Editing Antibody Design by AI (section)

== Stringent washing improves antibody EC50 prediction ==

The enrichment of a CDR-H3 sequence in a phage panning experiment is only an approximation of affinity for a target. During panning Fab fragments displayed on phages specific for the target are competing with both non-binding and poly-specific (“sticky”) Fab fragments. Standard washing conditions are designed to reduce non-binding Fab fragments with multiple washing cycles. Poly-specific Fab fragments are depleted by using blocking agents (Supplemental Methods).

We hypothesized that we could improve the correspondence between phage panning enrichment and IgG affinity by using stringent washing condition during phage panning to reduce non-specific interactions. We defined a stringent wash buffer with increased ionic strength to interrupt salt bridges (0.75M NaCl) and with a detergent to reduce hydrophobic interactions (2% Octyl β-D-glucopyranoside) in 0.1M Tris at pH 7.4. We expected that this stringent washing buffer would reduce both non-specific binding and weak specific binding. Weak specific binding can result from a low number of ionic and / or hydrophobic target interactions.

We performed panning experiments against ranibizumab using this stringent wash buffer starting in the second round of panning. Selected sequences at differing enrichments were produced as full-length IgG molecules, and used in an ELISA EC50 29 assay as a measure of antibody affinity. We found that the stringent buffer increased the correspondence between panning enrichment and affinity (Table S3, Fig. S3A). The Spearman rank correlations between enrichment and affinity for the sequences in Table S3 are 0.266 (p = 0.404, n = 12) for standard washing condition, and 0.371 (p = 0.236, n = 12) for stringent washing condition. Alternative machine learning methods for proposing sequences In addition to our neural network ensemble, three other machine learning frameworks were also applied to generate novel CDR-H3 sequences. Here, we detail these alternative computational approaches to antibody design that were less successful than our Ens-Grad methodology.