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Landscape-scale navigation unlocks antibody CDR structural logic for AI-guided rescue and therapeutic optimization
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Landscape-scale navigation unlocks antibody CDR structural logic for AI-guided rescue and therapeutic optimization

Changju Chun, Byeong-Kwon Sohn, Heesoo Ki +18 more
April 2026
 ·
bioRxiv
 ·
DOI 
10.64898/2026.04.21.719857
94
%
Reproducible
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This paper passed independent validation.
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It ranks in the top 1% of preprints assessed in Bioengineering this quarter.
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Why this is interesting

Overturning the view that CDRs are governed solely by antigen binding, this work reveals structural fitness as a master regulator of antibody productivity and delivers an AI-guided rescue strategy with 100-fold in vivo efficacy gains.

QED claims analysis

Summary

Landscape-scale navigation of antibody fitness landscapes, enabled by high-throughput single-molecule profiling of tens of thousands of variants, reveals that CDR sequences follow folded-protein structural logic governing cellular productivity, which can be accurately predicted by ProteinMPNN and leveraged for AI-guided rescue, while systematic mapping of rugged epistatic topologies identifies rare peak clusters yielding therapeutic antibodies with 20- to 100-fold enhanced in vivo efficacy through extended target residence time rather than improved equilibrium affinity.

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Main Claim 1 –

SPID profiles affinity, productivity, stability at tens-of-thousands scale

Supporting evidence

  • SPID enables reproducible, format-compatible CDR deep mutational scanning
Main Claim 2 –

Ten-antibody DMS reveals antibody-specific CDR functional signatures

Supporting evidence

  • Hydrophilic substitutions show higher functional tolerance across CDRs
Main Claim 3 –

Buried CDR residues impose depth-dependent constraints on productivity

Main Claim 4 –

Combinatorial CDR landscapes are rugged; rare peaks improve both traits

Supporting evidence

  • Fitness landscapes show peak clusters, valleys, and rugged interfaces
  • Landscape non-navigability blocks incremental routes to high-fitness variants
  • DMS pre-filtering is required for functional combinatorial CDR libraries
  • ML and modular assembly efficiently navigate rugged antibody landscapes

Abstract

While AI offers transformative potential for therapeutic antibody design, the lack of ground-truth data fundamentally constrains our ability to model the epistatic topology of fitness landscapes. Here, we establish a high-throughput workflow to characterize tens of thousands of antibody variants per week with gold-standard biophysical precision. By combinatorially assembling functional variants from deep mutational scanning, we charted antibody fitness landscapes comprising over 17,000 data points, which revealed an extremely rugged, non-navigable epistatic topology. Yet, navigating at this unprecedented scale enabled the discovery of rare peak clusters exhibiting simultaneous enhancements in affinity and productivity. Strikingly, ProteinMPNN predicted the CDR-dependent productivity landscape with remarkable accuracy, suggesting that sequence-structure compatibility within CDRs gates cellular productivity. This insight enabled a structure-guided rescue strategy combining AlphaFold3 and ProteinMPNN, which successfully restored the cellular productivity of high-affinity, low-productivity clones via single amino acid substitutions. Two elite variants drawn directly from peak clusters further demonstrated 20- to 100-fold in vivo efficacy gains in a murine psoriasis model. Our findings establish CDR structural fitness as a fundamental determinant of antibody cellular productivity and validate landscape-scale navigation as a powerful framework for therapeutic antibody optimization.

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