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Optimizing Mass Spectrometry Data with ProteoAnnotator

Why ProteoAnnotator is Essential for Your Research

Mass spectrometry (MS) based proteomics have achieved a near-complete proteome coverage in humans and in several other organisms, producing a wealth of information stored in databases and bioinformatics resources. Recent implementation of selected/multiple reaction monitoring (SRM/MRM) technology in targeted proteomics introduced the possibility of quantitatively follow-up specific protein targets in a hypothesis-driven experiment. In contrast to immunoaffinity-based workflows typically used in biological and clinical research for protein quantification, SRM/MRM is characterized by high selectivity, large capacity for multiplexing (approx. 200 proteins per analysis) and rapid, cost-effective transition from assay development to deployment. The concept of SRM/MRM utilizes triple quadrupole (QqQ) mass analyzer to provide inherent reproducibility, unparalleled sensitivity and selectivity to efficiently differentiate isoforms, post-translational modifications and mutated forms of proteins. SRM-like targeted acquisitions such as parallel reaction monitoring (PRM) are pioneered on high resolution/accurate mass (HR/AM) platforms based on the quadrupole-orbitrap (Q-orbitrap) mass spectrometer. The expansion of HR/AM also caused development in data independent acquisition (DIA). This review presents a step-by-step tutorial on development of SRM/MRM protein assay intended for researchers without prior experience in proteomics. We discus practical aspects of SRM-based quantitative proteomics workflow, summarize milestones in basic biological and medical research as well as recent trends and emerging techniques.

Mass spectrometry (MS) data is vital in analyzing proteins and genomes. ProteoAnnotator streamlines the process of integrating MS spectra with genomic data. Using an automated two-step pipeline, it allows you to:


  • Verify that predicted genes are translated into proteins.
  • Accurately identify peptides from your samples.
  • Filter out irrelevant spectra to focus on the key results.

Main Genome Annotation Steps

Genome annotation involves several key steps, such as repeat masking, protein homology prediction, and the alignment of open reading frames (ORFs) from other species. These steps often depend on data from previous genome assemblies and annotations. If these earlier annotations contain errors, those mistakes can be carried over, leading to reduced accuracy in the final annotation. This transfer of errors can affect the overall precision, making it important to carefully validate each step with reliable data to ensure better accuracy.

Applications in Various Research Fields


1. Genomic Research

ProteoAnnotator supports genomic research by validating whether predicted gene sequences are accurately expressed as proteins. This process is vital for ensuring the accuracy of genome annotations, which are essential for downstream analyses such as gene function identification and the study of genomic variability. By confirming gene-to-protein translation through mass spectrometry data, ProteoAnnotator helps researchers refine their genomic models and improve the overall quality of the genome sequence.


2. Biotechnology

In biotechnology, ProteoAnnotator plays a crucial role in optimizing recombinant protein production. It aids in ensuring that the proteins produced in biological systems match the expected peptide sequences, which is key for quality control in biomanufacturing. The tool is also useful for identifying new protein targets, whether for therapeutic applications or for understanding the molecular mechanisms underlying biological processes. The ability to identify and accurately map peptides enhances the development of novel biotechnological applications, from enzyme production to biosensors.


3. Proteomics

Proteomics research involves the large-scale study of proteins, which requires highly accurate identification and characterization of proteins in complex biological samples. ProteoAnnotator simplifies this process by filtering out irrelevant mass spectrometry data, allowing researchers to focus on the most relevant peptides and proteins. It helps ensure high-quality results in protein identification, including the detection of post-translational modifications (PTMs), which are essential for understanding protein functionality, interaction networks, and cellular pathways.

Start Using ProteoAnnotator Today

Convert your MS data into useful insights with ProteoAnnotator. Simplify your workflow, improve result accuracy, and speed up your research.


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