1. FUNCTIONAL ANALYSIS OF PROTEIN SEQUENCES:
ANNOTATION AND FAMILY CLASSIFICATION
Anastasia Nikolskaya
PIR (Protein Information Resource),
Georgetown University Medical Center

2. Overview Problem: Functional Analysis of Protein Sequences:
Most new protein sequences come from genome sequencing projects
Many have unknown functions
Large-scale functional annotation of these sequences based simply on BLAST best hit has pitfalls; results are far from perfect
Problem:
Functional Analysis of Protein Sequences:
Homology-based (sequence analysis, structure analysis)
Non-homology (genome context, phylogenetic distribution)
Solution for Large-scale Annotation:
Highly curated and annotated protein classification system
Automatic annotation of sequences based on protein families
PIRSF Protein Classification System
Whole-protein family classification based on evolution
Highly annotated, optimized for annotation propagation
Functional predictions for uncharacterized proteins
Used to facilitate and standardize annotations in UniProt

3. Proteomics and Bioinformatics
Data: Gene expression profiling Genome-wide analysis of gene expression
Data: Protein-protein interaction
Data: Structural genomics 3D structures of all protein families
Data: Genome projects (Sequencing) ….
Bioinformatics
Computational analysis and integration of these data
Making predictions (function etc), reconstructing pathways

4. What’s In It For Me? Sequence function
When an experiment yields a sequence (or a set of sequences), we need to find out as much as we can about this protein and its possible function from available data
Especially important for poorly characterized or uncharacterized (“hypothetical”) proteins
More challenging for large sets of sequences generated by large-scale proteomics experiments
The quality of this assessment is often critical for interpreting experimental results and making hypothesis for future experiments
Sequence function

5. Family Classification
Work with Protein, not DNA Sequence
DNA
Sequence
Gene
Protein Sequence
Function
Genomic DNA Sequence
5' UTR
Promoter
Exon1
Intron
Exon2
Exon3
3' UTR A G T
Gene Recognition C
Protein Sequence
Structure
Determination
Protein Structure
Function
Analysis
Gene Network
Metabolic Pathway
Protein Family
Molecular Evolution
Family Classification
Gene

6. The Changing Face of Protein Science
20th century
Few well-studied proteins
Mostly globular with enzymatic activity
Biased protein set
21st century
Many “hypothetical” proteins (Most new proteins come from genome sequencing projects, many have unknown functions)
Various, often with no enzymatic activity
Natural protein set
Credit: Dr. M. Galperin, NCBI

7. Knowing the Complete Genome Sequence
Advantages:
All encoded proteins can be predicted and identified
The missing functions can be identified and analyzed
Peculiarities and novelties in each organism can be studied
Predictions can be made and verified
Challenge:
Accurate assignment of known or predicted functions (functional annotation)

8. E. coli M. jannaschii S. cerevisiae H. sapiens
Characterized experimentally
Characterized by similarity
Unknown, conserved
Unknown, no similarity
from Koonin and Galperin, 2003, with modifications

9. Functional Annotation for Different Groups of Proteins
Experimentally characterized
Find up-to-date information, accurate interpretation
Characterized by similarity (“knowns”) = closely related to experimentally characterized
Avoid propagation of errors
Function can be predicted (no close sequence similarity, may be distant similarity to characterized proteins)
Extract maximum possible information, avoid errors and overpredictions
Most value-added (fill the gaps in metabolic pathways, etc)
“Unknowns” (conserved or unique)
Rank by importance

10. How are Protein Sequences Annotated?
“regular approach”
Protein Sequence
Function
Automatic assignment based on sequence similarity (best BLAST hit):
gene name, protein name, function
Large-scale functional annotation of sequences based simply on BLAST best hit has pitfalls;
results are far from perfect
To avoid mistakes, need human intervention (manual annotation)
Quality vs Quantity

11. Functional Annotation for Different Groups of Proteins
Experimentally characterized
Find up-to-date information, accurate interpretation
Characterized by similarity (“knowns”) = closely related to experimentally characterized
Avoid propagation of errors
Function can be predicted (no close sequence similarity, may be distant similarity to characterized proteins)
Extract maximum possible information, avoid errors and overpredictions
Most value-added (fill the gaps in metabolic pathways, etc)
“Unknowns” (conserved or unique)
Rank by importance

12. Problems in Functional Assignments for “Knowns”
Misinterpreted experimental results (e.g. suppressors, cofactors)
Biologically senseless annotations
Arabidopsis: separation anxiety protein-like
Helicobacter: brute force protein
Methanococcus: centromere-binding protein
Plasmodium: frameshift
“Goofy” mistakes of sequence comparison (e.g. abc1/ABC)
Multi-domain organization of proteins
Low sequence complexity (coiled-coil, transmembrane, non-globular regions)
Enzyme evolution:
Divergence in sequence and function (minor mutation in active site)
Non-orthologous gene displacement: Convergent evolution

13. Problems in Functional Assignments for “Knowns”: multi-domain organization of proteins
New sequence
ACT domain
BLAST
Chorismate
mutase
Chorismate mutase domain
ACT domain
In BLAST output, top hits are to chorismate mutases ->
The name “chorismate mutase” is automatically assigned to new sequence. ERROR ! (protein gets erroneous name, EC number, assigned to erroneous pathway, etc)

14. Problems in Functional Assignments for “Knowns”
Previous low quality annotations lead to
propagation of mistakes

15. Functional Annotation for Different Groups of Proteins
Experimentally characterized
Find up-to-date information, accurate interpretation
Characterized by similarity (“knowns”) = closely related to experimentally characterized
Avoid propagation of errors
Function can be predicted (no close sequence similarity, may be distant similarity to characterized proteins)
Extract maximum possible information, avoid errors and overpredictions
Most value-added (fill the gaps in metabolic pathways, etc)
“Unknowns” (conserved or unique)
Rank by importance

16. Functional Prediction: I
Functional Prediction: I. Sequence and Structure Analysis (homology-based methods)
in non-obvious cases:
Sophisticated database searches (PSI-BLAST, HMM)
Detailed manual analysis of sequence similarities
Structure-guided alignments and structure analysis
Often, only general function can be predicted:
Enzyme activity can be predicted, the substrate remains unknown (ATPases, GTPases, oxidoreductases, methyltransferases, acetyltransferases)
Helix-turn-helix motif proteins (predicted transcriptional regulators)
Membrane transporters

17. Using Sequence Analysis:
Hints
Proteins (domains) with different 3D folds are not homologous (unrelated by origin). Proteins with similar 3D folds are usually (but not always) homologous
Those amino acids that are conserved in divergent proteins within a (super)family are likely to be functionally important (catalytic or binding sites, ect).
Reaction chemistry often remains conserved even when sequence diverges almost beyond recognition

18. Using Sequence Analysis:
Hints
Prediction of 3D fold (if distant homologs have known structures!) and of general biochemical function is much easier than prediction of exact biological function
Sequence analysis complements structural comparisons and can greatly benefit from them
Comparative analysis allows us to find subtle sequence similarities in proteins that would not have been noticed otherwise
Credit: Dr. M. Galperin, NCBI

19. Functional Prediction: Role of Structural Genomics
Protein Structure Initiative: Determine 3D Structures of All Proteins
Family Classification:
Organize protein sequences into families, collect families without known structures
Target Selection:
Select family representatives as targets
Structure Determination:
X-Ray crystallography or NMR spectroscopy
Homology Modeling:
Build models for other proteins by homology
Attempt functional prediction based on structure
Structural genomics is the systematic determination of 3-dimensional structures of proteins representative of the range of protein structure and function found in nature. The aim, ultimately, is to build a body of structural information that will facilitate prediction of a reasonable structure and potential function for almost any protein from knowledge of its coding sequence. Such information will be essential for understanding the functioning of the human proteome, the ensemble of tens of thousands of proteins specified by the human genome.

20. Structural Genomics: Structure-Based Functional Predictions
Structural genomics is the systematic determination of 3-dimensional structures of proteins representative of the range of protein structure and function found in nature. The aim, ultimately, is to build a body of structural information that will facilitate prediction of a reasonable structure and potential function for almost any protein from knowledge of its coding sequence. Such information will be essential for understanding the functioning of the human proteome, the ensemble of tens of thousands of proteins specified by the human genome.
Methanococcus jannaschii MJ0577 (Hypothetical Protein)
Contains bound ATP => ATPase or ATP-Mediated Molecular Switch
Confirmed by biochemical experiments

21. Crystal Structure is Not a Function!
Credit: Dr. M. Galperin, NCBI

22. Functional Prediction: II. Computational Analysis Beyond Homology
Phylogenetic distribution (comparative genomics)
Wide most likely essential
Narrow - probably clade-specific
Patchy - most intriguing
Domain association – “Rosetta Stone”
Genome context (gene neighborhood, operon organization)
Clues: specific to niche, pathway type

23. Using Genome Context for Functional Prediction
SEED analysis tool
(by FIG)
Embden-Meyerhof and Gluconeogenesis pathway:
6-phosphofructokinase (EC )

24. Functional Prediction: Problem Areas
Identification of protein-coding regions
Delineation of potential function(s) for distant paralogs
Identification of domains in the absence of close homologs
Analysis of proteins with low sequence complexity

25. Prediction Verified: GGDEF domain
Case Study:
Prediction Verified: GGDEF domain
Proteins containing this domain: Caulobacter crescentus PleD controls swarmer cell - stalk cell transition (Hecht and Newton, 1995). In Rhizobium leguminosarum, Acetobacter xylinum, required for cellulose biosynthesis (regulation)
Predicted to be involved in signal transduction because it is found in fusions with other signaling domains (receiver, etc)
In Acetobacter xylinum, cyclic di-GMP is a specific nucleotide regulator of cellulose synthase (signalling molecule). Multidomain protein with GGDEF domain was shown to have diguanylate cyclase activity (Tal et al., 1998)
Detailed sequence analysis tentatively predicts GGDEF to be a diguanylate cyclase domain (Pei and Grishin, 2001)
Complementation experiments prove diguanylate cyclase activity of GGDEF (Ausmees et al., 2001)

26. The Need for Classification
Problem:
Most new protein sequences come from genome sequencing projects
Many have unknown functions
Large-scale functional annotation of these sequences based simply on BLAST best hit has pitfalls; results are far from perfect
Manual annotation of individual proteins is not efficient
Solution:
Highly curated and annotated protein classification system
Automatic annotation of sequences based on protein families
Automatic annotation of sequences based on protein families
Systematic correction of annotation errors
Protein name standardization
Functional predictions for uncharacterized proteins
Facilitates:
This all works only if the system is optimized for annotation

27. Levels of Protein Classification
Example
Similarity
Evolution
Class
/
Structural elements
No relationships
Fold
TIM-Barrel
Topology of backbone
Possible monophyly
Domain Superfamily
Aldolase
Recognizable sequence similarity (motifs); basic biochemistry
Monophyletic origin
Family
Class I Aldolase
High sequence similarity (alignments); biochemical properties
Evolution by ancient duplications
Orthologous group
2-keto-3-deoxy-6-phosphogluconate aldolase
Orthology for a given set of species; biochemical activity; biological function
Traceable to a single gene in LCA
Lineage-specific expansion
(LSE)
PA3131 and PA3181
Paralogy within a lineage
Recent duplication
Domain classification is easiest to handle, but insufficient for annotation. Example on next slide.

28. With enough similarity, one can trace back to a common origin
Protein Evolution
Domain: Evolutionary/Functional/Structural Unit
Sequence changes
Domain shuffling
This would be easy if proteins all evolved in a gradual manner, but…
With enough similarity, one can trace back to a common origin
What about these?

29. Consequences of Domain Shuffling
PIRSF001501
PIRSF006786
CM = chorismate mutase
PDH = prephenate dehydrogenase
PDT = prephenate dehydratase
ACT = regulatory domain
CM (AroQ type)
PDH
CM?
CM/PDH?
PDH?
CM (AroQ type)
PDH
PIRSF001499
PDH
ACT
PIRSF005547
PDT?
PDT
ACT
PIRSF001424
CM/PDT?
CM (AroQ type)
PDT
ACT
PIRSF001500

30. - Whole Protein = Sum of its Parts? PIRSF006256 Peptidase M22
Acylphosphatase
ZnF
YrdC -
On the basis of domain composition alone, biological function was predicted to be:
● RNA-binding translation factor
● maturation protease
Actual function:
● [NiFe]-hydrogenase maturation factor,
carbamoyltransferase
Despite the problem caused by domain shuffling, the previous slide actually showed a relatively easy case for annotation based on domain fusions. That is, the whole equals the sum of its parts. But this is not always the case.
Whole protein functional annotation is best done using annotated whole-protein families

31. Practical classification of proteins: setting realistic goals
We strive to reconstruct the natural classification of proteins to the fullest possible extent
BUT
Domain shuffling rapidly degrades the continuity in the protein structure (faster than sequence divergence degrades similarity)
THUS
The further we extend the classification, the finer is the domain structure we need to consider SO
We need to compromise between the depth of analysis and protein integrity
OR …
Credit: Dr. Y. Wolf, NCBI

32. Complementary Approaches
Domain Classification
Allows a hierarchy that can trace evolution to the deepest possible level, the last point of traceable homology and common origin
Can usually annotate only general biochemical function
Whole-protein Classification
Cannot build a hierarchy deep along the evolutionary tree because of domain shuffling
Can usually annotate specific biological function (preferred to annotate individual proteins)
Can map domains onto proteins
Can classify proteins even when domains are not defined

33. Levels of Protein Classification
Example
Similarity
Evolution
Class
/
Structural elements
No relationships
Fold
TIM-Barrel
Topology of backbone
Possible monophyly
Domain Superfamily
Aldolase
Recognizable sequence similarity (motifs); basic biochemistry
Monophyletic origin
Family
Class I Aldolase
High sequence similarity (alignments); biochemical properties
Evolution by ancient duplications
Orthologous group
2-keto-3-deoxy-6-phosphogluconate aldolase
Orthology for a given set of species; biochemical activity; biological function
Traceable to a single gene in LCA
Lineage-specific expansion
(LSE)
PA3131 and PA3181
Paralogy within a lineage
Recent duplication
Domain classification is easiest to handle, but insufficient for annotation. Example on next slide.

34. Protein Classification Databases
Domain classification
Pfam
SMART
CDD
Whole protein classification
PIRSF
Mixed
TIGRFAMS
COGs
Based on structural fold
SCOP
InterPro: integrates various types of classification databases

35. InterPro
Integrated resource for protein families, domains and sites. Combines a number of databases: PROSITE, PRINTS, Pfam, SMART, ProDom, TIGRFAMs, PIRSF
SF001500
Bifunctional chorismate mutase/ prephenate dehydratase CM
PDT
ACT

36. The Ideal System…
Comprehensive: each sequence is classified either as a member of a family or as an “orphan” sequence
Hierarchical: families are united into superfamilies on the basis of distant homology, and divided into subfamilies on the basis of close homology
Allows for simultaneous use of the whole protein and domain information (domains mapped onto proteins)
Allows for automatic classification/annotation of new sequences when these sequences are classifiable into the existing families
Expertly curated membership, family name, function, background, etc.
Evidence attribution (experimental vs predicted)

37. PIRSF Classification System
PIRSF Classification System
PIRSF:
Reflects evolutionary relationships of full-length proteins
A network structure from superfamilies to subfamilies
Definitions:
Homeomorphic Family: Basic Unit
Homologous: Common ancestry, inferred by sequence similarity
Homeomorphic: Full-length similarity & common domain architecture
Hierarchy: Flexible number of levels with varying degrees of sequence conservation
Network Structure: allows multiple parents
allow more accurate propagation of annotation and development of standard protein nomenclature and ontology.
Advantages:
Annotate both general biochemical and specific biological functions
Accurate propagation of annotation and development of standardized protein nomenclature and ontology

38. PIRSF Classification System
A protein may be assigned to only one homeomorphic family, which may have zero or more child nodes and zero or more parent nodes. Each homeomorphic family may have as many domain superfamily parents as its members have domains.
6 Subfamilies (IGFBP-1 through 6): correspond to 6 types known in literature
(Based on conserved intron/exon organization, sequence similarity, high binding affinity to IGFs, and function)

39. Creation and Curation of PIRSFs
UniProtKB proteins
New proteins
Automatic Procedure
Unassigned proteins
Computer-Generated (Uncurated) Clusters (35,000 PIRSFs)
Preliminary Curation (4,400 PIRSFs)
Membership
Signature Domains
Full Curation (3,200 PIRSFs)
Family Name, Description, Bibliography
PIRSF Name Rules
Automatic clustering
Preliminary Homeomorphic Families
Orphans
Map domains on Families
Automatic placement
Merge/split
clusters
Computer-assisted Manual Curation
Add/remove members
Curated Homeomorphic Families
Name, refs, description
Protein name rule/site rule
Final Homeomorphic Families
Create hierarchies (superfamilies/subfamilies)
Build and test HMMs

40. PIRSF Family Report: Curated Protein Family Information
Taxonomic distribution of PIRSF can be used to infer evolutionary history of the proteins in the PIRSF
Phylogenetic tree and alignment view allows further sequence analysis

41. PIRSF Hierarchy and Network: DAG Viewer

42. PIRSF Family Report (II)
Integrated value added information from other databases
Mapping to other protein classification databases

43. PIRSF Protein Classification: Platform for Protein Analysis and Annotation
Matching a protein sequence to a curated protein family rather than searching against a protein database
Provides value-added information by expert curators, e.g., annotation of uncharacterized hypothetical proteins (functional predictions)
Improves automatic annotation quality
Serves as a protein analysis platform for broad range of users

44. Family-Driven Protein Annotation
Objective: Optimize for protein annotation
PIRSF Classification Name
PIRSF Classification Name
Reflects the function when possible
Indicates the maximum specificity that still describes the entire group
Standardized format
Name tags: validated, tentative, predicted, functionally heterogeneous
Hierarchy
Subfamilies increase specificity (kinase -> sugar kinase -> hexokinase)
Hierarchy
Name Rules
Define conditions under which names propagate to individual proteins
Enable further specificity based on taxonomy or motifs
Names adhere to Swiss-Prot conventions (though we may make suggestions for improvement)
Name Rules
Site Rules
Define conditions under which features propagate to individual proteins
Site Rules

45. PIR Name Rules Monitor such variables to ensure accurate propagation
Account for functional variations within one PIRSF, including:
Lack of active site residues necessary for enzymatic activity
Certain activities relevant only to one part of the taxonomic tree
Evolutionarily-related proteins whose biochemical activities are known to differ
Monitor such variables to ensure accurate propagation
Propagate other properties that describe function:
EC, GO terms, misnomer info, pathway
Name Rule types:
“Zero” Rule
Default rule (only condition is membership in the appropriate family)
Information is suitable for every member
“Higher-Order” Rule
Has requirements in addition to membership
Can have multiple rules that may or may not have mutually exclusive conditions

46. Propagated Information
Example Name Rules
Rule ID
Rule Conditions
Propagated Information
PIRNR
PIRSF member and vertebrates
Name: S-acyl fatty acid synthase thioesterase
EC: oleoyl-[acyl-carrier-protein] hydrolase (EC )
PIRNR
PIRSF member and not vertebrates
Name: Type II thioesterase
EC: thiolester hydrolases (EC )
PIRNR
PIRSF member
Name: ACT domain protein
Misnomer: chorismate mutase
Note the lack of a zero rule for PIRSF000881

47. Name Rule Propagation Pipeline
Affiliation of Sequence: Homeomorphic Family or Subfamily
(whichever PIRSF is the lowest possible node)
Name rule exists?
Yes No
Nothing to propagate
Protein fits criteria for any higher-order rule?
Assign name from
Name Rule 1 (or 2 etc) No
Yes
Assign name from
Name Rule 0
PIRSF has zero rule? No
Yes
Nothing to propagate

48. Name Rule in Action at UniProt
Current:
Automatic annotations (AA) are in a separate field
AA only visible from
Future:
Automatic name annotations will become DE line if DE line
will improve as a result
AA will be visible from all consortium-hosted web sites

49. PIR Site Rules Position-Specific Site Features: Current requirements:
active sites
binding sites
modified amino acids
Current requirements:
at least one PDB structure
experimental data on functional sites: CATRES database (Thornton)
CC- Similarity line could be automatically generated
Rule Definition:
Select template structure
Align PIRSF seed members with structural template
Edit alignment to retain conserved regions covering all site residues
Build Site HMM from concatenated conserved regions

50. Match Rule Conditions
Only propagate site annotation if all rule conditions are met:
Membership Check (PIRSF HMM threshold)
Ensures that the annotation is appropriate
Conserved Region Check (site HMM threshold)
Residue Check (all position-specific residues in HMMAlign)

51. Site Rules Feed Name Rules
Rule-based Annotation of Protein Entries
Functional variations within one PIRSF (family or subfamily):
binding sites with different specificity
Monitor such variables for accurate propagation
Site Rules Feed Name Rules
Functional Site rule: tags active site, binding, other residue-specific information ?
Functional Annotation rule:
gives name, EC, other activity-specific information

52. Impact of Protein Bioinformatics and Genomics
Single protein level
Discovery of new enzymes and superfamilies
Prediction of active sites and 3D structures
Pathway level
Identification of “missing” enzymes
Prediction of alternative enzyme forms
Identification of potential drug targets
Cellular metabolism level
Multisubunit protein systems
Membrane energy transducers
Cellular signaling systems

53. Overview Problem: Functional Analysis of Protein Sequences:
Most new protein sequences come from genome sequencing projects
Many have unknown functions
Large-scale functional annotation of these sequences based simply on BLAST best hit has pitfalls; results are far from perfect
Problem:
Functional Analysis of Protein Sequences:
Homology-based (sequence analysis, structure analysis)
Non-homology (genome context, phylogenetic distribution)
Solution for Large-scale Annotation:
Highly curated and annotated protein classification system
Automatic annotation of sequences based on protein families
Automatic annotation of sequences based on protein families
Systematic correction of annotation errors
Name standardization in UniProt
Functional predictions for uncharacterized proteins
Facilitates:

54. What to do with a new porotein sequence
Basic:
- Domain analysis (SMART = most sensitive; PFAM, CDD
BLAST
Curated protein family databases (PIRSF, InterPro, COGs)
Literature (PubMed) from links from individual entries on the BLAST output (look for SwissProt entries first)
If not sufficient:
PSI-BLAST
Refined PubMed search using gene/protein names, synomims, function and other terms you found
Advanced:
Multiple sequence alignments (manual)
Structure-guided alignments and structure analysis
- Phylogenetic tree reconstruction

55. PIR Team Dr. Cathy Wu, Director Protein Classification team
Dr. Winona Barker Dr. Lai-Su Yeh Dr. Anastasia Nikolskaya
Dr. Darren Natale Dr. Zhangzhi Hu Dr. Raja Mazumder
Dr. CR Vinayaka Dr. Sona Vasudevan Dr. Cecilia Arighi
Informatics team
Dr. Hongzhan Huang Dr. Peter McGarvey Baris Suzek, M.S.
Dr. Leslie Arminski Dr. Hsing-Kuo Hua Yongxing Chen, M.S.
Jing Zhang, M.S. Dr. Xin Yuan
Students
Christina Fang Natalia Petrova