anastasia nikolskaya pir (protein information resource), georgetown university medical center

Anastasia NikolskayaPIR (Protein Information Resource),

Georgetown University Medical Center

FUNCTIONAL ANALYSIS OF PROTEIN SEQUENCES:ANNOTATION AND FAMILY CLASSIFICATION

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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: Overview

Highly curated and annotated protein classification system Automatic annotation of sequences based on protein families

Solution for Large-scale Annotation:

Full-length 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

PIRSF Protein Classification System

Functional Analysis of Protein Sequences: Homology-based (sequence analysis, structure analysis) Non-homology (genome context, phylogenetic distribution)

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Proteomics and Bioinformatics

BioinformaticsComputational analysis and integration of these data

Making predictions (function etc), reconstructing pathways

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) ….

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What’s In It For Me?

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

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Genomic DNA Sequence

5' UTRPromoter Exon1 Intron Exon2 Intron Exon3 3' UTR

A

G

GT

A

G

Gene Recognition

Exon2Exon1 Exon3

C

A

C

A

C

A

A

T

T

A

T

A

Protein Sequence

A

T

G

AA

T

A

A

A

Structure Determination

Protein Structure

Function Analysis

Gene NetworkMetabolic Pathway

Protein FamilyMolecular Evolution

Family Classification

GT

Gene Gene

DNASequence

Gene

Protein

Sequence

Function

Work with Protein, not DNA Sequence

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The Changing Face of Protein Science

20th century

Few well-studied proteins

Mostly globular with enzymatic activity

Biased protein set

21st centuryMany “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

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Knowing the Complete Genome Sequence

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

Advantages:

Challenge: Accurate assignment of known or predicted functions

(functional annotation)

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Escherichia coli Methanococcus jannaschii

Yeast Human

E. coli M. jannaschii S. cerevisiae H. sapiens Characterized experimentally 2046 97 3307 10189 Characterized by similarity 1083 1025 1055 10901 Unknown, conserved 285 211 1007 2723 Unknown, no similarity 874 411 966 7965 from Koonin and Galperin, 2003, with modifications

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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

Functional Annotationfor Different Groups of Proteins

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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)

How are Protein Sequences Annotated?“regular approach”

Quality vs Quantity

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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

Problems in Functional Assignments for “Knowns”

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Problems in Functional Assignments for “Knowns”: multi-domain organization of proteins

ACT domain

Chorismate mutase domain ACT domain

New sequence

Chorismate mutase

BLAST

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)

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Previous low quality annotations lead to

propagation of mistakes

Problems in Functional Assignments for “Knowns”

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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

Functional Prediction:I. Sequence and Structure Analysis

(homology-based methods)

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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

Using Sequence Analysis:Hints

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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

Using Sequence Analysis:Hints


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Structural Genomics: Structure-Based Functional Predictions

Methanococcus jannaschii MJ0577 (Hypothetical Protein)

Contains bound ATP => ATPase or ATP-Mediated Molecular Switch

Confirmed by biochemical experiments

Protein Structure Initiative: Determine 3D structures of all protein families

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Crystal Structure is Not a Function!


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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)

Functional Prediction:II. Computational Analysis Beyond Homology

Clues: specific to niche, pathway type

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Using Genome Context for Functional Prediction

Embden-Meyerhof and Gluconeogenesis pathway: 6-phosphofructokinase (EC 2.7.1.11)

SEED analysis tool (by FIG)

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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

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What to do with a new protein sequence Basic:- Domain analysis (SMART = most sensitive; PFAM= most complete, CDD)- BLAST- Curated protein family databases (PIRSF, InterPro, COGs)- Literature (PubMed) from links from individual entries on BLAST output

(look for SwissProt entries first)

If not sufficient:- PSI-BLAST- Refined PubMed search using gene/protein names, synonyms, function and other terms you found- Genome neighborhood (prokaryotes)

• Advanced: - Multiple sequence alignments (manual) - Structure-guided alignments and structure analysis- Phylogenetic tree reconstruction

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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)

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BLAST best hit has pitfalls; results are far from perfect Manual annotation of individual proteins is not efficient

Problem:

The Need for Classification


Solution:

Good quality and large-scale 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

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Levels of Protein ClassificationLevel 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

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Protein Evolution

With enough similarity, one can trace back to a

common origin

Sequence changes

What about these?

Domain shuffling

Domain: Evolutionary/Functional/Structural Unit

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PDT?

CM/PDH?

Consequences of Domain Shuffling

PIRSF001500CM (AroQ type) PDT ACT

PIRSF001501

CM (AroQ type)

PIRSF006786

PDH

PIRSF001499

PIRSF005547PDH ACT

PDT ACT PIRSF001424

CM = chorismate mutasePDH = prephenate dehydrogenase PDT = prephenate dehydrataseACT = regulatory domain

PDH?

CM/PDT?

CM?PDHCM (AroQ type)

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Peptidase M22Acylphosphatase ZnF YrdCZnF- - - -

Whole Protein = Sum of its Parts?

On the basis of domain composition alone, biological function was predicted to be: ● RNA-binding translation factor ● maturation protease

PIRSF006256

Actual function: ● [NiFe]-hydrogenase maturation factor, carbamoyltransferase

Full-length protein functional annotation is best done using annotated full-length protein families

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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

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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

Full-length 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

Complementary Approaches

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Levels of Protein ClassificationLevel 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

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Full-length protein classification

PIRSF

Domain classification

Pfam

SMART

CDD

Mixed

•TIGRFAMS

•COGs

Based on structural fold

•SCOP

Protein Classification Databases

InterPro: integrates various types of classification databases

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Integrated resource for protein families, domains and sites. Combines a number of databases: PROSITE, PRINTS, Pfam, SMART, ProDom, TIGRFAMs, PIRSF

SF001500Bifunctional chorismate mutase/ prephenate dehydratase

InterPro

CM PDT ACT

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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 full-length 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)

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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

Advantages: Annotate both general biochemical and specific biological functions Accurate propagation of annotation and development of standardized

protein nomenclature and ontology

http://pir.georgetown.edu/

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PIRSF001499: Bifunctional CM/PDH (T-protein)

PIRSF006786: PDH, feedback inhibition-insensitive

PIRSF005547: PDH, feedback inhibition-sensitive

PF02153: Prephenatedehydrogenase (PDH)

PIRSF017318: CM of AroQ class, eukaryotic type

PIRSF001501: CM of AroQ class, prokaryotic type

PIRSF026640: Periplasmic CM

PIRSF001500: Bifunctional CM/PDT (P-protein)


PF01817: Chorismatemutase (CM)

PIRSF006493: Ku, prokaryotic type

PIRSF500001: IGFBP-1

…


PIRSF Homeomorphic Subfamily

• 0 or more levels

• Functional specialization

PIRSF018239: IGFBP-related protein, MAC25 type

PIRSF001969: IGFBP

PIRSF003033: Ku70 autoantigen


PIRSF Homeomorphic Family• Exactly one level

• Full-length sequence similarity and common domain architecture

PIRSF Superfamily


• One or more common domains

PF00219: Insulin-like growth factor binding protein

(IGFBP)

PIRSF800001: Ku70/80 autoantigenPF02735: Ku70/Ku80 beta-barrel domain

Domain Superfamily• One common Pfam

domain


PIRSF006786: PDH, feedback inhibition-insensitive

PIRSF005547: PDH, feedback inhibition-sensitive

PF02153: Prephenatedehydrogenase (PDH)

PIRSF017318: CM of AroQ class, eukaryotic type

PIRSF001501: CM of AroQ class, prokaryotic type

PIRSF026640: Periplasmic CM

PIRSF001500: Bifunctional CM/PDT (P-protein)


PF01817: Chorismatemutase (CM)

PIRSF006493: Ku, prokaryotic type


…


PIRSF Homeomorphic Subfamily


• Functional specialization

PIRSF018239: IGFBP-related protein, MAC25 type

PIRSF001969: IGFBP



PIRSF Homeomorphic Family• Exactly one level

• Full-length sequence similarity and common domain architecture

PIRSF Superfamily


• One or more common domains

PF00219: Insulin-like growth factor binding protein

(IGFBP)

PIRSF800001: Ku70/80 autoantigenPF02735: Ku70/Ku80 beta-barrel domain

Domain Superfamily• One common Pfam

domain

PIRSF Classification SystemA 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.

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Creation and Curation of PIRSFsUniProtKB proteins

Preliminary Homeomorphic Families

Orphans

Curated Homeomorphic Families

Final Homeomorphic Families

Add/remove members

Name, refs, description

Automatic clustering

Computer-assisted Manual Curation

Automatic Procedure Unassigned proteins

Au

tom

atic

pla

ce

me

nt

Create hierarchies (superfamilies/subfamilies)

Map domains on Families

Merge/split clusters

New proteins

Protein name rule/site rule

Computer-Generated (Uncurated) Clusters

Preliminary Curation (4,700 PIRSFs) Membership Signature

Domains

Full Curation (3,300 PIRSFs) Family Name,

Description, Bibliography

PIRSF Name Rules

Build and test HMMs

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Taxonomic distribution of PIRSF can be used to infer evolutionary history of the proteins in the PIRSF

PIRSF Family Report: Curated Protein Family Information

Phylogenetic tree and alignment view allows further sequence analysis

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PIRSF Protein Classification: Platform for Protein Analysis and

Annotation

Improves automatic annotation quality Serves as a protein analysis platform for broad range of

users

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)

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Family-Driven Protein AnnotationObjective: Optimize for protein annotation

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

HierarchySubfamilies increase specificity (kinase -> sugar kinase -> hexokinase)

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Define conditions under which family properties propagate to individual proteins

Propagate protein name, function, functional sites, EC, GO terms, pathway

Enable further specificity based on taxonomy or motifs 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

Family-Driven Protein Annotation:Site Rules and Name Rules

Goal: Automatic annotation of sequences based on protein families

to address the quality versus quantity problem

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BLAST best hit has pitfalls; results are far from perfect

Problem:Overview


Solution for Large-scale Annotation:

Functional Analysis of Protein Sequences: Homology-based (sequence analysis, structure analysis) Non-homology (genome context, phylogenetic distribution)

Automatic annotation of sequences based on protein families Systematic correction of annotation errors Name standardization in UniProt Functional predictions for uncharacterized proteins

Facilitates:

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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

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PIR Team Dr. Cathy Wu, Director Protein Science team

Dr. Darren Natale (lead) Dr. Peter McGarvey Dr. Cecilia Arighi Dr. Anastasia Nikolskaya Dr. Winona Barker Dr. Sona Vasudevan Dr. Zhang-zhi Hu Dr. CR Vinayaka Dr. Raja Mazumder Dr. Lai-Su Yeh

Bioinformatics team Dr. Hongzhan Huang (lead) Yongxing Chen, M.S. Dr. Leslie Arminski Baris Suzek, M.S. Dr. Hsing-Kuo Hua Xin Yuan, M.S. Dr. Robel Kahsay Jian Zhang, M.S.

Students Natalia Petrova

UniProt Collaborators Dr. Rolf Apweiler (EBI) Dr. Amos Bairoch (SIB)

UniProt is supported by the National Institutes of Health, grant # 1 U01 HG02712-01

anastasia nikolskaya pir (protein information resource), georgetown university medical center

Documents

protein familiesdata

face of protein science

protein families solution

largescale annotation

set of sequences

close sequence similarity

genome projects sequencing

genome sequencing projects