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A virtual workbench for immunological questions with a focus on vaccine design. EpiToolKit offers an array of immunoinformatics tools covering MHC genotyping, epitope and neo-epitope prediction, epitope selection for vaccine design, and epitope assembly. In its recently re-implemented version 2.0, EpiToolKit provides a range of new functionality and for the first time allows combining tools into complex workflows. For inexperienced users it offers simplified interfaces to guide the users through the analysis of complex immunological data sets.
FRED / FRamework for Epitope Detection
A versatile immunoinformatics software framework enabling a unified interface to many tools, from epitope prediction, HLA typing, to epitope selection and assembly. Its openness and easy extensibility makes FRED 2 a perfect instrument for the development of advanced immunoinformatics pipelines that are needed for example in cancer immunotherapy development and other areas of personalized medicine. By building on top of popular modules such as BioPython and Pandas, FRED 2 allows rapid prototyping of complex and innovative immunoinformatics applications.
A server predicting MHC class II binding based on proteochemometrics. EpiTOP is a QSAR approach for ligands binding to several related proteins. It uses a quantitative matrix to predict binding to 12 HLA-DRB1 alleles. It identifies 89% of known epitopes within the top 20% of predicted binders, reducing laboratory labour, materials and time by 80%. EpiTOP is easy to use, gives comprehensive quantitative predictions and will be expanded and updated with new quantitative matrices over time.
ICES / Identification of cytotoxic T lymphocyte epitopes for swine viruses
Provides a web platform to detect cytotoxic T lymphocyte (CTL) epitopes of swine viruses. ICES is composed of four mains panels: (i) genome-wide scan that permits to scan the CTL epitopes; (ii) epitope search allowing their retrieval by binding affinity or sequence conservation as well as the detection of cross-subtypic ones; (iii) literature support that delivers information about experimentally validated CTL epitopes; and (iv) vaccine design and evaluation that gives access to a list of hypothetic CTL epitopes and in silico evaluation.
A self-contained web application for calculating and viewing epitope predictions. Epitopemap allows integration of multiple epitope prediction methods for any number of proteins in a genome. The tool is a front-end for various freely available methods. Features include visualisation of results from multiple predictors within proteins in one plot, genome-wide analysis and estimates of epitope conservation. The tool is easy to use and will assist in computational screening of viral or bacterial genomes.
Provides access to a collection of accurate major histocompatibility complex (MHC) class II binding prediction models, which were derived via molecular docking. EpiDOCK is structure-based and utilizes a combination of homology modeling and molecular docking techniques to derive quantitative models for MHC class II binding prediction. It identifies 90% of true binders and 76% of true non-binders, with an overall accuracy of 83%. It converts the input sequence into a collection of overlapping nonamers.
IMS / Immunogenetic Management Software
Permits multiplexed analysis of complex immunogenetic traits that are necessary for the accurate planning and execution of experiments involving large animal models, including nonhuman primates. IMS is capable of housing complex pedigree relationships, microsatellite-based major histocompatibility complex (MHC) typing data, as well as MHC pyrosequencing expression analysis of class I alleles. It includes an automated MHC haplotype naming algorithm and has accomplished an innovative visualization protocol that allows users to view multiple familial and MHC haplotype relationships through a single, interactive graphical interface. Detailed DNA and RNA-based data can also be queried and analyzed in a highly accessible fashion, and flexible search capabilities allow experimental choices to be made based on multiple, individualized and expandable immunogenetic factors. This web application is implemented in Java, MySQL, Tomcat, and Apache, with supported browsers including Internet Explorer and Firefox on Windows and Safari on Mac OS.
TEpredict / predicting T-cell epitopes
Allows T-cell epitope prediction. TEpredict allows prediction of (i) peptides binding to various allelic variants of class I and II Major Histocompatibility Complex (MHC) molecules, (ii) proteasomal and/or immunoproteasomal antigen processing, and (iii) binding of peptides to —transporters associated with antigen processing (TAPs). The software can also discard the peptides sharing local similarity with human proteins from the set of predicted epitopes and to estimate the expected population coverage by the selected peptides.
This server is meant for the prediction of binding affinity of peptide binders in an antigenic sequence for a MHC class II allele HLA-DRB1*0401. Methods developed in the past can only predict whether a peptide is a binder or non-binder of this allele. Moreover, determining the binding core is also a problem in case of developing prediction methods for MHC class-II alleles. This server tries to overcome this problem and can predict both the binding core as well as the binding affinity of a peptide in an antigenic protein sequence.
A computational method based on support vector machines with a weighted string kernel to predict peptide immunogenicity and identify important recognition positions. POPISK yields a mean 10-fold cross-validation accuracy of 68% in predicting T-cell reactivity of HLA-A2-binding peptides. POPISK is capable of predicting immunogenicity with scores that can also correctly predict the change in T-cell reactivity related to point mutations in epitopes reported in previous studies using crystal structures.
A web-based MHC-binder prediction server that uses support vector machines for predicting peptide binders of flexible lengths for 18 MHC class I and 12 class II alleles from sequence-derived physicochemical properties, which were trained by using 4,208 approximately 3,252 binders and 234,333 approximately 168,793 non-binders, and evaluated by an independent set of 545 approximately 476 binders and 110,564 approximately 84,430 non-binders. The binder prediction accuracies are 86 approximately 99% for 25 and 70 approximately 80% for five alleles, and the non-binder accuracies are 96 approximately 99% for 30 alleles.
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