Computational modeling of malarial parasite protein interactions reveals function on a genome-wide scale Chris Stoeckert Dept. of Genetics Center for Bioinformatics University of Pennsylvania School of Medicine Princeton PICASso talk Oct. 18, 2006

Genomic Data Integration Inferring relationships between genes and proteins –Databases –Knowledge representation –Computational models Application to problem of genome annotation

Conventional approach to genome sequence annotation …ACTGCGTATGCGTGCCTAGCTAGCATCGATCGATGCATCGATGCATCGATGCATCG;;; Predict gene models …ACTGCGTATGCGTGCCTAGCTAGCATCGATCGATGCATCGATGCATCGATAGCATCG… Similarity to characterized protein? (BLAST, homology) Yes! Name it after that protein. (maybe add “-like”) No. Call it a “hypothetical protein.”

Computational challenge Predict the functions of the many hypothetical proteins identified as genome sequences become available. Currently 3537 out of the 5444 protein- coding genes in the malarial parasite, Plasmodium falciparum, are annotated as “hypothetical.” How do you predict function when direct sequence comparisons to known proteins fail?

Genome Resarch Apr;16(4):542-9.

Some facts about Plasmodium Plasmodium falciparum is the causal organism of the most lethal form of Malaria Malaria is one of the three big killers (with TB and HIV/AIDS) >40% of the world’s population is exposed to the disease Million cases every year, and up to 2.7 Million deaths (mostly children under the age of 5, ~ one death / 30 seconds). Drug-resistant malaria strains found in Asia, Africa and South America

Some facts about Plasmodium D. Wirth Plasmodium has several distinct life stages in different hosts and cells Most expression studies focus on red blood cell stages which can be cultured.

P. falciparumS. cerevisiae Size22 Mb12 Mb No. of genes5,4445,770 Avg. gene length2,283 kb1,424 kb G+C content19.4 %38.3 % Hypothetical proteins3537 (~65%) ~30% Hypothetical proteins w/o pfam domain 2684 (~50%) Plasmodium falciparum Genome Characterizing these hypothetical proteins will increase our options for drug targets and vaccines.

Interactome modeling Goal: Reconstruct the network of functional protein-protein interactions –Calculate functional linkages between individual proteins using different functional genomics methods in-silico, or computational functional genomics methods Experimental functional genomics data –Combine the results within a suitable framework (Bayesian).

Interactome modeling: Yeast models Phylogenetic profiling Date & Marcotte, Nature Biotechnology, 2003 Combined experimental and functional genomics data Lee, Date, Adai & Marcotte, Science, 2004 Bayesian networks approach for predicting function from heterogeneous data sources Troyanskaya et al. PNAS 2003 MIPS complexes Subcellular loc. Bayesian networks approach for predicting protein-protein interactions from genomic data Jansen et al., Science, 2003

Predicting function through guilt-by-association Phylogenetic Profiles: Proteins that work together are present or absent together in different genomes. Rosetta Stone fusions: Proteins that get fused together are ones that work together. Expression Coherence: Genes that work together are expressed together.

Phylogenetic profile linkage data Phylogenetic profiles are a description of the presence or absence of a given protein in a set of reference genomes (Pellegrini et al, PNAS 1999). GenomesG1G2G3G4G5 Protein1      Protein2      Protein3       Presenc e  Absence Strong Presence ArchaeaBacteriaEukaryotes A phylogenetic profile constructed using BLAST E- values

Phylogenetic profile linkages Similarity between phylogenetic profiles is measured using the mutual information metric: MI(A,B) = H(A) + H(B) – H(A,B), where Intrinsic entropy H(A) = -  p(a) ln p(a) is the entropy of the probability distribution p(a) of gene A among all organisms Joint/Relative entropy H(A,B) = -   p(a,b) ln p(a,b) is the entropy of the joint probability distribution p(a,b) of occurrences of genes A and B together among all organisms. Use MI between pairs of proteins to predict functional interactions. Final dataset: Profiles of 2813 proteins that were found in at least one other organism, other than P. falciparum.

Rosetta stone (domain fusion) linkage data Proteins that appear as a single fused protein in one organism, but as two or more separate proteins in either the same or a different organism. Use linkage confidence between proteins to predict functional interactions measured using the hypergeometric distribution (Verjovsky Marcotte & Marcotte Applied Bioinformatics 2002). Final dataset: Fusion protein links between 993 proteins that were found in at least one other organism, other than P. falciparum. E.coli gyrA Yeast Topo II E.coli gyrB

Expression coherence 3 major blood stages Single peak and trough ~75% of genes are cyclic Genes with similar function have similar phase High correlation of related genes Bozdech, Z. et al PLoS Biol. 4: R9 Use Pearson correlation to predict functional interactions. Expression profiles were available for 3471 proteins.

Interactome modeling: Data sets Experimental functional genomics datasets –Microarray expression time-series (Bozdech et al. PLoS Biol 2003) –Microarray expression data for all stages (Le Roch et al. Science 2003) –Mass spectrometry (Florens et al. Nature 2002, Lasonder et al. Nature 2002) Computational functional genomics datasets –Phylogenetic profile linkages genomes –Rosetta stone linkages genomes Annotation datasets –Gene Ontology (GO) annotations (from Sanger & TIGR) [GOLD STD] –KEGG Pathway annotations [GOLD STD]

Interactome modeling: Model features G - gold standards P – phylogenetic profiles R – Rosetta stone links E1 – expression set 1 E2 – expression set 2 M1 – mass spec. set 1 M2 – mass spec. set 2

Interactome modeling: Gold standards +ve set Pair all non- Promiscuous 1 proteins sharing KEGG pathways G P -ve set Pair all proteins not sharing a pathway, then filter with GO hierarchy (7 levels) 2 G N’ GNGN 1 Positive set: remove “promiscuous” proteins found in multiple pathways 2 Negative set: remove protein pairs that are closely related based on GO hierarchy

Interactome modeling: Likelihood scores – 1 Bin 0.9 – 1 Bin LR(Bin pairs ) = P(Bin pairs | G P ) / P(Bin pairs | G N ) Assign this LR to each protein pair (A,B) in the bin. 0.8 – 0.9 Bin 0.8 – 0.9 Bin LR(A,B) = LR(A,B) Phylo x LR(A,B) Rosetta x LR(A,B) Expression Compare accuracy of each data set with the gold standards and derive likelihood ratio (LR) scores Example: Correlation values from expression profiles. Overlap of gold standard +ves (and -ves) for protein pairs with correlation values of 0.9 to 1, 0.8 to 0.9, 0.7 to 0.8, etc. Result is likelihood that proteins A and B are functionally linked +ve gold std -ve gold std

Interactome modeling: Reference priors - Assume ‘X’ the number of proteins linkages in reality. Divide X by the number of possible linkages to get O prior - Predict ‘Y’ number of interacting pairs with odds of 1 or greater (O posterior ), and measure the ratio of true and false positives in the predictions (LR). - Retain the prior with best coverage, and construct network O posterior = O prior x LR Bayes law

Interactome modeling: Validating results Expression (Winzeler) Mass spec (set 1) Mass spec (set 2) Random pairs 7-fold cross-validation and results of shuffled input Error Shuff. input Posterior probabilities based on Gold Standards improve with higher likelihood ratio thresholds on input datasets but do not improve with shuffled inputs. Overlap of input pairs with random pairs is small.

Interactome modeling: Validating results Expression (Winzeler) Mass spec (set 1) Mass spec (set 2) Posterior probabilities based on different benchmarks also improve with higher likelihood ratio thresholds on input datasets. Microarray expression data for all stages (Le Roch et al. Science 2003) Mass spectrometry (Florens et al. Nature 2002) Mass spectrometry (Lasonder et al. Nature 2002)

Likelihood score thresholds Overlap with gold standards (ratio of true positives to false positives) 0 Generating the interaction map and high- confidence subset Likelihood ratio of 2 gives an O posterior of 1. Interaction map. Likelihood ratio of 14 gives an O posterior of ~10. High confidence subset.

Attributes of the interaction map and high- confidence subset Note 9,428,653 pairs are possible for the 4343 proteins that had functional information.

Interactome modeling: High-confidence subset

Interactome modeling: Examples

Interactome Modeling: Summary Computational challenge is inferring function when sequence similarity alone fails How many inferences made? 2109 associations with characterized genes 107 hypothetical protein only linkages Computational success is using multiple lines of evidence tying genes together to infer functional interactions.

Data access and queries: PlasmoMAP Provide predicted functional interactions so that annotators and experimentalists can use these to establish protein annotations

Data access and queries: PlasmoDB

LaCount et al. Nature 2005

PlasmoDB is part of a federation of databases on protozoan parasites Penn: Brestelli J, Brunk B, Chakravartula P, Date S, Dommer J, Essien K, Fischer S, Gajria B, Gao X, Grant G, Innamorato F, Iodice J, Pinney D, Roos D, Stoeckert C, Whetzel P UGa: Aurrecoechea C, Heiges M, Kissinger J, Kraemer E, Miller J, Wang H, Wang S

Applying interactome modeling to other species Feng,C. et al Nuc. Acids Res. 34: D363-D368 Have generated phylogenetic profiles and Rosetta Stone linkages for Plasmodium vivax and Toxoplasma gondii Phylum apicomplexa Homo Sapiens Have also generated phylogenetic profiles and Rosetta Stone linkages. Will add expression data from tissue surveys.

Next computational challenges Compare functional interaction networks across related parasites Compare functional interaction networks across hosts and parasites Use functional interaction networks to look for regulators of biological processes (e.g., transcription for ribosomal protein genes) Use functional interactions to infer role of hypothetical protein families with novel domain

Next computational challenges Compare functional interaction networks across related parasites Compare functional interaction networks across hosts and parasites Use functional interaction networks to look for regulators of biological processes (e.g., transcription for ribosomal protein genes) Use functional interactions to infer role of hypothetical protein families with novel domain

RAP1, a myb-family transcription factor, regulates transcription of ribosomal proteins in yeast. Is this regulatory network conserved in P. falciparum? Ribosomal proteins RAP1 Yeast Ribosomal proteins ? P. falciparum No RAP1 ortholog in P. falciparum but there are myb proteins. PF13_0088 (myb1) expression is highly correlated with 43 ribosomal proteins (Pearson correlation of 0.9) Kobby Essien

Functional interaction network provides additional evidence for myb1 regulation of ribosomal proteins in Plasmodium 43 Ribosomal proteins myb1 60 Ribosomal proteins plus 331 others (5/13 high confidence links are to ribosomal proteins). In addition, cytoplasmic translational machinery proteins functionally linked to myb1 are significantly enriched in a conserved motif in their promoter sequence. Kobby Essien

Next computational challenges Compare functional interaction networks across related parasites Compare functional interaction networks across hosts and parasites Use functional interaction networks to look for regulators of biological processes (e.g., transcription for ribosomal protein genes) Use functional interactions to infer role of hypothetical protein families with novel domains

P. Falciparum contains hypothetical proteins with putative novel domains Family of 6 hypothetical proteins in P. falciparum identified by self BLAST (minimum 30% identity over 30% of length) These proteins have no known domains May be restricted to P. falciparum Top GO Biol. Process terms for proteins with predicted functional interactions with PFI0060c are GO: antigenic variation; GO: evasion of host immune response. Shailesh Date

Functional interactions provide a path to infer role of hypothetical protein family with novel domain Family of 5 hypothetical proteins in P. falciparum identified by self BLAST (minimum 30% identity over 30% of length) PFB0932w 14 GO: RNA metabolism 12 GO: RNA processing 9 GO: transcription PFB0930w 31 GO: protein biosynthesis 16 GO: antigenic variation MAL8P GO: protein biosynthesis 16 GO: RNA metabolism MAL7P GO: protein biosynthesis 16 GO: catabolism Top GO Biol. Processes for proteins with predicted functional interactions to family members. (none for PFB0075c) Shailesh Date

Connecting genes through data integration Regulatory interactions Physical interactions Functional interactions Functional genomics database