RamanProSeq: Plasmonic nanopore technologies for automatic manipulation and Raman spectroscopic sequencing of single proteins

The RamanProSeq project aims to develop a label-free optical single-molecule protein sequencing technology capable of reading protein primary sequence at single-amino-acid resolution with high accuracy (>99.9%). The proposed technology will revolutionise the field of proteomics towards personalized medicine and early diagnosis of diseases.

The project will be based on the partners' approaches: ultrafast Raman spectroscopy with 1 microsecond time resolution and plasmonic nanopore-enhanced Raman sensors that have demonstrated single-molecule identification of 20 proteogenic amino acids [1], their post-translational modifications [2,3] and discrimination of single amino acid residues within single peptides [4]. Such achievement has addressed the long-standing challenge of current single-molecule technologies that cannot detect all 20 proteinogenic amino acids.

Based on these approaches, the RamanProSeq project will integrate solid-state nanopores with a high-speed Raman detection system [5], an automated control system, computer simulations, and advanced Raman-based bioinformatics. The output of the project will be a novel nanopore-based device that will be used to sequence individual proteins at low concentrations and with high accuracy. This approach offers unprecedented accuracy in understanding protein structures and functions, with applications not only in medicine but also in agriculture and various areas of biological research.

Related works:

1. Zhao, Yingqi, et al., Label-Free Optical Analysis of Biomolecules in Solid-State Nanopores: Toward Single-Molecule Protein Sequencing, ACS Photonics, 2022, 9 (3), 730-742.
2. Zhao, Yingqi, et al. "Single-Molecule SERS Discrimination of Proline from Hydroxyproline Assisted by a Deep Learning Model." Nano Letters 25.18 (2025): 7499-7506.
3. Yaltaye, Mulusew W., et al. "Single-Molecule SERS Detection of Phosphorylation in Serine and Tyrosine Using Deep Learning-Assisted Plasmonic Nanopore." The Journal of Physical Chemistry Letters 16.33 (2025): 8418-8426.
4. J.A. Huang*et al., Multiplex characterization of single amino acid residue in polypeptide by single SERS hot spot, Angewandte Chemie International Edition, 59, 11423-11431 (2020).
5. Khozeymeh Sarbishe, Foroogh, et al. "High‐Speed Raman Readout of Single Polypeptides via Plasmonic Nanopores." Advanced Materials 37.39 (2025): 2504436.