Transcript Protein Sequence Databases, Peptides to Proteins

Protein Sequence Databases,
Peptides to Proteins, and
Statistical Significance
Nathan Edwards
Department of Biochemistry and Mol. & Cell. Biology
Georgetown University Medical Center
Protein Sequence Databases
• Link between mass spectra and proteins
• A protein’s amino-acid sequence provides
a basis for interpreting
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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:
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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
Literature
Function
• Biological process, molecular function,
cellular component
• Known and predicted
Features
• Polymorphism, Isoforms, PTMs, Domains
Derived 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
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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
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Permanent labels
Short, machine readable
Enable precise communication
Typos render them unusable!
Each database uses a different format
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Swiss-Prot: P17947
Ensembl: ENSG00000066336
PIR: S60367; S60367
GO: GO:0003700;
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Names / IDs
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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 VIIarelated 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
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Buffalo rat
Gunn rats
Norway rat
Rattus PC12 clone IS
Rattus norvegicus
Rattus norvegicus8
Rattus norwegicus
Rattus rattiscus
• Rattus sp.
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Rattus sp. strain Wistar
Sprague-Dawley rat
Wistar rats
brown rat
laboratory rat
rat
rats
zitter rats
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
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Indistinguishable Protein
Sequences
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Nesvizhskii, Aebersold, Mol Cell Proteomics, 2005
Indistinguishable Protein
Sequences
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Nesvizhskii, Aebersold, Mol Cell Proteomics, 2005
Protein Families
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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!
Nesvizhskii, 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
Fenyo & Beavis, Anal. Chem., 2003
Geer et al., J. Proteome Research, 2004
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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
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Distribution of scores
over all spectra
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180
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20
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-3.9
-2.3
-0.7
0.9
2.5
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4.1
5.7
7.3
Brian Searle, Proteome Software
Distribution of scores
over all spectra
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False
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160
140
120
100
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True
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40
20
0
-3.9
-2.3
-0.7
0.9
2.5
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4.1
5.7
7.3
Brian Searle, Proteome Software
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
Keller et al., Anal. Chem. 2002
Distribution of spectral scores in the results
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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
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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 (peptidelevel) results.
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