AbPACER: parent-aware, affinity-label-blind prioritization of affinity-matured scFv clones from phage-display NGS

BackgroundAffinity-maturation phage-display next-generation sequencing (NGS) yields more paired single-chain variable fragment clones than can be characterized experimentally, creating a fixed-budget prioritization problem. Read counts provide empirical support rather than direct affinity labels. We developed AbPACER (Antibody Parent-Aware Contextual Evidence Ranker), an affinity-label-blind neural ranker combining parent-relative mutation descriptors, frozen antibody-language-model context, and NGS evidence from related clones. AbPACER is campaign-adaptive rather than zero-shot: for each campaign, it is fitted to paired sequences and round-resolved R1-R3 counts before returning a 384-candidate assay list. We evaluated it in two retrospective phage-display campaigns and separately assessed its supervised mean-squared-error adaptation on AlphaSeq, denoted AbPACER-MSE. ResultsFrom frozen top-5% candidate sets containing 16,323 Fas-associated factor 1 (FAF1) and 7,487 vascular endothelial growth factor receptor (VEGFR) clones, each method ranked the complete target-specific set and selected 384 candidates. In FAF1, AbPACER recovered 2.00 {+/-} 0.00 of seven retrospective panel clones, recovering two in every seed, compared with 1/7 by total count, 1.00 {+/-} 0.00 by Ens-Grad CNN, 1.67 {+/-} 1.15 by A2Binder-HL, and 1.33 {+/-} 0.58 by AbAffinity. In VEGFR, AbPACER recovered 2.33 {+/-} 0.58 of three panel clones, the highest observed learned-method mean, whereas total count recovered 3/3. No learned method was uniformly best at broader hypothetical budgets. On the public AlphaSeq common split of 11,670 fixed-test variants, AbPACER-MSE recovered 187.0 {+/-} 2.6 of the true top-384, closely matching AbAffinity (188.0 {+/-} 2.6) and exceeding A2Binder (175.7 {+/-} 6.4) and Ens-Grad CNN (154.0 {+/-} 6.1). AbPACER-MSE updated 1.378 million task-specific parameters, compared with 651.04 million for AbAffinity, and achieved Pearson 0.687 {+/-} 0.003 and Spearman 0.652 {+/-} 0.002. ConclusionsAbPACER provides a campaign-specific, parent-aware framework for fixed-budget prioritization from affinity-label-blind phage-display NGS data. At the 384-candidate endpoint, it showed the highest mean recovery among learned methods in both retrospective campaigns. AbPACER-MSE closely matched AbAffinity in true top-384 recovery while updating substantially fewer task-specific parameters. These results motivate prospective evaluation of sequence-conditioned reranking as a complement to count-based prioritization.