Detection of Unknown Amino Acid Substitutions Using Error-Tolerant Database Search

Area of Science:

• Proteomics
• Mass Spectrometry
• Bioinformatics

Background:

• Mass spectrometry (MS)-based variant detection is a developing field.
• Current methods for identifying single-amino acid variants (SAAVs) in MS data rely on genomic or transcript databases, requiring additional data.
• These supplementary data requirements can limit the scope and efficiency of SAAV identification.

Purpose of the Study:

• To present a novel workflow for SAAV detection in MS data using only standard UniProt databases.
• To demonstrate the feasibility of identifying SAAVs without relying on external genomic or transcriptomic information.
• To avoid unnecessary search space extensions and provide an unbiased view of the proteomic data.

Main Methods:

• Utilized an error-tolerant peptide search engine (BICEPS) for variant identification.
• Employed standard UniProt databases, eliminating the need for custom target databases.
• Implemented a two-step identification process: initial variant discovery followed by verification and protein-level aggregation.

Main Results:

• Successfully identified SAAVs using only standard UniProt databases, bypassing the need for additional genomic or transcript data.
• The error-tolerant search approach avoided redundant search space expansions.
• The workflow provided an unbiased assessment of potential variants present in the MS data.

Conclusions:

• The presented method enables efficient and unbiased SAAV detection in MS data.
• This approach simplifies the variant identification pipeline by removing the dependency on external databases.
• The findings support the broader application of MS for variant discovery without pre-existing genetic or transcriptomic information.