Ligand conformational entropy as a regime-switching descriptor of the electrostatic-uptake relationship in nanocarrier-immune cell interactions.

Authors

Quynh Hoa Truong, Xuan Khanh Truong

Published in

PloS one. Volume 21. Issue 9. Pages e0358239. Epub Sep 18, 2026.

Abstract

Surface charge is widely regarded as a primary determinant of nanoparticle-immune cell interactions, yet nanocarriers with similar physicochemical profiles often exhibit markedly different biological outcomes. Here, we show that ligand conformational diversity, quantified through an information-theoretic descriptor (Hligand), is strongly associated with a regime transition in the relationship between surface charge and cellular uptake. Analysis of 105 gold nanoparticles from a benchmark protein corona dataset identifies a critical threshold at Hligand*≈1.76 bits (F = 47.3, p < 10-14): below this value, zeta potential is positively associated with uptake (r = +0.70), whereas above it the relationship reverses (r=-0.58), consistent with steric shielding by flexible surface ligands. Entropy-derived descriptors improve predictive performance across multiple model classes (ΔR2 up to +0.118), with gains concentrated in high-entropy regimes where physicochemical descriptors alone are insufficient. An analytical competition model captures this transition in a compact form (R2 = 0.550), with 2H*≈3.4 effective microstates marking a numerical balance point between electrostatic and steric contributions. Cross-dataset validation on 652 multi-material nanoparticles supports transferability of the descriptor framework (ΔR2=+0.181). We emphasize that Hligand is a constructed information-theoretic descriptor rather than a thermodynamic entropy, and that all findings are derived from retrospective analysis of in vitro datasets and not from controlled experimental manipulation. The identified threshold therefore represents a reproducible statistical pattern and a testable hypothesis for how ligand flexibility modulates interaction regimes. This framework provides a computable basis for organizing nanocarrier design hypotheses and motivates prospective validation in more complex carrier systems.

PMID:
42758725
Bibliographic data and abstract were imported from PubMed on 19 Sep 2026.

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