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A Protocol for Computer-Based Protein Structure and Function Prediction

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Protein Inference.

Zengyou He¹, Ting Huang², Can Zhao³

¹School of Software, Dalian University of Technology, Dalian, China. zyhe@dlut.edu.cn.

Advances in Experimental Medicine and Biology

|December 16, 2016

Summary

This summary is machine-generated.

Protein inference is crucial for identifying proteins from mass spectrometry data. This chapter introduces the protein inference problem and summarizes current methods for this essential bioinformatics task.

Keywords:

Protein identification Protein inference

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Area of Science:

Proteomics
Bioinformatics
Computational Biology

Background:

Protein identification from tandem mass spectrometry relies on accurate peptide identification.
The transformation of identified peptides into a protein list is a critical step known as protein inference.
Existing methods for protein inference vary in their algorithmic approaches and underlying assumptions.

Purpose of the Study:

To introduce the fundamental problem of protein inference in proteomics.
To provide a concise overview of the diverse protein inference strategies available in the scientific literature.
To serve as a foundational resource for researchers entering the field of protein identification.

Main Methods:

Review of existing literature on protein inference algorithms.
Categorization and summarization of common protein inference approaches.
Discussion of the challenges and considerations in peptide-to-protein mapping.

Main Results:

The chapter outlines the core challenge of distinguishing true protein identifications from false positives.
It highlights different algorithmic strategies, including those based on parsimony principles and statistical models.
A summary of key methods provides a landscape of current practices in the field.

Conclusions:

Protein inference is a pivotal step in mass spectrometry-based proteomics.
Understanding the spectrum of existing methods is essential for selecting appropriate tools.
This work provides a foundational overview for navigating the complexities of protein inference.