Applied Bioinformatics 2025�Week 1 Session 1�Intro to Proteomics

Natalie Turner, PhD

Postdoctoral Fellow – Yates Lab

Department of Molecular Medicine

naturner@scripps.edu

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Slide credit:

John R Yates III

Patrick Garrett (Grad student, Yates lab)

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Proteins are the workhorses of the cell�DNA mRNA Proteins Metabolites

• Heteropolymers of amino acids’
• “Linear” sequence of amino acids folds into a 3D structure
• Form the Structural and Functional elements of cells and tissues
• Enzymes – catalyze reactions, transmit signals, regulate processes
• Covalent modifications regulate activities and functions
• Protein sequences are derived from genes
• genomes are sequenced

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A key determinant to understanding protein function is discovering biological roles in a context.

Genome Analysis has Revolutionized Biology

An Unexpected Game Changer for Protein Biochemistry

What is Proteomics?

• Mass spectrometry is used to identify and quantify proteins in a sample
• Traditionally qualitative – now quantitative & qualitative
• Functional vs structural
• Bottom-up
• Top-down

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This Photo by Unknown Author is licensed under CC BY-SA

https://www.ebi.ac.uk/training/online/courses/proteomics-an-introduction/what-is-proteomics/

Proteomics strategies�

• Shotgun Proteomics:
• Identifies and quantifies proteins in complex mixtures using LC-MS/MS.
• Targeted Proteomics:
• Quantifies specific proteins/peptides using SRM/MRM.
• Quantitative Proteomics:
• Measures protein abundance using label-free, SILAC, or iTRAQ methods.
• Interaction Proteomics:
• Examines protein-protein interactions using affinity purification (AP)-MS and cross-linking (XL)-MS.
• Functional Proteomics:
• Analyzes protein activities and PTMs.

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• Structural Proteomics:
• Determines the three-dimensional structures of proteins and protein complexes to understand their functions and interactions.
• Top-Down Proteomics:
• Analyzes intact proteins and PTMs.
• Bottom-Up Proteomics:
• Digests proteins into peptides before analysis.
• Clinical Proteomics:
• Identifies biomarkers and disease-associated proteins.
• Metaproteomics:
• Studies protein composition in microbial communities.

Proteomics Applications

• Disease Biomarker Discovery: Identifying proteins associated with diseases for early diagnosis and prognosis.
• Drug Target Identification: Finding proteins that can be targeted by new drugs.
• Understanding Disease Mechanisms: Investigating how diseases affect protein expression and function.
• Personalized Medicine: Tailoring treatments based on individual protein profiles.
• Therapeutic Protein Production: Optimizing the production of therapeutic proteins, such as insulin and monoclonal antibodies.
• Vaccine Development: Identifying protein antigens for vaccine design.
• Environmental Monitoring: Analyzing protein changes in organisms exposed to pollutants.
• Agricultural Improvements: Studying plant and animal proteomes to enhance crop and livestock productivity.

Mass Spectrometers Made Rapid Identification of Proteins Easy

Ionization

Mass Analysis, e.g.

Ion Separation

Ion Detection

The three basic elements of an MS

Conversion of gases, liquids, solids

to the gas phase as ions

Separation of ions based

on mass to charge, m/z

Conversion of the separated

ions to an electrical signal

Bottom-up proteomics – enzymatic digest

Peptide Fragmentation

mass⁺ b-ions y-ions mass⁺

88.1 S PAFDSIMAETLK 1323.6

185.2 SP AFDSIMAETLK 1226.4

256.3 SPA FDSIMAETLK 1155.4

403.5 SPAF DSIMAETLK 1008.2

518.5 SPAFD SIMAETLK 893.1

605.6 SPAFDS IMAETLK 806.0

718.8 SPAFDSI MAETLK 692.3

850.0 SPAFDSIM AETLK 561.7

921.1 SPAFDSIMA ETLK 490.6

1050.2 SPAFDSIMAE TLK 361.5

1151.3 SPAFDSIMAET LK 260.4

1264.4 SPAFDSIMAETL K 147.2

Bottom-up database search

AELTVDPQGALAIRQLASVILKQYVETHWCAQSEKFRPPETTERAKIVIRELLPNGLRESISKVRSSVAYAVSAIAHWDWPEAWPQLFNLLMEMLVSGDLNAVHGAMRVLTEFTREVTDT

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QYVETHWCAQSEKFR

FRPPETTER

PPETTER

PPETTERAK

IVIRELLPNGLR

ELLPNGLR

ELLPNGLRESISK

...

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Sample

Search

Protein

Peptide

Spectra

Search Result

Xcorr: 3.5

Matched Intensity: 17.77 %

Protein Identification Using Data Dependent or Independent MS/MS

Venable et al Nature Methods 1, 39-45, 2004.

Genome Sequences Allow Fast and Accurate Lookup of Information

Schmidt, A., Forne, I. & Imhof, A. Bioinformatic analysis of proteomics data. BMC Syst Biol 8 (Suppl 2), S3 (2014). https://doi.org/10.1186/1752-0509-8-S2-S3

Module overview

Getting the notebooks

• In Github, click "New Repository", then "Import a repository"

Getting the notebooks

• Source repository: https://github.com/SuLab/Applied-Bioinformatics-2025
• Name: Applied-Bioinformatics-2025
• Make it a *private* repo

Getting the notebooks

• From RStudio, create a new project from that repo
• File -> "New Project" -> "Version Control" -> "Git"

Questions?

• Open your R Notebooks

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## Week 1: Data Import and Preprocessing

###Session 1: Introduction to Data Import

Dataframe columns - Descriptive

Run = Run name associated with the sample (usually an abbreviated form of ‘File.name’

Protein.Group = Inferred proteins. Primary (citable) Uniprot Accession for all assigned proteins.

Protein.Ids = All proteins matched to the precursor in the library or, in case of library-free search, in the sequence database.

Protein.Name = Names of the proteins in the Protein.Group.

Naming convention = X_Y, where X is a mnemonic protein identification code of at most 5 alphanumeric characters, ‘_’ is a separator, Y is a mnemonic species identification code of at most 5 alphanumeric characters.

Genes = Gene names corresponding to the proteins in Protein.Group.

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Dataframe columns - Quantitative

PG.MaxLFQ - *MaxLFQ normalised quantity for the protein group, channel-specific

PG.Q.Value - Run-specific q-value for the protein group, channel-specific

Lib.PG.Q.Value - protein group q-value for the respective library entry, 'global' if the library was created by DIA-NN. In case of MBR, this applies to the library created after the first MBR pass

*Intensity determination and normalization procedure. Protein abundance profiles are assembled using the maximum possible information from MS signals.

Cox et al 2014 https://doi.org/10.1074/mcp.M113.031591

Definitions

False Discovery Rate (FDR):

The expected ratio of false positive classifications to the total number of positive classifications.

Q value:

A statistical method for estimating the false discovery rate (FDR) of a set of tests. The q-value is the minimum FDR at which a test can be considered significant. It's used to balance the number of false positives and true positives in genome-wide studies.

Adjusted P Value (P adj value):

An adjusted P value, also known as a P adj value, is a statistical measurement that corrects for multiple comparisons in hypothesis testing. It's the smallest significance level at which a comparison is considered statistically significant.

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Week 1 Session 1 (continued)

###CamprotR

Database files (FASTA files)

• Proteomics data is searched against a background database (proteome) to obtain the protein sequences used for protein identification
• Proteomes can be downloaded from Uniprot.org/ according to species (or other) in .fasta format
• Additional protein sequences can be appended to these downloaded proteomes to create a custom database, which can then be used to identify potentially problematic/unwanted proteins

The common Repository of Adventitious Proteins, cRAP (pronounced "cee-RAP")

Adventitious proteins (unintended contaminants in proteomic samples), can:

• Significantly impact the accuracy and reliability of proteomic analyses
• Lead to false positives, misidentification of proteins, and skewed quantitative results, ultimately affecting the interpretation of experimental data and the conclusions drawn from it.

CamprotR: Cambridge Centre for Proteomics

• CamprotR is an R package that enables you to download an extensive cRAP database and customise it according to your project needs.
• The sample dataset provided to you has already been searched against a database file containing cRAP, however it’s important to understand the principle and basis for identifying and excluding problematic proteins from proteomics search results.

Refer to the camprotR exercise in your R notebooks and follow along with the tutorial contained therein.