Anastasia Nikolskaya PIR (Protein Information Resource), Georgetown University Medical Center

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

2. 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 Overview 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 • 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

3. Proteomics and Bioinformatics • Data: Gene expression profilingGenome-wide analysis of gene expression • Data: Protein-protein interaction • Data: Structural genomics3D 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? • 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. Genomic DNA Sequence Gene Gene Gene Recognition Exon1 Promoter 5' UTR Intron Exon2 Exon3 3' UTR Intron A C C T A G A G A A T A A A T T G G T C A T G A A T A A A Protein Sequence Exon1 Exon2 Exon3 Structure Determination Function Analysis Family Classification Protein Family Molecular Evolution Gene Network Metabolic Pathway Protein Structure Work with Protein, not DNA Sequence DNA Sequence Gene Protein Sequence Function

6. 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 The Changing Face of Protein Science 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 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

9. Functional Annotationfor 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 assignmentbased 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 Annotationfor 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 ACT domain New sequence 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 Annotationfor 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. 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 structuralcomparisons 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. Structural Genomics: Structure-Based Functional Predictions Protein Structure Initiative: Determine 3D structures of all protein families Methanococcus jannaschii MJ0577 (Hypothetical Protein) Contains bound ATP => ATPase or ATP-Mediated Molecular Switch Confirmed by biochemical experiments

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

21. 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,operonorganization) Clues: specific to niche, pathway type

22. Using Genome Context for Functional Prediction SEED analysis tool (by FIG) Embden-Meyerhof and Gluconeogenesis pathway: 6-phosphofructokinase (EC 2.7.1.11)

23. 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

24. 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

25. 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. Facilitates: • Good quality and large-scale • Systematic correction of annotation errors • Protein name standardization • Functional predictions for uncharacterized proteins 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 This all works only if the system is optimized for annotation

27. Levels of Protein Classification

28. Protein Evolution Domain: Evolutionary/Functional/Structural Unit Domain shuffling Sequence changes With enough similarity, one can trace back to a common origin What about these?

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

30. - - - - Acylphosphatase ZnF ZnF YrdC Peptidase M22 Whole Protein = Sum of its Parts? PIRSF006256 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 Full-length protein functional annotation is best done using annotated full-length protein families

31. 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 Practical classification of proteins:setting realistic goals We strive to reconstruct the natural classification of proteins to the fullest possible extent OR … Credit: Dr. Y. Wolf, NCBI

32. Domain Classification Allows ahierarchythat can trace evolution to thedeepest possible level, the last point of traceable homology and common origin Can usually annotate onlygeneral biochemical function Full-length protein Classification Cannot build a hierarchy deep along the evolutionary tree because ofdomain shuffling Can usually annotatespecific biological function(preferred to annotate individual proteins) Complementary Approaches • Can map domains onto proteins • Can classify proteins even when domains are not defined

33. Levels of Protein Classification

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

35. CM ACT PDT 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

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 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)

37. http://pir.georgetown.edu/ PIRSF Classification System • PIRSF: • Reflectsevolutionary relationshipsof full-lengthproteins • Anetworkstructure fromsuperfamiliestosubfamilies • 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

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.

39. Creation and Curation of PIRSFs UniProtKB proteins New proteins Unassigned proteins Automatic Procedure Automatic clustering • Computer-Generated (Uncurated) Clusters • Preliminary Curation (4,700 PIRSFs) • Membership • Signature Domains • Full Curation (3,300 PIRSFs) • Family Name, Description, Bibliography • PIRSF Name Rules Preliminary Homeomorphic Families Orphans Map domains on Families Automatic placement Merge/split clusters Add/remove members Computer-assisted Manual Curation Curated Homeomorphic Families Protein name rule/site rule Name, refs, description 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 Protein Classification: Platform for Protein Analysis and Annotation • Improves automatic annotationquality • 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)

42. Family-Driven Protein Annotation Objective: 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 • Hierarchy Subfamilies increase specificity (kinase -> sugar kinase -> hexokinase)

43. 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 • 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

44. 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 Facilitates: • Automatic annotation of sequences based on protein families • Systematic correction of annotation errors • Name standardization in UniProt • Functional predictions for uncharacterized proteins Overview 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

45. 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

46. 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