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Peptide Identification Using Tandem Mass Spectrometry

Peptide Identification Using Tandem Mass Spectrometry

Tandem mass spectrometry, also known as MS/MS or MS2, is an analytical technique that employs two mass analyzers. Essentially it is a series of mass spectrometers that helps isolate a particular biomolecule and then helps study its chemical properties.
This technique helps gather information regarding the protein from which the peptide was obtained and to study the peptides’ amino acid sequence. Identifying peptides from a complex mixture is an important component of the growing field of...

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Resolving Affinity Purified Protein Complexes by Blue Native PAGE and Protein Correlation Profiling

Resolving Affinity Purified Protein Complexes by Blue Native PAGE and Protein Correlation Profiling

Published on: April 1, 2017

Protein identification problem from a Bayesian point of view.

Yong Fuga Li¹, Randy J Arnold², Predrag Radivojac¹

¹School of Informatics and Computing, Indiana University, Bloomington, IN 47405, USA.

Statistics and Its Interface

|April 25, 2014

Summary

This summary is machine-generated.

We developed a Bayesian framework for peptide and protein identification in proteomics. This unified approach integrates database searching and de novo sequencing, improving accuracy for various prior information types.

Keywords:

Bayesian methods Mass spectrometry Protein identification Shotgun proteomics

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Area of Science:

Proteomics
Computational Biology
Bioinformatics

Background:

Peptide and protein identification are crucial in proteomics.
Current methods like database searching and de novo sequencing have limitations.
Integrating diverse prior information can enhance identification accuracy.

Purpose of the Study:

To present a generic Bayesian framework for peptide and protein identification.
To provide a unified interpretation of existing peptide identification approaches.
To incorporate various types of prior information into a probabilistic model.

Main Methods:

Developed a generic Bayesian framework.
Utilized probabilistic graphical models.
Incorporated diverse prior distributions (e.g., protein sequences, abundances, precursor masses, retention time, detectability).

Main Results:

The framework unifies database searching and de novo sequencing.
It allows modeling of various prior biological information.
Theoretically discussed applications for identifying modified peptides.

Conclusions:

The Bayesian framework offers a flexible and unified approach to peptide and protein identification.
It enhances the ability to incorporate prior knowledge for improved accuracy.
Potential applications include identifying peptides with mutations and post-translational modifications.