title: ABodyBuilder: Automated antibody structure prediction with data-driven accuracy estimation.
abstract: Computational modeling of antibody structures plays a critical role in therapeutic antibody design. Several antibody modeling pipelines exist, but no freely available methods currently model nanobodies, provide estimates of expected model accuracy, or highlight potential issues with the antibody's experimental development. Here, we describe our automated antibody modeling pipeline, ABodyBuilder, designed to overcome these issues. The algorithm itself follows the standard 4 steps of template selection, orientation prediction, complementarity-determining region (CDR) loop modeling, and side chain prediction. ABodyBuilder then annotates the 'confidence' of the model as a probability that a component of the antibody (e.g., CDRL3 loop) will be modeled within a root-mean square deviation threshold. It also flags structural motifs on the model that are known to cause issues during in vitro development. ABodyBuilder was tested on 4 separate datasets, including the 11 antibodies from the Antibody Modeling Assessment-II competition. ABodyBuilder builds models that are of similar quality to other methodologies, with sub-Angstrom predictions for the 'canonical' CDR loops. Its ability to model nanobodies, and rapidly generate models (∼30 seconds per model) widens its potential usage. ABodyBuilder can also help users in decision-making for the development of novel antibodies because it provides model confidence and potential sequence liabilities. ABodyBuilder is freely available at http://opig.stats.ox.ac.uk/webapps/abodybuilder .
authors.affiliation: a Department of Statistics , University of Oxford , Oxford , UK.
authors.forename: Jinwoo
authors.initials: J
authors.lastname: Leem
authors.affiliation: a Department of Statistics , University of Oxford , Oxford , UK.|b Roche Pharma Research and Early Development, Large Molecule Research, Roche Innovation Center Munich , Penzberg , Germany.
authors.forename: James
authors.initials: J
authors.lastname: Dunbar
authors.affiliation: b Roche Pharma Research and Early Development, Large Molecule Research, Roche Innovation Center Munich , Penzberg , Germany.
authors.forename: Guy
authors.initials: G
authors.lastname: Georges
authors.affiliation: c Informatics Department , UCB Pharma , Slough , UK.
authors.forename: Jiye
authors.initials: J
authors.lastname: Shi
authors.affiliation: a Department of Statistics , University of Oxford , Oxford , UK.
authors.forename: Charlotte M
authors.initials: CM
authors.lastname: Deane
mesh_terms: D000465:Algorithms; D000906:Antibodies / Q000737:chemistry*; D022801:Complementarity Determining Regions / Q000737:chemistry*; D003198:Computer Simulation; D015195:Drug Design; D008958:Models, Molecular
chemical_list: D000906:Antibodies; D022801:Complementarity Determining Regions
keywords: Antibody modeling; model quality assessment; modeling; nanobody; protein structure prediction