Antibody-antigen interaction software tools | Immune system data analysis
The determination of epitopes targeted by antibodies is useful for understanding virus escape, antibody optimization and epitope-based design of vaccines. Structure determination of antibody–antigen complexes can provide epitope information at the atomic level, but in many instances, atomic-level complex structures can be challenging to obtain. Additional experimental methods for epitope delineation are also available, although they are characterized with lower accuracy and typically require substantial experimental effort. Computational methods for epitope prediction have traditionally aimed at predicting antigen residues that could be part of any antibody epitope, and are thus not antibody specific. More recently, computational methods for antibody-specific epitope prediction (the prediction of the epitope targeted by an antibody of interest) have been developed.
Offers visualization of the information relevant to selection of immunogen peptide sequences AbDesigner identifies optimal immunizing peptides for antibody production using a peptide-based strategy. It includes commonplace measures, such as conservation, hydropathy, possible modification sites, transmembrane domains. It also provides specialized display of 3D structural information from the Protein Data Bank (PDB), allowing users to view all information extracted from UniProt and PDB databases interactively.
Allows to predict antibody–antigen affinity changes upon mutation which relies on graph-based signatures. mCSM-AB can help users which desire to realize design and development of therapeutic and diagnostic antibodies (Abs). It can provide insight into the development of escape mutations. To make an analysis, users have to enter a single mutation or upload a file. Then, the results are available on the web interface.
An efficient computational method to predict antibody-specific epitopes at the residue level based on neutralization panels of diverse viral strains. The method primarily utilizes neutralization potency data over a set of diverse viral strains representing the antigen, and enhanced accuracy is achieved by incorporating information from an unbound structure of the antigen.
A computational framework for finding desirable binding protein scaffolds by utilizing protein structure and sequence information. For each protein, its structure and the sequences of evolutionarily-related proteins were analyzed, and spatially contiguous regions composed of highly variable residues were identified. The analysis results for all 4,818 monomer structures can be accessed in the database section of our web server. The collective information for the proteins is displayed as a table.
An integrated computational framework to predict optimal structural domains and identify target molecules for antibodies. PAT automatically analyses various structural properties, evaluates the folding stability, and identifies possible structured units in a given protein sequence. PAT is able to identify the traditional domains with strongly conserved stretches of protein sequence and putative structural units with parts of the protein that adopt stable folds.
Detects characteristics related with viral antigenicity. GG-MTSL consists of model able to be trained from multi-sourced serologic data with the aim of recognizing variants and describing antigenic profiles in real time and on a large scale. This program provides a quantitative feature that starts from hemagglutinin (HA) protein sequences to evaluate antigenic distances between influenza A viruses.
Offers a method for measuring variant surface glycoprotein (VSG) diversity across strains and during infections. VAPPER supplies a method allowing users to study the host–parasite interaction at population and individual scales. It can be used for analyzing antigenic diversity in systems data (genomes, transcriptomes, and proteomes) called variant antigen profiling (VAP).