Reliable B Cell Epitope Predictions: Impacts of Method Development and Improved Benchmarking
By Jens Vindahl Kringelum, Claus Lundegaard, Ole Lund, Morten Nielsen. PLoS Comput. Biol., 2012
648 citations recorded by the source corpus.
The interaction between antibodies and antigens is one of the most important immune system mechanisms for clearing infectious organisms from the host. Antibodies bind to antigens at sites referred to as B-cell epitopes. Identification of the exact location of B-cell epitopes is essential in several biomedical applications such as; rational vaccine design, development of disease diagnostics and immunotherapeutics. However, experimental mapping of epitopes is resource intensive making in silico methods an appealing complementary approach. To date, the reported performance of methods for in silico mapping of B-cell epitopes has been moderate. Several issues regarding the evaluation data sets may however have led to the performance values being underestimated: Rarely, all potential epitopes have been mapped on an antigen, and antibodies are generally raised against the antigen in a given biological context not against the antigen monomer. Improper dealing with these aspects leads to many artificial false positive predictions and hence to incorrect low performance values. To demonstrate the impact of proper benchmark definitions, we here present an updated version of the DiscoTope method incorporating a novel spatial neighborhood definition and half-sphere exposure as surface measure. Compared to other state-of-the-art prediction methods, Discotope-2.0 displayed improved performance both in cross-validation and in independent evaluations. Using DiscoTope-2.0, we assessed the impact on performance when using proper benchmark definitions. For 13 proteins in the training data set where sufficient biological information was available to make a proper benchmark redefinition, the average AUC performance was improved from 0.791 to 0.824. Similarly, the average AUC performance on an independent evaluation data set improved from 0.712 to 0.727. Our results thus demonstrate that given proper benchmark definitions, B-cell epitope prediction methods achieve highly significant predictive performances suggesting these tools to be a powerful asset in rational epitope discovery. The updated version of DiscoTope is available at www.cbs.dtu.dk/services/DiscoTope-2.0.
Subfields: Immunology, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/2d3be1d9e06822435be2926a9d798357d53d81ea
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Immunology
- Bjoern Peters
- Morten Nielsen
- Alessandro Sette
- Sinu Paul
- Massimo Andreatta
- Swapnil Mahajan
- Sandeep Kumar Dhanda
- Paolo Marcatili
In Molecular Biology
- Bjoern Peters
- Morten Nielsen
- Alessandro Sette
- Massimo Andreatta
- Sinu Paul
- Swapnil Mahajan
- Sandeep Kumar Dhanda
- John Sidney
Citations in the corpus
- SAbPred: a structure-based antibody prediction server (2016, methodology): “Models of the antibody Fv can be used in many other ways including paratope prediction (13,14), epitope prediction (15,16) and protein docking (17).”
- An overview of bioinformatics tools for epitope prediction: Implications on vaccine development (2015, background): “This server also predicts epitopes in complexes of multiple chains [59].”
- A Sequence Homology and Bioinformatic Approach Can Predict Candidate Targets for Immune Responses to SARS-CoV-2 (2020, positive): “0 (Kringelum et al., 2012) method that also predicts epitopes based on protein conformation and residue exposure.”
- A Review of Deep Learning Methods for Antibodies (2020, background): “Despite being theorized as an unsolvable problem, several methods have been proposed and claim moderate success [21].”
- IEDB-AR: immune epitope database—analysis resource in 2019 (2019, methodology): “Since the last update of IEDB-AR, the recommended B cell epitope prediction methods, BepiPred (14) and DiscoTope (15), were updated to their 2.”
- ScanNet: an interpretable geometric deep learning model for structure-based protein binding site prediction (2021, methodology): “Prediction of B-cell Epitopes with ScanNet (a) Precision-Recall curve of B-cell epitope prediction for baseline methods, Discotope [51] and ScanNet.”
- The Immune Epitope Database and Analysis Resource in Epitope Discovery and Synthetic Vaccine Design (2017, methodology): “The performance of ElliPro was evaluated in two separate studies in 2007 (67) and 2012 (65).”