ABGEN: a knowledge-based automated approach for antibody structure modeling.

Abstract

Immunoglobulin (Ig) amino acid sequences are highly conserved and often have sequence homology ranging from 70 to 95%. Antigen binding fragments (Fab), variable region fragments (Fv), and single chain Fv (scFv) of more than 50 myeloma proteins and monoclonal antibodies (mAb) have been crystallized and display a high degree of structural similarity. Based on this observation, several homology modeling approaches have been developed for the prediction of Fab and Fv structures prior to their experimental determination. We have extracted features from existing Ig sequences, 44 known Fab and Fv structures to create an automated AntiBody structure GENeration (ABGEN) algorithm for obtaining structural models of antibody fragments. ABGEN utilizes a homology based scaffolding technique, and includes the use of invariant and strictly conserved residues, structural motifs of known Fab, canonical features of hypervariable loops, torsional constraints for residue replacements and key inter-residue interactions. The validity of the ABGEN algorithm has been tested using a five-fold cross validation with the existing Fab structures. Molecular mechanics and dynamics methods have been implemented with ABGEN models to accurately predict two Fab structures of anti-sweetener antibodies prior to crystallographic determinations.