| Literature DB >> 31318308 |
Joseph D Schrag1, Marie-Ève Picard2, Francis Gaudreault1, Louis-Patrick Gagnon1, Jason Baardsnes1, Mahder S Manenda2, Joey Sheff3, Christophe Deprez1, Cassio Baptista1, Hervé Hogues1, John F Kelly3, Enrico O Purisima1, Rong Shi2, Traian Sulea1.
Abstract
Solution stability is an important factor in the optimization of engineered biotherapeutic candidates such as monoclonal antibodies because of its possible effects on manufacturability, pharmacology, efficacy and safety. A detailed atomic understanding of the mechanisms governing self-association of natively folded protein monomers is required to devise predictive tools to guide screening and re-engineering along the drug development pipeline. We investigated pairs of affinity-matured full-size antibodies and observed drastically different propensities to aggregate from variants differing by a single amino-acid. Biophysical testing showed that antigen-binding fragments (Fabs) from the aggregating antibodies also reversibly associated with equilibrium dissociation constants in the low-micromolar range. Crystal structures (PDB accession codes 6MXR, 6MXS, 6MY4, 6MY5) and bottom-up hydrogen-exchange mass spectrometry revealed that Fab self-association occurs in a symmetric mode that involves the antigen complementarity-determining regions. Subtle local conformational changes incurred upon point mutation of monomeric variants foster formation of complementary polar interactions and hydrophobic contacts to generate a dimeric Fab interface. Testing of popular in silico tools generally indicated low reliabilities for predicting the aggregation propensities observed. A structure-aggregation data set is provided here in order to stimulate further improvements of in silico tools for prediction of native aggregation. Incorporation of intermolecular docking, conformational flexibility, and short-range packing interactions may all be necessary features of the ideal algorithm.Entities:
Keywords: Aggregation; native folding; prediction method; single point mutation; structure-aggregation relationship
Year: 2019 PMID: 31318308 PMCID: PMC6748613 DOI: 10.1080/19420862.2019.1632114
Source DB: PubMed Journal: MAbs ISSN: 1942-0862 Impact factor: 5.857