Unknown

Dataset Information

0

Constrained de novo sequencing of conotoxins.


ABSTRACT: De novo peptide sequencing by mass spectrometry (MS) can determine the amino acid sequence of an unknown peptide without reference to a protein database. MS-based de novo sequencing assumes special importance in focused studies of families of biologically active peptides and proteins, such as hormones, toxins, and antibodies, for which amino acid sequences may be difficult to obtain through genomic methods. These protein families often exhibit sequence homology or characteristic amino acid content; yet, current de novo sequencing approaches do not take advantage of this prior knowledge and, hence, search an unnecessarily large space of possible sequences. Here, we describe an algorithm for de novo sequencing that incorporates sequence constraints into the core graph algorithm and thereby reduces the search space by many orders of magnitude. We demonstrate our algorithm in a study of cysteine-rich toxins from two cone snail species (Conus textile and Conus stercusmuscarum) and report 13 de novo and about 60 total toxins.

SUBMITTER: Bhatia S 

PROVIDER: S-EPMC3412931 | biostudies-literature | 2012 Aug

REPOSITORIES: biostudies-literature

altmetric image

Publications

Constrained de novo sequencing of conotoxins.

Bhatia Swapnil S   Kil Yong J YJ   Ueberheide Beatrix B   Chait Brian T BT   Tayo Lemmuel L   Cruz Lourdes L   Lu Bingwen B   Yates John R JR   Bern Marshall M  

Journal of proteome research 20120702 8


De novo peptide sequencing by mass spectrometry (MS) can determine the amino acid sequence of an unknown peptide without reference to a protein database. MS-based de novo sequencing assumes special importance in focused studies of families of biologically active peptides and proteins, such as hormones, toxins, and antibodies, for which amino acid sequences may be difficult to obtain through genomic methods. These protein families often exhibit sequence homology or characteristic amino acid conte  ...[more]

Similar Datasets

| S-EPMC6901291 | biostudies-literature
| S-EPMC5161715 | biostudies-literature
| S-EPMC5547637 | biostudies-literature
| S-EPMC3216106 | biostudies-literature
| S-EPMC5642715 | biostudies-literature
| S-EPMC2754211 | biostudies-literature
| S-EPMC4604512 | biostudies-literature
| S-EPMC2891972 | biostudies-literature
| S-EPMC6280873 | biostudies-other
| S-EPMC3018809 | biostudies-other