A Multi-Engine Consensus Docking Pipeline for RNA Aptamer Screening Against ACC Oxidase (ACO): Statistical Validation, Machine Learning Analysis, and Pilot Cross-Target Evaluation Against ACC Synthase (ACS)
Year: 2026
Type: article
Abstract: Postharvest losses in climacteric fruits are largely driven by ethylene, and inhibiting its biosynthesis is an active research goal. RNA aptamers are attractive candidates for modulating the two rate-limiting enzymes-ACC oxidase (ACO) and ACC synthase (ACS), without the toxicity concerns of chemical inhibitors. We built a computational pipeline using three independent docking engines: ITD DOCK (Graphics Processing Unit [GPU]-accelerated, Assisted Model Building with Energy Refinement [AMBER] force fields), HDOCK Lite (knowledge-based geometric potentials), and HADDOCK3 (semi-flexible refinement), screened against large aptamer libraries. Applied to ACC oxidase with 9813 aptamers, engine scores were largely complementary, with all pairwise correlations statistically non-significant on the full dataset (Spearman |ρ|≤0.013, p>0.20 for all pairs, n=9813). A Random Forest on sequence-only features failed to predict docking scores (R2=-0.012, Root Mean Square Error [RMSE] = 21.79 kcal/mol), while a positive-control model on HADDOCK3 energy components achieved R2=0.991, confirming that predictive information only becomes available after docking. A preliminary pilot screening against ACC synthase (n=97 valid complexes) suggested pipeline generalizability to a second structurally distinct target and identified two putative dual-active candidates, pending confirmation against the full library. The complete workflow is automated in AptaDock, a standalone desktop application.
DOI: 10.3390/ijms27135947
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,499
OpenAlex ID: W7167054957