Protein domains, function and associated prediction. Experimental. Protein function categories. Issue when elucidating function experimentally

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1 C E T R E F O R I T E G R A T I V E B I O I F O R M A T I C S V U Lecture 14: Protein domains, function and associated prediction Introduction to Bioinformatics Metabolomics fluxomics Functional Genomics Systems Biology TERTIARY STRUCTURE (fold) TERTIARY STRUCTURE (fold) Genome Expressome Proteome Metabolome Experimental Structural genomics Functional genomics Protein-protein interaction Metabolic pathways Expression data Issue when elucidating function experimentally Typically done through knock-out experiments Partial information (indirect interactions) and subsequent filling of the missing steps egative results (elements that have been shown not to interact, enzymes missing in an organism) Putative interactions resulting from computational analyses Protein function categories Catalysis (enzymes) Binding transport (active/passive) Protein-DA/RA binding (e.g. histones, transcription factors) Protein-protein interactions (e.g. antibody-lysozyme) (experimentally determined by yeast two-hybrid (Y2H) or bacterial two-hybrid (B2H) screening ) Protein-fatty acid binding (e.g. apolipoproteins) Protein small molecules (drug interaction, structure decoding) Structural component (e.g. γ-crystallin) Regulation Signalling Transcription regulation Immune system Motor proteins (actin/myosin) Catalytic properties of enzymes K m k cat Michaelis-Menten equation: Moles/s V max V max/2 E + S ES E + P E = enzyme S = substrate K m ES = enzyme-substrate complex (transition state) P = product K m = Michaelis constant K cat = catalytic rate constant (turnover number) K cat /K m = specificity constant (useful for comparison) V max [S] V = K m + [S] [S] 1

2 Protein interaction domains Energy difference upon binding Examples of protein interactions (and of functional importance) include: Protein protein (pathway analysis); Protein small molecules (drug interaction, structure decoding); Protein peptides, DA/RA The change in Gibb s Free Energy of the protein-ligand binding interaction can be monitored and expressed by the following equation: G = H T S (H=Enthalpy, S=Entropy and T=Temperature) Protein function Many proteins combine functions Some immunoglobulin structures are thought to have more than 100 different functions (and active/binding sites) Alternative splicing can generate (partially) alternative structures Protein function & Interaction Active site / binding cleft Protein function evolution Chymotrypsin From a simple ancestral active site for cutting protein chains to a more elaborate active site with four different features, all helping to optimise proteolysis (cleavage) Shape complementarity Gene duplication has resulted in two-domain protein 2

3 Protein function evolution Chymotrypsin The active site lies between the two domains. It consists of residues on the same two loops (firstly between beta-strands 3 and 4, secondly between beta strands 5 and 6) of each of the two barrel domains. Four features of the active site are indicated in the figure. The Substrate Specificity Pocket Main Chain Substrate-binding The Oxyanion Hole (white) Catalytic triad Chymotrypsin cleaves peptides at the carboxyl side of tyrosine, tryptophan, and phenylalanine because those three amino acids contain phenyl rings. How to infer function Experiment Deduction from sequence Multiple sequence alignment conservation patterns Homology searching Deduction from structure Threading Structure-structure comparison Homology modelling A domain is a: Compact, semi-independent unit (Richardson, 1981). Stable unit of a protein structure that can fold autonomously (Wetlaufer, 1973). Recurring functional and evolutionary module (Bork, 1992). ature is a tinkerer and not an inventor (Jacob, 1977). Smallest unit of function Delineating domains is essential for: Obtaining high resolution structures (x-ray but particularly MR size of proteins) Sequence analysis Multiple sequence alignment methods Prediction algorithms (SS, Class, secondary/tertiary structure) Fold recognition and threading Elucidating the evolution, structure and function of a protein family (e.g. Rosetta Stone method) Structural/functional genomics Cross genome comparative analysis Domain connectivity linker Structural domain organisation can be nasty Pyruvate kinase Phosphotransferase β barrel regulatory domain α/β barrel catalytic substrate binding domain α/β nucleotide binding domain 1 continuous + 2 discontinuous domains 3

4 Domain size The size of individual structural domains varies widely from 36 residues in E-selectin to 692 residues in lipoxygenase-1 (Jones et al., 1998) the majority (90%) having less than 200 residues (Siddiqui and Barton, 1995) with an average of about 100 residues (Islam et al., 1995). Small domains (less than 40 residues) are often stabilised by metal ions or disulphide bonds. Large domains (greater than 300 residues) are likely to consist of multiple hydrophobic cores (Garel, 1992). 4

5 Analysis of chain hydrophobicity in multidomain proteins Analysis of chain hydrophobicity in multidomain proteins Domain characteristics Domains are genetically mobile units, and multidomain families are found in all three kingdoms (Archaea, Bacteria and Eukarya) underlining the finding that ature is a tinkerer and not an inventor (Jacob, 1977). The majority of genomic proteins, 75% in unicellular organisms and more than 80% in metazoa, are multidomain proteins created as a result of gene duplication events (Apic et al., 2001). Domains in multidomain structures are likely to have once existed as independent proteins, and many domains in eukaryotic multidomain proteins can be found as independent proteins in prokaryotes (Davidson et al., 1993). Protein function evolution - Gene (domain) duplication - Active site Chymotrypsin 5

6 Pyruvate phosphate dikinase 3-domain protein Two domains catalyse 2-step reaction A B C Third so-called swivelling domain actively brings intermediate enzymatic product (B) over 45Å from one active site to the other Pyruvate phosphate dikinase 3-domain protein Two domains catalyse 2-step reaction A B C Third so-called swivelling domain actively brings intermediate enzymatic product (B) over 45Å from one active site to the other / The DEATH Domain Present in a variety of Eukaryotic proteins involved with cell death. Six helices enclose a tightly packed hydrophobic core. Some DEATH domains form homotypic and heterotypic dimers. Detecting Structural Domains A structural domain may be detected as a compact, globular substructure with more interactions within itself than with the rest of the structure (Janin and Wodak, 1983). Therefore, a structural domain can be determined by two shape characteristics: compactness and its extent of isolation (Tsai and ussinov, 1997). Measures of local compactness in proteins have been used in many of the early methods of domain assignment (Rossmann et al., 1974; Crippen, 1978; Rose, 1979; Go, 1978) and in several of the more recent methods (Holm and Sander, 1994; Islam et al., 1995; Siddiqui and Barton, 1995; Zehfus, 1997; Taylor, 1999). Detecting Structural Domains However, approaches encounter problems when faced with discontinuous or highly associated domains and many definitions will require manual interpretation. Detecting Domains using Sequence only Even more difficult than prediction from structure! Consequently there are discrepancies between assignments made by domain databases (Hadley and Jones, 1999). 6

7 Integrating protein multiple sequence alignment, secondary and tertiary structure prediction in order to predict structural domain boundaries in sequence data Protein structure hierarchical levels PRIMARY STRUCTURE (amino acid sequence) SECODARY STRUCTURE (helices, strands) VHLTPEEKSAVTALWGKVVDE VGGEALGRLLVVYPWTQRFFE SFGDLSTPDAVMGPKVKAHG KKVLGAFSDGLAHLDLKGTFA TLSELHCDKLHVDPEFRLLG VLVCVLAHHFGKEFTPPVQAAY QKVVAGVAALAHKYH SnapDRAGO Richard A. George George R.A. and Heringa, J. (2002) J. Mol. Biol., 316, QUATERARY STRUCTURE TERTIARY STRUCTURE (fold) Protein structure hierarchical levels PRIMARY STRUCTURE (amino acid sequence) SECODARY STRUCTURE (helices, strands) Protein structure hierarchical levels PRIMARY STRUCTURE (amino acid sequence) SECODARY STRUCTURE (helices, strands) VHLTPEEKSAVTALWGKVVDE VGGEALGRLLVVYPWTQRFFE SFGDLSTPDAVMGPKVKAHG KKVLGAFSDGLAHLDLKGTFA TLSELHCDKLHVDPEFRLLG VLVCVLAHHFGKEFTPPVQAAY QKVVAGVAALAHKYH VHLTPEEKSAVTALWGKVVDE VGGEALGRLLVVYPWTQRFFE SFGDLSTPDAVMGPKVKAHG KKVLGAFSDGLAHLDLKGTFA TLSELHCDKLHVDPEFRLLG VLVCVLAHHFGKEFTPPVQAAY QKVVAGVAALAHKYH QUATERARY STRUCTURE TERTIARY STRUCTURE (fold) QUATERARY STRUCTURE TERTIARY STRUCTURE (fold) Protein structure hierarchical levels PRIMARY STRUCTURE (amino acid sequence) SECODARY STRUCTURE (helices, strands) VHLTPEEKSAVTALWGKVVDE VGGEALGRLLVVYPWTQRFFE SFGDLSTPDAVMGPKVKAHG KKVLGAFSDGLAHLDLKGTFA TLSELHCDKLHVDPEFRLLG VLVCVLAHHFGKEFTPPVQAAY QKVVAGVAALAHKYH SAPDRAGO Domain boundary prediction protocol using sequence information alone (Richard George)! "# $ ##! % #"#! &'((( ) % $ # ##"# ' #% $ # $$ QUATERARY STRUCTURE TERTIARY STRUCTURE (fold) *+, # " '+-!.'/!(,/0 7

8 SnapDragon SAPDRAGO Domain boundary prediction protocol using sequence information alone (Richard George) # 12,-3& # ((4 "$ % " -&5 6&* ((! %132 7* '((( 8 ) 17& *+, # " '+-!.'/!(,/0 Domain prediction using DRAGO Distance Regularisation Algorithm for Geometry Optimisatio (Aszodi & Taylor, 1994) Folded protein models based on the requirement that (conserved) hydrophobic residues cluster together. First construct a random high dimensional Cα distance matrix. Distance geometry is used to find the 3D conformation corresponding to a prescribed target matrix of desired distances between residues. SAPDRAGO Domain boundary prediction protocol using sequence information alone (Richard George) 9: &'(() "# $ ## ; " % ## <##% # ; 1 "# %$ %$ #" β, ; 2" ## 6α 6β = ; "#!" %=" %$ %>% DRAGO = Distance Regularisation Algorithm for Geometry Optimisatio Cα distance matrix Target matrix 3 9)9 100 randomised initial matrices 100 predictions 3 Input data 1- The Cα distance matrix is divided into smaller clusters. Separately, each cluster is embedded into a local centroid. The final predicted structure is generated from full embedding of the multiple centroids and their corresponding local structures. 8

9 # " " 1# # ##, 1-1- Taylor method (1999) DOMAI-3D Taylor method (1999)! % #" #! &'((( Easy and clever method Uses a notion of spin glass theory (disordered magnetic systems) to delineate domains in a protein 3D structure Steps: 1. Take sequence with residue numbers (1..) 2. Look at neighbourhood of each residue (first shell) 3. If ( average nghhood residue number > res no) resno = resno+1 else resno = resno-1 4. If (convergence) then take regions with identical residue number as domains and terminate repeat until convergence if 41 < ( )/5 then Res (up 1) else Res (down 1) Taylor,WR. (1999) Protein structural domain identification. Protein Engineering 12 : Taylor method (1999) Iterate until convergence initial situation SAPDRAGO Domain boundary prediction protocol using sequence information alone (Richard George) ) % $ # ##"# ' #% $ # $ $ continuous discontinuous? i A? E 2"$ *+, # " '+-!.'/!(,/0 W i i 9

10 SAPDRAGO "F 8 6<G H, 8 2" I# % " F 8 & ##% < %" ##%< 8 G0 % # 8 G "# 8 6 <"#% %<#0 8 2"% <G I <" # % SnapDRAGO prediction assessment Test set of 414 multiple alignments;183 single and 231 multiple domain proteins. Boundary predictions are compared to the region of the protein connecting two domains (maximally ±10 residues from true boundary) SnapDRAGO prediction assessment Baseline method I: Divide sequence in equal parts based on number of domains predicted by SnapDRAGO Baseline method II: Similar to Wheelan et al., based on domain length partition density function (PDF) PDF derived from 2750 non-redundant structures (deposited at CBI) Given sequence, calculate probability of onedomain, two-domain,.., protein Highest probability taken and sequence split equally as in baseline method I Average prediction results per protein SnapDRAGO Baseline 1 Baseline 2 Continuous set Discontinuous set Full set Coverage 63.9 (± 43.0) 35.4 (± 25.0) 51.8 (± 39.1) Success 46.8 (± 36.4) 44.4 (± 33.9) 45.8 (± 35.4) Coverage 43.6 (± 45.3) 20.5 (± 27.1) 34.7 (± 40.8) Success 34.3 (± 39.6) 22.2 (± 29.5) 29.6 (± 36.6) Coverage 45.3 (± 46.9) 22.7 (± 27.3) 35.7 (± 41.3) Success 37.1 (± 42.0) 23.1 (± 29.6) 31.2 (± 37.9) Coverage is the % linkers predicted (TP/TP+F) Success is the % of correct predictions made (TP/TP+FP) Average prediction results per protein Protein-protein interaction networks 10

11 Protein Function Prediction How can we get the edges (connections) of the cellular networks? We can predict functions of genes or proteins so we know where they would fit in a metabolic network There are also techniques to predict whether two proteins interact, either functionally (e.g. they are involved in a two-step metabolic process) or directly physically (e.g. are together in a protein complex) Protein Function Prediction The state of the art it s not complete Many genes are not annotated, and many more are partially or erroneously annotated. Given a genome which is partially annotated at best, how do we fill in the blanks? Of each sequenced genome, 20%-50% of the functions of proteins encoded by the genomes remains unknown! How then do we build a reasonably complete networks when the parts list is so incomplete? Protein Function Prediction For all these reasons, improving automated protein function prediction is now a cornerstone of bioinformatics and computational biology ew methods will need to integrate signals coming from sequence, expression, interaction and structural data, etc. Classes of function prediction methods (recap) Sequence based approaches protein A has function X, and protein B is a homolog (ortholog) of protein A; Hence B has function X Structure-based approaches protein A has structure X, and X has so-so structural features; Hence A s function sites are. Motif-based approaches a group of genes have function X and they all have motif Y; protein A has motif Y; Hence protein A s function might be related to X Function prediction based on guilt-by-association gene A has function X and gene B is often associated with gene A, B might have function related to X Phylogenetic profile analysis Function prediction of genes based on guilt-byassociation a non-homologous approach The phylogenetic profile of a protein is a string that encodes the presence or absence of the protein in every sequenced genome Because proteins that participate in a common structural complex or metabolic pathway are likely to co-evolve, the phylogenetic profiles of such proteins are often ``similar' This means that such proteins have a good chance of being physically or metabolically connected Phylogenetic profile analysis Phylogenetic profile (against genomes) For each gene X in a target genome (e.g., E coli), build a phylogenetic profile as follows If gene X has a homolog in genome #i, the ith bit of X s phylogenetic profile is 1 otherwise it is 0 11

12 gene Phylogenetic profile analysis Example phylogenetic profiles based on 60 genomes genome orf1034: orf1036: orf1037: orf1038: orf1039: orf104: orf1040: orf1041: orf1042: orf1043: orf1044: orf1045: orf1046: orf1047: orf105: orf1054: By correlating the rows (open reading frames (ORF) or genes) you find out about joint presence or absence of genes: this is a signal for a functional connection Genes with similar phylogenetic profiles have related functions or functionally linked D Eisenberg and colleagues (1999) Phylogenetic profile analysis Phylogenetic profiles contain great amount of functional information Phlylogenetic profile analysis can be used to distinguish orthologous genes from paralogous genes Example: Subcellular localization: 361 yeast nucleusencoded mitochondrial proteins were identified at 50% accuracy with 58% coverage through phylogenetic profile analysis Functional complementarity: By examining inverse phylogenetic profiles, one can find functionally complementary genes that might have evolved through one of several mechanisms of convergent evolution. Phylogenetic profiling typically has low accuracy (specificity) but can have high coverage. Domain fusion example n Vertebrates have a multi-enzyme protein (GARs- AIRs-GARt) comprising the enzymes GAR synthetase (GARs), AIR synthetase (AIRs), and GAR transformylase (GARt) n In insects, the polypeptide appears as GARs- (AIRs) 2 -GARt nin yeast, GARs-AIRs is encoded separately from GARt nin bacteria each domain is encoded separately (Henikoff et al., 1997). GAR: glycinamide ribonucleotide AIR: aminoimidazole ribonucleotide Using observed domain fusion for prediction of protein-protein interactions Rosetta stone method Gene fusion is the an effective method for prediction of protein-protein interactions If proteins A and B are homologous to two domains of a multidomain protein C, A and B are predicted to have interaction A Though gene-fusion has low prediction coverage, its false-positive rate is low (high specificity) B C Protein interaction database There are numerous databases of protein-protein interactions DIP is a popular protein-protein interaction database The DIP database catalogs experimentally determined interactions between proteins. It combines information from a variety of sources to create a single, consistent set of protein-protein interactions. Protein interaction databases BID - Biomolecular Interaction etwork Database DIP - Database of Interacting Proteins PIM Hybrigenics PathCalling Yeast Interaction Database MIT - a Molecular Interactions Database GRID - The General Repository for Interaction Datasets InterPreTS - protein interaction prediction through tertiary structure STRIG - predicted functional associations among genes/proteins Mammalian protein-protein interaction database (PPI) InterDom - database of putative interacting protein domains FusionDB - database of bacterial and archaeal gene fusion events IntAct Project The Human Protein Interaction Database (HPID) ADVICE - Automated Detection and Validation of Interaction by Co-evolution InterWeaver - protein interaction reports with online evidence PathBLAST - alignment of protein interaction networks ClusPro - a fully automated algorithm for protein-protein docking HPRD - Human Protein Reference Database 12

13 Protein interaction database etwork of protein interactions and predicted functional links involving silencing information regulator (SIR) proteins. Filled circles represent proteins of known function; open circles represent proteins of unknown function, represented only by their Saccharomyces genome sequence numbers ( Solid lines show experimentally determined interactions, as summarized in the Database of Interacting Proteins 19 ( Dashed lines show functional links predicted by the Rosetta Stone method 12. Dotted lines show functional links predicted by phylogenetic profiles 16. Some predicted links are omitted for clarity. etwork of predicted functional linkages involving the yeast prion protein 20 Sup35. The dashed line shows the only experimentally determined interaction. The other functional links were calculated from genome and expression data 11 by a combination of methods, including phylogenetic profiles, Rosetta stone linkages and mra expression. Linkages predicted by more than one method, and hence particularly reliable, are shown by heavy lines. Adapted from ref. 11. STRIG - predicted functional associations among genes/proteins STRIG is a database of predicted functional associations among genes/proteins. Genes of similar function tend to be maintained in close neighborhood, tend to be present or absent together, i.e. to have the same phylogenetic occurrence, and can sometimes be found fused into a single gene encoding a combined polypeptide. STRIG integrates this information from as many genomes as possible to predict functional links between proteins. Berend Snel (UU), Martijn Huynen (RU) and the group of Peer Bork (EMBL, Heidelberg) STRIG - predicted functional associations among genes/proteins STRIG is a database of known and predicted protein-protein interactions. The interactions include direct (physical) and indirect (functional) associations; they are derived from four sources: STRIG - predicted functional associations among genes/proteins Conserved eighborhood 1. Genomic Context (Synteny) 2. High-throughput Experiments 3. (Conserved) Co-expression 4. Previous Knowledge STRIG quantitatively integrates interaction data from these sources for a large number of organisms, and transfers information between these organisms where applicable. The database currently contains proteins in 179 species This view shows runs of genes that occur repeatedly in close neighborhood in (prokaryotic) genomes. Genes located together in a run are linked with a black line (maximum allowed intergenic distance is 300 bp). ote that if there are multiple runs for a given species, these are separated by white space. If there are other genes in the run that are below the current score threshold, they are drawn as small white triangles. Gene fusion occurences are also drawn, but only if they are present in a run. 13

14 Wrapping up Understand chymotrypsin example: evolution via gene duplication of an optimised two-domain barrel enzyme with active site residues from either domain. Understand domain issues: structural and functional Understand the basic steps of the Snap-DRAGO method for domain boundary prediction but no need to memorize it all Understand phylogenetic profiling and the Rosetta Stone method (guilt-by-association) Understand that conservation patterns in the order of genes that are nearby on the genome (synteny) indicate functional relationships (used in STRIG method) Also co-expression (genes being expressed (or not) at the same time) indicates a functional relationship (used in STRIG method) 14