PreciAbTM
As we all know, during the protein drug therapy, immunogenicity is a big obstacle to the success. Nowadays, immune response to foreign proteins is mostly well understood, on account of years of thorough study of parameters influencing vaccine efficacy. A number of factors are related to the protein immunogenicity, including delivery route, delivery vehicle, dose regimen, aggregation, innate immune system activation and the capacity of the protein to interface with the humoral (B cell) and cellular (T cell) immune systems. Based on the well-established in silico technology platform in Creative Biolabs, now, it is available to evaluate the T-cell epitope content of a protein relatively accurately, and also to measure the regional and overall immune potential of a protein therapeutic.
T-cell Epitope Prediction
It has been widely recognized that T-cell epitope prediction is a big challenge because of the high degree of MHC polymorphism and disparity in the volume of data on various steps encountered in the generation and presentation of T-cell epitopes in the living systems. It has been a long time for the predictions of T-helper epitopes through in silico technology. And their application to the selection of autoimmunity epitopes and vaccine design has been well demonstrated. With years of experience, we can provide best-match method to meet your requirements in T-cell epitope prediction.
Based on the sequence and structure analyses of T-cell epitopes, direct methods rely on features like the presence of amphipathicity, MHC-binding motifs, etc.
Indirect methods use some of the elegant techniques to predict MHC-peptide binding based on statistical learning theory, such as artificial neural networks (ANN), support vector machines (SVM), and molecular docking simulations, etc.
Fig 1. A summary of the various T-cell antigen discovery approaches with respect to both antigen-presentation strategies and assay methods. (Sharma, 2014)
B-cell Epitope Prediction
Predicting B-cell antigenicity is helpful to identify neutralizing antibody targets. However, it is complicated to predict B-cell epitopes by computational tools, due to the conformational based on antibody: antigen interactions. 3DEX and CEP, B-cell epitope prediction tools, are able to be used to predict B-cell epitopes on a high-throughput basis. Specifically, sometimes, defining a T-cell epitope may result in identification of a B-cell epitope, because B-cell epitopes have been shown to colocalize with T-helper epitopes. Creative Biolabs offers high quality b-cell epitope prediction services by using in silico techniques to contribute to the antibody immunogenicity prediction.
Fig 2. B-cell epitope prediction. (Sela-Culang, 2014)
De-immunization
On account of the disruption of HLA binding, an underlying demand for T-cell stimulation, the de-immunization method by epitope modification has been developed. The idea of rational epitope modification is based on the natural process which occurs when tumor cells and pathogens evolve to escape immune pressure by accumulating mutations, in order to decrease the binding of their constituent epitopes to host HLA, rendering the host cell unable to ‘signal’ to T cells the presence of the tumor or pathogen. Creative Biolabs offers high-quality de-immunization services by using in silico techniques to customers to promote your project success.
With our comprehensive antibody immunogenicity prediction services, designing and engineering novel antibodies with desired therapeutic properties is available. We customize the service according to the specific requirements from the customers. We also provide other structure-based antibody reformatting services. Please contact us for more information and a detailed quote.
References
All services provided on this site are intended to support preclinical research only. Do not use our services or final products on humans.
More than 10 years of exploration and expansion
The whole new drug discovery and development pipeline
