American Quantum Silicon Company (Quantum-Si, Inc) Brian D. Reed and other researchers developed new single-molecular protein sequencing technology , high-sensitivity protein/peptide sequencing, parsing protein mutations/modifications and components in protein mixtures (1).
This technology is mainly based on the -terminal amino acid of the protein/peptide to be tested based on the -terminal amino acid of the protein/peptide to be tested, as well as the enzyme (Aminopeptidases) (Aminopeptidases) that can cleave amino acid from the end of the protein. Characteristic fluorescence mode of when fluorescent recognizers protein binds to terminal amino acids (fluorescence lifetime, intensity (to distinguish fluorescence type) and binding kinetic "fingerprint", etc.) can be captured by imaging system, and then locates its amino acid type ; subsequent cleavage of end with protease can further recognize subsequent amino acid (see the figure below) (1).
Figure 1: Single-molecular protein sequencing technology based on terminal amino acid recognition (1)
Figure 2: Recognizer protein recognizes characteristic fluorescence patterns of different terminal amino acids (1)
This work was published in Science on October 13, 2022; researchers said that the development of more specifically recognized recognizer proteins and terminal cleavage protease are expected to achieve high sensitivity protein de novo sequencing to help protein research (1).
Comment(s):
has quite a potential technology. In addition to the authors' mentions that developing better recognizer proteins and proteases, training suitable machine learning model (such as building a framework for generative adversarial network (GAN)) , and in conjunction with mass spectrometry data , it can also improve the parsing ability of proteome in complex biological samples (2).
References:
1. B. D. Reed et al., Real-time dynamic single-molecule protein sequencing on an integrated semiconductor device. Science (80-. ). 378, 186–192 (2022).
2. I. J. Goodfellow et al., Generative Adversarial Networks. arXiv. 11046 LNCS, 1–9 (2014).
Original link:
https://www.science.org/doi/10.1126/science.abo7651
