The identification of proteins is essential in many areas of proteomics, such as determining the absence or presence of an expected protein in a sample of interest, identifying a protein responsible for a biochemical activity in an isolated protein fraction, or identifying an unknown protein in a biological sample.
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Mass spectrometry or affinity-based techniques may be appropriate in some cases, but these techniques can lead to significant challenges when the protein of interest is unknown, with specific particular amino acid sequences or reliable detection of post-translational modifications (PTMs).
To overcome these challenges using direct sequencing and to offer more accessible tools for protein science discovery, Quantum-Si has integrated single-molecule sequencing output with automated cloud-based algorithms to provide proteome-wide mapping of sequencing data for the accurate identification of proteins.
Connecting proteolyzed peptides with their corresponding antecedent proteins is an impressive ambition in bottom-up proteomics, which depends on the digestion of intact proteins.
In standard peptide-centric methods, peptides are fractionated, and their spectra are subsequently matched with a database of simulated spectra. This database is produced using in silico protein digestion.
However, the explicit, proteome-wide mapping of peptides is restricted by various confounding factors. For instance, unanticipated or missed cleavages and post-translational modifications can result in the search algorithm failing to detect peptides.1
Single-molecule protein sequencing provides an alternative technique for identifying proteins using the kinetic signature of binding between recognizers and N-terminal amino acids.
This method delivers the required peptide-level resolution to distinguish peptides with similar sequences or physicochemical properties.
Quantum-Si has advanced the technology for mapping the proteome by developing cloud-based software that automatically maps sequencing data to the human proteome to identify proteins.
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References and further reading
Gessulat, S. et al. Prosit: proteome-wide prediction of peptide tandem mass spectra by deep learning. Nat. Methods. 2019, 16, 509–518
About Quantum-SI
Inspired by Ion Torrent’s success at shrinking next-generation sequencing technology into a benchtop instrument, Jonathan Rothberg founded Quantum-Si™ to bring the same semiconductor technology to protein sequencing with the launch of the Platinum™ Next-Generation Protein Sequencer.
That was in Guilford, CT, back in 2013. Fast forward to today and we now have over 1,000 patents issued and applications pending, plus a groundbreaking single-molecule protein sequencing technology platform, the Platinum.
Along the way, we solved critical challenges around sensitive and unambiguous amino acid detection, blending biology, chemistry, and semiconductor technology to help biologists see what other approaches cannot deliver. We also set the stage for a revolution in how scientists understand biology and build new treatments for disease by making single molecule protein sequencing accessible to every lab everywhere.
We are now entering a new phase of our development as a company. Starting with an initial public offering in June 2021 (QSI on the NASDAQ) and continuing with a new product development and operations facility in San Diego, CA, in 2022, we have entered a period of rapid growth. Through this expansion, we will be able to fuel a new era of biology, the post-genomic era, where biologists accelerate basic scientific insight and biomedical advances through the power of next generation protein sequencing.
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