Protein Sequence Databases,

Peptides to Proteins, and

Statistical Significance

Protein Sequence Databases,

Peptides to Proteins, and

Statistical Significance

Nathan EdwardsDepartment of Biochemistry and Mol. & Cell. BiologyGeorgetown University Medical Center

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Protein Sequence Databases

• Link between mass spectra and proteins• A protein’s amino-acid sequence provides

a basis for interpreting• Enzymatic digestion• Separation protocols• Fragmentation• Peptide ion masses

• We must interpret database information as carefully as mass spectra.

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More than sequence…

Protein sequence databases provide much more than sequence:

• Names• Descriptions• Facts• Predictions• Links to other information sources

Protein databases provide a link to the current state of our understanding about a protein.

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Much more than sequence

Names• Accession, Name, Description

Biological Source• Organism, Source, Taxonomy

LiteratureFunction

• Biological process, molecular function, cellular component

• Known and predictedFeatures

• Polymorphism, Isoforms, PTMs, DomainsDerived Data

• Molecular weight, pI

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Database types

Curated• Swiss-Prot• UniProt• RefSeq NP

Translated• TrEMBL• RefSeq XP, ZP

Omnibus• NCBI’s nr• MSDB• IPI

Other• PDB• HPRD• EST• Genomic

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SwissProt

• From ExPASy • Expert Protein Analysis System• Swiss Institute of Bioinformatics

• ~ 515,000 protein sequence “entries”• ~ 12,000 species represented• ~ 20,000 Human proteins• Highly curated• Minimal redundancy• Part of UniProt Consortium

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TrEMBL

• Translated EMBL nucleotide sequences• European Molecular Biology Laboratory

• European Bioinformatics Institute (EBI)• Computer annotated • Only sequences absent from SwissProt• ~ 10.5 M protein sequence “entries”• ~ 230,000 species• ~ 75,000 Human proteins• Part of UniProt Consortium

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UniProt

• Universal Protein Resource• Combination of sequences from

• Swiss-Prot• TrEMBL

• Mixture of highly curated/reviewed (SwissProt) and computer annotation (TrEMBL)

• “Similar sequence” clusters are available• 50%, 90%, 100% sequence similarity

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RefSeq

• Reference Sequence• From NCBI (National Center for

Biotechnology Information), NLM, NIH• Integrated genomic, transcript, and

protein sequences.• Varying levels of curation

• Reviewed, Validated, …, Predicted, …• ~ 9.7 M protein sequence “entries”

• ~ 209,000 reviewed, ~ 90,000 validated• ~ 39,000 Human proteins

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RefSeq

• Particular focus on major research organisms• Tightly integrated with genome projects.

• Curated entries: NP accessions• Predicted entries: XP accessions• Others: YP, ZP, AP

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IPI

• International Protein Index• From EBI

• For a specific species, combines• UniProt, RefSeq, Ensembl• Species specific databases: HInv-DB, VEGA, TAIR

• ~ 87,000 (from ~ 307,000 ) human protein sequence entries

• Human, mouse, rat, zebra fish, arabidopsis, chicken, cow

• Slated for closure November 2010, but still going…

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MSDB

• From the Imperial College (London)• Combines

• PIR, TrEMBL, GenBank, SwissProt• Distributed with Mascot

• …so well integrated with Mascot• ~ 3.2M protein sequence entries• “Similar sequences” suppressed

• 100% sequence similarity• Not updated since September 2006

(obsolete)

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NCBI’s nr

• “non-redundant”• Contains

• GenBank CDS translations• RefSeq Proteins• Protein Data Bank (PDB)• SwissProt, TrEMBL, PIR• Others

• “Similar sequences” suppressed• 100% sequence similarity

• ~ 10.5 M protein sequence “entries”

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Human Sequences

• Number of Human genes is believed to be between 20,000 and 25,000

SwissProt ~ 20,000

RefSeq ~ 39,000

TrEMBL ~ 75,000

IPI-HUMAN ~ 87,000

MSDB ~130,000

nr ~230,000

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DNA to Protein Sequence

Derived from http://online.itp.ucsb.edu/online/infobio01/burge

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UCSC Genome Browser

• Shows many sources of protein sequence evidence in a unified display

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Accessions

• Permanent labels• Short, machine readable• Enable precise communication• Typos render them unusable!• Each database uses a different format

• Swiss-Prot: P17947• Ensembl: ENSG00000066336• PIR: S60367; S60367• GO: GO:0003700;

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Names / IDs

• Compact mnemonic labels• Not guaranteed permanent• Require careful curation• Conceptual objects

• ALBU_HUMAN• Serum Albumin

• RT30_HUMAN• Mitochondrial 28S ribosomal protein S30

• CP3A7_HUMAN• Cytochrome P450 3A7

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Description / Name

• Free text description• Human readable• Space limited• Hard for computers to interpret!• No standard nomenclature or format• Often abused….

• COX7R_HUMAN• Cytochrome c oxidase subunit VIIa-

related protein, mitochondrial [Precursor]

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FASTA Format

• >• Accession number

• No uniform format• Multiple accessions separated by |

• One line of description• Usually pretty cryptic

• Organism of sequence?• No uniform format• Official latin name not necessarily used

• Amino-acid sequence in single-letter code• Usually spread over multiple lines.

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FASTA Format

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Organism / Species / Taxonomy

• The protein’s organism…• …or the source of the biological sample

• The most reliable sequence annotation available

• Useful only to the extent that it is correct• NCBI’s taxonomy is widely used

• Provides a standard of sorts; Heirachical• Other databases don’t necessarily keep up

• Organism specific sequence databases starting to become available.

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Organism / Species / Taxonomy

• Buffalo rat• Gunn rats• Norway rat• Rattus PC12 clone IS• Rattus norvegicus• Rattus norvegicus8• Rattus norwegicus• Rattus rattiscus

• Rattus sp.

• Rattus sp. strain Wistar• Sprague-Dawley rat• Wistar rats• brown rat• laboratory rat• rat• rats• zitter rats

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Controlled Vocabulary

• Middle ground between computers and people

• Provides precision for concepts• Searching, sorting, browsing• Concept relationships

• Vocabulary / Ontology must be established• Human curation

• Link between concept and object:• Manually curated• Automatic / Predicted

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Gene Ontology

• Hierarchical• Molecular function• Biological process• Cellular component

• Describes the vocabulary only!• Protein families provide GO association

• Not necessarily any appropriate GO category.• Not necessarily in all three hierarchies.• Sometimes general categories are used because

none of the specific categories are correct.

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Gene Ontology

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Protein Families

• Similar sequence implies similar function• Similar structure implies similar function• Common domains imply similar function

• Bootstrap up from small sets of proteins/domains with well understood characteristics

• Usually a hybrid manual / automatic approach

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Protein Families

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Protein Families

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Sequence Variants

• Protein sequence can vary due to• Polymorphism• Alternative splicing• Post-translational modification

• Sequence databases typically do not capture all versions of a protein’s sequence

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Swiss-Prot Variant Annotations

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Swiss-Prot Variant Annotations

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Omnibus Database Redundancy Elimination

• Source databases often contain the same sequences with different descriptions

• Omnibus databases keep one copy of the sequence, and • An arbitrary description, or• All descriptions, or• Particular description, based on source preference

• Good definitions can be lost, including taxonomy

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Description Elimination

• gi|12053249|emb|CAB66806.1| hypothetical protein [Homo sapiens]

• gi|46255828|gb|AAH68998.1| COMMD4 protein [Homo sapiens]

• gi|42632621|gb|AAS22242.1| COMMD4 [Homo sapiens]

• gi|21361661|ref|NP_060298.2| COMM domain containing 4 [Homo sapiens]

• gi|51316094|sp|Q9H0A8|COM4_HUMAN COMM domain containing protein 4

• gi|49065330|emb|CAG38483.1| COMMD4 [Homo sapiens]

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Peptides to Proteins

Nesvizhskii et al., Anal. Chem. 2003

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Peptides to Proteins

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Peptides to Proteins

• A peptide sequence may occur in many different protein sequences• Variants, paralogues, protein families

• Separation, digestion and ionization is not well understood

• Proteins in sequence database are extremely non-random, and very dependent

Indistinguishable Protein Sequences

38 Nesvizhskii, Aebersold, Mol Cell Proteomics, 2005

Indistinguishable Protein Sequences

39 Nesvizhskii, Aebersold, Mol Cell Proteomics, 2005

Protein Families

40 Nesvizhskii, Aebersold, Mol Cell Proteomics, 2005

Protein Grouping Scenarios

• Parsimony• Minimum # of proteins

• Weighted• Choose proteins

with the most confident peptides(ProteinProphet)

• Show all • Mark repeated peptides

• Often no (ideal) resolution is possible!

41Nesvizhskii, Aebersold, Mol Cell Proteomics, 2005

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High Quality Peptide Identification: E-value < 10-8

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Moderate quality peptide identification: E-value < 10-3

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Peptide Identification

• Peptide fragmentation by CID is poorly understood

• MS/MS spectra represent incomplete information about amino-acid sequence• I/L, K/Q, GG/N, …

• Correct identifications don’t come with a certificate!

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Peptide Identification

• High-throughput workflows demand we analyze all spectra, all the time.

• Spectra may not contain enough information to be interpreted correctly• …bad static on a cell phone

• Peptides may not match our assumptions• …its all Greek to me

• “Don’t know” is an acceptable answer!

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What scores do “wrong” peptides get?

• Generate random peptide sequences• Real looking fragment masses• Empirical distribution• Require similar precursor mass

• Arbitrary score function can model anything we like!

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Random Peptide Scores

Fenyo & Beavis, Anal. Chem., 2003

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Random Peptide Scores

Fenyo & Beavis, Anal. Chem., 2003

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Random Peptide Scores

• Truly random peptides don’t look much like real peptides

• Just use peptides from the sequence database!

• Assumptions:• IID sampling of “score” values per spectra

• Caveats:• Correct peptide (non-random) may be included• Peptides are not independent

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Extrapolating from the Empirical Distribution

• Often, the empirical shape is consistent with a theoretical model

Geer et al., J. Proteome Research, 2004

Fenyo & Beavis, Anal. Chem., 2003

E-values vs p-values

• Need to adjust for the size of the sequence database• Best false/random score goes up with number

of trials• E-value makes this adjustment

• Expected number of incorrect peptides (with this score) from this sequence database.

• E-value = # Trials * p-value (to 1st approx.)

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False Discovery Rate

• Which peptide IDs to accept?• E-value only provides a per-spectrum statistic• With enough spectra, even these can be

misleading!• Decide which spectra (w/ scores) will be

accepted:• SEQUEST Xcorr, E-value, Score, etc., plus...• Threshold on identification criteria

• Control the proportion of incorrect identifications in the result for entire dataset

Distribution of scores over all spectra

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-3.9 -2.3 -0.7 0.9 2.5 4.1 5.7 7.3

Brian Searle, Proteome Software

Distribution of scores over all spectra

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-3.9 -2.3 -0.7 0.9 2.5 4.1 5.7 7.3

False

True

Brian Searle, Proteome Software

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False Discovery Rate

• FDRscore ≥ x = # false ids with score ≥ x # all ids with score ≥ x

• Need to estimate numerator!• Assumes the false (and true) scores, sampled

over spectra, are IID• Not true for some peptide-spectrum scores• (Mostly) true for E-values

• Can compute the # false ids using a decoy search…

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Peptide Prophet

Distribution of spectral scores in the results

Keller et al., Anal. Chem. 2002

Decoy searches

• Shuffle or reverse sequence database• Same size as original• Known false identifications• Estimate “False” distribution

• Alternatively, merge target+decoy results:• Competition between target and decoy scores• Assume false target and false decoys each

win half the time• FDRscore ≥ x = 2 * # decoy ids with score ≥ x

# target ids with score ≥ x 57

Summary

• Protein sequence databases have varying characteristics, choose wisely!

• Inferring proteins from peptides can be (very) tricky!

• Statistical significance can help control the proportion of errors in the (peptide-level) results.

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