for breast cancer

In Silico Analysis of Six Known Leishmania major Antigens and In Vitro Evaluation of Specific Epitopes Eliciting HLA-A2 Restricted CD8 T Cell Response

Leishmaniasis is currently a serious health as well as economic problem in underdeveloped and developing countries in Africa, Asia, the Near and Middle East, Central and South America and the Mediterranean region. Cutaneous leishmaniasis is highly endemic in Iran, remarkably in Isfahan, Fars, Khorasan, Khozestan and Kerman provinces. Since effective prevention is not available and current curative therapy is expensive, often poorly tolerated and not always effective, alternative therapies including vaccination against leishmaniasis are of priority to overcome the problem. Although Th1 dominant response is so far considered as a pre-requisite for the immune system to overcome the infection, CD8+ T cell response could also be considered as a potent arm of immune system fighting against intracellular Leishmania. Polytope vaccine strategy may open up a new way in vaccine design against leishmaniasis, since they act as a potent tool to stimulate multi-CD8 T cell responses. Clearly there is a substantial need to evaluate the promising epitopes from different proteins of Leishmania parasite species. Some new immunoinformatic tools are now available to speed up this process, and we have shown here that in silico prediction can effectively evaluate HLA class I-restricted epitopes out of Leishmania proteins

Genome-wide analysis of excretory/secretory proteins in Echinococcus multilocularis: insights into functional characteristics of the tapeworm secretome

Venn diagrams of expression levels of ES protein coding genes in different developments. The venn diagrams are displayed for: the genes that have expressions (A), the genes with very high expression levels (B), the genes with high expression levels (C), the genes with medium expression levels (D), the genes with low expression levels (E), and the genes without expressions (F). (PDF 285 kb

Novel urinary and serological markers of prostate cancer using proteomics techniques: an important tool for early cancer diagnosis and treatment monitoring

In Africa, Prostate cancer (PCa) is the most frequently diagnosed solid organ tumour in males and use of prostate specific antigen (PSA) is presently fraught with diagnostic inaccuracies. Not least, in a multi-ethnic society like South Africa, proteome differences between African, Caucasian and Mixed-Ancestry PCa patients are largely unknown. Hence, discovery and validation of affordable, non-invasive and reliable diagnostic biomarkers of PCa would expand the frontiers of PCa management. We have employed two high-throughput proteomics technologies to identify novel urine- and blood-based biomarkers for early diagnosis and treatment monitoring of prostate cancer in a South African cohort as well as elucidate proteome differences in patients from our heterogeneous cohort. We compared the urinary proteomes of PCa, Benign Prostatic Hyperplasia (BPH), disease controls comprising patients with other uropathies (DC) and normal healthy controls (NC) both by pooling and individual discovery shotgun proteomic assessment on a nano-Liquid chromatography (nLC) coupled Hybrid Quadrupole-Orbitrap Mass Spectrometer platform. In-silico verification of identified biomarkers was performed using the Human Protein Atlas (HPA) as well as SRMAtlas; and verified potential biomarkers were experimentally prevalidated using a targeted parallel reaction monitoring (PRM) proteomics approach. Further, we employed the CT100+ antigen microarray platform to assess the differential humoral antibody response of PCa, DC and BPH patients in our cohort to a panel of 123 tumour-associated cancer antigens. Candidate antigen biomarkers were analyzed for ethnic group variation in our cohort and potential cancer diagnostic and immunotherapeutic inferences were drawn. Using these approaches, we identified 5595 and 9991 non-redundant peptides from the pooled and individual experiments respectively. While nine proteins demonstrated ethnic trend, 37 and 73 proteins were differentially expressed by pooled and individual analysis respectively. All 32 verified biomarkers were prevalidated with parallel reaction monitoring. Good PRM signals for 12 top ranking biomarker was observed, including PSA and prostatic acid phosphatase. We also identified 41 potential diagnostic and immunotherapeutic antigen biomarkers. Proteogenomic functional pathway analyses of differentially expressed antigens showed similar enrichments of biologic processes. We identified herein novel urinary and blood-based potential diagnostic biomarkers and immunotherapeutic targets of PCa in a South African PCa Cohort using multiple proteomics approaches

FungalRV: adhesin prediction and immunoinformatics portal for human fungal pathogens

<p>Abstract</p> <p>Background</p> <p>The availability of sequence data of human pathogenic fungi generates opportunities to develop Bioinformatics tools and resources for vaccine development towards benefitting at-risk patients.</p> <p>Description</p> <p>We have developed a fungal adhesin predictor and an immunoinformatics database with predicted adhesins. Based on literature search and domain analysis, we prepared a positive dataset comprising adhesin protein sequences from human fungal pathogens <it>Candida albicans, Candida glabrata, Aspergillus fumigatus, Coccidioides immitis, Coccidioides posadasii, Histoplasma capsulatum, Blastomyces dermatitidis, Pneumocystis carinii, Pneumocystis jirovecii and Paracoccidioides brasiliensis</it>. The negative dataset consisted of proteins with high probability to function intracellularly. We have used 3945 compositional properties including frequencies of mono, doublet, triplet, and multiplets of amino acids and hydrophobic properties as input features of protein sequences to Support Vector Machine. Best classifiers were identified through an exhaustive search of 588 parameters and meeting the criteria of best Mathews Correlation Coefficient and lowest coefficient of variation among the 3 fold cross validation datasets. The "FungalRV adhesin predictor" was built on three models whose average Mathews Correlation Coefficient was in the range 0.89-0.90 and its coefficient of variation across three fold cross validation datasets in the range 1.2% - 2.74% at threshold score of 0. We obtained an overall MCC value of 0.8702 considering all 8 pathogens, namely, <it>C. albicans, C. glabrata, A. fumigatus, B. dermatitidis, C. immitis, C. posadasii, H. capsulatum </it>and <it>P. brasiliensis </it>thus showing high sensitivity and specificity at a threshold of 0.511. In case of <it>P. brasiliensis </it>the algorithm achieved a sensitivity of 66.67%. A total of 307 fungal adhesins and adhesin like proteins were predicted from the entire proteomes of eight human pathogenic fungal species. The immunoinformatics analysis data on these proteins were organized for easy user interface analysis. A Web interface was developed for analysis by users. The predicted adhesin sequences were processed through 18 immunoinformatics algorithms and these data have been organized into MySQL backend. A user friendly interface has been developed for experimental researchers for retrieving information from the database.</p> <p>Conclusion</p> <p>FungalRV webserver facilitating the discovery process for novel human pathogenic fungal adhesin vaccine has been developed.</p

MalVac: Database of malarial vaccine candidates

<p>Abstract</p> <p>Background</p> <p>The sequencing of genomes of the Plasmodium species causing malaria, offers immense opportunities to aid in the development of new therapeutics and vaccine candidates through Bioinformatics tools and resources.</p> <p>Methods</p> <p>The starting point of MalVac database is the collection of known vaccine candidates and a set of predicted vaccine candidates identified from the whole proteome sequences of Plasmodium species provided by PlasmoDb 5.4 release (31st October 2007). These predicted vaccine candidates are the adhesins and adhesin-like proteins from Plasmodium species, <it>Plasmodium falciparum</it>, <it>Plasmodium vivax </it>and <it>Plasmodium yoelii</it>. Subsequently, these protein sequences were analysed through 20 publicly available algorithms to obtain Orthologs, Paralogs, BetaWraps, TargetP, TMHMM, SignalP, CDDSearch, BLAST with Human Ref. Proteins, T-cell epitopes, B-cell epitopes, Discotopes, and allergen predictions. All of this information was collected and organized with the ORFids of the protein sequences as primary keys. This information is relevant from the view point of Reverse Vaccinology in facilitating decision making on the most probable choice for vaccine strategy.</p> <p>Results</p> <p>Detailed information on the patterning of the epitopes and other motifs of importance from the viewpoint of reverse vaccinology has been obtained on the most probable protein candidates for vaccine investigation from three major malarial species. Analysis data are available on 161 adhesin proteins from <it>P. falciparum</it>, 137 adhesin proteins from <it>P. vivax </it>and 34 adhesin proteins from <it>P. yoelii</it>. The results are displayed in convenient tabular format and a facility to export the entire data has been provided. The MalVac database is a "community resource". Users are encouraged to export data and further contribute by value addition. Value added data may be sent back to the community either through MalVac or PlasmoDB.</p> <p>Conclusion</p> <p>A web server MalVac for facilitation of the identification of probable vaccine candidates has been developed and can be freely accessed.</p

Search of Potential Vaccine Candidates against Trueperella pyogenes Infections through Proteomic and Bioinformatic Analysis

Trueperella pyogenes is an opportunistic pathogen, responsible for important infections in pigs and significant economic losses in swine production. To date, there are no available commercial vaccines to control diseases caused by this bacterium. In this work, we performed a comparative proteomic analysis of 15 T. pyogenes clinical isolates, by “shaving” live cells, followed by LC-MS/MS, aiming at the identification of the whole set of surface proteins (i.e., the “pan-surfome”) as a source of antigens to be tested in further studies as putative vaccine candidates, or used in diagnostic tools. A total of 140 surface proteins were detected, comprising 25 cell wall proteins, 10 secreted proteins, 23 lipoproteins and 82 membrane proteins. After describing the “pan-surfome”, the identified proteins were ranked in three different groups based on the following criteria: to be (i) surface-exposed, (ii) highly conserved and (iii) widely distributed among different isolates. Two cell wall proteins, three lipoproteins, four secreted and seven membrane proteins were identified in more than 70% of the studied strains, were highly expressed and highly conserved. These proteins are potential candidates, alone or in combination, to obtain effective vaccines against T. pyogenes or to be used in the diagnosis of this pathogen