SVMTriP: A Method to Predict Antigenic Epitopes Using Support Vector Machine to Integrate Tri-Peptide Similarity and Propensity

Yao, Bo; Zhang, Lin; Liang, Shide; Zhang, Chi

doi:10.1371/journal.pone.0045152

Cited by 292 publications

(214 citation statements)

References 29 publications

(31 reference statements)

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“…SVMTriP achieves a sensitivity of 80.1% and a precision of 55.2% and the AUC value 0.702, when tested on nonredundant epitopes extracted from IEDB [78]. A comparative study concluded that the methods based on sequence analysis do not predict epitopes better than chance.…”

Section: In Silico B-cell Epitope Predictionmentioning

confidence: 99%

An Introduction to B-Cell Epitope Mapping and In Silico Epitope Prediction

Potocnakova

Bhide

Pulzová

2016

Journal of Immunology Research

260

193

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Identification of B-cell epitopes is a fundamental step for development of epitope-based vaccines, therapeutic antibodies, and diagnostic tools. Epitope-based antibodies are currently the most promising class of biopharmaceuticals. In the last decade, in-depth in silico analysis and categorization of the experimentally identified epitopes stimulated development of algorithms for epitope prediction. Recently, various in silico tools are employed in attempts to predict B-cell epitopes based on sequence and/or structural data. The main objective of epitope identification is to replace an antigen in the immunization, antibody production, and serodiagnosis. The accurate identification of B-cell epitopes still presents major challenges for immunologists. Advances in B-cell epitope mapping and computational prediction have yielded molecular insights into the process of biorecognition and formation of antigen-antibody complex, which may help to localize B-cell epitopes more precisely. In this paper, we have comprehensively reviewed state-of-the-art experimental methods for B-cell epitope identification, existing databases for epitopes, and novel in silico resources and prediction tools available online. We have also elaborated new trends in the antibody-based epitope prediction. The aim of this review is to assist researchers in identification of B-cell epitopes.

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Section: In Silico B-cell Epitope Predictionmentioning

confidence: 99%

An Introduction to B-Cell Epitope Mapping and In Silico Epitope Prediction

Potocnakova

Bhide

Pulzová

2016

Journal of Immunology Research

260

193

View full text Add to dashboard Cite

show abstract

“…The proteins that are localized on cell surface were analyzed, for antigenic property by Vaxijen 2.0 [27], presence of transmembrane helices by TMHMM [28], identifying the domains that have the capability to bind to immune cells of humans by domain search against InterProScan [29], and finally characterizing the vaccine candidate by identifying the epitopes by SVMTriP [30]. Then, broad spectrum analysis and non-human gut flora analysis were carried out for drug target and vaccine candidate by BLASTP option from Human Microbiome Project [31,32].…”

Section: Data Collection Databases and Tools Employedmentioning

confidence: 99%

Screening of Putative Therapeutic Candidates in Superbug (Staphylococcus Aureus): A Systematic in Silico Approach

Patnala

Zaveri²

2016

Asian J Pharm Clin Res

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Objective: Staphylococcus aureus, a superbug and antibiotic resistant pathogen, is one of the most infection causing organism, ranging from skin allergies to severe lethal conditions. The prolonged use of different antibiotics and lack of optimal treatment over the antibiotic resistant species, led to the identification of new, better and promising therapeutic candidates.Methods: A systematic in silico filtration process was employed, which includes subtractive channels and reverse vaccinology techniques.Results: Here, we report 12 possible drug targets and two vaccine candidates based on essentiality, non-human homolog, virulent and localization, commonly in all the strains. Further characterization studies such as pathway analysis, chokepoint and structure prediction revealed, two proteins as the best drug targets one being novel and the other druggable. Only one protein has shown the characteristic feature of vaccine candidate, having antigenic property and an IgG binding domain. Conclusion:Two best drug targets were commonly identified in all the strains of S. aureus namely UDP-N-acetylmuramoyl-L-alanyl-D-glutamate--L-lysine ligase (MurE) and cell division protein FtsA, whereas the best common vaccine candidate includes Peptidoglycan binding protein. The therapeutic candidates reported in the present study might facilitate screening of new and better antimicrobial compounds, for an optimal treatment of S. aureus infections.

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“…BcpredB-cell epitope prediction (EL-Manzalawy et al 2008) server uses Artificial Intelligence Research Laboratory at http://ailab.cs.iastate.edu/bcpreds/index.html. Svmtrip (Yao et al 2012) at http://sysbio.unl.edu/SVMTriP/prediction.php predicts antigenic epitope within sequence input. In this method, Support Vector Machine (SVM) has been used by combining the Tri-peptide similarity and Propensity scores (SVMTriP) in order to achieve the higher accuracy and specificity by leave-one-out test.…”

Section: Sequence-based B Cell Epitope Predictionmentioning

confidence: 99%

Functional Exposed Amino Acids of BauA as Potential Immunogen Against Acinetobacter baumannii

et al. 2015

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Multidrug-resistant Acinetobacter baumannii is recognized to be among the most difficult antimicrobial-resistant gram negative bacilli to control and treat. One of the major challenges that the pathogenic bacteria face in their host is the scarcity of freely available iron. To survive under such conditions, bacteria express new proteins on their outer membrane and also secrete iron chelators called siderophores. Antibodies directed against these proteins associated with iron uptake exert a bacteriostatic or bactericidal effect against A. baumanii in vitro, by blocking siderophore mediated iron uptake pathways. Attempts should be made to discover peptides that could mimic protein epitopes and possess the same immunogenicity as the whole protein. Subsequently, theoretical methods for epitope prediction have been developed leading to synthesis of such peptides that are important for development of immunodiagnostic tests and vaccines. The present study was designed to in silico resolving the major obstacles in the control or in prevention of the diseases caused by A. baumannii. We exploited bioinformatic tools to better understand and characterize the Baumannii acinetobactin utilization structure of A. baumannii and select appropriate regions as effective B cell epitopes. In conclusion, amino acids 26-191 of cork domain and 321-635 of part of the barrel domain including L4-L9, were selected as vaccine candidates. These two regions contain functional exposed amino acids with higher score of B cell epitopes properties. Majority of amino acids are hydrophilic, flexible, accessible, and favorable for B cells from secondary structure point of view.

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SVMTriP: A Method to Predict Antigenic Epitopes Using Support Vector Machine to Integrate Tri-Peptide Similarity and Propensity

Cited by 292 publications

References 29 publications

An Introduction to B-Cell Epitope Mapping and In Silico Epitope Prediction

An Introduction to B-Cell Epitope Mapping and In Silico Epitope Prediction

Screening of Putative Therapeutic Candidates in Superbug (Staphylococcus Aureus): A Systematic in Silico Approach

Functional Exposed Amino Acids of BauA as Potential Immunogen Against Acinetobacter baumannii

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