CSM-AB is a machine learning method capable of predicting antibody-antigen binding affinity by modelling interaction interfaces as graph-based signatures.
mCSM-AB is a web server for predicting antibody-antigen affinity changes upon mutation with graph-based signatures
mCSM-AB2 is an updated and refined version approach, capable of accurately modelling the effects of mutations on antibody-antigen binding affinity, through the inclusion of evolutionary and energetic terms.
mmCSM-AB is a tool for analysing the effects of introducing multiple point mutations on antibody-antigen binding affinity.
Antigen Explorer is an interactive resource for browsing antigen combinations for more precise tumor recognition. Leveraging expression data from TCGA and GTEx, the discrimination potential of all possible combinations of surface antigens were scored for 33 tumor types. Users can explore the top predictions and make interactive plots to evaluate an antigen pair against normal tissue cross-reactivity.
BASELINe, a new computational framework for Bayesian estimation of Antigen-driven selection in Immunoglobulin sequences, provides a more intuitive means of analyzing selection by actually quantifying it.
ABAG allows to compute the endpoint titer and concentration of Antibody(Ab) or Antigen(Ag) from ELISA data. It calculate the Ab/Ag concentration, using the graphical method