Proteins are the executors of life and are composed of twenty proteinogenic amino acids. Compared with the rapid development of DNA sequencing technology, the development of highly sensitive and high-throughput protein sequencing technology is lagging behind. Currently, only Edman degradation and mass spectrometry (MS) have the capacity to sequence peptides. However, the detection length of Edman degradation is limited. The dynamic range and detection limit of MS pose challenges when analyzing of low-abundance proteins and identifying new biomarkers. Single-molecule protein sequencing, which provides greater sensitivity and accuracy, is expected to contribute to the advancement of single-cell proteomics. Single-molecule protein sequencing has been recognized by Nature as one of the seven new technologies that may have a significant impact on scientific innovation in 2023.
Proteins are composed of 20 amino acids, which is more diverse than the four nucleotide building blocks found in nucleic acids. Regardless of the readout method, the detection of 20 distinguishable signals is a difficult task. The accurate identification of post-translational modifications on proteins also requires high detection resolution.
Recently, Professor Shuo Huang's group constructed an engineered Mycobacterium smegmatis porin A (MspA) nanopore to detect all proteinogenic amino acids and their post-translational modifications. A nickel-nitrilotriacetic acid (Ni-NTA) aptamer was introduced to the constriction region of the nanopore, enabling direct detection and differentiation of all proteinogenic amino acids using coordination interactions, with an accuracy of 98.8%. This Ni-NTA modified MspA nanopore is the first nanopore in the world that can completely distinguish all 20 protein amino acids. Additionally, they also used the same nanopore to distinguish four common post-translational modified amino acids.
Fig.1：Discrimination of 20 amino acids using Ni-NTA modified nanopore.
Afterwards, this approach was applied to the analysis of amino acid composition of peptides. The authors used aminopeptidase to digest the peptide into individual amino acids. Then, the amino acids were detected by engineered nanopore. Machine learning was used to automatically identify amino acid identities. Ultimately, the amino acid map of the original peptide sequence was reconstructed.
Fig.2：Identification of proteolytically cleaved amino acids.
This nanopore sensor can be also used to discriminate common post-translational modifications, including phosphorylation, methylation, glycosylation, and acetylation. This achievement is a significant milestone in nanopore protein sequencing and post-translational modification analysis.
Fig.3：Identification of 20 proteinogenic amino acids and four modified amino acids by machine learning.
The related paper entitled “Unambiguous discrimination of all 20 proteinogenic amino acids and their modifications by nanopore” has been published on Nature Methods on September 25th, 2023 (DOI: https://doi.org/10.1038/s41592-023-02021-8). Prof. Shuo Huang from our department is the corresponding author. Ph.D. students Kefan Wang and Shanyu Zhang from our department and Ph.D. student Xiao Zhou from School of Physics, Nanjing University are co-first authors. This project was funded by the National Key R&D Program of China (grant no. 2022YFA1304602), National Natural Science Foundation of China (grant no. 22225405 and no. 31972917), the Fundamental Research Funds for the Central Universities (grant no. 020514380257), Programs for high-level entrepreneurial and innovative talents introduction of Jiangsu Province (individual and group program), Natural Science Foundation of Jiangsu Province (grant no. BK20200009), State Key Laboratory of Analytical Chemistry for Life Science (grant no. 5431ZZXM2204) and the China Postdoctoral Science Foundation (grant no. 2021M691508 and grant no. 2022T150308).