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FUNCTIONAL ANALYSIS OF PROTEIN SEQUENCES: ANNOTATION AND FAMILY CLASSIFICATION Anastasia Nikolskaya PIR (Protein Information Resource), Georgetown University Medical Center Overview 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 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 2 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 3 pathways 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 4 Work with Protein, not DNA Sequence Genomic DNA Sequence DNA Sequence Gene Gene Gene Recognition Promoter Gene Protein Sequence C A C A C A A T Exon1 5' UTR T A T A A T G T Exon2 A G Exon3 Intron G T Protein Sequence Exon1 Exon2 Structure Determination Family Classification 3' UTR A A T A A A A G G Protein Structure Function Intron Exon3 Function Analysis Protein Family Molecular Evolution Gene Network Metabolic Pathway 5 The Changing Face of Protein Science 20th century Few well-studied proteins 21st century Many “hypothetical” proteins (Most new proteins come from genome sequencing projects, many have unknown functions) Mostly globular with enzymatic activity Various, often with no enzymatic activity Biased protein set Natural protein set 6 Credit: Dr. M. Galperin, NCBI 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) 7 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 8 from Koonin and Galperin, 2003, with modifications 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 9 How are Protein Sequences Annotated? “regular approach” Protein Sequence 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 Function To avoid mistakes, need human intervention (manual annotation) Quality vs Quantity 10 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 11 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, nonglobular regions) Enzyme evolution: Divergence in sequence and function (minor mutation in active12site) Non-orthologous gene displacement: Convergent evolution 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 13 number, assigned to erroneous pathway, etc) Problems in Functional Assignments for “Knowns” Previous low quality annotations lead to propagation of mistakes 14 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 15 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) 16 Membrane transporters 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 17 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 18 Credit: Dr. M. Galperin, NCBI 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 19 Confirmed by biochemical experiments Crystal Structure is Not a Function! 20 Credit: Dr. M. Galperin, NCBI Functional Prediction: II. Computational Analysis Beyond Homology Phylogenetic distribution (comparative genomics) Wide - most likely essential Narrow - probably clade-specific Patchy - most intriguing Clues: specific to niche, pathway type Domain association – “Rosetta Stone” Genome context (gene neighborhood, operon organization) 21 Using Genome Context for Functional Prediction SEED analysis tool (by FIG) Embden-Meyerhof and Gluconeogenesis pathway: 6-phosphofructokinase (EC 2.7.1.11) 22 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 23 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 • 24 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 25 of GGDEF (Ausmees et al., 2001) 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 Facilitates: Automatic annotation of sequences based on protein families Systematic correction of annotation errors Protein name standardization Functional predictions for uncharacterized proteins 26 This all works only if the system is optimized for annotation Levels of Protein Classification Level 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-6phosphogluconate aldolase Orthology for a given set of species; biochemical activity; biological function Traceable to a single gene in LCA Lineagespecific expansion (LSE) PA3131 and PA3181 Paralogy within a lineage Recent duplication 27 Protein Evolution Domain: Evolutionary/Functional/Structural Unit Sequence changes With enough similarity, one can trace back to a common origin Domain shuffling What about these? 28 Consequences of Domain Shuffling PIRSF001501 PIRSF006786 CM (AroQ type) PDH CM? CM (AroQ type) PDH CM = chorismate mutase PDH = prephenate dehydrogenase PDT = prephenate dehydratase ACT = regulatory domain PIRSF001499 PDH? PDT? CM/PDH? PDH ACT PIRSF005547 PDT ACT PIRSF001424 PDT ACT PIRSF001500 CM/PDT? CM (AroQ type) 29 Whole Protein = Sum of its Parts? PIRSF006256 Acylphosphatase - ZnF - ZnF - YrdC - Peptidase M22 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 30 using annotated full-length protein families 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 … 31 Credit: Dr. Y. Wolf, NCBI Complementary Approaches Full-length protein Classification 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 Can Can Cannot build a hierarchy deep along the evolutionary tree because of domain shuffling Can usually annotate specific biological function (preferred to annotate individual proteins) map domains onto proteins classify proteins even when domains are not defined32 Levels of Protein Classification Level 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-6phosphogluconate aldolase Orthology for a given set of species; biochemical activity; biological function Traceable to a single gene in LCA Lineagespecific expansion (LSE) PA3131 and PA3181 Paralogy within a lineage Recent duplication 33 Protein Classification Databases Domain classification Pfam SMART Full-length protein classification PIRSF CDD Mixed Based on structural fold •TIGRFAMS •SCOP •COGs 34 InterPro: integrates various types of classification databases InterPro Integrated resource for protein families, domains and sites. Combines a number of databases: PROSITE, PRINTS, Pfam, SMART, ProDom, TIGRFAMs, PIRSF CM PDT ACT SF001500 Bifunctional chorismate mutase/ prephenate dehydratase 35 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) 36 http://pir.georgetown.edu/ PIRSF Classification System PIRSF: 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 Reflects evolutionary relationships of full-length proteins A network structure from superfamilies to subfamilies Advantages: Annotate both general biochemical and specific biological functions Accurate propagation of annotation and development of standardized protein nomenclature and ontology 37 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. Domain Superfamily • One common Pfam domain PIRSF Superfamily • 0 or more levels • One or more common domains PIRSF Homeomorphic Family • Exactly one level • Full-length sequence similarity and common domain architecture PIRSF Homeomorphic Subfamily • 0 or more levels • Functional specialization PIRSF003033: Ku70 autoantigen PF02735: Ku70/Ku80 beta- barrel domain PIRSF800001: Ku70/80 autoantigen PIRSF016570: Ku80 autoantigen PIRSF006493: Ku, prokaryotic type PIRSF500001: IGFBP-1 PF00219: Insulin-like growth factor binding protein (IGFBP) PIRSF001969: IGFBP … PIRSF500006: IGFBP-6 PIRSF018239: IGFBP-related protein, MAC25 type 38 Creation and Curation of PIRSFs UniProtKB proteins Membership Signature Domains Full Curation (3,300 PIRSFs) Family Name, Description, Bibliography PIRSF Name Rules Unassigned proteins Automatic Procedure Automatic clustering Preliminary Homeomorphic Families Automatic placement ComputerGenerated (Uncurated) Clusters Preliminary Curation (4,700 PIRSFs) New proteins Orphans Map domains on Families Computerassisted Manual Curation Merge/split clusters Add/remove members Curated Homeomorphic Families Name, refs, description Protein name rule/site rule Final Homeomorphic Families Create hierarchies (superfamilies/subfamilies) 39 Build and test HMMs 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 40 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 43 Family-Driven Protein Annotation Objective: Optimize for protein annotation PIRSF Classification Name Hierarchy Subfamilies increase specificity (kinase -> sugar kinase -> hexokinase) Name Rules Reflects the function when possible Indicates the maximum specificity that still describes the entire group Standardized format Name tags: validated, tentative, predicted, functionally heterogeneous 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) Site Rules Define conditions under which features propagate to individual proteins 44 PIR Name Rules 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 45 Can have multiple rules that may or may not have mutually exclusive conditions Example Name Rules Rule ID Rule Conditions Propagated Information PIRNR000881-1 PIRSF000881 member and vertebrates Name: S-acyl fatty acid synthase thioesterase PIRNR000881-2 PIRSF000881 member and not vertebrates Name: Type II thioesterase PIRNR025624-0 PIRSF025624 member Name: ACT domain protein Misnomer: chorismate mutase Note the lack of a zero rule for PIRSF000881 46 Name Rule Propagation Pipeline Affiliation of Sequence: Homeomorphic Family or Subfamily (whichever PIRSF is the lowest possible node) Name rule exists? Yes Protein fits criteria for any higher-order rule? PIRSF has zero rule? No No No Yes Yes Nothing to propagate Assign name from Name Rule 1 (or 2 etc) Assign name from Name Rule 0 47 Nothing to propagate Name Rule in Action at UniProt Current: • Automatic annotations (AA) are in a separate field • AA only visible from www.ebi.uniprot.org Future: • Automatic name annotations will become DE line if DE line will improve as a result 48 • AA will be visible from all consortium-hosted web sites PIR Site Rules Position-Specific Site Features: Current requirements: active sites binding sites modified amino acids at least one PDB structure experimental data on functional sites Rule Definition: Select template structure Align PIRSF seed members with structural template Edit alignment to retain conserved regions covering all site residues 49 Build Site HMM from concatenated conserved regions 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) 50 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 51 Overview 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 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 Facilitates: Automatic annotation of sequences based on protein families Systematic correction of annotation errors Name standardization in UniProt Functional predictions for uncharacterized proteins 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 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 54