Generalized Protein Parsimony and Spectral Counting

for FunctionalEnrichment Analysis

Nathan EdwardsDepartment of Biochemistry and

Molecular & Cellular BiologyGeorgetown University Medical Center

Systems Biology

2

Structured High-Throughput

Experiments

KnowledgeDatabases

molecular biology ↕

phenotype

Systems Biology

3

KnowledgeDatabases

Structured High-Throughput

Experiments• Localization• Function• Process• Interactions• Pathway• Mutation

• Proteomics• Sequencing• Microarrays• Metabolomics

molecular biology↕

biology

molecular biology ↕

phenotype

Systems Biology

4

MathematicalModels

Structured High-Throughput

Experiments• Localization• Function• Process• Interactions• Pathway• Mutation

• Proteomics• Sequencing• Microarrays• Metabolomics

molecular biology↕

biology

KnowledgeDatabases

molecular biology ↕

phenotype

Systems Biology

5

MathematicalModels

Structured High-Throughput

Experiments• Localization• Function• Process• Interactions• Pathway• Mutation

• Proteomics• Sequencing• Microarrays• Metabolomics

molecular biology↕

biology

KnowledgeDatabasesFunctional

AnnotationEnrichment

molecular biology ↕

phenotype

Systems Biology

6

MathematicalModels

Structured High-Throughput

Experiments• Localization• Function• Process• Interactions• Pathway• Mutation

• Proteomics• Sequencing• Microarrays• Metabolomics

molecular biology↕

biology

KnowledgeDatabasesFunctional

AnnotationEnrichment

molecular biology ↕

phenotype

Systems Biology

7

MathematicalModels

Structured High-Throughput

Experiments• Localization• Function• Process• Interactions• Pathway• Mutation

• Proteomics• Sequencing• Microarrays• Metabolomics

molecular biology↕

biology

KnowledgeDatabasesFunctional

AnnotationEnrichment

Functional Annotation Enrichment In any draw, we expect:

~ 5 "evens", ~ 2 "≤ 10", etc. Each ball is equally likely Balls are independent p-value is surprise! For transcriptomics:

Genes ↔ Balls Genome ↔ Tumbler Diff. Expr. ↔ Draw Annotation ↔ "evens",…

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Draw 10 of 50!

Why not in proteomics? Double counting and false positives…

…due to traditional protein inference

Proteomics cannot see all proteins… …proteins are not equally likely to be drawn

Good relative abundance is hard… …extra chemistries, workflows, and software …missing values are particularly problematic

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In proteomics… Double counting and false positives…

Use generalized protein parsimony

Proteomics cannot see all proteins… Use identified proteins as background

Good relative abundance is hard… Model differential spectral counts directly

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Ignore some PSMs FDR filtering leaves some false PSMs Enforce strict protein inference criteria Leave some PSMs uncovered

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10%

Proteins

PSMs

Ignore some PSMs FDR filtering leaves some false PSMs Enforce strict protein inference criteria Leave some PSMs uncovered

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Proteins

PSMs

90%

Match uncovered PSMs to FDR

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Plasma membrane enrichment Pellicle enrichment of plasma membrane

Choksawangkarn et al. JPR 2013 (Fenselau Lab) Six replicate LC-MS/MS analyses each

Cell-lysate (44,861 MS/MS) Fe3O4-Al2O3 pellicle (21,871 MS/MS)

625 3-unique proteins to match 10% FDR: Lysate: 18,976 PSMs; Pellicle: 13,723 PSMs 89 proteins with significantly (< 10-5) increased counts

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Plasma membrane enrichment Na/K+ ATPase subunit alpha-1 (P05023):

Lysate: 1; Pellicle: 90; p-value: 5.2 x 10-33 Transferrin receptor protein 1 (P02786):

Lysate: 17; Pellicle: 63; p-value: 2.0 x 10-11 DAVID Bioinformatics analysis (89/625):

Plasma membrane (GO:0005886) : 29 (5.2 x 10-5) Transmembrane (SwissProtKW): 24 (1.3 x 10-6)

Transmembrane (SwissProtKW): Lysate: 524; Pellicle: 1335; p-value: 2.6 x 10-158

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A protein's PSMs rise and fall together!

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A protein's PSMs rise and fall together?

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Anomalies indicate proteoforms

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Nascent polypeptide-associated complex subunit alpha

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7.3 x 10-8

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Pyruvate kinase isozymes M1/M22.5 x 10-5

Summary Functional annotation enrichment for

proteomics too: Careful counting (generalized parsimony) Differential abundance by spectral counts

Use (multivariate-)hypergeometric model for Differential abundance by spectral counts Proteoform detection

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HER2/Neu Mouse Model of Breast Cancer Paulovich, et al. JPR, 2007 Study of normal and tumor mammary tissue by LC-

MS/MS 1.4 million MS/MS spectra

Peptide-spectrum assignments Normal samples (Nn): 161,286 (49.7%) Tumor samples (Nt): 163,068 (50.3%)

4270 proteins identified in total 2-unique generalized protein parsimony

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Distribution of p-values (Yeast)

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