Antibody modeling assessment II. Structures and models

Abstract

To assess the state-of-the-art in antibody structure modeling, a blinded study was conducted. Eleven unpublished Fab crystal structures were used as a benchmark to compare Fv models generated by seven structure prediction methodologies. In the first round, each participant submitted three non-ranked complete Fv models for each target. In the second round, CDR-H3 modeling was performed in the context of the correct environment provided by the crystal structures with CDR-H3 removed. In this report we describe the reference structures and present our assessment of the models. Some of the essential sources of errors in the predictions were traced to the selection of the structure template, both in terms of the CDR canonical structures and VL/VH packing. On top of this, the errors present in the Protein Data Bank structures were sometimes propagated in the current models, which emphasized the need for the curated structural database devoid of errors. Modeling non-canonical structures, including CDR-H3, remains the biggest challenge for antibody structure prediction.

MoFvAb: Modeling the Fv region of antibodies

“…The identification of sequence-identical template structures to exclude from model building is performed using the software CD-HIT. 61,62 To evaluate how MoFvAb compares to state-of-the-art antibody modeling software, we downloaded the crystal structures and participant models of the 2011 and 2014 AMA1 and AMA2 [17][18][19] blind modeling studies from http:// www.3dabmod.com. AMA1 Despite the fact that we try to mimic a blind modeling simulation by template exclusion, we, at the time of model building, had access to more template structures than the original AMA participants (in particular with regard to AMA1 in 2011) so that the direct comparison in terms of all-atom RMSD is not altogether fair, but possibly still indicative.…”

“…During the all-atom RMSD calculation, we correct for possible symmetry variants of the sidechains of arginine, aspartate, glutamate, leucine, phenylalanine, tyrosine and valine. For the sake of consistency with AMA2, we use the b-sheet core and CDR loop definition of Teplyakov et al 19 as highlighted in Figures SI1 and SI2.…”

“…[12][13][14][15][16] Among these computational methods is a number of homology modeling applications that have been tailored to exclusively generate atomistic models of the antigen-binding (Fv) region of antibodies, [17][18][19] a functionality that is meant to complement and relieve experimental structure elucidation by crystallography.…”

“…Predicting the conformation of CDR-H3 with satisfactory accuracy is thus the key challenge in antibody homology modeling. 19 The situation is complicated further because the Fv region is composed of 2 separate immunoglobulin domains, the variable region of the heavy chain and the variable region of the light chain (VH and VL), with 3 CDRs distributed on each of the 2. The relative orientation of the 2 domains exhibits a notable variability over the known repertoire of antibody crystal structures, [30][31][32] and it has been suggested that VH-VL orientation, in addition to length and sequence of the CDRs, is a co-modulator of antigen specificity and affinity.…”

“…In summary, another challenge of antibody homology modeling lies in finding the correct relative orientation of the 2 variable domains. 19 The state of current antibody modeling technology was benchmarked in the 2011 and 2014 Antibody Modeling Assessment studies (AMA1 and AMA2), [17][18][19] where sets of previously unpublished crystal structures were compared to their blindly modeled counterparts in terms of backbone coordinate accuracy (root-mean-square deviation, RMSD) and structure quality (e.g., number of steric clashes, percentage of backbone conformations in the favored Ramachandran region 34 ). The 2014 AMA iteration added the analysis of the models' VH-VL orientation parameters to the set of criteria to be assessed.…”