Antibodies are used extensively in diagnostics and as therapeutic agents. Achieving high-affinity binding is important for expanding detection limits, extending dissociation half-times, decreasing drug dosages and increasing drug efficacy. However, antibody-affinity maturation in vivo often fails to produce antibody drugs of the targeted potency, making further affinity maturation in vitro by directed evolution or computational design necessary. Here we present an iterative computational design procedure that focuses on electrostatic binding contributions and single mutants. By combining multiple designed mutations, a tenfold affinity improvement to 52 pM was engineered into the anti-epidermal growth factor receptor drug cetuximab (Erbitux), and a 140-fold improvement in affinity to 30 pM was obtained for the anti-lysozyme model antibody D44.1. The generality of the methods was further demonstrated through identification of known affinity-enhancing mutations in the therapeutic antibody bevacizumab (Avastin) and the model anti-fluorescein antibody 4-4-20. These results demonstrate computational capabilities for enhancing and accelerating the development of protein reagents and therapeutics.
Submitter: Marie Ary
Submission Date: Dec. 3, 2018, 5:39 p.m.
|Number of data points||390|
|Proteins||Anti-epidermal growth factor receptor (EGFR) (cetuximab) scFv ; Anti-hen egg white lysozyme D44.1 quad mutant scFv ; Anti-hen egg white lysozyme D44.1 scFv ; Anti-hen egg white lysozyme D1.3 scFv|
|Assays/Quantities/Protocols||Experimental Assay: High salt Kd ; Experimental Assay: k_on ; Experimental Assay: k_off ; Experimental Assay: Kd_WT/Kd_mut ; Experimental Assay: ΔΔG_binding_Exp ; Experimental Assay: Kd ; Derived Quantity: SD of k_on ; Derived Quantity: SD of k_off ; Derived Quantity: SD of ΔΔG_binding_Exp ; Derived Quantity: SD of Kd ; Derived Quantity: SD of Kd_WT/Kd_mut ; Derived Quantity: SD of High salt Kd ; Derived Quantity: k_off/k_on ; Derived Quantity: SD of k_off/k_on ; Computational Protocol: ΔΔG_binding_Total calc ; Computational Protocol: ΔΔG_binding_Electrostatics calc ; Computational Protocol: ΔΔG_folding_Total calc ; Computational Protocol: ΔΔG_folding_Electrostatics calc|
|Libraries||Cetuximab predicted binding affinities (Table 1d) ; Cetuximab exp binding affinities (Table 1d) ; D44.1 quad mutant experimental and predicted binding affinities (Table S2) ; D44.1 experimental and predicted binding data (Table 1a-c) ; D1.3 experimental and predicted single mutant binding affinities (Table S1)|
|Percent Identity||Matching Chains||Protein||Accession||Entry Name|
|93.8||Anti-epidermal growth factor receptor (EGFR) (cetuximab) scFv||P01821||HVM45_MOUSE|
|95.6||Anti-epidermal growth factor receptor (EGFR) (cetuximab) scFv||P01642||KV5A9_MOUSE|
|96.9||Anti-hen egg white lysozyme D1.3 scFv||P01820||HVM44_MOUSE|
|96.7||Anti-hen egg white lysozyme D1.3 scFv||P01635||KV5A3_MOUSE|