Proteomics and Protein Bioinformatics: Functional Analysis of Protein Sequences Anastasia Nikolskaya Assistant Professor (Research) Protein Information Resource Department of Biochemistry and Molecular Biology Georgetown University Medical Center

Overview Role of bioinformatics/computational biology in proteomics research Functional annotation of proteins = assigning correct name, describing function or predicting function for a sequence Classification of proteins = grouping them into families of related sequences Annotating a family helps the annotation of its members Sequence function

Bioinformatics as related to proteins 1. Sequence analysis Genome projects -> Gene prediction Protein sequence analysis Comparative genomics Protein sequence and family databases (annotation and classification) 2. Structural genomics 3. Data analysis and integration for: Large scale gene expression analysis Protein-protein interaction Intracellular protein localization 4. Integration of all data on proteins to reconstruct pathways and cellular systems, make predictions and discover new knowledge

Functional Genomics and Proteomics Proteomics studies biological systems based on global knowledge of protein sets (proteomes). Functional genomics studies biological functions of proteins, complexes, pathways based on the analysis of genome sequences. Includes functional assignments for protein sequences. GenomeTranscriptome ProteomeMetabolome

Proteomics Bioinformatics Analysis and integration of these data Data: Gene expression profiling Genome-wide analyses of gene expression (DNA Microarrays/Chips ) Data: Protein-protein interaction (Yeast Two-Hybrid Systems) Data: Structural genomics Determine 3D structures of all protein families Data: Genome projects (Sequencing)

Bioinformatics and Genomics/Proteomics Sequence, Other Data Pathways and Regulatory Circuits Hypothetical Cell Unknown Genes Putative Functional Groups

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

Bioinformatics as related to proteins 1. Sequence analysis Genome projects -> Gene prediction Protein sequence analysis Comparative genomics Protein sequence and family databases (annotation and classification) 2. Structural genomics 3. Data analysis and integration for: Large scale gene expression analysis Protein-protein interaction Intracellular protein localization 4. Integration of all data on proteins to reconstruct pathways and cellular systems, make predictions and discover new knowledge

Genomic DNA Sequence 5' UTRPromoter Exon1 IntronExon2 Intron Exon33' UTR A G G T A G Gene Recognition Exon2Exon1Exon3 C A C A C A A T T A T A Protein Sequence A T G A A T A A A Structure Determination Protein Structure Function Analysis Gene Network Metabolic Pathway Protein Family Molecular Evolution Family Classification G T Gene DNA Sequence Gene Protein Sequence Function Work with protein sequence, not DNA sequence

Most new proteins come from genome sequencing projects Mycoplasma genitalium proteins Escherichia coli - 4,288 proteins S. cerevisiae (yeast) - 5,932 proteins C. elegans (worm) ~ 19,000 proteins Homo sapiens ~ 30,000 proteins... and have unknown functions

Advantages of 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

The changing face of protein science 20 th century Few well-studied proteins Mostly globular with enzymatic activity Biased protein set 21 st century Many “hypothetical” proteins Various, often with no enzymatic activity Natural protein set Credit: Dr. M. Galperin, NCBI

Properties of the natural protein set Unexpected diversity of even common enzymes (analogous, paralogous enzymes) Conservation of the reaction chemistry, but not the substrate specificity Functional diversity in closely related proteins Abundance of new structures Credit: Dr. M. Galperin, NCBI

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

Protein Sequence Function From new genomes Automatic assignment based on sequence similarity: gene name, protein name, function To avoid mistakes, need human intervention (manual annotation) Functional annotation of proteins (protein sequence databases) Best annotated protein database: UniProt (unites Swiss-Prot, TrEMBL, and PIR)

Experimentally characterized –Up-to-date information, manually annotated (curated database!) Problems: avoid misinterpreting experimental results (e.g. suppressors, cofactors) “Knowns” = Characterized by similarity (closely related to experimentally characterized) – Make sure the assignment is plausible Function can be predicted – Extract maximum possible information – Avoid errors and overpredictions – Fill the gaps in metabolic pathways “Unknowns” (conserved or unique) – Rank by importance Objectives of functional analysis for different groups of proteins

Problems in functional assignments for “knowns” Previous low quality annotations lead to propagation of mistakes Biologically senseless annotations Arabidopsis: separation anxiety protein-like Helicobacter: brute force protein Methanococcus: centromere-binding protein Plasmodium: frameshift Propagated mistakes of sequence comparison

Problems in functional assignments for “knowns” Multi-domain organization of proteins ACT domain Chorismate mutase domainACT 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 ! Can be propagated, protein gets erroneous EC number, assigned to erroneous pathway, etc

Enzyme evolution: -Divergence in sequence and function -Non-orthologous gene displacement: Convergent evolution Low sequence complexity (coiled-coil, non-globular regions) Problems in functional assignments for “knowns”

Experimentally characterized “Knowns” = Characterized by similarity (closely related to experimentally characterized) – Make sure the assignment is plausible Function can be predicted (non-obvious predictions) – Extract maximum possible information – Avoid errors and overpredictions – Fill the gaps in metabolic pathways “Unknowns” (conserved or unique) – Rank by importance Objectives of functional analysis for different groups of proteins

Dealing with “hypothetical” proteins Computational analysis –Sequence and genome context analysis Structural analysis –Determination of the 3D structure Mutational analysis Functional analysis –Expression profiling –Tracking of cellular localization Possible Functional Prediction Experimental

Cluster analysis of protein families (family databases) Use of sophisticated database searches (PSI-BLAST, HMM) Detailed manual analysis of sequence similarities Functional prediction: sequence analysis in non-obvious cases

Proteins (domains) with different 3D folds are not homologous (unrelated by origin) 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

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 Credit: Dr. M. Galperin, NCBI

Sequence 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

Phylogenetic distribution (comparative genomics) – Wide - most likely essential – Narrow - probably clade-specific – Patchy - most intriguing –(e.g., niche-specific, pathway type Domain association – “Rosetta Stone” (for multidomain proteins) Gene neighborhood (operon organization) Functional prediction: computational analysis beyond sequence Clues: specific to niche, pathway type

Using genome context for functional prediction Leucine biosynthesis

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 –Functional prediction based on structure

Structural Genomics: Structure-Based Functional Assignments Methanococcus jannaschii MJ0577 (Hypothetical Protein) Contains bound ATP => ATPase or ATP-Mediated Molecular Switch Confirmed by biochemical experiments

Crystal structure is not a function! Credit: Dr. M. Galperin, NCBI

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

Experimentally characterized –Up-to-date information, manually annotated “Knowns” = Characterized by similarity (closely related to experimentally characterized) – Make sure the assignment is plausible Function can be predicted – Extract maximum possible information – Avoid errors and overpredictions – Fill the gaps in metabolic pathways “Unknowns” (conserved or unique) – Rank by importance (e.g., using phylogenetic deistribution) Objectives of functional analysis for different groups of proteins

Functional annotation and protein classification Protein families are real and reflect evolutionary relationships Function often follows along the family lines Therefore, matching a new protein sequence to well-annotated and curated family provides information about this new protein and helps predicting its function. This is more accurate than comparing the new sequence to individual proteins in a database (search classification database vs search protein database) For large-scale accurate anotation, need “natural” protein classification Functional annotation needs to be both accurate and large-scale (efficient) Can protein classification help?

Protein Evolution Tree of Life & Evolution of Protein Families (Dayhoff, 1978) Can build a tree representing evolution of a protein family, based on sequences Orthologous Gene Family: Organismal and Sequence Trees Match Well

Protein Evolution Homolog –Common Ancestors –Common 3D Structure –Usually at least some sequence similarity (sequence motifs or more close similarity) Ortholog –Derived from Speciation Paralog –Derived from Duplication A ancestor Ax Az paralogs Ax 1 Az 1 Ax 2 Az 2 Species 1 Species 2

Levels of protein classification LevelExampleSimilarityEvolution FoldTIM-BarrelTopology of folded backbone Possible monophyly Domain Superfamily AldolaseRecognizable sequence similarity (motifs); basic biochemistry Monophyletic origin Class I AldolaseHigh 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 Origin traceable to a single gene in LCA Lineage- specific expansion (LSE) PA3131 and PA3181 Paralogy within a lineageEvolution by recent duplication and loss

Protein Family vs Domain Domain: Evolutionary/Functional/Structural Unit A protein can consist of a single domain or multiple domains. Proteins have modular structure. Recent domain shuffling: CM (AroQ type) SF SF SF SF SF PDH CM (AroQ type) PDT ACT PDH ACT PDT ACT SF001424

Protein Evolution: Sequence Change vs. Domain Shuffling If enough similarity remains, one can trace the path to the common origin What about these?

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

Complementary approaches Classify domains Allows to build a hierarchy and trace evolution all the way to the deepest possible level, the last point of traceable homology and common origin Can usually annotate only generic biochemical function Classify whole proteins Does not allow to build a hierarchy deep along the evolutionary tree because of domain shuffling Can usually annotate specific biological function (value for the user and for the automatic individual protein annotation)  Can map domains onto proteins  Can classify proteins when some of the domains are not defined

Levels of protein classification LevelExampleSimilarityEvolution FoldTIM-BarrelTopology of folded backbone Possible monophyly Domain Superfamily AldolaseRecognizable sequence similarity (motifs); basic biochemistry Monophyletic origin Class I AldolaseHigh 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 Origin traceable to a single gene in LCA Lineage- specific expansion (LSE) PA3131 and PA3181 Paralogy within a lineageEvolution by recent duplication and loss

Whole protein classification PIRSF Domain classification Pfam SMART CDD Mixed TIGRFAMS COGs Based on structural fold SCOP Protein classification databases

CM PDT ACT Protein family – domain – site (motif) InterPro is an integrated resource for protein families, domains and sites. Combines a number of databases: PROSITE, PRINTS, Pfam, SMART, ProDom, TIGRFAMs, PIRSF, PANTHER SF Bifunctional chorismate mutase/prephenate dehydratase InterPro Entry Type defines the entry as a Family, Domain, Repeat, or Site Family = protein family. “Contains” field lists domains within this protein “Found in” field: for domain entries, lists families which contain this domain

Whole protein functional annotation is best done using annotated whole protein families [NiFe]-hydrogenase maturation factor, carbamoyl phosphate-converting enzyme PIRSF Acylphosphatase – Znf x2 – YrdC - On the basis of domain composition alone, can not predict biological function related to Peptidase M22

PIRSF protein classification system  Basic concept: A network classification system based on evolutionary relationship of whole proteins A network classification system based on evolutionary relationship of whole proteins From Superfamilies to Subfamilies, flexible number of levels  Basic unit = PIRSF Family Homeomorphic (end-to-end similarity with common domain architecture) Homeomorphic (end-to-end similarity with common domain architecture) Homologous (Common Ancestry): Inferred by sequence similarity  Domains and motifs are mapped onto PIRSF  Advantages Annotation of both generic biochemical and specific biological functions Accurate propagation of annotation and development of standardized protein nomenclature and ontology

Levels of protein classification LevelExampleSimilarityEvolution FoldTIM-BarrelTopology of folded backbone Possible monophyly Domain Superfamily AldolaseRecognizable sequence similarity (motifs); basic biochemistry Monophyletic origin Class I AldolaseHigh 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 Origin traceable to a single gene in LCA Lineage- specific expansion (LSE) PA3131 and PA3181 Paralogy within a lineageEvolution by recent duplication and loss

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.

PIRSF DAG Viewer (coming soon) PIRSF family hierarchy based on evolutionary relationships Standardized PIRSF family names Network structure for PIRSF family classification system

PIRSF curation and annotation Curated family name Description of family Sequence analysis tools 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 Uncurated ( Computationally generated clusters) Preliminary curation (preliminary membership, domains) Full curation: membership, family name, bibliography, description (opt.) …Scroll for other database cross-refs, etc

Protein classification systems can be used to: Provide accurate large-scale automatic annotation for new sequences Detect and correct genome annotation errors systematically Drive other annotations (active site etc) Improve sensitivity of protein identification, simplify detection of non-obvious relationships Provide basis for evolutionary and comparative research Discovery of new knowledge by using information embedded within families of homologous sequences and their structures

PIRSF-Based Protein Annotation in UniProt Rule-Based annotation system using curated PIRSFs Name Rules (PIRNR): transfer name from PIRSF to individual proteins –Define a subgroup if necessary –Protein Name (may differ from family name), synonyms, acronyms –EC, Misnomers, GO Terms, Function, etc Site Rules (PIRSR): Position-Specific Site Features (active sites, binding sites, modified sites, other functional sites)

Systematic correction of annotation errors: Chorismate mutase Chorismate Mutase (CM), AroQ class –PIRSF – CM (Prokaryotic type) [PF01817] –PIRSF – TyrA bifunctional enzyme (Prok) [PF01817-PF02153] –PIRSF – PheA bifunctional enzyme (Prok) [PF01817-PF00800] –PIRSF – CM (Eukaryotic type) [Regulatory Dom-PF01817] Chorismate Mutase, AroH class –PIRSF – CM [PF01817] AroH AroQ Euk AroQ Prok

Systematic correction of annotation errors: IMPDH IMPDH Misnomer in Methanococcus jannaschii IMPDH Misnomers in Archaeoglobus fulgidus

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