Slide 1: March 28, 2003 NIH Proteomics Workshop Bethesda, MD Anastasia Nikolskaya, Ph.D. Research Assistant Professor Protein Information Resource Department of Biochemistry and Molecular Biology Georgetown University Medical Center

Slide 2:Overview Role of Bioinformatics/Computational Biology in Proteomics Research Genomics Functional Annotation of Proteins Classification of Proteins Bioinformatics Databases and Analytical Tools (Dr. Yeh and Dr. Hu) Sequence function

Slide 3:Functional Genomics/Proteomics Proteomics studies biological systems based on global knowledge of genomes, transcriptomes, proteomes, metabolomes. Functional genomics studies biological functions of proteins, complexes, pathways based on the analysis of genome sequences. Includes functional assignments for protein sequences. Genome: All the Genetic Material in the Chromosomes Transcriptome: Entire Set of Gene Transcripts Proteome: Entire Set of Proteins Metabolome: Entire Set of Metabolites

Slide 4:Proteomics Data: Gene Expression Profiling - Genome-Wide Analyses of Gene Expression Data: Structural Genomics - Determine 3D Structures of All Protein Families Data: Genome Projects (Sequencing) - Functional genomics - Knowing complete genome sequences of a number of organisms is the basis of the proteomics research

Slide 6:Bioinformatics and Genomics/Proteomics

Slide 7:Most new proteins come from genome sequencing projects Mycoplasma genitalium - 484 proteins Escherichia coli - 4,288 proteins S. cerevisiae (yeast) - 5,932 proteins C. elegans (worm) ~ 19,000 proteins Homo sapiens ~ 40,000 proteins

Slide 8: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

Slide 9:The changing face of protein science 20th century Few well-studied proteins Mostly globular with enzymatic activity Biased protein set 21st century Many “hypotheti- cal” proteins Various, often with no enzymatic activity Natural protein set

Slide 10:Properties of the natural protein set Unexpected diversity of even common enzymes (analogous, paralogous, xenologous, etc. enzymes ) Conservation of the reaction chemistry, but not the substrate specificity Functional diversity in closely related proteins Abundance of new structures

Slide 11:Experimentally characterized Best annotated protein database: SwissProt “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

Slide 13:Problems in functional assignments for “knowns” Previous low quality annotations

Slide 15:Problems in functional assignments for “knowns”

Slide 17:Experimentally characterized “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

Slide 18:Functional Prediction:Dealing with “hypothetical” proteins Computational analysis Sequence analysis of the new ORFs Mutational analysis Functional analysis Expression profiling Tracking of cellular localization Structural analysis Determination of the 3D structure

Slide 19: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 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. 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.

Slide 20:Structural Genomics: Structure-Based Functional Assignments 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. 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.

Slide 22:Detailed manual analysis of sequence similarities Cluster analysis of protein families (family databases) Use of sophisticated database searches (PSI-BLAST, HMM) Improving functional assignments for “unknowns” (Functional Prediction)

Slide 23:Those amino acids that are conserved in divergent proteins (archaeal and bacterial, hyperthermophilic and mesophilic) are likely to be important for catalytic activity. Comparative analysis allows us to find subtle sequence similarities in proteins that would not have been noticed otherwise Prediction of the 3D fold and general function is much easier than prediction of exact biological (or biochemical) function.

Slide 24:For some reason, the reaction chemistry often remains conserved even when sequence diverges almost beyond recognition Sequence database searches that use exotic or highly divergent query sequences often reveal more subtle relationships than those using queries from humans or standard model organisms (E. coli, yeast, worm, fly). Sequence analysis complements structural comparisons and can greatly benefit from them

Slide 25:Poorly characterized protein families Enzyme activity can be predicted, the substrate remains unknown (ATPases, GTPases, oxidoreductases, methyltransferases, acetyltransferases) Helix-turn-helix motif proteins (predicted transcriptional regulators) Membrane transporters

Slide 26:Phylogenetic distribution Wide - most likely essential Narrow - probably clade-specific Patchy - most intriguing, niche-specific Domain association – Rosetta Stone for multidomain proteins Gene neighborhood (operon organization) Improving functional assignments for “unknowns”

Slide 28:Problems in functional assignments/predictions Identification of protein-coding regions Delineation of potential function(s) for distant paralogs Identification of domains in the absense of close homologs Analysis of proteins with low sequence complexity

Slide 29:“Unknown unknowns” Phylogenetic distribution Wide - most likely essential Narrow - probably clade-specific Patchy - most intriguing, niche-specific

Slide 30:To deal with the ocean of new sequences, need “natural” protein classification Protein families are real and reflect evolutionary relationships Protein classification systems can be used to Improve sensitivity of protein identification Provide new protein sequence annotation, simplifying the search for non-obvious relationships Detect and correct genome annotation errors systematically Drive other annotations (actve site etc) Provide basis for evolution, genomics and proteomics research

Slide 31:The ideal system would be: Comprehensive, with each sequence classified either as a member of a family or as an “orphan” sequence, a family of one Hierarchical, with families united into superfamilies on the basis of distant homology Allow for simultaneous use of the whole protein and domain information (domains mapped onto proteins) Allow for automatic classification/annotation of new sequences when these sequences are classifiable into the existing families Expertly curated (family name, function, evidence attribution (experimental vs predicted), background etc). This is the only way to avoid annotation errors and prevent error propagation

Slide 32:The ideal system has yet to be created, but there are several very useful systems

Slide 33:Levels of Protein Classification

Slide 34:Protein Evolution Tree of Life & Evolution of Protein Families (Dayhoff, 1978) Can build a tree representing evolution of a protein family, based on sequences Othologus Gene Family: Organismal and Sequence Trees Match Well

Slide 35:Protein Evolution Homolog Common Ancestors Common 3D Structure Common Active Sites or Binding Domains Ortholog Derived from Speciation Paralog Derived from Duplication Homology: Similarity in DNA or protein sequences between individuals of the same species or among different species. Evolutionary approaches, including cross species sequence comparisons and studies on features of genome organization, evolution and conserved synteny are critical. Homology: Similarity in DNA or protein sequences between individuals of the same species or among different species. Evolutionary approaches, including cross species sequence comparisons and studies on features of genome organization, evolution and conserved synteny are critical.

Slide 36:Orthologs and Paralogs

Slide 37:Orthologs and Paralogs

Slide 38:Orthologs and Paralogs

Slide 39:Orthologs and Paralogs

Slide 40:Orthologs and Paralogs

Slide 41:Levels of Protein Classification

Slide 42:Protein Family-Domain-Motif Domain: Evolutionary/Functional/Structural Unit Domain = structurally compact, independently folding unit that forms a stable three-dimentional structure and shows a certain level of evolutionary conservation. Usually, corresponds to an evolutionary unit. A protein can consist of a single domain or multiple domains. Proteins have modular structure. Motif: Conserved Functional/Structural Site Here, for example, are diagrammatic representations of 5 superfamilies containing the calcineurin-like phosphoesterase domain, in several cases in association with other known types of domains.Here, for example, are diagrammatic representations of 5 superfamilies containing the calcineurin-like phosphoesterase domain, in several cases in association with other known types of domains.

Slide 43:Protein Evolution:Sequence Change vs. Domain Shuffling

Slide 44:Recent Domain Shuffling

Slide 45:Protein classification: proteins and domains Option 1: classify domains - take individual domain sequences, consider them as independently evolving units and build a classification system allows to go all the way to the deepest possible level, the last point of traceable homology and common origin (fold) domain databases (Pfam, SMART, CDD) allow to map domains onto a query sequence

Slide 46:Protein classification: proteins and domains Option 2: classify full-length proteins In cases of multidomain proteins, does not allow to go deep along the evolutionary tree All proteins in a family will often have a common biological function, which is very convenient for annotation Domains will be mapped onto protein families

Slide 47:Practical Classification of Proteins:Setting Realistic Goals

Slide 48:Clasification: current status PIR Superfamilies: Proteins in PIRPSD: 283,289 Proteins  classified:   187,871 2/3 of the PIR proteins COGs: ~ 70% of each microbial genome ~ 50% of each Eukaryotic genome in 3-clade COG ~ 20% ? of each Eukaryotic genome in LSEs

Slide 49:PIR Web Site (http://pir.georgetown.edu)

Slide 50:PIR Superfamily Concept

Slide 51:PIR Superfamilies Created by automated clustering by % identity with coverage-by-length requirements. Creation of new Superfamilies is an ongoing process. Automated classification rules are refined by expert curation: - Evolution rates are very different in different “branches” of the protein universe, so need very different score cutoffs Verify/add members Annotation (at level of orthology): Superfamily Name, Description, Bibliography In some cases, more than one orthologous group will be included into a single Superfamily; these Superfamilies will often be very large and diverse Depth of hierarchy will be different for single-domain and multidomain proteins This is work in progress and will become available through PIR (iProClass) and InterPro

Slide 52:CM-Related Superfamilies Chorismate Mutase (CM), AroQ class SF001501 – CM (Prokaryotic type) [PF01817] SF001499 – tyrA bifunctional enzyme (Prok) [PF01817-PF02153] SF001500 – pheA bifunctional enzyme (Prok) [PF01817-PF00800] SF017318 – CM (Eukaryotic type) [Regulatory Dom-PF01817] Chorismate Mutase, AroH class SF005965 – CM [PF01817] CSM: Class: All alpha proteins Fold: Chorismate mutase II multihelical; core: 6 helices, bundle 1DBF: Class: Alpha and beta proteins (a+b) Mainly antiparallel beta sheets (segregated alpha and beta regions) Fold: Bacillus chorismate mutase-like core: beta-alpha-beta-alpha-beta(2); mixed beta-sheet: order: 1423, strand 4 is antiparallel to the rest CSM: Class: All alpha proteins Fold: Chorismate mutase II multihelical; core: 6 helices, bundle 1DBF: Class: Alpha and beta proteins (a+b) Mainly antiparallel beta sheets (segregated alpha and beta regions) Fold: Bacillus chorismate mutase-like core: beta-alpha-beta-alpha-beta(2); mixed beta-sheet: order: 1423, strand 4 is antiparallel to the rest

Slide 53:iProClass Superfamily Report (I)

Slide 54:iProClass Superfamily Report (II)

Slide 55:InterPro

Slide 56:InterPro Entry

Slide 57:PIR Superfamilies are being integrated into InterPro

Slide 58:complete genomes- reciprocal best hits- no score cutoffs Comparative genomics - a branch of computational biology that uses complete genome sequences

Slide 60:Construction of COGs:

Slide 62:Construction of COGs:Add all homologs

Slide 69:Two Groups of Unusual RRs [Receiver-X] SF006198, COG3279 1. AlgR-related Pseudomonas aeruginosa (AlgR): alginate biosynthesis Klebsiella pneumoniae (MrkE): formation of adhesive fimbriae Clostridium perfringens (VirR): virulence factors 2. Regulators of autoinduced peptide-controlled regulons Staphylococcus aureus (AgrA): virulence factors Lactobacillus plantarum (PlnC, PlnD): bacteriocin production Streptococcus pneumoniae (ComE): competence Properties of the CheY- LytTR transcriptional regulators Regulate secreted and extracellular factors Often regulate their own expression Bind to imperfect direct repeat sites in -80 to - 40 area (or in UAS) Can be phosphorylated by His kinases, but form operons with HisK-type sensor ATPases Contain a conserved LytTR-type DNA-binding domain

Slide 71:Domain organization of LytTR proteinsother than CheY-LytTR Stand-alone LytTR Streptococcus pneumoniae BlpS Pseudomonas phage D3 Orf50 40aa - LytTR Lactococcus lactis L121252 Listeria monocytogenes Lmo0984 Staphylococcus aureus SA2153 Streptococcus pneumoniae SP0161 ABC - LytTR Bacillus halodurans BH3894 MHYT - LytTR Oligotropha carboxydovora CoxC, CoxH 3TM - LytTR Xanthomonas campestris RpfD Caulobacter crescentus CC1610 Mesorhizobium loti mll0891 3TM - LytTR Caulobacter crescentus CC0295 4TM - LytTR Caulobacter crescentus CC0330, CC3036 8TM - LytTR Caulobacter crescentus CC0551 PAS - LytTR Burkholderia cepacia Geobacter sulfurreducens

Slide 72:    Consensus binding site for the LytTR domains

Slide 73:Predicted LytTR-regulated genes Expected Bacillus subtilis natAB (Na+-ATPase) Oligotropha carboxidovorans   comC, comH (CO growth) Staphylococcus aureus lrgAB (autolysis) Streptococcus pneumoniae hld (hemolysin delta) Unexpected  Bacillus subtilis alr, dinB, rapI, veg, ybaJ, ybbI, yceA, ydbS, ydjL, yebB, yfiV, ykuA Staphylococcus aureus capO, coa, hsdR, SA0096, SA0257, SA0285, SA0302, SA0357, SA0358, SA0513

Slide 75:Examples for analysis: 1. Retrieve one of the following protein sequences: PIR: C69086 D64376 GenBank GI:15679635. Using analysis tools available on the web, check if the functional annotation is correct, and provide correct annotation without looking at internal PIR or COG annotations (Run BLAST with CDsearch and SMART to start with). When you are done, look at the PIR curated SF annotation (still at internal site only): http://pir.georgetown.edu/test-cgi/sf/pirclassif.pl?id=SF006549 http://pir.georgetown.edu/cgi-bin/ipcSF1?id=SF006549 (compare with original automatic SF annotation at the public site), and at COG annotations. What caused the wrong annotations? In BLAST outputs for these sequences, do you see other wrongly annotated proteins? Next, analyze the C-terminal domain of these proteins by PSI-BLAST (and alignment analysis) and suggest any speculations as to its function (homework).  

Slide 76:Examples for analysis: 2. Retrieve the following sequence: GI:7019521 Take a look at the associated publication (reference). Analyze the sequence to see if any additional information can be obtained (run PSI-BLAST, and (as a homework) construct multiple alignment). Take a look at taxonomy report: what does it tell you? Find experimental paper associated with one of the sequences found by PSI-BLAST. What annotation is appropriate for this sequence and for the entire family?

Slide 77:Examples for analysis: 3. Predict the function of the following proteins: GenBank: GI: 27716853 E. coli YjeE protein Verify and/or correct the following functional annotations. Can you explain why the erroneous annotations were made? PIR: H87387 GenBank: GI:15606003 GI:15807219 PIR: F70338

Slide 78:Examples for analysis: 4. Homework: an exercise in transitive relationships:Start with>gi|20093648|ref|NP_613495.1| Uncharacterized membrane protein, conserved in Archaea [Methanopyrus kandleri AV19](this is a short membrane protein); run PSI-BLAST, make sure you have filtering, complexity and CD-search off. There are no good hits but a bunch of sub-threshold ones. Collect "suspect" relations, use them as queries and expand the net. You will be able to come up with two proteins:>gi|21227474|ref|NP_633396.1| hypothetical protein [Methanosarcina mazei Goe1] and>gi|14324537|dbj|BAB59464.1| hypothetical protein [Thermoplasma volcanium]When used as a PSI-BLAST query, the first will tie the Methanopyrus protein into a group, while the second will tie this group to the Sec61 subunit of preprotein translocase.Then, of course, you can obtain the same result with CD-search in a single step ?.