Introduction
Mass spectrometry (MS) is a commonly used, high-throughput tool for studying proteins. The procedure of MS-based
protein identification involves digesting proteins into peptides, which are then separated, fragmented, ionised, and
captured by mass spectrometers. Proteins are finally identified from the peaks of the captured mass spectra using
computational methods, where each peak theoretically represents a peptide fragment ion. Existing Mass spectrometry
based protein identification methods can be categorised into 2 approaches: the database search approach and the de
novo sequencing approach. The database search approach identifies proteins by generating theoretical spectra in
silico from a given protein sequence database and comparing experimental spectra with the theoretical ones to find
the closest matches. The de novo sequencing approach identifies proteins by extracting protein sequence information
directly from the spectrum peaks derived from peptide fragment ions without recourse to any protein database.
Fig.
1. Brief overview of the NovoDB identification method. (Wang P H, et al., 2013)
Our Services
Mass spectrometry-based protein identification is a very challenging task. Creative Proteomics has
developed a new integrative protein identification method which can integrate de novo sequencing more efficiently
into database searching. Our scientists firstly infer partial peptide sequences, known as tags, directly from tandem
spectra through de novo sequencing, and then puts these sequences into a database search to find a close peptide
match.
At Creative Proteomics, we developed the following complete set of protein identification workflows,
including spectrum preprocessing, sequence tag inference, sequence tag query, spectrum ion match, and final scoring.
Ⅰ. Our scientists use two versions of the peak intensities to preprocess the spectra and normalise the peak
intensities, and peak into five regions based on the mass to charge ratio and utilises this information in the
sequence tag inference.
Ⅱ. A number of peptide sequence tags directly are infered from the spectrum according to the above spectrum
information.
Ⅲ. After sequence tags are obtained, the next stage is to query a database to find see if matches can be
found.Spectrum ion match.
Ⅳ. Then based on the dot-product between the observed ions were matched with the theoretical ions generated in silico
from the sequence database.
Ⅴ. The target protein is identified according to the final score calculated by the bioinformatics software analysis,
and a corresponding report is given.
Advantages of Our Services
- The method incorporates a sophisticated de novo sequencing algorithm and infers the peptide sequences in a
data-driven manner. Much longer sequence tags can be inferred accurately.
- The method does not rely on finding exact sequence tag matches in the database but employs a dynamic programming
approach to better tolerate sequencing errors.
- The method employs a simple scoring model that gives more weight to the de novo sequencing. generally our method
is able to identify more proteins at the same false discovery rate (FDR) when compared to other methods.
Thanks to our powerful mass spectrometry sequencing platform, Creative Proteomics, as a first-class
protein sequencing service provider, is committed to providing global customers with all protein mass spectrometry
identification services to accelerate their project research journey. Our services guarantees accurate and reliable
results, at quick turnaround time! Please contact us If you are interested
in our services.
References
- Wang P, Wilson S R. (2013) Mass spectrometry-based protein identification by integrating de novo sequencing with
database searching. BMC Bioinformatics. 14 Suppl 2(Suppl 2):S24.
- Johnson R S., Taylor J A. (2002) Searching sequence databases via de novo peptide sequencing by tandem mass
spectrometry. Molecular biotechnology. 22(3):301-315.
