Pair-complete structural gating enables Pareto optimization of de novo cyclic peptides with preserved binding geometry and improved developability
- 1School of Pharmacy,Shihezi University
Abstract
Cyclic peptides can engage extended protein interfaces, but sequence changes that improve developability often disrupt the selected target-bound geometry. Existing property-guided design methods lack a principled way to preserve this geometry during local multiproperty optimization. Here we present RF-PepTune, which separates structural eligibility from property prioritization. Before any property score is queried, RF-PepTune evaluates single substitutions and all position-distinct pairs induced by admitted substitutions to build a parent-specific, pair-complete admission graph. Pareto Monte Carlo tree search then optimizes predicted permeability, solubility, non-fouling, and low-haemolysis scores exclusively within this graph, and selected sequences undergo full structural re-evaluation. In a matched three-arm comparison of 334 parents across MDM2, GABARAP, and MCL1, joint property-structure success rose from 10.2% without admission to 51.5% with single-substitution admission and 77.5% with pair-complete admission; pair-complete gating improved success by 26.0 percentage points over single-substitution gating (95% CI, 20.7-31.4). Property-positive but structure-failing endpoints fell from 88.3% to 9.3%. In a separate target-balanced set, all 90 prioritized outputs improved all four model-native scores while satisfying prespecified structural criteria. This work establishes pairwise structural gating as a general strategy for constrained multi-objective molecular optimization.
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BibTeX
@misc{hou2026paircomplete,
title = {{Pair-complete structural gating enables Pareto optimization of de novo cyclic peptides with preserved binding geometry and improved developability}},
author = {Y. Hou and Y. Zhang and Z. Liu and X. Peng and Z. Liu and M. Yusupujiang and Y. Xu and J. Hu and Z. Liu and S. Yang},
howpublished = {bioRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.24.754053},
url = {https://www.biorxiv.org/content/10.64898/2026.09.24.754053v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - Pair-complete structural gating enables Pareto optimization of de novo cyclic peptides with preserved binding geometry and improved developability
AU - Y. Hou
AU - Y. Zhang
AU - Z. Liu
AU - X. Peng
AU - Z. Liu
AU - M. Yusupujiang
AU - Y. Xu
AU - J. Hu
AU - Z. Liu
AU - S. Yang
PY - 2026
DA - 2026/09/24
PB - bioRxiv
DO - 10.64898/2026.09.24.754053
UR - https://www.biorxiv.org/content/10.64898/2026.09.24.754053v1
AB - Cyclic peptides can engage extended protein interfaces, but sequence changes that improve developability often disrupt the selected target-bound geometry. Existing property-guided design methods lack a principled way to preserve this geometry during local multiproperty optimization. Here we present RF-PepTune, which separates structural eligibility from property prioritization. Before any property score is queried, RF-PepTune evaluates single substitutions and all position-distinct pairs induced by admitted substitutions to build a parent-specific, pair-complete admission graph. Pareto Monte Carlo tree search then optimizes predicted permeability, solubility, non-fouling, and low-haemolysis scores exclusively within this graph, and selected sequences undergo full structural re-evaluation. In a matched three-arm comparison of 334 parents across MDM2, GABARAP, and MCL1, joint property-structure success rose from 10.2% without admission to 51.5% with single-substitution admission and 77.5% with pair-complete admission; pair-complete gating improved success by 26.0 percentage points over single-substitution gating (95% CI, 20.7-31.4). Property-positive but structure-failing endpoints fell from 88.3% to 9.3%. In a separate target-balanced set, all 90 prioritized outputs improved all four model-native scores while satisfying prespecified structural criteria. This work establishes pairwise structural gating as a general strategy for constrained multi-objective molecular optimization.
ER -