Literature DB >> 20576624

EpiTOP--a proteochemometric tool for MHC class II binding prediction.

Ivan Dimitrov1, Panayot Garnev, Darren R Flower, Irini Doytchinova.   

Abstract

MOTIVATION: T-cell epitope identification is a critical immunoinformatic problem within vaccine design. To be an epitope, a peptide must bind an MHC protein.
RESULTS: Here, we present EpiTOP, the first server predicting MHC class II binding based on proteochemometrics, a QSAR approach for ligands binding to several related proteins. EpiTOP 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. AVAILABILITY: EpiTOP is freely accessible at http://www.pharmfac.net/EpiTOP.

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Year:  2010        PMID: 20576624     DOI: 10.1093/bioinformatics/btq324

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  14 in total

1.  Proteochemometric model for predicting the inhibition of penicillin-binding proteins.

Authors:  Sunanta Nabu; Chanin Nantasenamat; Wiwat Owasirikul; Ratana Lawung; Chartchalerm Isarankura-Na-Ayudhya; Maris Lapins; Jarl E S Wikberg; Virapong Prachayasittikul
Journal:  J Comput Aided Mol Des       Date:  2014-10-26       Impact factor: 3.686

2.  Comparative evaluation of MPT83 (Rv2873) for T helper-1 cell reactivity and identification of HLA-promiscuous peptides in Mycobacterium bovis BCG-vaccinated healthy subjects.

Authors:  Abu S Mustafa
Journal:  Clin Vaccine Immunol       Date:  2011-08-18

3.  NetMHCIIpan-2.0 - Improved pan-specific HLA-DR predictions using a novel concurrent alignment and weight optimization training procedure.

Authors:  Morten Nielsen; Sune Justesen; Ole Lund; Claus Lundegaard; Søren Buus
Journal:  Immunome Res       Date:  2010-11-13

4.  Prediction of peptide reactivity with human IVIg through a knowledge-based approach.

Authors:  Nicola Barbarini; Alessandra Tiengo; Riccardo Bellazzi
Journal:  PLoS One       Date:  2011-08-24       Impact factor: 3.240

Review 5.  Fundamentals and Methods for T- and B-Cell Epitope Prediction.

Authors:  Jose L Sanchez-Trincado; Marta Gomez-Perosanz; Pedro A Reche
Journal:  J Immunol Res       Date:  2017-12-28       Impact factor: 4.818

6.  Two Faces of Milk Proteins Peptides with Both Allergenic and Multidimensional Health Beneficial Impact- Integrated In Vitro/In Silico Approach.

Authors:  Anna Maria Ogrodowczyk; Ivan Dimitrov; Barbara Wróblewska
Journal:  Foods       Date:  2021-01-14

Review 7.  Allergic Diseases: A Comprehensive Review on Risk Factors, Immunological Mechanisms, Link with COVID-19, Potential Treatments, and Role of Allergen Bioinformatics.

Authors:  Fahad M Aldakheel
Journal:  Int J Environ Res Public Health       Date:  2021-11-18       Impact factor: 3.390

Review 8.  T-cell epitope vaccine design by immunoinformatics.

Authors:  Atanas Patronov; Irini Doytchinova
Journal:  Open Biol       Date:  2013-01-08       Impact factor: 6.411

9.  Designing of interferon-gamma inducing MHC class-II binders.

Authors:  Sandeep Kumar Dhanda; Pooja Vir; Gajendra P S Raghava
Journal:  Biol Direct       Date:  2013-12-05       Impact factor: 4.540

10.  Computational Modeling and Analysis to Predict Intracellular Parasite Epitope Characteristics Using Random Forest Technique.

Authors:  Amir Javadi; Ali Khamesipour; Farshid Monajemi; Marjan Ghazisaeedi
Journal:  Iran J Public Health       Date:  2020-01       Impact factor: 1.429

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