期刊
MABS
卷 8, 期 7, 页码 1259-1268出版社
TAYLOR & FRANCIS INC
DOI: 10.1080/19420862.2016.1205773
关键词
Antibody modeling; model quality assessment; modeling; nanobody; protein structure prediction
资金
- Engineering Physical Sciences Research Council (EPSRC) [EP/G037280/1]
- UCB Pharma
- Roche Diagnostics GmbH
- Engineering and Physical Sciences Research Council [1231608] Funding Source: researchfish
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 (approximate to 30seconds 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.
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