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EPITOPE PREDICTION

Accurately predict B-cell epitopes and antigenic regions based on protein sequence data. PRICING DOWNLOAD FREE TRIAL

Use Protean 3D to accurately perform B-cell epitope prediction using only sequence-based information.

B-cell epitopes–the part of an antigen recognized by an antibody–are conformational in nature; however, accurate epitope prediction is difficult for proteins without a known 3D structure. Protean 3D, part of Lasergene Protein, uses a machine learning approach that improves the ability to perform B-cell epitope prediction using only sequence-based information, providing better overall predictive accuracy than other leading epitope prediction methods. Protean 3D enables you to view predicted epitopes and antigenic regions along your sequence and structure, and compare predictions to other characteristics, such as surface probability. Easily create molecular and solvent accessible surfaces around your structure and view predicted epitopes relative to the protein surface, view confidence scores for predicted antigenic regions, and change the appearance of predicted epitopes on the sequence and structure for optimized visualization and publication-quality images.

Epitope prediction in 4 simple steps

Epitope Prediction Step 1

Step 1

Open a protein sequence or structure file

Epitope Prediction Step 2

Step 2

Apply the B-Cell Epitopes (DNASTAR) track to the sequence

Epitope Prediction Step 3

Step 3

Modify options, if desired

Epitope Prediction Step 4

Step 4

View and analyze predicted B-cell epitopes

computer

Learn more about Epitope Prediction

Resources | Tutorials | FAQs | Citations | User Guide

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Resources

Please see our resources below for more information on using epitope prediction in Protean 3D.

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Precise Predictions of Linear
B Cell Epitopes in Protean 3D

Read White Paper

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Lasergene Protein Overview

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Protean 3D Overview Webinar

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How Can DNASTAR’s Protein Tools Help You?

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Tutorials

Watch one of our videos or check out our user guide to learn more about using Protean 3D for epitope prediction.

Lasergene Protein Overview

This video gives an overview of Lasergene Protein, consisting of Protean 3D, plus the optional services NovaFold, NovaFold Antibody and NovaDock.

Changing Rendering Styles to Highlight Features of Interest

This video demonstrates how Protean 3D makes it easy to highlight features of interest in your structure by changing rendering styles.

Creating Surfaces and Adjusting Lighting on Structures in Protean 3D

In this video, we show you how to create surfaces around your macromolecular structures in Protean 3D. We also show how to adjust lighting to optimize the structure view for presentation.

Protean 3D's Analysis View

Protean 3D’s Analysis View enables you to evaluate secondary structural characteristics of a sequence using a variety of known bioinformatics prediction methods with customizable parameters.

FAQs

What file types are supported for importing protein sequences for epitope prediction?

Protean 3D supports the following protein file formats: .aa, .fap, .fas, .fasta, .gp, .gbk, .sbd, .pro.

What method does Protean 3D use for epitope prediction?

Protean 3D uses the NeoClone method, a machine learning approach developed in collaboration with
NeoClone® Biotechnology International, that improves the ability to predict linear B cell epitopes using only sequence-based information. The NeoClone method is a predictive model that considers secondary structure, flexibility, hydropathy, and antigenicity, and is based on NeoClone’s antibody development and bioinformatics approach. The NeoClone method is described further in the white paper, Precise Predictions of Linear B Cell Epitopes in Protean 3D.

How are antigenic and highly antigenic regions predicted?

A cross-validated machine learning approach was used to train two models that predict antigenic and highly antigenic regions. The antigenic models consider sequence-based helix, sheet, beta turn, and flexibility predictions as well as hydropathy, antigenicity, and stability indices when predicting antigenic regions; the highly antigenic models consider a modified combination of secondary structure predictions, hydropathy, antigenicity, and stability.

Can I change the confidence score threshold for B-cell epitope prediction?

Yes. A confidence score with a value from 0 to 1 is assigned to each residue in a protein sequence. By default, residues with a confidence score greater than 0.5 are classified as putative antigenic residues. You can edit the confidence score threshold by first selecting the B-cell epitopes (DNASTAR) track from the Tracks panel, then opening the Options area of the panel. Changing this threshold value will update the Analysis View accordingly.

Can I export an image of the epitope prediction track?

Yes. Protean 3D lets you export an image of the Analysis view, which displays the B-cell epitope prediction track. To do this, choose  File > Export Image > Analysis and save the image as a .pdf, .png, .jpg, or .pptx (Microsoft PowerPoint) file.

Citations

Preparation of monoclonal antibodies against gamma-type phospholipase A2 inhibitors and immunodetection of these proteins in snake blood
Li, J., Xiong, Y., Sun, S. et al. J Venom Anim Toxins Incl Trop Dis 23, 37 (2017).

Development of Rift Valley fever (RVF) vaccine by genetic joining of the RVF-glycoprotein Gn with the strong adjuvant subunit B of cholera toxin (CTB) and expression in bacterial system
Ibrahim, Essam H., Taha, Ramadan, Ghramh, Hamed A., Kilany, Mona. Saudi Journal of Biological Sciences, Volume 26, Issue 7 (2019).

Identification and Pilot Evaluation of Salivary Peptides from Anopheles albimanus as Biomarkers for Bite Exposure and Malaria Infection in Colombia
Londono-Renteria, B.; Drame, P.M.; Montiel, J.; Vasquez, A.M.; Tobón-Castaño, A.; Taylor, M.; Vizcaino, L.; Lenhart, A.E. Int. J. Mol. Sci. 2020, 21, 691.

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Results are second to none

“Protean 3D is innovative. It allows me answer questions that were not explored previously, and the in-silico accuracy of results are second to none as determined by comparing with in-vitro techniques.”

Alebachew Messele, Addis Ababa University

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