Machine Learning-Enhanced Solid-State Nanopore Analysis for Single-Molecule Discrimination of Albumin and Aptamer-Bound Glycated Albumin Complexes
4 affiliations
- 1National Science and Technology Development Agency (NSTDA) , , Thailand Science Park , ,
- 2University of Strathclyde, Technology and Innovation Centre , , 99 George Street , ,
- 3Vidyasirimedhi Institute of Science and Technology (VISTEC) , , ,
- 4Mahidol University , , ,
Abstract
Discriminating glycated human serum albumin from unmodified albumin and aptamer-bound complexes at the single-molecule level is a sensing challenge unmet by conventional bulk analytical methods, which cannot resolve individual protein modification states. Here, we present a proof-of-concept solid-state nanopore platform combined with aptamer recognition and supervised machine learning for single-event discrimination of human serum albumin (HSA), glycated HSA (GHSA), and aptamer-bound GHSA (Apt+GHSA). Using a 14 nm silicon nitride nanopore, synthetic HSA, GHSA, and Apt+GHSA produced distinguishable ionic current blockade signatures, while dwell times remained similar. Serum-derived HSA showed broader, heterogeneous blockade distributions consistent with mixed albumin proteoforms; aptamer treatment generated a higher-blockade population consistent with Apt+GHSA-like events. A modified fluorescence aptamer assay estimated serum glycated albumin content at approximately 30%. To resolve overlapping signatures, five signal-derived features were extracted from individual translocation events and used to train supervised machine learning classifiers. Random Forest and Gradient Boosting achieved the highest three-class test accuracy of 94.50% for HSA, Apt+GHSA, and serum-derived HSA; Gradient Boosting achieved the highest macroaverage AUC of 0.9915. Applied to aptamer-treated serum-derived HSA, Gradient Boosting classified 54.6% of events as Apt+GHSA-like, reflecting aptamer-responsive nanopore signatures rather than a direct quantification of the glycated albumin percentage. This study establishes a nanopore–aptamer–machine learning framework for resolving albumin modification and binding states in biologically derived samples.
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BibTeX
@article{pitakjakpipop2026machine,
title = {{Machine Learning-Enhanced Solid-State Nanopore Analysis for Single-Molecule Discrimination of Albumin and Aptamer-Bound Glycated Albumin Complexes}},
author = {Harit Pitakjakpipop and Ibrar Alam and Kiatnida Treerattrakoon and Warittha Thongkham and Kawin Loha and Thanaya Saeyang and Jarunee Vanichtanankul and Itthi Chatnuntawech and Surat Teerapittayanon and Rawisara Tuncharoen and Phattaraporn Pongsalee and Thanapun Thanomputsa and Poramin Boonbanjong and Nuankanya Sathirapongsasuti and Thitikorn Boonkoom and Deanpen Japrung},
journal = {ACS Omega},
year = {2026},
month = oct,
doi = {10.1021/acsomega.6c09079},
url = {https://pubs.acs.org/acsodf/article/doi/10.1021/acsomega.6c09079/5460396/Machine-Learning-Enhanced-Solid-State-Nanopore},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - JOUR
TI - Machine Learning-Enhanced Solid-State Nanopore Analysis for Single-Molecule Discrimination of Albumin and Aptamer-Bound Glycated Albumin Complexes
AU - Harit Pitakjakpipop
AU - Ibrar Alam
AU - Kiatnida Treerattrakoon
AU - Warittha Thongkham
AU - Kawin Loha
AU - Thanaya Saeyang
AU - Jarunee Vanichtanankul
AU - Itthi Chatnuntawech
AU - Surat Teerapittayanon
AU - Rawisara Tuncharoen
AU - Phattaraporn Pongsalee
AU - Thanapun Thanomputsa
AU - Poramin Boonbanjong
AU - Nuankanya Sathirapongsasuti
AU - Thitikorn Boonkoom
AU - Deanpen Japrung
PY - 2026
DA - 2026/10/28
JO - ACS Omega
DO - 10.1021/acsomega.6c09079
UR - https://pubs.acs.org/acsodf/article/doi/10.1021/acsomega.6c09079/5460396/Machine-Learning-Enhanced-Solid-State-Nanopore
AB - Discriminating glycated human serum albumin from unmodified albumin and aptamer-bound complexes at the single-molecule level is a sensing challenge unmet by conventional bulk analytical methods, which cannot resolve individual protein modification states. Here, we present a proof-of-concept solid-state nanopore platform combined with aptamer recognition and supervised machine learning for single-event discrimination of human serum albumin (HSA), glycated HSA (GHSA), and aptamer-bound GHSA (Apt+GHSA). Using a 14 nm silicon nitride nanopore, synthetic HSA, GHSA, and Apt+GHSA produced distinguishable ionic current blockade signatures, while dwell times remained similar. Serum-derived HSA showed broader, heterogeneous blockade distributions consistent with mixed albumin proteoforms; aptamer treatment generated a higher-blockade population consistent with Apt+GHSA-like events. A modified fluorescence aptamer assay estimated serum glycated albumin content at approximately 30%. To resolve overlapping signatures, five signal-derived features were extracted from individual translocation events and used to train supervised machine learning classifiers. Random Forest and Gradient Boosting achieved the highest three-class test accuracy of 94.50% for HSA, Apt+GHSA, and serum-derived HSA; Gradient Boosting achieved the highest macroaverage AUC of 0.9915. Applied to aptamer-treated serum-derived HSA, Gradient Boosting classified 54.6% of events as Apt+GHSA-like, reflecting aptamer-responsive nanopore signatures rather than a direct quantification of the glycated albumin percentage. This study establishes a nanopore–aptamer–machine learning framework for resolving albumin modification and binding states in biologically derived samples.
SN - 2470-1343
ER -