Work

Raman and SERS analytical platform with machine learning for classifying wild type p53 and hotspot mutants R175H and R273H

Year: 2026

Type: article

Abstract: The development of analytical approaches capable of detecting subtle protein conformational changes is of significant interest in biomedicine, particularly for disease diagnostics and biomolecular characterization. In this work, the tumor suppressor p53 protein was selected as a model system to evaluate an analytical platform based on Raman spectroscopy combined with surface-enhanced Raman spectroscopy (SERS) and supervised machine learning for the classification of closely related protein variants. Label-free Raman and SERS spectra of recombinant wild-type p53 and its hotspot mutants R175H and R273H were acquired on bare Al and Al coated with gold nanospheres, gold nanorods, and silver nanoparticles. Principal Component Analysis (PCA) revealed distinct spectral fingerprints among the p53 variants, with relevant contributions in the amide III and CH stretching regions, indicating subtle conformational differences. Supervised classification was performed using several machine learning algorithms under a nested, group-disjoint cross-validation protocol (holding out entire acquisition lines) to account for within-line spectral correlation. Among tested models, a linear Support Vector Machine (Linear-SVM) achieved the highest performance, reaching an accuracy of 92.9 ± 6.9% on AuNS@Al substrates. These results demonstrate that the integration of optimized nanostructured SERS substrates, Raman spectroscopy, and machine learning constitutes a robust analytical platform for the structural classification of wild-type and mutant p53 proteins. The proposed approach provides a versatile framework for studying conformational changes in biomolecules of biomedical relevance and supports its potential application in cancer biomarker detection and protein misfolding studies. • Raman/SERS–ML platform enables label-free discrimination of closely related proteins. • Wild-type and mutant p53 (R175H, R273H) identified using ~5 μL sample volume. • PCA reveals vibrational markers linked to p53 conformational differences. • Group-disjoint nested cross-validation ensures unbiased classification performance. • AuNS@Al with Linear-SVM achieves robust p53 variant discrimination (92.9% accuracy).

Source: Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy

DOI: 10.1016/j.saa.2026.127522

Authors: Karen Hernández-Vidales, Juan A. Muñoz Castillo, Selene R. Islas, Andrés Rodríguez-Rodríguez, Vanesa Olivares-Illana, José M. Saniger

Institutions: Universidad Nacional Autónoma de México, Autonomous University of San Luis Potosí

Language: English

Topic: Spectroscopy Techniques in Biomedical and Chemical Research

Subfield: Biophysics

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: hybrid

Estimated APC paid: $3,750