Epitope Selection Software
The Tepitool provides prediction of peptides binding to MHC class I and class II molecules. Tool is designed as a wizard with 6 steps as described below. Each field (except sequences and alleles) is filled with default recommended settings for prediction and selection of optimum peptides. The input parameters can be adjusted as per your specific needs. You can go back to previous steps to change your selection before submission of the job. Once you submit the job (at the end of step-6), you will not be able to make any more changes and will have to start the prediction all over again with updated input parameters.
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T Cell Epitopes - Processing Prediction These tools predict epitope candidates based upon the processing of peptides in the cell. The deimmunization tool is attempt to identify immunodominant regions in a given therapeutically important protein, and suggest amino-acid substitutions that create non-immunogenic versions of the proteins. So we have opted a two steps process; 1) In the first step, the deimmunization tool will list all the immunogenic regions or peptides based on selected threshold. These peptides will be generated from the protein with 15mer window size and 10mer overlap. 2) In the second step, the user can select one or more peptides listed in the results and final result window will display the non-immunogenic substitution of each selected peptides. The default threshold is 8.5 (which is difference in the median of percentile rank from 26 reference alleles set for MHC class II).
In the final result window, the tools will also take care of the fact that non-immunogenic substitution in the immunogenic peptides, should not create new immunogenic site in the neighboring peptides. Therefore, the result window will also display the effect of substitution on the neighboring peptides. Structure Tools.
Epitope Selection Software For Windows
Abstract DESCRIPTION (provided by applicant): Human health has benefited tremendously from the therapeutic application of monoclonal antibodies (mAb), treating painful and devastating diseases such as rheumatoid arthritis and cancer, among others. However, mAb development is a laborious and time consuming process.
The health benefits gained from faster mAb development are clear, creating a great need for tools to guide scientists toward discovering the most promising antigenic targets-particularly with regard to B-cell epitopes (the part of an antigen recognized by an antibody). The critical barrier to progress in this domain is the inability to deduce the conformational characteristics of protein sequence in the absence of known structure for predicting linear B-cell epitopes-the largest, most diverse, and pharmaceutically valuable class of known epitopes. The general criticism of existing prediction methods is that they are inaccurate and do not address the conformational nature of B-cell epitopes. DNASTAR proposesto create a software pipeline that guides the prediction of B-cell epitopes, models the dynamic structural interface between a monoclonal antibody and its experimentally identified antigen, and screens in silico site-directed mutations to engineer more potent antibodies with enhanced binding affinity. The Phase I goal is to improve the prediction of antigenic peptides from target protein sequences and experimental or predicted structures.
Toward this goal, DNASTAR has established collaborations with experts in monoclonal antibody production, 3D structure prediction, and protein structure and dynamics, including access to their experimental methods, data, and software tools. Our predictive models will benefit from three key innovations: 1) a superior data set and professional insights into monoclonal antibody production, 2) the introduction of state of the art 3D structure prediction for training our epitope predictors, and 3) the first use of structure-based protein dynamics in B-cell epitope prediction. Atthe conclusion of Phase I, we will deliver an enhanced sequence-only B-cell epitope prediction model when compared to current top prediction methods (Aim 1) and a superior sequence and structure-based epitope prediction model using 3D structure predictionand protein dynamics (Aim 2).
Epitope Selection Software For Windows 7
In creating these models, we will account for the chemical and physical properties of a protein sequence and the biophysics that mediate protein-protein interactions, including solvent accessibility, hydrogen bonding, residueflexibility, binding nuclei, and geometric contours of the molecular surface. The proposed software pipeline will be built upon Protean 3D, our new molecular structure and simulation viewer, and will elevate the technical capability of a broad range of experimental scientists to estimate key antigenic structural properties from proteins without known structure-all on their desktop computer. Upon achieving these aims, scientists will recognize that it is no longer adequate to describe B-cell epitopes usingamino acid frequencies or propensity scales alone.
PUBLIC HEALTH RELEVANCE: Monoclonal antibodies are invaluable tools for diagnosing and treating human diseases. Unfortunately, the experimental methods used today to identify the most promising immunogenic targets are time consuming and less than totally effective. By taking the novel approach of incorporating both protein sequence information and structural features derived from high quality 3D structure predictions within our desktop computer software product, we propose to advance the ability of a broad range of life scientists to properly predict B-cell epitopes (the part of an antigen recognized by an antibody) applicable to their area of interest. This will accelerate the discovery of new monoclonal antibody pharmaceuticals, leading to improved human health across many diseases. information listed above is at the time of submission.